Citation
Forage and animal responses in pasture-based dairy production systems for lactating cows

Material Information

Title:
Forage and animal responses in pasture-based dairy production systems for lactating cows
Creator:
Macoon, Bisoondat, 1959-
Publication Date:
Language:
English
Physical Description:
xix, 307 leaves : ill. ; 29 cm.

Subjects

Subjects / Keywords:
Coats ( jstor )
Estimation methods ( jstor )
Forage ( jstor )
Grazing ( jstor )
Milk ( jstor )
Milk production ( jstor )
Pastures ( jstor )
Seasons ( jstor )
Stocking rate ( jstor )
Sward ( jstor )
Agronomy thesis, Ph. D ( lcsh )
Dissertations, Academic -- Agronomy -- UF ( lcsh )
City of Gainesville ( local )
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Florida, 1999.
Bibliography:
Includes bibliographical references (leaves 291-304).
General Note:
Printout.
General Note:
Vita.
Statement of Responsibility:
by Bisoondat Macoon.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Resource Identifier:
002531435 ( ALEPH )
43757252 ( OCLC )
AMP7357 ( NOTIS )

Downloads

This item has the following downloads:


Full Text












FORAGE AND ANIMAL RESPONSES IN PASTURE-BASED DAIRY
PRODUCTION SYSTEMS FOR LACTATING COWS













BY

BISOONDAT MACOON


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


1999



















To my wife, Yonette,


for her love, patience, endless understanding,
and moral support



To my sons, Ron and Russ,


for providing unconditional love and
inspiration and giving final meaning to my
life



To my mother, Agnes,


for her love and for instilling motivation for
learning



And to all those people,


who stretched out a helping hand when I
stumbled and fell as I traveled life's path to
where I am today













ACKNOWLEDGMENTS


The author wishes to express his sincere appreciation and deep gratitude to Dr.

Lynn E. Sollenberger, chairman of the supervisory committee, for his constant support,

outstanding guidance, and extraordinary patience prior to and throughout the doctoral

program, for being an excellent mentor, and for his considerable contributions to the

dissertation research and preparation of this manuscript. Much appreciation is extended to

Dr. Charles R. Staples, co-chair of the supervisory committee, for his diligent guidance

and exceptional contribution to the dissertation research and also the preparation of this

manuscript. Appreciation and sincere gratitude is extended to Dr. John E. Moore for his

exceptional teaching, outstanding guidance, and for being a significant mentor throughout

the author's graduate career. Appreciation and sincere gratitude is further extended to Dr.

Kenneth M. Portier, for his outstanding guidance with statistical matters and for serving

on the supervisory committee and reviewing this manuscript. Special appreciation and

sincere gratitude is also extended to Dr. Carrol G. Chambliss for his excellent guidance,

encouragement, and support throughout the doctoral program and for serving on the

supervisory committee and reviewing this manuscript.

Special appreciation is extended to University-wide faculty members who always

encouraged and supported the author's professional development. Prominent among

these are Drs. Kenneth H. Quesenberry, David S. Wofford, Kenneth L. Buhr, the late








Edwin C. French III, Clarence B. Ammerman, and Peter E. Hildebrand. Sincere gratitude

is extended to Dr. Jerry Bennett, Agronomy Department Chairman, and the administrative

staff of the department for their help and support during the author's graduate program.

The award of the teaching assistantship from the Agronomy Department, which made

financial support for this doctoral program possible, is gratefully acknowledged. The

National Agricultural Research Institute of Guyana is gratefully recognized for its role in

facilitating the author's graduate studies.

Many people contributed to the successes of the author's program of study and

their assistance is gratefully acknowledged. Special thanks to Richard Fethiere and Nancy

Carter of the Forage Evaluation Support Laboratory for their friendship and help. The

long hours spent in the field with Sid Jones and Dwight Thomas of the Forage Evaluation

Field Laboratory were vital to the successful completion of the dissertation research and

the author will always be grateful for their contribution. The help and support of John

Funk, Pam Miles, and Nancy Wilkinson of the Animal Science Department and Jocelyn

Jennings of the Dairy Science Department are gratefully acknowledged. The support of

the staff of the Dairy Research Unit, including Farm Manager, Mr. Dale Hissem, Ms.

Mary Russell, and Mr. James Lindsey, and the many people who worked specifically with

the grazing study, including Ryan Richards, Shane Brooks, and Danny Tomlinson, is

gratefully recognized. The help and friendship of Dr. Masaaki Hanada, a visiting scientist

from Japan, is gratefully acknowledged. The author will always be grateful for the

cooperative research responsibilities and special friendship shared with John Fike,

graduate student in the Dairy and Poultry Sciences Department.








The author is grateful to former and current fellow students, Steve Welch, Renato

Fontaneli, and Yoana Newman, who worked relentlessly to help on the author's research

project, and also Elide Valencia, Marjatta Eilitta, Nadia Douglas, Brett Wade, Stuart

Rymph, Alberta Chiteka, Liana Jank, Marcos Freire, Jean Thomas, Celina Johnson, and

many others for their friendship and help during his graduate school life and wishes them

the best in their careers. Gratitude is also extended to visiting scientists, Ricardo Reis and

Dianelis Urbano, for the sharing of ideas and their friendship.

Special gratitude is extended to Dr. Leslie A. Simpson for his friendship and

encouragement in pursuing the opportunity for doctoral studies and to Richard N.

Cumberbatch and Godfrey A. Nurse for being friends and mentors. The efforts by Dr.

Ronald W. Rice to help the Macoon family reach the United States of America and his

special friendship, moral support, and help will forever be remembered and is recognized

with deepest gratitude. Finally, the author wishes to express his most sincere appreciation,

love, and gratitude to his wife, Yonette, for her love, endless understanding, and support

throughout their life together and more so during the doctoral program, to his sons, Ron

and Russ, for their unconditional love and for being the sources of his inspiration, and to

his mother, Agnes, for her love and for instilling the desire to seek knowledge.














TABLE OF CONTENTS


ACKNOWLEDGMENTS....................................................................................

LIST OF TABLES............................................................................................

LIST OF FIGURES............................................................................ ...........

A B STRA CT.....................................................................................................

CHAPTERS

1 IN TRODUCTION ..............................................................................

2 LITERATURE REVIEW..............................................................

Pasture-based Milk Production Systems........................... ..............
Economic Aspects of Pasture-based Milk Production.....................
Animal Responses to Management Variables..................................
Forage Responses on Grazed-pasture Dairy Systems......................
Plant-animal Interactions................................................................
Factors Influencing Intake of Grazing Animals...............................
Role of Grazing Time in the Plant-animal Interface..............................
Coat Color Relationships with Grazing Behavior and
Animal Performance.................................................................
Estimation of Intake on Pasture..................................... ................


3 GRAZING MANAGEMENT EFFECTS ON FORAGE PRODUCTION
AND ANIMAL PERFORMANCE OF LACTATING DAIRY
COWS ON SUBTROPICAL WINTER PASTURES.....................

Introduction.. .............................................................................
Materials and Methods...................................................................
Pasture Variables.....................................................................
Plant-animal Interface Variables............................... ...............
Animal Response Variables..................................... .................


page
iii

ix

xvii

xviii



1

8

8
9
12
16
21
22
34

39
42




53

53
57
60
62
65








Statistical Analysis........................................................................... 66
Results and Discussion................................................................... 69
Herbage Mass......................................................................... 69
Herbage Disappearance............................................................ 74
Herbage Allowance.................................................................. 78
Forage Nutritive Value............................................................ 81
Organic Matter Intake.................................................................... 86
Milk Production per Cow........................................................ 96
Milk Production per Hectare........................................................... 101
4% Fat Corrected Milk Production per Cow.................................... 103
4% Fat Corrected Milk Production per Hectare.............................. 104
Milk Composition............................................................................ 105
Animal Body Weight Changes.......................................................... 113
Body Condition Score Changes........................................................ 115
Blood Glucose Concentration............................................................ 116
Summary and Conclusions..................................................................... 117

4 GRAZING MANAGEMENT, COAT COLOR, AND SEASON
EFFECTS ON GRAZING BEHAVIOR AND PERFORMANCE
OF LACTATING DAIRY COWS................................................. 122

Introduction........................................................................................... 122
Materials and Methods........................................................................... 125
Statistical Analysis........................................................................... 130
Results and Discussion........................................................................... 133
Responses to Grazing Management.................................................. 133
Coat Color Effects on Grazing Behavior........................................ 168
Coat Color Effects on Animal Responses....................................... 175
Summary and Conclusions..................................................................... 181

5 COMPARISON OF THREE TECHNIQUES FOR ESTIMATING
FORAGE INTAKE OF LACTATING DAIRY COWS
ON PASTURE................................................................................ 187

Introduction........................................................................................... 187
Materials and Methods........................................................................... 191
Statistical Analysis........................................................................... 194
Results and Discussion.......................................................................... 196
Forage DMI Data for 1996............................................................ 196
Forage DMI Data for 1997.............................................................. 206
Summary and Conclusions..................................................................... 214

6 GENERAL DISCUSSION AND SUMMARY................................... 219








APPENDIX

TABLES OF CLIMATOLOGICAL DATA, VARIABLE LISTS,
RAW DATA AND PROBABILITY VALUES ASSOCIATED
WITH RESPONSES REPORTED IN THE DISSERTATION........ 232

REFERENCES................................................................................................. 291

BIOGRAPHICAL SKETCH ................................................................................... 305














LIST OF TABLES


Table page

3.1 Period by stocking rate (SR) interaction effect on pregraze herbage mass
during winter 1996........................................................................... 70

3.2 Period by forage system (FS) by stocking rate (SR) interaction effect on
pregraze herbage mass during winter 1997........................................... 71

3.3 Forage system (FS) by stocking rate (SR) by concentrate supplement (CS)
interaction effect on pregraze herbage mass during winter 1997............ 74

3.4 Period by forage system (FS) by stocking rate (SR) interaction effect on
herbage disappearance during winter 1997........................................... 75

3.5 Period by stocking rate (SR) interaction effect on herbage allowance
during winter 1996.................................................. .................... 78

3.6 Forage system (FS) by stocking rate (SR) by concentrate supplement (CS)
interaction effect on herbage allowance during winter 1997.............. 79

3.7 Period by forage system interaction effect on herbage allowance during
w inter 1997..................................................................................... ......80

3.8 Period by stocking rate (SR) interaction effect on pasture in vitro digestible
organic matter concentration during winter 1996................................. 82

3.9 Period by stocking rate (SR) interaction effect on pasture neutral detergent
fiber concentration during winter 1996................................. ....... 85

3.10 Period by stocking rate (SR) interaction effect on forage organic matter
intake (OMI) and total OMI during the 1996 winter grazing season....... 87

3.11 Period by stocking rate (SR) interaction effect on average daily forage
organic matter intake (OMI) and total OMI relative to cow body
weight, during the 1996 winter grazing season..................................... 88








3.12 Period by concentrate supplement (CS) interaction effect on forage
organic matter intake (OMI) and total OMI during the 1996 winter
grazing season........................................................ .................. 89

3.13 Period by concentrate supplement (CS) interaction effect on average daily
forage organic matter intake (OMI) and total OMI, relative to cow
body weight, during the 1996 winter grazing season............................ 90

3.14 Period by forage system interaction effect on forage organic matter intake
(OMI) and total OMI during the 1997 winter grazing season............... 91

3.15 Period effect on forage organic matter intake (OMI) and period by forage
system interaction effect total OMI, both relative to cow body weight,
during the 1997 winter grazing season............................... ......... 92

3.16 Forage system by stocking rate (SR) interaction trend on forage organic
matter intake (OMI) and total OMI during the 1997 winter grazing
season................................................................................................ 94

3.17 Period by stocking rate (SR) interaction effect on milk production per
cow during winter 1997.............................................................. 97

3.18 Period by stocking rate (SR) interaction effect on milk production per
hectare during winter 1997.................................................................... 102

3.19 Period by stocking rate (SR) interaction effect on 4% fat corrected milk
production per cow during winter 1997............................................... 104

3.20 Period by stocking rate (SR) interaction effect on 4% fat corrected milk
production per hectare during winter 1997.......................................... 105

3.21 Period by stocking rate (SR) interaction effect on milk fat concentration
during w inter 1997................................................................................ 106

3.22 Period by stocking rate (SR) interaction effect on milk crude protein
concentration during winter 1996........................................................ 108

3.23 Period by forage system interaction effect on milk urea nitrogen
concentration during winter 1997........................................................ 110

3.24 Forage system (FS) by stocking rate (SR) by concentrate supplement (CS)
interaction effect on milk urea nitrogen concentration during winter
1997............................................................................................... 111








3.25 Stocking rate (SR) by concentrate supplement (CS) interaction effect on
blood glucose concentration during winter 1997.................................... 117

4.1 Season by forage system by stocking rate (SR) by concentrate supplement
(CS) interaction effect on pre-graze herbage mass............................... 134

4.2 Season by forage system by stocking rate (SR) by concentrate supplement
(CS) interaction effect on herbage allowance....................................... 135

4.3 Season by forage system by stocking rate (SR) interaction effect on
herbage in vitro digestible organic matter concentration......................... 136

4.4 Season by forage system by stocking rate (SR) interaction effect on
herbage neutral detergent fiber concentration...................................... 137

4.5 Stocking rate (SR) by concentrate supplement (CS) interaction effect
on time cows spent grazing during daytime hours in summer 1996......... 139

4.6 Season by forage system by stocking rate (SR) interaction effect on total
time cows spent grazing during a 24-h cycle........................................ 144

4.7 Season by concentrate supplement (CS) interaction effect on total time
cows spent grazing during a 24-h cycle................................................ 145

4.8 Temperature (maximum, minimum, and average) and solar radiation
intensity recorded on pasture for observation days................................. 147

4.9 Season by forage system by stocking rate (SR) interaction effect on forage
organic matter (OM) intake................................................................... 154

4.10 Season by forage system (FS) interaction effect on forage organic matter
(OM) intake relative to animal body weight......................................... 155

4.11 Season by forage system by stocking rate (SR) interaction effect on total
organic matter (OM) intake................................................................... 158

4.12 Season by stocking rate (SR) interaction trend on total organic matter
(OM) intake relative to animal body weight......................................... 159

4.13 Season by forage system (FS) interaction effect on total organic matter
(OM) intake relative to animal body weight......................................... 160








4.14 Season by stocking rate (SR) interaction effect on average daily milk
production............................................................................................. 160

4.15 Season by forage system (FS) interaction effect on average daily milk
production............................................................................................. 161

4.16 Season by concentrate supplement (CS) interaction effect on average
daily milk production...................................................................... 162

4.17 Season by stocking rate (SR) interaction effect on animal body weight
changes................................................................................................. 166

4.18 Forage system (FS) by concentrate supplementation (CS) interaction
effect on animal body weight changes.................................................. 167

4.19 Temperature (maximum, minimum, and average) and solar radiation
intensity recorded on pasture for coat-color study observation days....... 170

4.20 Season by coat color effects on daily forage organic matter (OM) intake
for cows grazing summer and winter pastures...................................... 176

4.21 Season by coat color effects on daily total OM intake for cows grazing
summer and winter pastures.................................................................. 176

4.22 Season by coat color effects on average daily milk production for cows
grazing summer and winter pastures...................................................... 178

4.23 Season by coat color effects on 4% fat corrected milk production for
cows grazing summer and winter pastures........................................... 179

4.24 Season by coat color effects on average daily body weight changes for
cows grazing summer and winter pastures........................................... 180

5.1 Period by concentrate supplement (CS) interaction effect on forage dry
matter intake estimates predicted by the pulse dose marker
technique in 1996................................................................................... 197

5.2 Period by stocking rate (SR) interaction trend on forage dry matter (DM)
intake estimates predicted by the pulse dose marker technique in 1996... 198

5.3 Period by concentrate supplement (CS) interaction effect on deviation of
forage dry matter intake estimates predicted by the pulse dose marker
technique from base estimates in 1996................................................. 199








5.4 Period by stocking rate (SR) interaction effect on deviation of forage dry
matter intake estimates predicted by the pulse dose marker technique
from base estimates in 1996................................................................... 200

5.5 Period by stocking rate (SR) interaction effect on forage dry matter intake
estimated by the herbage disappearance technique in 1996................... 204

5.6 Period by forage system (FS) interaction effect on deviation of forage dry
matter intake estimates predicted by herbage disappearance technique
from base estimates in 1997.................................................................. 211

A-1 Climatological data starting from October 1995 to April 1997, i.e., from
seeding of winter pastures in the first year to end of the study period..... 233

A-2 List of abbreviations used to describe response variables in appendix tables
(in order of appearance)........................................................................ 234

A-3 Ingredient composition of concentrate supplement fed to animals in the
winter 1996 and 1997 grazing studies.................................................. 236

A-4 Chemical composition of concentrate supplement fed to animals in the
winter 1996 and 1997 grazing studies................................................... 237

A-5 Pregraze herbage mass (PRHM), herbage disappearance (HD) and
herbage allowance (HA) for each sample time within each period in
1996 reported in Chapter 3.................................................................. 238

A-6 Forage in vitro digestible organic matter (IVDOM), crude protein (CP)
and neutral detergent fiber (NDF) concentration for each sample time
within each period during 1996 reported in Chapter 3......................... 240

A-7 Probability levels for tests of fixed effects of period (P) stocking rate (SR),
and concentrate supplement (CS), and their interactions on response
variables associated with 1996 herbage data reported in Chapter 3......... 241

A-8 Pregraze herbage mass (PRHM), herbage disappearance (HD) and herbage
allowance (HA) for each sample time (week) within each period in
1997 reported in Chapter 3.................................................................... 242

A-9 Forage in vitro digestible organic matter (IVDOM), crude protein (CP)
and neutral detergent fiber (NDF) concentration for each sample time
(week) within each period during 1997 reported in Chapter 3................ 246








A10 Probability levels for tests of fixed effects of forage system (FS), stocking
rate (SR), concentrate supplement (CS), period (P), and their
interactions on response variables associated with 1997 herbage data
reported in Chapter 3........................................................................ 250

A- 1 Forage organic matter intake (FOMI), total organic matter intake (TOMI),
FOMI relative to body weight (FOMIBW), and TOMI relative to body
weight (TOMIBW) of cows during 1996 reported in Chapter 3............. 251

A-12 Probability levels for tests of fixed effects of period (P) stocking rate (SR),
and concentrate supplement (CS), and their interactions on response
variables associated with 1996 organic matter intake data reported
in Chapter 3.................................................................................... 252

A-13 Forage organic matter intake (FOMI), total organic matter intake (TOMI),
FOMI relative to body weight (FOMIBW), and TOMI relative to body
weight (TOMIBW) of cows during 1997 reported in Chapter 3............ 253

A-14 Probability levels for tests of fixed effects of forage system (FS), stocking
rate (SR), concentrate supplement (CS), period (P), and their
interactions on response variables associated with 1997 organic matter
intake data reported in Chapter 3......................................................... 255

A-15 Calculated daily nutrient intake by cows in the winter 1997 grazing study
for each forage system (FS) by stocking rate (SR) by concentrate
supplement (CS) treatment combination vs. NRC tabulated
require ents.......................................................................................... 256

A-16 Animal performance responses observed in 1996....................................... 258

A-17 Probability levels for tests of fixed effects of period (P) stocking rate (SR),
and concentrate supplement (CS), and their interactions on response
variables associated with 1996 animal performance data reported
in Chapter 3........................................................................................... 259

A-18 Animal performance responses observed in 1997....................................... 260

A-19 Probability levels for tests of fixed effects of forage system (FS), stocking
rate (SR), concentrate supplement (CS), period (P), and their
interactions on response variables associated with 1997 animal
performance data reported in Chapter 3............................................... 261








A-20 Milk composition and blood glucose concentration data obtained during
1996 reported in Chapter 3.............................................................. 262

A-21 Probability levels for tests of fixed effects of period (P) stocking rate (SR),
and concentrate supplement (CS), and their interactions on response
variables associated with 1996 milk composition and blood glucose
concentration data reported in Chapter 3............................................. 264

A-22 Milk composition, milk urea N, and blood glucose concentration data
obtained during 1997 reported in Chapter 3........................................... 265

A-23 Probability levels for tests of fixed effects of forage system (FS), stocking
rate (SR), concentrate supplement (CS), period (P), and their
interactions on response variables associated with 1997 milk
composition, milk urea N, and blood glucose concentration data
reported in Chapter 3............................................................................. 267

A-24 Program in SAS to compute new estimates of forage intake and total intake
based on adjusted diet digestibility of mixed forage-concentrate
supplem ent diet.................................................................................... 268

A-25 Program in SAS to compute parameters for fecal output calculations based
on chromium concentrations in fecal samples and time of fecal
collection............................................................................................... 268

A-26 Pregraze herbage mass, postgraze herbage mass, and herbage allowance
data obtained on 1996 summer pastures reported in Chapter 4............... 269

A-27 Forage nutritive value, organic matter intake, body weight and animal
performance data obtained in 1996 summer reported in Chapter 4......... 272

A-28 Probability levels for tests of fixed effects of forage system (F), stocking
rate (SR), concentrate supplement (CS), season (S), period within
season (P[S]), and their interactions on pasture response variables
reported in Chapter 4............................................................................. 273

A-29 Probability levels for tests of fixed effects of forage system (F), stocking
rate (SR), concentrate supplement (CS), season (S), period within
season (P[S]), and their interactions on animal response variables
reported in Chapter 4............................................................................. 274

A-30 Time spent grazing, loafing (under and outside shade), and eating
concentrate supplement during 1996 summer....................................... 275








A-31 Time spent grazing, loafing, and eating concentrate during 1997 winter....... 278

A-32 Probability levels for tests of fixed effects of forage system (F), stocking
rate (SR), concentrate supplement (CS), season (S), and their
interactions on animal grazing behavior reported in Chapter 4............. 280

A-33 Grazing behavior, organic matter intake, and animal performance of
animals on the coat color study in summer 1996.................................. 281

A-34 Grazing behavior, organic matter intake, and animal performance of
animals on the coat color study in winter 1997..................................... 284

A-35 Probability levels for tests of fixed effects of season (S), period within
season (P[S]), coat color (CC), observation day (OBD), and their
interactions on animal grazing behavior and animal performance
reported in Chapter 4............................................................................. 286

A-36 Forage dry matter intake predicted by different techniques during winter
1996 and the deviation of these estimates from the base estimate.......... 287

A-37 Forage dry matter intake predicted by different techniques during winter
1997 and the deviation of these estimates from the base estimate.......... 288

A-38 Probability levels for tests of fixed effects of period (P) stocking rate
(SR), and concentrate supplement (CS), and their interactions on
forage dry matter intake estimated by different techniques during
1996 reported in Chapter 5.................................................................. 289

A-39 Probability levels for tests of fixed effects of forage system (F), stocking
rate (SR), concentrate supplement (CS), period (P), and their
interactions on forage dry matter intake estimated by different
techniques and their deviation from base estimates during 1997
reported in Chapter 5............................................................................. 290














LIST OF FIGURES


Figure age

3.1 Regression relationships between pregraze herbage mass (HM) and herbage
disappearance (HD) for (a) all 1996 data combined, (b) high stocking
rate data for 1997, and (c) low stocking rate data for 1997.................... 77

4.1 Average daily temperature (AVT; OC) effects on daytime grazing (DG; a),
nighttime grazing (NG; b), and total grazing time (TG; c) during
sum m er................................................................................................. 150

4.2 Average solar radiation intensity (RAD; pmol m"2 s'") effects on daytime
grazing (DG; a), nighttime grazing (NG; b), and total grazing time
(TG; c) during summ er................ ........................................................... 151

4.3 Average solar radiation intensity (RAD) effects on time spent grazing during
daytime by black (BHC) and white (WHC) hair coat cows during
sum m er........................................................ .................................. 172

4.4 Average solar radiation intensity (RAD) effects on time spent under shade
by black (BHC) and white (WHC) hair coat cows during summer.......... 174

5.1 Relationship between estimates of forage dry matter intake (MI) predicted
by the pulse dose marker technique (PDME) and the deviation of these
estimates from the base estimates (DBE) in 1996................................. 201

5.2 Relationship between estimates of forage dry matter intake (DMI) predicted
by the herbage disappearance technique (HDE) and the deviation of
these estimates from the base estimates (DBE) in 1996....................... 205

5.3 Relationship between estimates of forage dry matter intake (DMI) predicted
by the pulse dose marker technique (PDME) and the deviation of these
estimates from the base estimates (DBE) in 1997................................ 209

5.4 Relationship between estimates of forage dry matter intake (DMI) predicted
by the herbage disappearance technique (HDE) and the deviation of
these estimates from the base estimates (DBE) in 1997.......................... 213


xvii














Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

FORAGE AND ANIMAL RESPONSES IN PASTURE-BASED DAIRY
PRODUCTION SYSTEMS FOR LACTATING COWS

By

Bisoondat Macoon

December, 1999

Chairman: Dr. Lynn E. Sollenberger
Major Department: Agronomy

In response to greater volatility in milk prices and higher costs of production,

Florida's dairy producers have begun to consider use of grazed-pasture for their lactating

cows. Insufficient research information is available to provide comprehensive grazing

recommendations. During 1996 and 1997, research was conducted to quantify forage and

animal production when lactating dairy cows grazed two cool-season forage systems (FS;

N-fertilized rye [Secale cereal L.]-ryegrass [Lolium multiflorum L.] pastures vs. rye-

ryegrass-crimson clover [Trifolium incarnatum L.]-red clover [Trifoliumpratense L.]

pastures) at two stocking rates (SR; 5 vs. 2.5 cows ha"') and two concentrate

supplementation levels (CS; 1 kg [as-fed] per 2.5 kg of milk vs. 1 kg per 3.5 kg).

Additionally, studies conducted in summer 1996 and winter 1997 evaluated management,

season, and cows' coat color effects on grazing behavior. A further study compared the


xviii








pulse dose marker and herbage disappearance techniques for estimating forage dry matter

intake (DMI) vs. the energy requirement for animal performance method.

Stocking rate was the key factor affecting forage production and animal

performance. Greater herbage mass occurred at low than high SR (1390 vs. 1020 kg DM

ha"') resulting in greater intake (17.6 vs 15.8 kg OM d-') and milk production per animal

(23.5 vs. 20.1 kg cow' d-'). Milk production per hectare was greater at high SR.

Production based on high SR, however, may not be sustainable because of loss in body

weight and condition. Animals fed at high CS had better energy balance and less body

weight loss. Grass-N fertilizer systems were more adapted to intensive grazing than grass-

clover systems in this environment.

Cows grazed for less time per day in summer (161 min) than winter (249 min)

because of heat stress. In summer, animals on rhizoma peanut pastures grazed less (179

vs. 143 min) but achieved greater forage intake (14.9 vs. 11.0 kg OM d"') and milk

production (17.2 vs. 14.9 kg cow"' d"') than cows grazing bermudagrass. In winter, cows

on low SR grazed longer (494 vs. 419 min d-') and achieved greater forage intake (12.5

vs. 10.2 kg OM d-') and milk production (20.0 vs 16.2 kg cow' d'') than those on high

SR. Cows with predominantly white coats grazed on average 13 min d1- longer than those

with predominantly black coats, regardless of season, resulting in higher intake (12.5 vs.

11.2 kg d-') and greater milk production (11.5 vs. 8.0 kg cow' d'') in summer but not in

winter. Forage DMI estimates obtained by the different techniques indicated that

predictions by the herbage disappearance method more closely matched energy

requirements compared to estimates by the pulse dose marker technique.














CHAPTER 1
INTRODUCTION


Animal products contribute to the dietary energy requirements of humans, and

protein from animal sources is especially important in human diets. The extent of this

contribution varies widely throughout the world depending on geographical location,

culture, and state of development (Henzell, 1983). Milk and milk products are major

components of the contribution of animals to human food needs; milk is the major

component of the dietary requirement of infants.

Historically, the major source of nutrients for milk- and meat-producing domestic

herbivores, largely cattle, has been forages. Native grasslands and improved pastures still

contribute the great majority of nutrients for cattle in many parts of the world, with

additional nutrients coming from crop byproducts. Since the mid- to late 1950s, the dairy

industry in the USA and other developed areas has adopted an approach to dairying that

involves much greater use of concentrate feeds, greater reliance on stored forage, and

removal of dairy cattle from pastures. This came about in an era of advances in scientific

knowledge that allowed for agricultural production to become more intensified. Since

then, milk production per cow has increased greatly, largely due to increased grain

consumption but also to improved cattle genetics and to the ability of animal nutritionists

to formulate diets that more accurately and uniformly meet the nutritional requirements of








2

the lactating cow. Total confinement systems of dairy production based on greater use of

concentrate feeds and stored forages require high capital inputs for buildings and

equipment, which, along with the cost of feedstuffs, incur high production costs.

Florida's dairy industry is an integral part of the state's agriculture with revenue

from milk sales generating more than $400 million annually in the last several years

(Florida Agricultural Statistics, 1999). Before recent legislation requiring use of animal

wastes to grow crops, dairies in Florida typically comprised large herds housed on

relatively small land areas and they grew little of their feed. Like the rest of the USA dairy

industry since the mid- to late 1950s, most Florida dairy farms are managed based on high

concentrate feed use, stored forage, and drylot feeding of total mixed rations (TMR) in

total confinement. On these farms, pastures contribute little as a source of nutrition for

dairy cows and are more used as exercise lots for dry cows and heifers. This system

concentrates nutrients in a small area, creating conditions where loss to the environment

can occur. Thus, nutrient management has become a major focus of regulatory agencies

and a major expense and constraint to profitability of producers. Recent regulations

require dairy farms to recycle nutrients from animal wastes into crop production.

Changes in market conditions in the dairy industry (Elbehri and Ford, 1995) and

growing pressure by regulatory agencies and environmental interests regarding nutrient

management on dairy farms have led to a search for alternatives to total confinement

systems. In recent years, dairying based on grazed pastures as sources of nutrients for

lactating cows is once again being considered. Improved forages and forage management

practices, improved and cheaper fencing materials to support rotational grazing, reduced








3

forage harvesting and feeding costs, possible improvements in herd health with animals off

of concrete, lower capital outlay and reduced day to day costs for handling animal wastes,

and an attempt to improve public perception of the dairy industry have been cited

(Bernard and Chandler, 1994) as reasons for reverting to pasture-based dairy systems. In

recent years, there has been a trend of adoption of pasture-based systems in temperate

areas of the USA (Parker et al., 1992). Some Florida producers have begun to adopt

similar systems during the last 5 yr. Insufficient information on pasture-based dairying in

Florida, however, precludes recommendation of widespread use of grazed pastures for

dairy cows. Research efforts are needed to address this void.

Studies in the northeastern USA have demonstrated that there are economic

benefits from incorporating intensive grazing in the dairy production system; however,

these benefits depend on the producers' managerial abilities (Elbehri and Ford, 1995).

There is a need for targeted extension and research programs to improve feeding

programs with grazing as well as the overall quality of pasture management. Fales et al.

(1995) contended that stocking rate was the key management variable in determining

productivity and profitability of grazing systems. It should be noted that in northeastern

USA dairies, because of the high protein concentration and digestibility of temperate

forages, there was little or no reduction in milk production when the system changed from

feeding in confinement to one in which the forage component of the diet was grazed

pasture (Fales et al., 1993). This situation may not be the same for the tropical and

subtropical forage grasses grown in Florida because they generally contain more fiber and

less protein and have lower digestibility than their temperate counterparts (Sollenberger








4

and Chambliss, 1991). It is questionable whether or not high producing dairy animals

grazing tropical grass pastures can achieve the level of nutrient intake required and cope

with heat stress adequately to maintain a profitable milk production enterprise. On the

other hand, Florida's climate allows for growing of cool-season forage crops during

winter months when temperatures in temperate areas of the USA prohibit crop growth.

This may be advantageous to Florida's producers by providing an opportunity for year-

round grazing management and perhaps decreasing dependence on grain consumption and

stored forages, and further, reducing waste management concerns compared to their

counterparts in temperate areas.

Developing an information base on which to make grazing management decisions

should include collection of data that help explain how animal responses are coupled to

pasture production and environmental conditions and how these components interact at

the plant-animal interface. It is well established that animal performance is largely

determined by nutrient consumption (Mertens, 1994). Nutrient intake and subsequent

animal performance of grazing animals in forage-based systems are dependent on forage

and non-forage factors (Moore, 1994). Forage factors include quality-related

characteristics and quantity, while non-forage factors include animal characteristics,

environmental conditions, and supplemental feed management (Moore, 1980). Factors

affecting forage intake may act through metabolic, physical, and/or behavioral control

mechanisms (Hodgson, 1985). Moore (1983) suggested that these control mechanisms

probably operate simultaneously, with one or the other being dominant depending on








5

pasture characteristics. Hodgson (1985) indicated that the behavioral mechanism may

override the others when herbage mass or herbage allowance are limiting.

The behavioral mechanism expresses forage intake as the product of grazing time,

bite weight, and biting rate. Bite weight and biting rate are influenced by animal and

sward characteristics, but biting rate and grazing time are considered the primary

compensatory responses of the animal to limitations in bite weight (Hodgson et al., 1994).

These authors reported that grazing time increases in response to limitations in intake rate.

Forbes et al. (1985) reported that there was evidence of differences in grazing time

between breeds of cows and between animals of differing physiological state. In cattle,

animals with higher energy requirements seem to spend more time grazing. Positive

relationships between grazing time and milk yield have been reported in the tropics

(Stobbs, 1970; Cowan, 1975). There also is evidence that black hair coat cows produce

less milk than white-coated cows under conditions of heat stress (Hansen, 1990), likely

because cows with black hair coat absorb more solar radiation than white hair coat cows.

No studies of coat color effects on grazing behavior in lactating dairy cows were found in

the literature, but there are anecdotal reports that dairy cows with predominantly white

coats spent more time grazing than cows with black coats in the southeastern USA.

Intake measurements may help to explain observed variation in animal outputs and

can be related to pasture characteristics. Estimates of forage intake by grazing livestock

are inherently difficult to obtain, and every method has unique advantages and

disadvantages. Several practical approaches to estimating pasture intake were suggested

by Moore (1996). These include estimates for individual animals as well as estimates for








6

groups of animals, or a pasture. Comparisons among methods may aid researchers in

choosing which is best suited for the objectives and conditions of particular experiments.

Deriving from problems facing the dairy industry and the related scientific

literature, a three-component research program was developed. The first component of

the dissertation research addressed grazing management of cool-season forages during the

winter months. The study was based on the null hypotheses that a) forage species does

not affect pasture production or animal responses, b) neither pasture nor animal responses

are a function of stocking rate imposed, and c) amount of concentrate fed does not affect

animal performance nor pasture intake. A grazing trial using lactating Holstein cows (Bos

taurus) was conducted to test these hypotheses. The objective was to quantify pasture

and animal responses when lactating dairy cows graze two different cool-season forage

mixtures (rye [Secale cereale L.] and ryegrass [Lolium multiflorum Lam.] mixed pastures

vs. rye-ryegrass-crimson clover [Trifolium incarnatum L.]-red clover [T. pratense L.]

mixed pastures) at two stocking rates (5 vs. 2.5 cows per hectare) and two levels of

concentrate supplementation (1 kg concentrate per 2.5 kg of milk produced vs. 1 kg

concentrate per 3.5 kg of milk produced).

A second component of the research examined grazing time of lactating cows on

summer and winter pastures. This experiment was based on the null hypothesis that

grazing time of lactating dairy cows is not influenced by pasture characteristics, rate of

supplemental feeding, coat color, nor prevailing climatic conditions. The overall objective

was to examine grazing management variables, coat color, and seasonal (summer vs.

winter) effects on grazing time of lactating dairy cows.








7

A third component of the research compared methods of estimating forage intake

of lactating dairy cows in both the summer and the winter grazing seasons. The three

methods of intake estimation for animals grazing pasture suggested by Moore (1996) were

studied, viz., the animal performance method, the pulse dose marker method, and the

herbage disappearance method. The null hypothesis was that these three methods of

estimating pasture intake are equally useful in determining intake of grazing dairy cows.

The objective was to compare differences in estimates among the three methods, which

may help identify the most useful approach or combination of approaches for estimating

pasture intake in pasture-based dairy production research.

As a result of these studies it is anticipated that applied and fundamental

knowledge will be gained. Specifically, data will be available to inform producer choices

of forage, stocking rate, and concentrate feeding rate in pasture-based systems.

Additionally, greater fundamental understanding will be gained of the pasture and animal

factors affecting grazing behavior, intake, and performance of lactating dairy cows.














CHAPTER 2
LITERATURE REVIEW


Pasture-Based Milk Production Systems


Historically, forages have been the major source of nutrients for cattle for both

meat and milk production. Native grasslands and improved pastures still contribute the

majority of nutrients for cattle in many parts of the world, with additional nutrients coming

from crop byproducts. The dairy industry in the USA and other developed countries has

adopted an approach to dairying that involves much greater use of concentrate feeds,

greater reliance on stored forages, and removal of dairy cattle from pastures. This

occurred during the 1950s, an era when advances in scientific knowledge led the way

toward more intensified agricultural production. Since then, milk production per cow has

increased greatly, largely due to increased grain consumption but also to improved cattle

genetics and to the ability of animal nutritionists to formulate diets that more accurately

and uniformly meet the nutritional requirements of the lactating cow (Mertens, 1986).

Present-day livestock production utilizes a substantial quantity of potential human

food crops. Grain fed to livestock accounted for more than 40% of the world's

production by the early 1980s (Henzell, 1983). Morley (1981) noted that pastures are

available for much of the year in many parts of the world, with minimal costs in non-

renewable resources because they are harvested by the grazing animal. Other sources of








9

livestock feed may not be available or are too costly in many regions of the world.

Improved management of pastures emphasizing increased efficiency of pasture utilization

can be the basis for improving human nutrition and welfare (Morley, 1981). Additionally,

ever changing economic conditions (Elbehri and Ford, 1995) and increased environmental

regulations have led dairymen to consider reverting from intensive to pasture-based

systems.


Economic Aspects of Pasture-Based Milk Production


More economical milk production was listed among reasons why some dairy

producers are reverting to utilization of pasture as a nutrient source (Bernard and

Chandler, 1994). Elbehri and Ford (1995) cautioned that economic impacts of grazing-

based dairies have not been extensively researched. Additionally, most of the reported

studies used the partial cost approach which does not account for such factors as fixed

costs, machinery costs, and labor requirement changes. They suggested that whole-farm

economic analysis are needed in order to quantify the benefits of grazing-based systems.

A comparative survey of Pennsylvania dairymen using or not using pasture-based

systems was conducted to acquire empirical data on which to make decisions relative to

adoption of intensive grazing systems (Parker et al., 1993). Of the dairymen using

pasture-based systems, 74% ranked reduced feed costs as the most important reason for

adopting grazing. Feed cost is estimated to account for between 50 and 60% of total

operating costs (Elbehri and Ford, 1995). Parker et al. (1993) found that feed purchase

costs were similar among systems, suggesting that pasture-based systems were not








10

capturing the potential savings in feed costs that were available to them. There were no

differences in average milk production between Holstein herds on grazed pastures

compared to conventional confined systems (7878 vs. 8153 kg cow"') in the surveyed area

(Parker et al., 1993). Overall operating costs tended to be lower on the pasture-based

systems. Tietz (1993) in an article summarizing research done by a University of

Minnesota group reported that the pastured cows produced less milk than barn-fed cows

in both years of the study but net returns favored the pasture systems because of cost

savings associated with less feeding of stored feeds, use of facilities and equipment,

manure disposal costs, and labor.

Miller and Schnitkey (1994) studied economic patterns and labor utilization in a

comparison of intensive grazing vs. conventional systems in Ohio. They reported that

total fixed cost per cow in the pasture system was 27 to 30% lower than in the

conventional system. Returns above total cost were higher than for most major grain

crops in the USA, hence grazing compared favorably to other cropping alternatives.

These authors suggested that returns from intensive grazing systems were dependent on

stocking rate (SR). Fales et al. (1995) found that on a per unit land area basis,

profitability increased with increased SR due to an increase in milk production per land

area. On a per cow basis, profitability was greatest at a low SR because of the value of

silage associated with maintaining similar production levels at each SR in their study.

Parker et al. (1992) compared an intensive grazing system to a drylot feeding

system using linked spreadsheet models and found that gross margin was $121 per cow

higher on the grazing farm. Their analysis suggested that the average Pennsylvania dairy








11

could reduce operating costs by $6,000 to $7,000 annually through intensive grazing, but

overall income would not be improved if milk production per cow fell by more than 450

kg per lactation. Elbehri and Ford (1995) evaluated economic impact of intensive grazing

for a representative Pennsylvania dairy farm using a simulation model. Under the

assumption of equal milk production levels, annual net cash farm income of the intensively

grazed farm was 14 to 25% higher compared to farms without intensive grazing systems.

In terms of net income per cow, intensive grazing farms generated between $140 and $207

per year more than farms without intensive grazing, depending on the forage scenario

modeled. It should be noted that the simulated economic benefits assumed above average

managerial expertise to produce forage crops of good yield and quality and good herd

management, particularly in providing well-balanced nutrients and cost-effective feeds

(Elbehri and Ford, 1995).

Data from surveys conducted in Wisconsin in 1993 and 1995 (Jackson-Smith et

al., 1996) showed that total returns to labor and management were, on average, highest

for fully intensive grazing operations ($16,034), less for semi-intensive grazing ($12,739),

and considerably lower for non-intensive grazing and total confinement systems ($7,855

and $10,680). Total farm income was $69,379 for fully intensive graziers, $99,414 for

semi-intensive graziers, $105,368 for non-intensive graziers, and $146,061 for

confinement operations.

Despite the demonstrated potential to improve profitability of dairy farms,

producers are cautious to change because of their lack of confidence in the ability of

pastures to provide high quality forage and the dearth of information describing grazing








12

systems and ration programs necessary to maintain milk production (Parker et al., 1992).

Jensen (1995) summarized the benefits of grazing vs. traditional dairy very well when she

suggested that the economic advantage appears to be for producers now starting a dairy

operation. Start up costs for grazing dairies run close to $1,900 per cow, less than half

the $4,300 per cow initial costs associated with the traditional confined dairy (Jensen,

1995). She also quoted a Florida producer, Ron St. John, suggesting that grazing was an

attractive way to expand, with fewer costs and more favorable reaction from local

residents.


Animal Responses to Management Variables


Animal performance on pasture is likely coupled to pasture quantity and quality

when all other variables are similar. Numerous management factors are involved in

determining the supply and quality of forage. Major factors include forage species,

grazing frequency, grazing intensity, season of year, soil fertility, and agronomic

management. Concentrate supplementation fed to animals on pasture and its inherent

associative effects (Moore, 1992) will also influence intake and subsequent performance of

grazing animals.

No recent reports in the USA comparing the effects of different forage systems on

performance of lactating cows were found in the literature. Moss and Lowe (1993) in

subtropical Australia found that milk production per cow was not different between N-

fertilized ryegrass and ryegrass-clover (Trifolium spp.) pasture systems. In Florida,

Baltensperger et al. (1986) compared responses from an N-fertilized rye-ryegrass mixture








13

and a rye-ryegrass-clover mixture with no additional N that were rotationally grazed by

steers. They reported that carrying capacity was not different between the two forage

systems. Average daily gains were higher on the grass-clover pastures in the first two

years of the study (0.8 vs. 0.6 and 0.8 vs. 0.5 kg d', respectively) and were similar in the

third year (0.9 kg d-'; Baltensperger et al., 1986).

Fales et al. (1995) contended that SR is a key management variable in determining

productivity and profitably of grazing systems. Milk production responses to SR occur

primarily through its effects on herbage allowance and intake. These authors suggest that

SR has not been researched adequately for high producing dairy cows in the USA. The

trend of milk production responses in several studies of SR effects in grazing dairy

systems appears to be lower production per animal and higher production per land area

with increasing SR (Davidson et al., 1997b; Fales et al., 1995; Kristensen, 1993; Moss and

Lowe, 1993). Body weight (BW) changes were not as definitive, possibly because of

confounding effects due to mobilization of body tissues by cows prioritizing milk

production (Moe et al., 1970; Orskov and Ryle, 1990).

In a study conducted in Pennsylvania, daily milk production (25 kg cow') was not

different among cows grazing temperate pastures at three different SR, mainly because

cows were managed to have similar production by feeding silage when pasture was

limiting (Fales et al., 1995). Milk fat concentration, crude protein (CP), or somatic cell

count (SCC) were not affected by SR treatments. Also, there were no SR effects on BW

or body condition score (BCS) changes. Milk production per unit land area was greater at

the high SR (Fales et al., 1995).








14

Moss and Lowe (1993) studied grazing dairy systems integrating irrigated

temperate pastures with rain-fed tropical pastures in subtropical Australia. They found

that milk production per cow increased when SR decreased on N-fertilized ryegrass or

ryegrass-clover pastures. In another Australian study in which tropical pastures were

grazed at 2.0, 2.5, 3.0, or 3.5 cows ha"', fat corrected milk (FCM) production per cow

decreased with increasing SR, but FCM yield per ha increased linearly with increasing SR

(Davidson et al., 1985). Forage dry matter (DM) on offer was closely related to milk

production in that study. Body weight tended to decrease with increasing SR, and there

were marked differences in BCS (data were not presented). More recently, Davidson et

al. (1997b) showed that milk production per cow decreased with increasing SR while milk

yield per unit land area had a positive linear relationship to SR. A study conducted in the

Irish Republic demonstrated that increasing SR above levels that allowed for adequate

forage supply resulted in decreased milk production (O'Brien et al., 1999). That study

also concluded that supplementation in the presence of adequate availability of good

quality forage was not economical.

Concentrate feeding has been the basis of intensive dairy production in the USA.

Pastures can potentially reduce costs associated with grain-based, stored-forage dairy

systems, but will require supplementation with concentrate for high producing dairy cows

to achieve optimum milk production (Kolver and Muller, 1998). Hoffman et al. (1993)

investigated effects of three levels of concentrate supplementation on performance of

lactating cows on rotationally stocked pastures. The treatments were 1) fixed levels of a

control grain at 1 kg per 3 kg milk, or 2) 1 kg grain per 4 or 5 kg milk, depending on








15

quantity of pasture available, and 3) grain reformulated biweekly based on pasture quality

at 1 kg per 4 or 5 kg milk depending on pasture availability. Average daily milk

production was not different among ration levels (range = 23.2 to 24.7 kg cow'). Cows

fed the control ration at the higher level gained more BW and had better BCS than those

fed less ration. Berzaghi and Polan (1992) found that grazing lactating cows fed 5.7 kg d-'

cracked corn had higher daily milk production than those not given any supplemental feed

(23.7 vs. 19.5 kg cow'). Supplementation with 2.3 kg d"' corn silage DM had no effect

on milk production, milk composition, BW changes, or BCS of Holstein cows grazing

temperate pastures (Holden et al., 1995). Total dry matter intake (DMI) of cows in that

study remained the same because forage intake was reduced with supplementation. Jones-

Endsley et al. (1997) showed that supply and digestion of nutrients in grazing dairy cows

may be improved through an increase in the CP concentration of supplement or the

amount of supplement offered. The effects of this on milk yield or milk components,

however, may be small. Kolver and Muller (1998) compared nutrient intake and animal

performance of high producing Holstein cows consuming pasture or total mixed ration

(TMR). They concluded that although high nutrient intake from pasture can be achieved,

loss of body condition required to maintain high milk production necessitates supplemental

energy and, in some cases, protein to achieve potential milk production.

A study in the UK evaluated the effect of sward characteristics, viz., tiller density,

of perennial ryegrass (Lolium perenne L.) and supplement type (based on high or low fiber

concentration) on herbage intake and milk production of grazing dairy cows (Fisher et al.,

1996). Sward, but not supplement type, affected herbage intake. Cows grazing swards








16

with high tiller density had greater intake than those on low tiller density swards (14.5 vs.

11.6 kg DM d'). There were no treatment effects on milk production, milk composition,

BW, or BCS change, but the authors suggested that small increases in energy intake from

sward and supplement effects may have been used primarily to ameliorate BW loss (Fisher

et al., 1996). The treatment sward characteristics were achieved by manipulating grazing

intensity as a prerequisite to the experiment, thus the relationship with SR. Studies using

this approach, viz., altering grazing management to achieve desired sward characteristics,

were also done in New Zealand. In one study (Holmes et al., 1992), swards were

manipulated so that pregraze herbage mass (HM) was either high (5.1 Mg DM ha"') or

low (2.9 Mg DM ha"'), and pastures were grazed by lactating dairy cows offered a

common daily herbage allowance. Cows grazing the low HM swards had higher milk

production, milk fat, and milk protein because of higher forage quality due to less

senescent material, lower concentrations of stem, more clover component, and higher

digestibility than the high mass counterpart. In a second New Zealand study

(Hoogendoorn et al., 1992), pastures were managed in the same manner to achieve low,

medium, and high HM swards but cows were now allowed a common daily allowance of

green leaf DM (total DM allowance varied widely). Milk production and composition did

not differ among treatments in this study.


Forage Responses on Grazed-Pasture Dairy Systems


Defoliation is the most important influence of the grazing animal on the pasture.

Grazing management allows the manipulation of (and defoliation is characterized by)








17

grazing frequency how often the sward is defoliated, grazing intensity how much of the

sward is removed at defoliation, and timing the stage of plant development or time of

season when defoliation is imposed. In addition, species of animal, prehension method,

treading, excreta deposited on pastures, and even saliva deposited on plants during grazing

are contributing factors that influence persistence, productivity, and botanical composition

of the sward and the regrowth rate of plants following grazing (Matches, 1992).

Few recent studies have been done in the USA to compare forage responses from

pasture systems based on different forage species for dairying. Casler et al. (1998) noted

that perennial cool-season grasses (used in pasture-based dairy systems) have historically

been bred and evaluated under hay managements with mechanical harvesting. These

researchers have begun development of a data base of forage yield and persistence

responses of cool-season grass cultivars to management intensive grazing systems (Casler

et al., 1998). Differences among and within species for herbage availability, apparent

intake, and ground cover were demonstrated in their study. Many of the USA grazing

studies with lactating dairy animals have been done in the Northeast, and have utilized

pastures with forage species that were already acceptable for use in grazed-dairy systems

(e.g., Fales et al., 1995; Hoffman et al., 1993; Jones-Endsley et al., 1997; Kolver and

Muller, 1998). It seems logical that forage species will affect pasture production and

quality, but apparently, evaluation of forage species impact on pasture-based dairying are

not deemed critically important for pastures based on temperate species in the northeast

USA.








18

A recent study in Missouri comparing responses from two ryegrass cultivars,

'Surrey' and 'Marshall' (Hafley, 1996), indicated that there was a tendency (p = 0.12) for

Surrey to have less forage mass but there were no differences in forage nutritive value

between the two cultivars. In Australia, Moss and Lowe (1993) reported more herbage

production in N-fertilized ryegrass pastures than in ryegrass-clover pastures (2.3 vs 1.5

Mg DM ha"') grazed by lactating dairy cows at SR of 5 or 10 cows ha"'. Average pasture

digestibility (750 g kg'') and CP (above 250 g kg-') were not different between forage

systems.

Stocking rate is a major determinant of both animal and plant production (Fales et

al., 1995; Kristensen, 1988; Le Duc et al., 1979). In a review, Hart and Norton (1988)

concluded that forage production usually decreases with increasing SR. With increased

SR, there is less herbage available per animal, thus animals become less selective in

choosing plant or plant parts (Matches, 1992). This likely leads to an increasing level of

plant defoliation along with changes in sward morphology and composition (Matches et

al., 1981). Maturity is the major factor affecting plant morphology and in determining

forage quality (Nelson and Moser, 1994), so grazing management effect on plant

morphology and maturity obviously will affect quality of the pasture sward. Increased SR

led to increases in young leaves in continuously stocked perennial ryegrass pastures, and

proportion of leaf in the canopy increased during regrowth following severe defoliation

with rotational stocking (Parsons et al., 1988; Parsons and Penning, 1988). Younger

leaves and high proportion of leaves has implications for improved forage nutritive value

and greater herbage intake (Minson, 1982; Nelson and Moser, 1994). When pastures are








19
underutilized due to lenient SR, animals tend to concentrate grazing on the shorter and

less mature vegetation (Hodgson et al., 1984), likely resulting in areas of progressively

more mature herbage in the sward, leading to a deterioration of overall pasture quality.

There is evidence of decreases in herbage production with increasing SR in recent

studies of grazed-pasture dairy systems (Fales et al., 1995; Hoogendoorn et al., 1992;

Moss and Lowe, 1993). Fales et al. (1995) conducted a 2-yr grazing study with lactating

dairy cows on pastures typical of many in the northeast USA. Temperate species

orchardgrass (Dactylis glomerata L.), Kentucky bluegrass (Poapratensis L.), and smooth

bromegrass (Bromus inermis L.) were dominant along with an assortment of herbaceous

weeds and very little clover. Pregraze HM was inversely related to SR in both years, a

result of greater accumulation of herbage during the season at lower SR. Pasture growth

rate (unit DM d"') tended to be directly related to SR in both years (Fales et al., 1995).

They attributed the differences in pasture growth rates to the positive effects of higher

grazing pressure on removal of old tissue and production of new growth as was discussed

by Bircham and Hodgson (1984). Fales et al. (1995) suggested that herbage removal is an

important consideration in pasture management because it reflects the efficiency of the

system. Herbage rejected in one cycle is unlikely to be grazed in subsequent cycles, hence

is wasted. Where SR is too low, herbage removal may not be optimal. Herbage nutritive

value was positively related to higher SR, due to more complete removal of leaf tissue at

higher grazing pressures during the first year of the study. Nutritive value differences due

to SR occurred mainly in summer when CP and in vitro dry matter digestibility (IVDMD)

were positively related and NDF concentration was negatively related to SR. In Australia,








20

low SR pastures had greater forage available for grazing than high SR (2.4 vs. 1.2 Mg ha'

for N-fertilized ryegrass pastures and 1.6 vs. 0.7 Mg ha7' for ryegrass-clover mixtures;

Moss and Lowe, 1993). Forage nutritive value was not affected by SR in that study.

Davidson et al. (1985) reported decreased forage DM on offer and increased CP

concentration with increasing SR on tropical pastures grazed by lactating cows. In

another more recent Australian study using lactating cows to graze kikuyu (Pennisetum

clandestinum Hochst.)-clover pastures, similar results were obtained, viz., pasture DM on

offer was highest at the lowest SR and CP concentration increased with increasing SR

(Davidson et al., 1997). On temperate pastures (mixed species dominated by perennial

ryegrass) in Denmark, there were large differences in HM production between severe

(high) and lax (low) SR treatments, with growth rates depressed significantly on the

severely defoliated treatment (Kristensen, 1988). The high SR treatments in that study

also had higher CP (262 vs. 229 g kg-'), greater leaf concentration (856 vs. 730 g kg-'),

and lower NDF (413 vs 491 g kg-'), but OM digestibility (731 vs. 719 g kg'') was similar

between SR treatments.

Although SR effects on the herbage production responses in the studies cited here

are similar, Matches (1992) cautioned that forage species may respond differently to

intensity of grazing and he indicated that no single description of plant response to grazing

is applicable to all swards under all environments.








21

Plant-Animal Interactions


Production from domestic livestock is largely influenced by the feed intake of

animals. Grazing systems are by definition dependent on forages; thus, any system of

forage-based animal production is concerned with growth, production, and management

of plants (Forbes et al., 1985). Economically viable livestock production from forages

depends largely on the quantity and quality of forage produced, the animal's capacity to

harvest and utilize that forage efficiently, and on the producer's managerial ability given

his resources (Forbes, 1988). Efficient grazing management systems require an

understanding of the role of the different components in a forage-based livestock

production system. Understanding the system as a whole rather than the system

components in isolation is a requirement for successful livestock production on grazed

pastures. Unfortunately, much of the forage-livestock research in the past focused on

examining output of a particular system rather than interactions between the grazed forage

and the grazing animal (Forbes, 1988).

The study of the plant-animal interface involves understanding the mechanism

whereby the components of the plant and animal systems interface or interact. The

specific interactions of interest are 1) forage characteristics effects on the grazing animal

(viz., effects on intake, digestibility, efficiency of nutrient utilization, and animal

performance) and, 2) how the act of grazing affects pasture growth and development,

persistence, composition, and quality. Forbes (1988) suggested that investigating








22

ingestive behavior of grazing ruminants is an integral part of the development of grazing

systems.


Factors Influencing Intake of Grazing Animals


Generally, factors influencing feed intake of the grazing animal can be categorized

as forage, animal, environmental, and management (including supplemental feed) effects

(Forbes et al., 1985; Hodgson et al., 1994; Moore, 1994). Minson (1982) reported that

the quantity of herbage eaten by the grazing animal depends on the availability of suitable

forage, the physical and chemical nature of that forage, and the nutrient requirements of

the animal. Forbes et al. (1985) indicated that intake of grazing ruminants is controlled

largely by the effects of diet composition, principally diet digestibility, on the rate of

disappearance of material from the reticulo-rumen, and by the effects of sward structure

on ingestive behavior. These two effects were discussed by Raymond (1969) as being

intrinsic and extrinsic factors controlling voluntary herbage intake.

Forbes (1988) reported that much work was done to develop stochastic simulation

models that predict forage production and animal consumption but in many cases lack of

suitable data has limited these efforts. Grazing animals spend much of their time obtaining

food, so it seems logical to assume that the forage being grazed will have large influences

on intake. Long-term intake control may be influenced by energy balance of the animal

(Moe and Tyrell, 1973), but short-term intake of meals is probably controlled by a

combination of plant structural attributes that affect rate of ingestion (Minson, 1981;

Moore and Sollenberger, 1986), the effect of masticated forage on gut fill (Forbes, 1996;








23

Kennedy and Doyle, 1993), and social behavior and environmental factors affecting the

appetite-satiety complex (Forbes, 1988). Baumont et al. (1990) concluded that feed

intake control has to be considered a multiple-factor process based on evidence that

showed sensory responses overriding satiety signals due to rumen fill. At a recent ASAS

annual meeting, a special symposium focused on several complex models which had been

proposed to explain and quantify regulation of intake and ruminal flows, and to predict

voluntary feed consumption and animal performance based on mathematical synthesis

mimicking in vivo performance (Allen, 1996; Forbes, 1996; Fisher, 1996). This

demonstrates that active work is ongoing to continue elucidation of the intake and

digestion process.

Intake control mechanisms

Factors affecting intake may act through metabolic, physical, and/or behavioral

control mechanisms (Hodgson, 1985). Variations in digestion and rate of passage of

forages in the digestive system of grazing animals may be determinants of, or responses to,

variations of intake. The metabolic mechanism (calculated as Intake [I] = digestible

energy intake [DEI] / digestible energy concentration [DE]) responds to chemostatic

sensory signals (Fisher, 1996) and assumes some upper limit on DEI. The DE

concentration of the diet determines intake. The physical or distension mechanism (I = gut

fill / retention time) is controlled by mechanical sensors (receptors in the stomach wall

sensitive to pressure; Allen, 1996) and assumes some upper limit on rumen fill. Retention

time of particles in the rumen (or rate of passage from the rumen) determines intake after

that limit is reached. When the behavioral mechanism (I = number of bites per day x bite








24

weight; where number of bites per day = biting rate x grazing time) is acting, there are

upper limits to both biting rate and bite weight. Compensating changes can occur in biting

rate when bite weight is reduced, but how much this results in reducing intake variation

depends on sward characteristics (Hodgson, 1994). In short swards, increase in biting rate

is unlikely to balance the reduction in bite weight. Grazing time is usually extended in

response to limitations in biting rate and bite weight, but this increase is seldom great

enough to compensate for reductions in intake rate (Hodgson, 1985).

These control mechanisms probably operate simultaneously, with one or the other

being dominant depending on pasture characteristics (Moore, 1983). The metabolic

mechanism applies to very high-quality forages (Waldo, 1986). In a rotationally stocked

pasture, the metabolic mechanism can be acting when forage quantity is not limiting and

when the animal has the opportunity the select high quality plants and plant parts. As

animals continue to graze, pasture quality may become limiting because the animals had

earlier selected the more highly digestible components of the canopy but quantity is not

yet limiting, so the distension mechanism becomes active. With further grazing, forage

quantity may become limiting which causes the behavioral mechanism to act because bite

weight is reduced due to limited forage availability. Thus, pasture conditions dictate

which mechanism is operating and in turn the act of grazing affects pasture characteristics

and their influence on the grazing animal. At the time when these ideas for intake control

mechanisms on grazed pastures were being developed, conventional wisdom was that the

intake control mechanisms switched from one to the other in response to the changing

conditions, but recently Forbes (1996) suggested that it is more of an additive nature.








25

Hodgson (1985) suggested that the behavioral mechanism may override the others when

herbage availability is limiting.

Forage characteristics affecting intake

Herbage intake responses to pasture characteristics are mediated by ingestive

behaviors that change in response to changing sward conditions (Forbes et al., 1986).

Forage factors affecting animal intake can be broadly classified as quantity and quality-

related characteristics. Quantity is generally expressed as HM, i.e., total amount of DM

on pasture during grazing or as herbage allowance, amount of herbage available per

animal, and depends on SR and accessibility of the canopy to the grazing animal. Quality-

related character of the pasture relates to chemical composition of the grazed herbage,

physical characteristics as it relates to plant anatomy and morphology, and canopy

structure viz., height, density, botanical composition, plant-part composition, and dead vs.

green tissue (Minson, 1981; 1982; Moore, 1994).

Herbage mass can have a major effect on intake. Forage quantity has to be

adequate to avoid restrictions to intake but pasture availability in excess of animal

requirement leads to an accumulation of mature material with low digestibility and intake

(Wilson and Minson, 1980). These factors have implications for managing pastures to

ensure adequate forage availability. Hodgson (1985) reported that intake per bite or bite

weight is the variable most directly affected by sward conditions, and normally falls

sharply as HM declines. The general result is a reduction in herbage intake when HM is

limiting. The amount of herbage available to the grazing animal is a function of both HM

and SR, expressed as grazing pressure (animal BW per unit HM) or herbage allowance








26

(the reciprocal of grazing pressure). High grazing pressure has been shown to decrease

intake but pasture utilization was reportedly improved on tropical grass pastures (Adjei et

al., 1980). Many models of relationships between grazing pressure (or herbage allowance)

and animal performance (Adjei et al., 1980; Duble et at., 1971, McCartor and Rouquette,

1977; Mott and Moore, 1985; Peterson et al., 1965; Stobbs, 1977) show increased

performance with increasing herbage allowance up to an optimum grazing pressure.

Several studies have reported increased intake at low vs. high SR when dairy cows grazed

temperate pastures (Hoogendoorn et al., 1992; Kristensen, 1988). Cows grazing tall

fescue (Festuca arundinacea Schreb.) pastures had a linear increase in DM intake per

grazing meal with increasing herbage allowances (Dougherty et al., 1992). Time spent

grazing was related to herbage intake because even at the highest herbage allowance,

available DM was still limiting so it appeared that cows grazed up to a particular horizon

then stopped. In a second experiment conducted on the same field (Dougherty et al.,

1992), herbage intake increased with increasing herbage allowance up to a point where

there were no further increases; herbage allowances this time covered a higher range and

herbage availability was not limiting at the higher levels. The higher rates of intake were

attributed to larger DM intake per bite in the second experiment.

Sward height and density of tillers are factors that contribute to HM on a given

area of pasture. In temperate pastures, there is a body of evidence supporting a positive

relationship with these two factors and herbage intake (Dougherty et al., 1992; Fisher et

al., 1996; Forbes, 1988; Gibb et al., 1997; 1999; Hodgson, 1985; Kristensen, 1988). In

fact, Forbes (1988) reported that results from a number of studies in the UK indicate








27

sward height is the primary factor influencing intake and, hence, production. This led to a

series of grazing systems studies concerned with managing pastures based on sward

height. Hodgson (1985) reported that depth of the leafy horizon of a sward appears to set

an effective limit to the depth of a bite thus influencing bite weight. Additionally,

Dougherty et al. (1992) reported that cattle stopped grazing after a particular horizon.

Sward density, or number of tillers per unit area, seems to have a positive effect on

herbage intake in temperate pastures, mainly because of larger bite weight (Dougherty et

al., 1992). Hodgson (1985) stated that variations in bite weight appear to be attributable

to variations in sward height for temperate pastures. In tropical pastures on the other

hand, bite weight was found to decrease with increasing sward height (Forbes, 1988). The

reason given to explain this contrast was generally lower bulk density with greater height

in the tropical swards. Hodgson (1985) cautioned that there are apparent conflicts in the

results of some of these field studies. Laca et al. (1992) found that even in homogeneous

swards, both density and height are necessary to predict bite weight but reported that bite

weight was more sensitive to sward height than to bulk density. Findings from a more

recent study, focused on tiller density characteristics as a main effect, support a positive

relationship between tiller density of a sward and herbage intake on temperate pastures

(Fisher et al., 1996).

Apart from being a major determinant of quality and quantity of forage consumed,

herbage allowance determines the opportunity for selection. In most practical conditions,

there is sward heterogeneity in both vertical and horizontal dimensions and with time

(Hodgson et al., 1994). Particular characteristics of the sward may stimulate selective








28

intake by the grazing animal conditioned by environment and plant factors. Selective

grazing occurs when an excess of forage is available, and may be tempered by plant

species composition of the sward and their canopy structure (Lascano, 1987; Sollenberger

et al., 1987). The focus of many studies of sward canopy structure effects on selective

grazing has been on vertical and horizontal distribution of plant components. In studies of

vertical distribution, it has been shown in temperate pastures that there is little difference

between composition of the diet and that of the upper strata of canopy within which the

animals were known to be grazing (Hodgson et al., 1994). These authors caution,

however, that it would be ridiculous to argue for nondiscriminatory selection being the

general case since other studies have demonstrated that this is not so.

Degree of selectivity may be due to difference in maturity of vegetation and its

constituent species, morphological characteristics of particular plant species, and

differences in grazing strategy between different animal species (Hodgson et al., 1994)

Animals generally select leaf in preference to stem, green and young tissues in preference

to dead or old tissues, particularly in mature pastures, or a specific plant species in

preference to another (Minson, 1981). Often, the selected plant or plant part is higher in

CP, soluble carbohydrates, and digestibility and lower in structural carbohydrates than the

pasture as a whole. Animals seem to select for plants or plant parts that allow for

increased voluntary intake (Minson and Wilson, 1994). Minson (1982) reported that this

is often positively correlated with digestibility but may be more related to physical factors

of the plant material and how it relates to rate of passage of indigestible residues through

the rumen. Burs et al. (1991) reported that differences in DMI were due to differences in








29

diet IVDMD rather than digesta kinetics. Hodgson et al. (1994) suggested that selection

strategy was based on visual and olfactory/gustatory cues mediated by the effects of

physical and structural characteristics of vegetation influencing ease and rate of intake.

They reported that there is clear evidence that choice is strongly influenced by potential

intake rate and these results appear to hold good for substantial ranges of variation in

sward structure.

Dairy cows grazing temperate swards with higher green leaf concentration, less

dead material, and higher organic matter digestibility did not have higher intake than cows

on less digestible pastures in a New Zealand study (Holmes et al., 1992). This intake

response is confounded, however, by herbage availability, thus opportunity for selection,

since the less digestible (more mature) pastures also had higher herbage allowance. In a

second experiment with the same sward characteristics but with common herbage

allowance, herbage intake was greater on swards that had greater concentration of green

leaf DM and higher digestibility (Hoogendoorfet al., 1992). In a Danish study, Kristensen

(1988) found that herbage intake was depressed by increasing herbage maturity due to

lower nutritive value, but in particular due to reduced green leaf and increased

reproductive development. These observations of increased intake due to the presence of

greater leaf concentrations may be related to shorter retention time in the rumen of leaf vs.

stem tissue, even at similar digestibility, leading to higher intake as noted by Minson

(1982).










Animal characteristics affecting intake

The animal itself is one of the factors affecting animal performance (hence nutrient

intake) in forage-based livestock systems (Moore, 1994). Ingestive behavior and grazing

selectivity can be influenced by interactions among the effects of variation in body size,

mouth dimensions, and productive state within and between animal species, and by

species-specific differences in behavior, some of which may relate to body shape and jaw

size (Hodgson et al., 1994). Increased nutrient demand (as with lactating vs. non-lactating

cows) will usually increase forage intake. Thus, both grazing time and herbage intake may

be substantially higher in lactating than in non-lactating cows, and thin animals may have

higher rates of intake than fat animals, though the relative magnitude of these effects may

differ in different circumstances (Hodgson, 1985). Evidence to support these observations

was recently demonstrated by Gibb et al. (1999). Generally, patterns of response in

ingestive behavior or herbage intake to variations in sward characteristics appear to be

similar for sheep and cattle and, within animal species, for different age and productive

state (Hodgson, 1985).

In each of the intake control mechanisms described earlier, there are animal- and

plant-dependent components that set limits to feed intake. The animal-dependent

component of the metabolic mechanism is the DEI requirement of the animal, a function of

species, physiological state, type, production situation, etc., and intake is regulated mainly

by chemostatic feedback (Fisher, 1996; Forbes, 1996). In grazing situations, the herbage

intake of productive animals is seldom affected by metabolic limits (Hodgson, 1985). In

the distention mechanism, rumen fill is the animal factor, and limits are set by capacity of








31

the reticulorumen (Allen, 1996; Fisher, 1996). It has been suggested that both cattle and

sheep eat to a constant level of rumen fill but this does not appear to hold true for all

ruminants (Forbes et al., 1985). It was demonstrated that rumen fill in sheep can be

variable, responding to a number of factors including protein nutrition, and does not

operate as a mechanism controlling intake at a preset level. Baumont et al. (1990)

demonstrated that sensory responses induced by supplementary distribution of palatable

hay is sufficient to override the satiety signals due to rumen fill in sheep.

Limitations on number of bites per day is primarily an animal factor in the

behavioral mechanism. Other animal-dependent factors that play a role in controlling

herbage intake of grazing animals have to do with the animals requirement for rest,

drinking, or carrying out social activities, and also the effect of environmental conditions

on these activities (Forbes et al., 1985). These factors likely act upon time spent grazing.

Concentrate supplementation effects on intake

Supplemental feed has two important consequences when introduced into forage

feeding systems; its direct influence on animal performance and its effects on/interactions

with forage consumed (Mott and Moore, 1969). Raymond (1969) noted that

understanding the biological basis of feed interactions, including the effect of other

components of the total ration on amount of forage that the ruminant will eat, is

important. Feeding supplemental concentrates in restricted amounts can result in an

increase, a decrease, or no change in herbage intake (Caton and Dhuyvetter, 1997; Moore,

1992; Raymond, 1969). When concentrates are fed as supplements, animal responses may

not always be as expected. Deviations between expected and observed performance may








32

be due to effects of concentrates upon voluntary intake of herbage (Moore, 1992). Also,

there may be associative effects such that digestibility of the total diet is not the same as

that calculated from the weighted intake and digestibilities of the components of the diet

(Merchen and Bourquin, 1994). Associative effects between concentrates and forages can

result in metabolizable energy (ME) concentrations of the mixed diet being higher or

lower than the expected values (Dixon and Stockdale,1999; Moe, 1981; Moore, 1992).

Herbage intake changes due to supplemental feed appear to be related to forage

characteristics such as potential intake when fed alone, digestibility and CP concentration,

and supplement characteristics such as amount, DE concentration, source (starch vs.

fiber), and protein concentration and type (ruminally degradable vs. undegradable; Moore,

1992; 1994; Raymond, 1969). Generally, forage intake will decrease as increasing

amounts of supplementary concentrates are fed. This decrease is more marked with

forages of high digestibility (Moore, 1994; Raymond, 1969), when other nutrients are in

balance with energy, and concentrates are fed in large amounts (Moore, 1994). In some

cases, small amounts of concentrate may increase voluntary forage intake when forages

are of low quality, especially when they have high ratios of total digestible nutrients

(TDN) to CP (Caton and Dhuyvetter, 1997; Moore, 1994; Moore et al., 1995). Positive

associative effects, where supplemental concentrate feeding increases forage intake, are

usually due to provision of a limiting nutrient (e.g., N, P) in the supplement which is

deficient in the forage (Dixon and Stockdale, 1999). Delagarde et al. (1999)

demonstrated that supplementation with soybean meal increased herbage intake by way of

improved ruminal digestion due to input of N for dairy cows grazing perennial ryegrass








33

pastures fertilized with low levels of N. Supplementation with energy-based concentrates

did not affect herbage intake in their study.

Decreases in forage intake due to concentrate feeding is called substitution of

forage with concentrate. When substitution occurs, animal response to supplemental

energy concentrates may be lower than expected based upon calculations for the forage

fed alone and the amount of concentrate being fed (Moore, 1994). Reductions in ruminal

pH, often cited to be the cause of fiber digestion reduction, may not always explain

reduced forage intake and digestibility associated with energy supplementation (Caton and

Dhuyvetter, 1997).

Jones-Endsley et al. (1997) fed mid-lactation Holstein cows grazing alfalfa

(Medicago sativa L.)- orchardgrass pastures with supplement of different CP

concentration (120 or 160 g kg-' on a DM basis) at 6.4 or 9.6 kg cow"' d"'. Forage intake

tended to increase when CP in supplement increased but was unaffected by amount of

supplement. They concluded that supply and digestion of nutrients in grazing dairy cows

may be improved through an increase in CP concentration of the supplement or the

amount of concentrate offered. This did not, however, have much effect on milk

production or composition in their study. In a Virginia study, lactating cows grazing

permanent pastures with a supplement of 5.7 kg d"` cracked corn had lower forage intake

than those that were not given any supplement, but total intake was greater for the

supplemented animals (Berzaghi and Polan, 1992). Feeding supplemental corn silage to

lactating cows that were intensively grazing primarily orchardgrass pastures demonstrated

that each unit of corn silage replaced 1.2 units of pasture, but total DMI was not different








34

from those of animals that did not receive any corn silage (Holden et al., 1995). Mueller

et al. (1995) reported that, in general, the drop in forage intake that occurs with grain

feeding is less than the increase in grain intake, so total DMI is increased. Substitution

rates (decrease of forage intake per unit of concentrate feed) in dairy cows vary, but in

general, pasture DMI decreases about 0.5 to 0.9 units per unit of grain (Muller et al.,

1995).


Role of Grazing Time in the Plant-Animal Interface


Behavioral limits to herbage intake may be the overriding intake control

mechanism functioning in grazing animals (Hodgson, 1985). Intake per bite or bite weight

is considered the dominant influence on herbage intake (Forbes et al., 1985; Hodgson,

1981; 1985; Moore and Sollenberger, 1986). Biting rate and grazing time are considered

the primary compensating responses of the animal to limitations in bite weight. Hodgson

et al. (1994) reported that the general rate of jaw movement prehensionn, biting, and

chewing) is remarkably constant. They suggested that variations in biting rate (bites min"')

reflect variations of the relative proportions of the three jaw movements to obtain a

particular bite, and are therefore largely determined by the manipulation necessary to graze

effectively in swards of different structure. Evidence suggests that on tall swards this can

result in substantial reduction in biting rate but compensating changes which can occur in

intake per bite and biting rate result in little variation on short-term herbage intake

(Hodgson et al., 1994). On shorter swards, however, increase in biting rate does not

balance reduced bite weight.








35

Animals will extend grazing time in response to limitations in intake rate

(Hodgson, 1981). It is believed, however, that there is a limit to grazing time extension in

a given day so the degree of compensation is again limited (Hodgson, 1985). Grazing

time may increase as soon as sward conditions start to limit short-term rate of herbage

intake. Hodgson (1994) surmised that there currently is no good basis for quantifying this

relationship or for relating it directly to sward conditions. They suggested that although

bite weight may be the primary animal response to variations in sward conditions, the

value of studies of grazing behavior effects on herbage intake will ultimately depend on

the effectiveness with which herbage intake predictions can be associated with adaptive

changes in grazing time.

Grazing time has two dimensions; one in the context of the behavior of animals as

a whole and the other as a variable influencing herbage intake. Research on grazing time

as a component of grazing behavior has shown that, in general, domestic animals likely

spend about one-third of the day actually grazing. Forbes et al. (1985) reported that this

is influenced by animal characteristics and sward conditions and may range from

approximately 4 to 13V2 h.

There have been some reports of differences in grazing time between cattle breeds

(Forbes et al., 1985). Recent studies in two Texas environments did not detect any

differences in grazing time among various purebred and crossbred heifers (Forbes et al.,

1998), suggesting that breed differences depend on degree of adaptability of animals to

environmental conditions. There is also evidence of differences in grazing time (and

intake) due to different physiological state, with lactating cows grazing longer than dry








36

cows (Forbes et al., 1985; Gibb et al., 1999). Younger animals may graze longer than

mature animals but the evidence for this is not conclusive (Forbes et al., 1985).

Grazing time appears to increase when a decline in herbage availability limits

intake per bite (Allden and Whittaker, 1970; Gibb et al., 1999; Popp et al., 1998). Gibb et

al. (1999) demonstrated that this phenomena occurred for lactating as well as dry Holstein

cows in response to varying sward heights grazed either continuously or rotationally, and

was sufficient to compensate for reduced bite weight so that average daily herbage intake

was similar among sward heights. In a 3-yr study, Popp et al. (1998) reported that cattle

spent less time grazing as herbage availability increased in predominantly alfalfa pastures

grazed either continuously or rotationally. The longer grazing time on the heavily stocked

pastures was not sufficient to compensate for reduced intake in late season of the first

year, but intake was similar in the subsequent years. Rate of intake (product of bite

weight and biting rate) tended to be greater (p < 0.10) at the higher forage availability

(Popp et al., 1998). Sward characteristics other than factors related to herbage availability

also influence grazing time because of their influences on rate of intake, i.e., grazing time

increases when rate of intake decreases (Forbes et al., 1985). Canopy structure and

composition (Minson, 1981; 1982; Moore and Sollenberger, 1986), and green leaf and

dead tissue fraction (Hodgson, 1985; Minson, 1981; 1982) have been shown to influence

bite weight and bite rate. Whether or not extension of grazing time is sufficient to

compensate for reduced intake rate depends on the particular situation (Gibb et al., 1999;

Popp et al., 1998), but some evidence suggests that it is seldom great enough (Gibb et al.,

1997; Hodgson, 1981). Gibb et al. (1997) suggested that increased grazing time as a








37

response to reduced intake rate is limited by the requirement for ruminating and non-

grazing, non-ruminating activities, which is influenced by qualitative and quantitative

aspects of the herbage ingested. Their study suggests that managing sward conditions to

ensure maximum intake rate is the preferable option, since the cows' only effective

strategy to compensate for any reduction in intake rate is to increase grazing time and,

given the limitations to grazing time extension, the amount of compensation required to

balance daily intake may not be met.

Not many reports of supplementation effects on grazing time were found. In one

study, Krysl and Hess (1993) reported that cattle given supplemental feed spent less time

grazing than those not receiving any supplemental feed.

Grazing during a 24-h cycle is marked by periodicity. The evidence suggests that

animals graze on four to five occasions during the day (Forbes et al., 1985). These

occasions may not have a strict pattern, but the major periods in terms of length of grazing

occur at dawn, mid-morning, mid-afternoon, and at dusk (Cowan, 1975; Gary et al., 1970;

Gibb et al., 1997). The length of each period of grazing is determined largely by the rate

of intake; i.e., faster rate of feed ingestion results in quicker gut fill or achieving satiety,

and is thus dependent on sward conditions. Chilibroste et al. (1997) found that grazing

time and DMI, were greater after longer periods of fasting and were reduced by the

presence of indigestible material in the rumen. Numbers of grazing periods likely are

dependent on rapidity with which the rumen is filled and subsequently emptied, and by

environmental factors and length of the day, because there is evidence of a marked

influence of sunrise and sunset on initiation and cessation of grazing (Forbes et al., 1985).








38

Gary et al. (1970) reported that temperature was not correlated with the different grazing

behavior on different days. It should be noted, however, that in their study (conducted in

Pennsylvania) mean daily temperature was 19.3C with a 3.6 standard deviation, which

may not represent sufficient extremes in temperature to demonstrate any differences in

grazing behavior due to effects of temperature.

Night grazing makes up only a small proportion of total grazing time in temperate

areas (Gary et al., 1970) but may increase with increasing daylength (Forbes et al., 1985).

Night grazing can be a significant contributor to total grazing time in tropical regions

(Cowan, 1975; Stobbs, 1970). Recent research with sheep demonstrated that there may

be compensatory responses to restriction from grazing during the night (lason et al.,

1999). They found that sheep that were fasted during the night extended daytime grazing

by fewer, longer bouts compared to sheep that had continuous access to pasture, but total

grazing time in a 24-h period was still shorter. Also, on pastures where forage availability

was not limiting, overnight restricted sheep had larger intake rates mainly via larger bite

weight and were able to counteract the reduction in total grazing time. In a another study

that examined two durations of fasting (16.5 and 2.5 h) during the night before grazing,

both DMI and grazing time for the first bout of grazing were greater with longer fasting,

but bite weight and biting rate were not affected by period of fasting (Chilibroste et al.,

1997). It would have been interesting to see what responses would occur had the

researchers included a restricted-during-the-day-graze-at-night treatment to explore the

possibility of compensatory night grazing.








39

Animals must make decisions whether to continue grazing at low rate of intake on

certain swards at some stage. In conditions of severely limited feed intake due to the

physical absence of feed or a lack of desire on the animals part to continue grazing the

herbage on offer due to poor pasture characteristics, it may be advantageous for animals

to reduce energy expenditure by reducing grazing activity (Forbes et al., 1985). There is

evidence to suggest that when animals are moved from good pasture to poor pasture their

energy expenditure is reduced (Young and Corbett, 1972), likely due to less grazing

activity, though their energy intake will be less also. Gary et al. (1970) suggested, based

on their observations, that there may be group facilitation of behavior, so the decision to

stop grazing may be simultaneous.


Coat Color Relationship with Grazing Behavior and Animal Performance


Typical Florida weather conditions are considered adverse for high producing dairy

cows. High temperature, solar radiation, and humidity are common, particularly during

summer and can result in stress to dairy cattle (Collier et al., 1982). Decreased milk

production (Becerril et al., 1991; 1993; Hanson, 1990), changes in milk composition

(Becerril et al., 1993; Schleger, 1967), and poorer reproductive performance (King et al.,

1988) can result from climatic stress. High temperature has been shown to depress

growth, reduce production, and lower reproductive efficiency in several species of

domestic animals (Campbell and Lasley, 1985). The principal cause of decreased milk

secretion in hot weather is lowered feed consumption (Campbell and Lasley, 1985; Collier

et al., 1982). Incident solar radiation seems to have overwhelming importance for the heat








40

balance of animals (Walsberg et al., 1997) because it can directly increase body

temperature. The heat load on cows' bodies from solar radiation is produced by

absorption of light and associated heat on the animal surface exposed to sunlight. This

along with other climatic factors can result in heat stress.

Absorptivity, the ability of an object to absorb radiation, is a measure of the

proportion of radiant energy that is absorbed when the object comes in contact with that

energy. Textbook values for absorptivity for solar radiation of black coat cattle is 0.90

while for white coat cattle the value is 0.50. Thus, the amount of heat absorbed from

solar radiation is directly related to hair coat color of cattle. Breed development in cattle

used color patterns as a trade mark, but coat color relationships with production and

reproduction were not always considered. Becerril et al. (1993) reported that evidence of

studies investigating relationships between coat color and animal performance began

surfacing in the late 1920s and early 1930s.

In a study conducted during summer months, Hansen (1990) reported that a

number of physiological variables including rectal temperature, skin surface temperature,

and respiration rates increased for Holstein cows kept in a dry lot with no shade provided

vs. cows kept in a concrete-floored shed open on all four sides. Moreover, the difference

in magnitude of these responses in unshaded vs. shaded environments was greater for

black coat cows than for white coat cows. Milk production tended (P = 0.106) to be

influenced by a similar interaction. The depression in milk production caused by keeping

cows in the unshaded environment was 1.5 kg cow' d`' for white coat cows and 3.3 kg

cow" d" for black coat cows. These results suggest that coat color may influence








41

resistance to heat stress in environments characterized by high solar radiation (Hansen,

1990). Intensity of coat color was demonstrated to have strong relationships with milk

production and milk fat production in nine commercial Australian Illawarra Shorthorn

herds (Schleger, 1967). Lighter colored animals produced higher milk yield. Schleger

(1967) also noted that this relationship tended to be stronger in lower producing herds.

Analysis of data from records of first lactation dairy cows in Florida demonstrated a linear

relationship between milk yield and percentage of white coat (Becerril et al., 1991; 1993);

fat percentage declined with increasing percentage white coat but actual fat production

tended to be higher in cows with more white coat (Becerril et al., 1993). Most cows for

which the data were obtained were producing in management conditions designed to avoid

heat and climatic stress; shade, fans, and sprinklers were provided. King et al. (1988)

reported that coat color or seasonal effects did not affect milk production but white coat

cows had better reproductive efficiency than black coat cows when freshened during

summer months, requiring fewer services per conception and having fewer open days.

These data were from a commercial Holstein herd in Arizona. The authors suggested that

cooling facilities provided during the first 130 d of lactation may have removed any

advantage white cows may have held (for milk production) during summer months. None

of the studies of coat color effects in dairy animals cited in this review reported feed intake

data associated with the observed performance levels nor were any of them conducted on

pasture.

Reports of studies examining coat color effects on grazing behavior were not

found in the literature. There are anecdotal reports that white coat cows spent more time








42

grazing than black coat cows in the southeastern USA. Godfrey and Hansen (1996)

analyzed 27 years of records of Holstein cows on the Caribbean island of St. Croix (where

the feeding system is based on grazing) to determine the influence of hair coat color and

season on reproduction and lactation. The data indicated that there were seasonal patterns

of reproduction and milk yield, likely related to forage availability, but no influence of hair

coat color (Godfrey and Hansen, 1996). The authors suggested that with a feeding system

based on grazing, nutritional influences may override the effect of coat color on milk yield.

Coat color is a fairly heritable trait (Becerril et al., 1991; 1993; 1996; King et al.,

1988) should it be determined to be useful to select for favorable coat color where cattle

are in hot environments. Becerril et al. (1993) found that small positive increases in

heritability estimates occurred for production and reproductive traits when percent white

coat was included in the model to estimate heritability, but suggested that the differences

may not be important.


Estimation of Intake on Pasture


Quantifying DMI in grazing systems is necessary for estimating nutrient

consumption by grazing animals. Nutrient consumption is the product of DMI and

nutrient concentration of the DM consumed. Estimation of daily quantity of nutrient

consumed on pasture is critical in coupling animal performance and pasture characteristics,

and in quantifying nutrient recycling in the soil-plant-animal system. Forage nutrient

concentration may be estimated by accurate sampling and analysis of the grazed herbage

and is routine in forage research. In grazing systems it is important but inherently difficult








43

to quantify forage intake (Burns et al., 1994; Moore, 1996; Reeves et al., 1996). Many

methods have been developed but choice of technique is likely determined by available

resources and the experimental objectives. Techniques for estimating intake on pasture

may be based on the use of internal or external markers, ingestive behavior, disappearance

of herbage mass, prediction from forage characteristics, and animal performance (Moore,

1996). All of the commonly used techniques have unique advantages and disadvantages

and results from all methods are only estimates of intake with an associated error that

varies in magnitude (Burs et al., 1994; Moore, 1996). While none of the methods are

completely adequate, it may be possible to obtain precise measurements of differences

among pastures at specific times (Moore, 1996). Better accuracy is required, however,

for them to be useful in quantitative applications (Burs et al., 1994).

Moore (1996) categorizes the commonly used approaches to estimating forage

intake by grazing animals based on suitability as estimates for a) individual animals, or b)

groups of animals or a pasture. Approaches considered suitable for estimates of intake in

individual animals are techniques based on the use of markers or on ingestive behavior,

while estimates for groups of animals or a pasture are techniques based on disappearance

of herbage mass, prediction from forage characteristics, or calculations of energy

requirements for observed animal performance (Moore, 1996).

A direct method of estimating intake is to weigh the animal before or after they eat

(Burns et al., 1994). In grazing situations, this will have limited practicability, but

according to Burs et al. (1994) it has been used successfully in studies where ingestive

behavior is of interest and the duration of grazing can be restricted. Major limitations for








44
expanded use of this technique are its short-term nature and the need to account for

weight adjustments caused by defecation and urination (Burs et al., 1994).

Intake of grazing animals has often been estimated by the difference in HM before

and after grazing, i.e., disappearance of herbage mass. Daily herbage intake of the grazing

animal is assumed to be herbage disappearance observed in a paddock divided by the

product of number of animals and days grazed. This method is very simple and in theory

is the most direct and accurate of all intake estimation methods because of its similarity to

the method used for confined studies; i.e., intake is the difference between what is offered

and refused. The technique is extremely labor intensive since forage HM has to be

estimated both before and after grazing for rotationally stocked pastures or frequently for

continuously stocked pastures. Also, many sites have to be sampled to improve accuracy.

Double sampling, i.e., estimating HM at many sites by an indirect method (visual or

mechanical) and clipping a percentage of the sites to measure actual HM, then using a

regression relationship between clipped and indirect samples to determine pasture HM

(Burs et al., 1989), is often used to ensure representative sampling.

The method may be too simplistic since it does not consider loss of HM due to

trampling or removal by nonexperimental animals, feral animals as well as insects (Bums

et al., 1994; Moore, 1996). Not accounting for growth during grazing may lead to

underestimation of intake. These problems can be minimized in rotationally stocked

systems if the grazing period is kept short (1 to 3 d) or by placing cages in the pasture to

prevent grazing in selected sample areas that will be used to estimate the growth rate in

the period between sampling dates. The cage technique is used for continuously stocked








45

pastures to measure herbage accumulation since before and after measurement is not

possible. Well managed, uniform pastures with high growth rates may provide acceptable

estimates of intake using the herbage disappearance method (Moore, 1996). Another

situation where this technique may be useful is in studies of ingestive behavior where the

interest is in herbage intake for a specific meal such as done by Dougherty et al. (1992).

Moore (1996) feels that the HM disappearance method, although theoretically sound, is

not reliable enough to be recommended for routine estimates of pasture intake.

Indirect methods of estimating DMI of grazing animals have evolved because of

the inherent difficulty of making direct determinations. In this approach, estimates of

intake are made for individual animals grazing a pasture at a particular time (Moore,

1996). These techniques estimate intake based on fecal output (FO) and the indigestibility

of the diet, i.e., forage intake is estimated by the equation:

DMI (kg d"') = 100 (FO [kg DM d']) / (100 DM digestibility [%])

Intake and digestibility are not correlated highly across a wide range of forages, so FO

must vary independent of both intake and digestibility (Moore, 1996). Thus, independent

estimates of both FO and DM digestibility are required. Errors in either estimate will

result in erroneous intake values. Because digestibility is in the denominator of the

equation, however, errors in its determination will result in larger error in the estimation of

intake than for errors in determining FO, which will only result in proportional errors for

intake estimates.

Fecal output may be obtained by collection of all defecation during the grazing

period using a harness and collection bag apparatus (Bums et al., 1994). In theory, this








46
method is simple and straight forward. Rapid results are obtained and extensive

laboratory facilities are not needed since only DM and ash determinations are required.

Major disadvantages include significant reductions in animal performance, incomplete

collection of feces, distortion of hind legs due to weight of feces in bag, high labor input

for sampling and weighing, and it likely interferes with normal grazing behavior (Burs et

al., 1994; Moore, 1996). Total fecal collection is generally not recommended, particularly

with cattle, since determination of FO indirectly using inert markers offers opportunities to

reduce negative effects on animal health.

Estimation of FO using markers is based on the ratio between quantity of a marker

dosed to an animal and its concentration in the feces [FO [g d"'] = (ug of marker

administered) / (ug of marker per g of feces)] (Burs et al., 1994). Diet digestibility data

are then used to calculate intake. Daily administration of a known dose of an external

marker (one or two doses per day) has been the classical approach (Burs et al., 1994;

Moore, 1996). Chromic oxide (chromium sesquioxide, Cr203) is the oldest known and

has been used extensively (Carter et al., 1960; Moore, 1996; Pond et al., 1986). This

technique is logistically very labor intensive, requiring animals be removed from pasture

and restrained or the animal be trained to permit dosing through a rumen cannula.

Grazing behavior, FO, and forage intake may be affected by dosing activities. Other

approaches to administer chromic oxide have included dosing with chromic oxide

impregnated paper, feeding small quantities of the marker with some feedstuffs, and use of

intraruminal controlled release device. Moore (1996) suggested that in all of these

approaches, however, there may be incomplete recovery of the marker and/or diurnal








47

variation in fecal chromic oxide concentration, both of which lead to errors in estimate of

FO.

An alternative approach is to use the pulse dose method suggested by Pond et al.

(1986) in which the chromium is mordanted onto forage samples, or esophageal extrusa,

that have been extracted with neutral detergent reagent. This mordanted fiber is

administered at the beginning of the measurement period. Frequent fecal collection is

required for at least 96 h and each sample is analyzed for chromium concentration to

characterize the "pulse" in marker concentrations found in the feces. A non-linear

equation is used to describe the relationship between marker concentrations and time after

dosing. With the characteristics of this excretion curve, it is possible to estimate rate of

digest passage, mean residence time, and digestive tract fill, as well as FO. This

technique also has limitations, probably more than with daily dosing (Burs et al., 1994).

Labor requirements are not reduced. While perhaps not as stressful as daily dosing,

animals are disturbed every 2 to 3 h for fecal collections that may require rectal (grab)

sampling. This may lead to interruptions of normal grazing behavior which may in turn

result in misleading estimates of kinetic parameters. Sampling from recent defecations

helps to alleviate this problem (Moore, 1996). The large number of chemical analyses

required to develop the marker excretion curve, the complexity of modeling marker flow

to calculate kinetic parameters, and proper interpretation of the resulting data add a

degree of difficulty. Additionally, kinetic properties of the dosed material must be similar

to those of the digesta, thus the marker has to be mordanted to the dietary component of

interest, typically forage fiber. The mordanted fiber should have a particle size distribution








48

similar to the digesta. Even though the list of limitations is long, Burs et al. (1994)

reported successful use of this technique in their research.

Another approach to estimating herbage intake by grazing animals is based on

calculating, in retrospect, the amount of energy an animal would need to ingest daily to

achieve the observed rate of performance (e.g., weight gain, milk production, etc.). Then

DMI is estimated based on the intake of energy considered the requirement and the energy

concentration of the diet (Moore, 1996; Reeves et al., 1996). The energy requirement for

given performance levels of particular classes of animals may be obtained from tabulated

values.

Moore (1996) suggested that energy intake and forage energy concentration must

be expressed as some measure of available energy rather than total or Gross Energy and

must be expressed in the same units. He noted that Effective Feed Units, TDN, Digestible

Energy (DE), Metabolizable Energy, and Net Energy (NE) have all been used as

expressions of available energy, and suggested that NE may be more appropriate to use in

this calculation because of differences in efficiency of ME utilization among forages.

Requirements of NE are partitioned into NE for maintenance (NE,) and for gain (NE) in

the NRC (1984) system based on efficiency of ME utilization, and energy intake

calculations have to be done separately for each (NRC, 1984). Separate calculations are

not necessary for requirements of lactating dairy cows, however, because it has been

shown that they use DE or ME with similar degree of efficiency for maintenance, growth,

and lactation (NRC, 1988). In estimating energy requirements for grazing lactating dairy

cows, adjustments have to be made for NE requirements related to activities such as actual








49

grazing and walking since NRC (1988) only gives tabulated values for lactating cows in

confined systems. Concentrations of forage NEm and NE, are calculated from ME

concentration by NRC (1984) equations and ME is calculated from DE. Forage DE

concentration may be estimated based on digestibility determinations.

Accurate measurement of production responses such as body weight changes are

critical to quantification of energy requirements. Weighing errors for grazing ruminants

may be quite large and contribute to error of estimates of mean weights and liveweight

changes (Moore, 1996). An additional disadvantage is that extraneous factors such as

nutrient imbalances and environmental effects may affect conversion of NE to animal

product. An advantage of this method is that the intake estimates reflect changes in forage

quality and quantity integrated over reasonably long time periods. Additionally, this

technique lends itself to mathematical simulation modeling (Moore, 1996).

Intake estimation based on ingestive behavior is also possible, where DMI is the

product of bite weight, biting rate, and grazing time. It is important to identify actual bites

as separate from jaw movements related to prehension and swallowing. Biting rate can be

recorded by observing or listening for the distinct sound of forage being severed, or by use

of electronic devices designed to record biting activity (Forbes, 1988). Bite weight is best

estimated by esophageal extrusa collected over measured short time periods and grazing

time can be recorded by observation or using automatic devices (Forbes, 1988; Moore,

1996). This approach may provide useful data about a sward during short periods when

the observations are made. This may not reflect, however, behavior throughout the day

and behavior on one day may not reflect that of other days (Moore, 1996). Forbes (1988)









50

suggested that the usefulness of this technique is limited to intake estimation for short

periods of grazing such as studies of the influence of sward characteristics on ingestive

behavior.

Forage intake estimates can be predicted based on forage characteristics where

DMI (as a function of body weight) is determined from regression coefficients and

concentrations of various quality-related characteristics of forages. Multiple regression

equations have been shown to provide better estimates than equations based on single

"predictors" (Moore et al., 1994). This approach can provide acceptable estimates of

potential intake. Frequent pasture sampling to represent the variation of forage quality

within a pasture due to location and time will enhance the usefulness of the intake

estimates. This approach may provide misleading results because estimates of intake

predicted from laboratory analyses have higher relative error than do estimates of

digestibility obtained in the same manner (Moore et al., 1994). Further, the method needs

to incorporate effects on intake due to variation in forage availability, animal

characteristics, supplemental feeding, and environmental conditions for it to be taken

seriously as an approach to estimating pasture intake.

The importance of diet digestibility in estimating intake cannot be over

emphasized. Perhaps more important than analyses of forage samples to determine their

digestibility is obtaining samples that represent what the animals are consuming. Two

basic approaches have been used to collect forage samples. One is a manual method in

which the experimenter selects samples to represent what the animal is consuming based

on observing the animals grazing behavior, and the other method is to use surgically








51

altered (rumen or esophageal cannula) animals (Bums et al., 1994). The first procedure is

often criticized for being subjective while the second can lead to biased estimates if

adequate sampling is not achieved.

Once representative samples are collected, accuracy is dependent on the method

used to estimate the digestibility of the diet. It appears that the two-stage in vitro bioassay

is the preferred method and has the broadest application (Burs et al., 1994; Moore,

1996). Moore (1996) cautioned that whatever laboratory method was used, it should be

calibrated against in vivo digestibility in order to improve the accuracy of the estimate.

Internal markers, natural plant constituents that are neither digested nor absorbed

by the animal, can be used to estimate forage digestibility by knowing the ratio of the

marker in the diet and feces and calculated as digestibility (%) = 100 [100 (marker in

diet/marker in feces)]. Substances evaluated as internal markers include silica, lignin, fecal

N, chromogen, indigestible NDF, and acid soluble ash (Pond et al., 1986). Of these, lignin

has been the most widely used (Bums et al., 1994) but they all have limitations to their

usefulness. An infallible and totally dependable internal marker has not yet been found

(Cochran et al., 1986).

New methods for estimating intake are constantly evolving (Burns et al., 1994).

Alkanes, found in cuticular waxes, have been investigated recently both as internal and

external markers (Moore, 1996). Use of alkanes may improve digestibility estimates

(Burs et al., 1994). Reeves et al. (1996) reported that alkane techniques provided a

direct and precise estimate of pasture intake compared to estimates derived from herbage

disappearance or energy requirement calculations, and can be obtained on a daily basis if








52

required. Like other techniques, there are short comings to the use of alkanes (e.g.,

concentrations of odd-numbered alkanes may be too low in tropical forages; Moore,

1996) but ways to overcome some of these are being sought (Burs et al., 1994). Also,

controlled release devices have been developed for the delivery of Cr203 to overcome

limitations related to frequent dosing and diurnal variations in Cr2O3 output (Bums et al.,

1994). Several shortcomings have led to caution for the use of these release devices in a

research setting. The devices may be useful in the future if current problems can be

solved. In the meantime, new technologies to improve intake estimation must

continuously be sought.














CHAPTER 3
GRAZING MANAGEMENT EFFECTS ON FORAGE PRODUCTION AND ANIMAL
PERFORMANCE OF LACTATING DAIRY COWS ON SUBTROPICAL WINTER
PASTURES


Introduction


Florida's dairy industry is an integral part of the state's agriculture with revenue

from milk sales exceeding $400 million annually during the last several years. Like the

rest of the USA dairy industry since the mid- to late 1950s, most Florida dairy farms gave

up traditional pasture-based dairying and changed to management systems based on high

concentrate feed use, stored forage, and drylot feeding of total mixed rations (TMR) in

total confinement. With these intensive production systems, milk production per cow has

increased greatly, largely due to increased grain consumption but also to improved cattle

genetics and to the ability of animal nutritionists to formulate diets that more accurately

and uniformly meet the nutritional requirements of the lactating cow (Mertens, 1986).

Total confinement systems of dairy production based on greater use of concentrate feeds

and stored forages require high capital inputs for buildings and equipment, which, along

with the cost of feedstuffs, incur high production costs.

Dairies in Florida are typically comprised of large herds, housed on relatively small

land areas that grow little of their feed. Pastures contribute little as a source of nutrition

for dairy cows and are more used as exercise lots for dry cows and heifers. This system








54

concentrates nutrients in a small area where loss to the environment can occur. Thus,

nutrient management has become a major focus of regulatory agencies and a major

expense and constraint to profitability of producers. Recent regulations require dairy

farms to recycle nutrients from animal wastes into crop production or export from the

farm.

Fluctuating market conditions in the dairy industry (Elbehri and Ford, 1995)

combined with growing pressure by regulatory agencies and environmental interests

regarding nutrient management have led producers to search for alternatives to total

confinement systems. Improved forages and forage management practices, improved and

cheaper fencing materials to support rotational stocking, reduced forage harvesting and

feeding costs, possible improvements in herd health with animals off of concrete, lower

capital outlay and reduced day to day costs for handling animal wastes, and improved

public perception of the dairy industry (Bernard and Chandler, 1994) have made reverting

to pasture-based dairy systems seem attractive. In recent years, significant numbers of

dairy producers in temperate areas of the USA have adopted pasture-based systems

(Parker et al., 1992). Florida producers have begun to adopt similar systems during the

last 5 yr. Insufficient information on pasture-based dairying in Florida, however,

precludes recommendation of widespread use of grazed pastures for dairy cows. Research

efforts are needed to address this void.

Studies in the northeastern USA have demonstrated that there are economic

benefits from incorporating intensive grazing in the dairy production system, however,

these benefits depend on the producers' managerial abilities (Elbehri and Ford, 1995).








55

Milk production in the northeastern USA dairies was not greatly affected when the system

changed from feeding in confinement to one in which the forage component of the diet

was grazed pasture (Fales et al., 1993), likely because of the high protein concentration

and digestibility of temperate forages. This situation may not be the same for the tropical

and subtropical forage grasses grown in Florida because they generally contain more fiber

and less protein and have lower digestibilities than their temperate counterparts

(Sollenberger and Chambliss, 1991). On the other hand, Florida's climate allows for

growing of cool-season forage crops during winter months when temperatures in

temperate areas of the USA prohibit crop growth. This may be advantageous to Florida's

producers by providing an opportunity for year-round grazing and perhaps decreasing

dependence on grain consumption and stored forages, and further, reducing waste

management concerns compared to their counterparts in temperate areas.

Development of an information base on which to make grazing management

decisions for Florida's winter pasture systems should include data that help explain how

animal responses are coupled to pasture production and how these components interact.

It is well established that animal performance is largely determined by nutrient

consumption (Mertens, 1994), which is influenced by pasture characteristics,

environmental conditions, and management factors. Baltensperger et al. (1986) reported

that rye (Secale cereale L.), ryegrass (Lolium multiflorum Lam.), and clovers (Trifolium

spp.) are valuable forages for winter pasture programs in Florida. The use of clover in

mixed swards to supplement N fertilization in rye-ryegrass pastures in subtropical

locations is beneficial (Baltensperger et al., 1986; Moss and Lowe, 1993). Forage








56

availability or herbage allowance (Davidson et al., 1985; Dougherty et al., 1992; Holmes

et al., 1992) and pasture quality (Minson, 1982; Holmes et al., 1992; Hoogendoorfet al.,

1992; Kristensen, 1988) are major determinants of limitations to intake and, hence, animal

performance on pasture-based systems. Stocking rate (SR) is a key management variable

in determining productivity and profitability of grazing systems (Fales et al., 1995) because

it determines herbage allowance and is linked to herbage mass and pasture nutritive value

responses (Davidson et al., 1985; Dougherty et al., 1992; Fisher et al., 1996). Although

high nutrient intake from pasture may be possible, the high nutrient requirements of

lactating dairy cows necessitate concentrate supplementation so that near potential milk

production may be achieved (Kolver and Muller, 1998). To reduce production costs

associated with intensive systems, concentrate supplementation studies on pasture-based

systems need to focus on feeding levels to ensure maximum nutrient intake from pasture

while reducing substitution of forage for concentrate.

This research addressed grazing management of cool-season forages during the

winter months in Florida. The objective was to quantify pasture and animal responses

when lactating dairy cows grazed two different cool-season forage systems (FS; N-

fertilized rye and ryegrass mixed pastures vs. rye-ryegrass-crimson clover [Trifolium

incarnatum L.]-red clover [Trifolium pratense L.] mixed pastures) at two SR (5 vs. 2.5

cows per hectare) and two levels of concentrate supplementation (CS; 1 kg concentrate

[as-fed] per 2.5 or 3.5 kg of milk produced).








57

Materials and Methods


This research was conducted during the 1995-1996 and 1996-1997 winter seasons

at the University of Florida Dairy Research Unit at Hague, 18 km north of Gainesville, FL

(290 60' north lat.). The approximately 10-ha experimental area is mapped as a

heterogenous mixture of Chipley fine sand (thermic, coated, Aquic Quartzipsamments),

less than 5% slope, Sparr fine sand (loamy, siliceous, hyperthermic, Grossarenic

Paleudults), and Tavares fine sand (hyperthermic, uncoated, Typic Quartzipsamments), 0

to 5% gradient. These soils are somewhat poorly to moderately well drained. Prior to

initiation of summer grazing at the site in 1995, mean soil pH (1:2 soil:deionized H20

ratio) was 5.9, and Mehlich-I (0.05 MHCL + 0.0125 MH2SO4) extractable P, K, Mg, and

Ca in the Apl horizon (0- to 15-cm depth) were 99, 26, 50, and 598 mg kg-',

respectively.

Starting dates for the trials in the 2-yr study were 28 Jan. 1996 and 13 Jan. 1997.

Evaluations were conducted during three continuous 28-d periods (viz., a first, second,

and third period [P1, P2, and P3]) each year, ending 22 April and 7 April in 1996 and

1997, respectively. Climatic data during months relevant to the study are presented in the

Appendix (Table A-i). In 1996, rainfall for January and February were both much lower

and in March more than twice that of the 70-yr average for Gainesville. In 1997, rainfall

in January was the same as the 70-yr average, but, it was lower for the next two months.

Rainfall totals for the November-December-January period prior to implementation of

grazing (i.e., coinciding with establishment phase of pastures) were 150 mm in the first








58

year and 239 mm in the second year, compared to the 70-yr average of 195 mm. Average

ambient temperatures during the study months were generally lower in 1996 and higher in

1997 compared to normal temperatures (Table A-1).

In the fall before both winters (mid-October), 'Tifton 85' bermudagrass (Cynodon

spp.) summer pastures were overseeded with a mixture of rye and ryegrass and

'Florigraze' rhizoma peanut (Arachis glabrata Benth.) summer pastures were seeded with

a mixture of the two cool-season grasses along with crimson and red clovers. Both

summer forages had been grazed previously using treatment arrays similar to those

described for the present trial. All pastures received an initial application of 40 kg N and

40 kg K ha7' during establishment in both years. An additional 40 kg N ha' were applied

to all pastures when the trial period began each year. Subsequently, 40 kg N ha7' were

applied to rye-ryegrass pastures at the beginning of each of the following two, 28-d

grazing periods. In 1997, grass-clover pastures received one additional mid-season

application of 40 kg N ha-' to ensure sufficient grass growth. Thus, the N-fertilized FS

received a total of 160 kg N ha"' each winter growing season while in the 1996 and 1997

winter growing seasons pastures of grass-legume mixtures received 80 and 120 kg N ha'',

respectively.

Treatments were two FS (N-fertilized rye-ryegrass mixture [GN] vs. rye-ryegrass-

clovers mixture [GL]), two SR (high and low SR; 5 vs. 2.5 cows per hectare), and two

levels of CS (high and low CS; 1 kg concentrate per 2.5 or 3.5 kg milk produced) in a 2 x

2 x 2 factorial experiment. Each treatment was replicated twice in a completely

randomized design so there were a total of 16 pastures (experimental units) in the








59

experiment. During fall 1995, irrigation was unavailable and drought caused establishment

failure of the clovers. Data from these pastures will not be reported; thus, in the first year

of the study, only four treatments were evaluated (i.e., no FS factor, just a 2 x 2 factorial

combination of the two levels each of SR and CS). Clover establishment was successful in

the second year. Pasture sizes were 0.8 ha for the low SR treatment and 0.4 ha for the

high SR treatment.

Two lactating Holstein cows (Bos taurus) blocked by parity (viz., one primiparous

and one multiparous cow) were assigned to each pasture during each of the three 28-d

periods. All cows were more than 100 d in lactation at the beginning of the trial period in

both years. A given pasture received the same treatment in each period, but cows were

reassigned to different experimental units at the beginning of every period. Pastures were

subdivided for rotational stocking so that cows were offered a fresh paddock every

morning. Pastures for GN were subdivided into 22 paddocks and GL pastures into 29

paddocks to allow for 21-d and 28-d rest periods, respectively, between grazing. Cows

were actually allowed two paddocks at a time to give them additional room for movement,

so they were on the paddock that was grazed the day before and a fresh paddock during

every 24-h period. Animals were milked in the morning and evening each day.

Concentrate (four parts of a corn [Zea mays L.]-soybean [Glycine max (L.) Merr.]

meal-byproduct based formulation [120 g CP kg-', 20 g fat kg', and 150 g NDF kg-']

mixed with one part cotton [Gossypium hirsutum L.] seed; ingredient and chemical

composition are presented in Appendix Tables A-3 and A-4) was fed to animals at the

appropriate CS level after each milking. During 1996, animals were group fed in high or








60

low CS groups at a loafing area adjacent to the milking parlor, but in 1997 cows were fed

by experimental units via feed troughs in each pasture. The amount of concentrate per

experimental group was adjusted twice weekly based on average milk production during

the preceding 3- to 4-d period. A minimum of 4.5 kg concentrate cow' d-' was fed. All

pastures had water tubs fitted with float-control devices to ensure continuous drinking

water availability.

Pasture and animal responses to the treatments were measured during each period.

The first 14 d of each period were considered an adaptation phase, and animal response

data were recorded during the latter 14 d of the period.


Pasture Variables


Sampling for pregraze and postgraze herbage mass was done weekly in each

period using a double sampling technique (Burs et al., 1989). Pregraze herbage mass

samples were taken from a particular paddock the day before it was grazed and postgraze

herbage mass samples were taken at the end of the 2 d grazing (since the rotational

stocking scheme dictated that animals occupy a particular paddock for 2 d). Indirect

estimates of herbage mass were done using a 0.25-m2 disk meter at 20 sites per paddock

(sites were selected randomly but spatially arranged to represent the entire paddock).

These indirect estimates were calibrated with direct estimates taken at the first and third

sampling times during each period. At these times, direct samples were taken at three

sites in each paddock to represent low, intermediate, and high disk meter readings.

Circular quadrats of similar size to the area covered by the disk meter (0.25 m2) were








61

harvested using battery-powered hand clippers. Samples were clipped to a consistent

stubble height of 3 cm that was below the expected grazing height, as suggested by Bums

et al. (1989). Harvested samples were dried at 600C in a forced-air oven. Regression

techniques were used to develop equations relating direct (harvested sample) and indirect

(disk meter height) measures of herbage mass on pastures. Equations developed for a

particular double sampling date (first and third week of each period) were used to estimate

herbage mass for that date and for the indirect measures taken the following week (i.e.,

second and fourth week of the period, respectively). Herbage removed from pasture

during grazing herbagee disappearance) was the difference between pregraze and

postgraze herbage mass.

In 1996, samples to estimate nutritive value of the grazed portion of the pastures

were taken twice in each period, once each week during the latter 2 wk of the period. The

number of samples for nutritive value was doubled in 1997; samples were taken four times

(once each week) during each of the three periods. Hand-plucked samples, selected to

represent what the animals were consuming, were composite from 15 to 20 random sites

in the paddock that was to be grazed the following day. These samples were dried at 60C

in a forced-air oven then ground in a Wiley mill to pass a 1-mm stainless steel screen.

Samples for N analysis were digested using a modified aluminum block digestion

procedure (Gallaher et al., 1975). Ammonia in the digestate was determined using semi-

automated colorimetry (Hambleton, 1997). Crude protein (CP; dry matter [DM] basis)

was calculated by multiplying N concentration by 6.25. Herbage in vitro organic matter

digestibility (IVOMD) was determined using a modification of the two-stage procedure








62

(Moore and Mott, 1974). Neutral detergent fiber (NDF) was determined using techniques

outlined by Golding et al. (1985).


Plant-Animal Interface Variables


Herbage allowance (HA) was computed as the ratio of average available herbage

(kg DM) to animal liveweight (kg) per hectare during the grazing period (Sollenberger

and Moore, 1997). Average available herbage was calculated by summing pregraze and

postgraze herbage mass then dividing the sum by two.

Total organic matter intake (OMI) was computed based on fecal output (FO) and

diet digestibility, viz., OMI = organic matter (OM) output of feces / (1 [OM

digestibility/100]). Fecal output was estimated using a pulse dose marker technique (Pond

et al., 1986). Chromium-mordanted fiber was used as the external marker. Hand-plucked

samples that represent the grazed portion of the pastures were used as the source of fiber

for marking. The forage was dried at 600C in a forced-air oven then ground in a Wiley

mill to pass a 2-mm stainless steel screen. The neutral detergent soluble component of the

ground forage was extracted by boiling in detergent, and the fiber was washed and

mordanted with chromium following recommended procedures (Uden et al., 1980) then

packed into gelatin capsules.

All study animals were dosed with the pulse markers (30 g mordanted fiber cow-')

at approximately 1800 h on Day 24 of each period. The gelatin capsules were

administered orally via balling gun. Fecal samples were obtained from rectal grab samples

when animals were in the milking parlor or collected in the pasture by following the








63

animals and grabbing a sample when they defecated. These samples were taken at dosing

time (0 h) and approximately 12, 15, 18, 21, 24, 27, 36, 42, 48, 60, 72, and 84 h after

dosing.

Fecal samples were dried at 600C in a forced-air oven then ground in a Wiley mill

to pass a 2-mm stainless steel screen. Marker concentration in the feces was assayed by

atomic absorption spectrophotometry following the procedure outlined by Williams et al.

(1962). All samples were analyzed in duplicate and analyses were repeated for samples

where the difference of Cr concentration between duplicates exceeded 10%.

The one-compartment model with time delay and gamma two age dependency

(Pond et al., 1986; Pond et al., 1988) was used to describe the marker appearance curve.

The model is:


y = (Ko* 1 ,-t)*e-' [d)) /0.59635

where y = concentration of marker,

Ko = concentration of marker if instantaneously mixed in the compartment,

1, = age dependent rate parameter,

td = time delay,

t = time after marker administration.

The age dependent rate parameter assumes equal probability for escape of all particles

from the rumen based on exponentially distributed residence times and the proportion of

the dose remaining in the compartment at a specific time (Pond et al., 1988). Fecal output

was then computed as:








64

FO = (Marker dosed / Ko)*,,*24*0.59635


Total OMI was first calculated based on these estimates of FO (called observed FO

[FOO]) and IVOMD data of the respective pastures. Forage OMI was the difference

between total OMI and the known amount of concentrate OM fed. Total diet digestibility

data were then recalculated (called expected digestibility [EXPD]) to balance for the

estimated proportion of forage and concentrate supplement OM consumed and their

respective digestibilities, viz., EXPD = (forage OMI*forage digestibility + supplement

OMI*supplement digestibility) / total OMI. This EXPD was further adjusted to help

account for differences in partial efficiency of utilization of Metabolizable Energy (ME)

from concentrate and forage due to associative effects (Dixon and Stockdale, 1999;

Moore, 1992) that may result from mixed forage-concentrate diets. This new calculation

of total diet digestibility, called adjusted digestibility (AJD), was obtained using an

equation developed from a wide range of published data of ME concentrations showing

deviation from the expected (based on calculations from the weighted intake and

digestibilities of the forage and concentrate supplement components) when mixed diets are

fed (Dr. John E. Moore, Emeritus Professor, Univ. of Florida, personal communication).

The equation is:

ADJ = 59.71 0.8948 EXPD + 0.01399 EXPD2

Using this adjusted digestibility value, a prediction of FO (FOP) from total OMI is

computed thus:

FOP = Total OMI (1 ADJ)








65

A new estimate of forage OMI is then recalculated via a series of iterations (given that

concentrate OMI is fixed) to make the difference between FOO and FOP less than 0.01;

thus recalculated total OMI will now be the sum of the new estimate of forage OMI and

concentrate OMI. The SAS (SAS Institute Inc., 1987) program used for these

computations is shown in the Appendix (Table A-24).


Animal Response Variables


Milk yield was recorded at each milking during the latter 14 d of each period for

each cow. Samples were taken for six consecutive milkings each week during the third

and fourth week of each period and sent to an external laboratory (Southeast Dairy

Laboratory, Inc., McDonough, GA) for analysis of milk fat, milk protein, and somatic cell

count (SCC). Milk urea N was determined only for 1997 samples.

All animals were weighed after milking for three consecutive mornings at the

beginning of the trial, at each change of period, and at the end of the trial to determine

body weight (BW) changes. Average BW was the mean weight of the 3 d. Body

condition scores (BCS) of all animals were evaluated using a five-point scale (where 1 =

thin to 5 = obese; Wildman et al., 1982) also at the beginning of the trial, each change of

period, and end of the trial. Blood samples to determine blood glucose concentrations

were taken at the end of each period. Blood was collected via venipuncture of the

coccygeal vein.










Statistical Analysis


Data to predict the marker appearance curve were analyzed using the NLIN

procedure of SAS (SAS Institute Inc., 1982b); iterative method used was Marquardt. The

model fitted to the data was:

Y = ([Ko*L,*T]*e-LI*T)/0.59635

where Y = Cr concentration in feces (ppm)

Ko = concentration of marker if instantaneously mixed in the compartment,

L, = age dependent rate parameter,

T = Time after marker administration time delay (T).

Starting values for the parameters were set at 100, 400, and 700 for Ko, 0.03, 0.05, and

0.08 for L,, and and 3, 5, and 7 for T and bounds were set so that all of these parameters

were > 0 (SAS program shown as Table A-25 in Appendix). Rate of passage (kg h"') was

then calculated as the product of L, and 0.59635, retention time as the reciprocal of the

average of the sum of L, and T (viz., 2 / [L, + r]), and fill as ag Cr administered in dose /

Ko. Fecal output then was the product of fill and rate of passage in 24 h. Intake estimates

based on these data were adjusted (described in previous section) prior to statistical

analysis to make inferences about intake responses to experimental variables.

All responses were analyzed by fitting mixed effects models (Littell et al., 1996)

using the PROC MIXED procedure of SAS (SAS Institute Inc., 1992). Because the FS

factor was lost in the first year of the study, data were analyzed separately for each year.

For the 1996 data, the model used was:








67

Yk = + P, + S+(PS) + Ck + (PC)k + (SC)k + (PSC)k +e

where Yijk is the dependent variable

1 is the overall mean

P, is the period effect

Sj is the SR effect

(PS), is the period by SR interaction

Ck is the CS effect

(PC), is the period by CS interaction

(SC)jk is the SR by CS interaction

(PSC)4k is the period by SR by CS interaction

ek is the error.

In 1997, the model used was:

Y!, = + P, + F + (PF) + S, + (PS)k + (FS)jk + (PFS)k + CC + (PC) + (FC) +

(SC)k + (PFC),I + (PSC),kI + (PFSC)k, + ek,

where Yi is the dependent variable

iz is the overall mean

P, is the period effect

F, is the FS effect

(PF), is the period by FS interaction

Sk is the SR effect

(PS),k is the period by SR interaction

(FS)jk is the FS by SR interaction








68

(PFS)Uk is the period by FS by SR interaction

C, is the CS effect

(PC), is the period by CS interaction

(FC),, is the FS by CS interaction

(SC)k, is the SR by CS interaction

(PFC)U, is the period by FS by CS interaction

(PSC)i, is the period by SR by CS interaction

(PFSC),, is the period by FS by SR by CS interaction

ejki is the error.

All effects were considered fixed except for the error term which was considered random.

Period in both models was considered to be a repeated measure in time since it did not

have a chance to be assigned randomly (Littell, 1989). Subjects were the experimental

units, viz., each replication x FS x SR x CS combination. The REPEATED statement in

the SAS program allowed for modeling the covariance structure within subjects. Options

were used to specify three commonly used covariance structures to model, viz.,

autoregressive order one, compound symmetric, and unstructured row-column variance-

covariance components (Littell et al., 1996; SAS Institute Inc., 1996). The covariance

structure to assume in the model for final inference for each response variable was

determined by first examining the Pr > IZI value to determine if it was within acceptable

range (< 0.10), then examining the model-fit criteria computed by PROC MIXED,

Aikaike's Information Criterion and Schwarz' Bayesian Criterion. The covariance

structures with values of the criteria closest to zero, as suggested by Littell et al. (1996),








69

were accepted for final inferences. Means separation using probability of difference tests

(PDIFF in SAS; SAS Institute Inc., 1987) was conducted only for effects in the respective

models that were statistically significant (P < 0.05) or where there was a trend (P > 0.05

but < 0.10). Means reported are least squares means (LSmeans) and were considered

different at P < 0.05 unless otherwise stated.

Regression and correlation techniques (PROC REG and PROC CORR procedures

in SAS; SAS Institute Inc., 1982a; 1982b) were used to explore relationships between

variables.


Results and Discussion


Herbage Mass


In the 1996 winter grazing season, there was a trend toward a period by SR

interaction effect on pregraze herbage mass (P = 0.076). The interaction was due mainly

to the lack of SR differences in P1 (Table 3.1). This was expected, because some time is

required before management treatments affect pregraze herbage mass. Pregraze herbage

mass was similar during P1 and P2 within each SR, increased in P3, and was greater at

low SR than at high SR in both P2 and P3 (Table 3.1).

In the 1997 winter grazing season, there was a period by FS by SR interaction on

pregraze herbage mass (P = 0.003). During P1, there were no differences between SR

treatments within levels of FS, or vice-versa (Table 3.2). Again, lack of differences during

P1 was because pastures had been subjected to grazing treatments for only a short time.








70

During P2, low SR pastures had greater pregraze herbage mass than high SR, regardless

of FS (Table 3.2). Also, GN pastures had greater pregraze herbage mass than GL

pastures regardless of SR (Table 3.2). In P3, GL pastures had greater pregraze herbage

mass than GN pastures when SR was high but there was no difference due to FS when SR

was low. Also, pregraze herbage mass was greater for low SR than for high SR on GN

pastures, but there was no difference due to SR in GL pastures (Table 3.2). After the first

week of P2, animals were taken off the high SR treatments on GL pastures due to low

forage availability, so the higher pregraze herbage mass on these pastures in P3 resulted

from longer regrowth interval than for other pastures.


Table 3.1. Period by stocking rate (SR) interaction effect on pregraze herbage mass
during winter 1996.

SR
Period
High Low
------------ kg DM ha"' ----------- P valuet
1 760 b: 1040 b 0.1037
2 550 b 980 b 0.0030
3 1320 a 2080 a 0.0001



t Probability of difference value for comparisons between SR means within a period.
: Means followed by the same letter within a column are not different (P > 0.05)


Increased pregraze herbage mass during the last period of the trial in 1996 may be

due to improved conditions for growth as the season progressed. Rainfall during the latter

period of the study was higher than average and daylength was increasing. Increased










pregraze herbage mass in GL pastures during P3 in 1997 likely was due to removal of

cattle from high SR pastures during P2, and the additional mid-season application of 40 kg

Nha-'.


Table 3.2. Period by forage system (FS) by stocking rate (SR) interaction effect on
pregraze herbage mass during winter 1997.

Forage SR
Period Systemt
High Low
------------ kg DM ha' --------- P value

GL 1640 1590 0.7018
1
GN 1800 1790 0.9271
Pvalue1 0.1988 0.1153


GL 420 970 0.0002
2
GN 970 1370 0.0015
P value1 0.0002 0.0015


GL 1350 1350 0.9504
3
GN 750 1420 0.0001
P value1 0.0001 0.5663



t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover
mixture; GN = forage system comprising of N-fertilized rye and ryegrass mixture.
$ Probability of difference value for comparisons between SR means within a FS by
period.
Standard errors for interaction means ranged from 86.9 to 117.1.
Probability of difference value for comparisons between FS means within a SR by
period.








72

Nitrogen fertilization conferred an advantage to the GN system for herbage

production in this study. Moss and Lowe (1993) demonstrated similar responses on N-

fertilized ryegrass pastures vs. ryegrass-clover mixtures. Longer rest period on GL

pastures did not seem to be as advantageous as N fertilization for herbage production.

Many grazing studies have demonstrated similar responses to SR, viz., greater herbage

mass at lenient SR (Davidson et al., 1985; Fales et al., 1995; Hoogendoorn et al., 1992;

Moss and Lowe, 1993). The inverse relationship between pregraze herbage mass and SR

found in the present study could be attributed to greater accumulation of herbage at low

SR, which likely is linked to postgraze HM. In 1996, postgraze herbage mass was 370 kg

DM ha-' on the high SR compared to 820 kg DM ha' on the low SR pastures (P = 0.001).

In 1997, postgraze herbage mass on high SR pastures was 350 and 620 kg DM ha" for

GL and GN, respectively, compared to 640 (GL pastures) and 1000 kg DM ha'' (GN

pastures) for low SR treatments (P = 0.0001; SR main effect). Herbage accumulation

(difference of pregraze herbage mass of the current cycle and postgraze herbage mass of

the previous cycle) was 800 (high SR) vs. 1020 kg DM ha-' (low SR) in 1996 and 520

(GL) and 250 kg DM ha-' (GN) on high SR vs. 490 (GN) and 550 kg DM ha-' (GL) for

low SR pastures in 1997 (P = 0.0001 for FS main effect). Herbage accumulation results

for GL pastures were confounded because high SR pastures were not grazed for most of

P2 in 1997. In a New Zealand study on perennial ryegrass-white clover (Trifolium repens

L.) pastures, L'Huillier (1987) observed no differences in pregraze herbage mass when the

grazing season started but greater pregraze herbage mass was associated with lower SR as

the season progressed. Herbage accumulation for 28-d regrowth after grazing in that








73

study responded in the same pattern as pregraze herbage mass. Fales et al. (1995)

reported that greater herbage accumulation at lenient SR was responsible for greater

pregraze herbage mass in their study.

Pregraze herbage mass during the 1997 winter grazing season also was affected by

an FS by SR by CS interaction (P = 0.029). At the high CS level, there was greater

pregraze herbage mass at low than at high SR on GN pastures, but there was no difference

due to SR in GL pastures (Table 3.3). Also at high CS, GN pastures had greater pregraze

herbage mass than GL systems when SR was low, but there was no difference due to FS

when SR was high (Table 3.3). At the low CS level, pregraze herbage mass at the low SR

tended to be greater than that of the high SR in GL pastures and there was no difference

due to SR in GN pastures (Table 3.3). There were no differences in pregraze herbage

mass between FS, regardless of SR, when CS was low. Examination of these data

suggests that the main reason for the interaction is the high value for pregraze herbage

mass on the low SR treatment of GN system when fed at the high CS. It is likely that

cows on this treatment, with forage availability not as limiting as at the high SR, were not

grazing as much as cows fed the low CS at similar SR. Herbage accumulation, apart from

being greater at the low SR treatment on GN pastures, tended to be greater (P = 0.06; 360

vs. 290 kg DM ha7') on low CS treatments. Discussion of supplementation effects on

pasture herbage production were not found in reports of studies involving

supplementation of lactating cows on pasture.








74

Table 3.3. Forage system (FS) by stocking rate (SR) by concentrate supplement (CS)
interaction effect on pregraze herbage mass during winter 1997.

CS

High Low
SR SR
Forage
System High Low High Low
----- kg DM ha' ----- P value ------ kg DM ha' ------ P value

GL 1090s 1170 0.4841 1180 1430 0.0541
GN 1140 1690 0.0007 1210 1350 0.1959
P value' 0.6606 0.0011 0.8509 0.4620



t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover
mixture; GN = forage system comprising of N-fertilized rye and ryegrass mixture.
$ Probability of difference value for comparisons between SR means within a FS by CS.
Standard errors for interaction means ranged from 74.1 to 82.8.
Probability of difference value for comparisons between FS means within a SR by CS.


Herbage Disappearance


In the 1996 winter grazing season, herbage disappearance during grazing did not

respond to SR or CS treatments (P > 0.10) but was influenced by a period effect (P =

0.0001). Herbage disappearance was 640 kg ha'1 in P1, decreased to 360 kg ha' in P2,

then increased back to 600 kg ha-' in P3.

During the 1997 winter grazing season, there was a period by FS by SR interaction

effect on herbage disappearance (P = 0.0076). Response patterns were similar for P1 and










P3, when there was greater herbage disappearance at high SR than at low SR in GL

pastures, but no difference due to SR in the GN system (Table 3.4). Also, there was


Table 3.4. Period by forage system (FS) by stocking rate (SR) interaction effect on
herbage disappearance during winter 1997.

Forage SR
Period Systemt
High Low
----------- kg DM ha-' ----------- P value

GL 1150 750 0.0003
1
GN 610 510 0.3156

P value' 0.0001 0.0201


GL 190 460 0.0359
2
GN 470 470 0.9711
P value' 0.0305 0.9615


GL 1040 800 0.0274
3
GN 580 590 0.9804
P value' 0.0001 0.0388



t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover
mixture; GN = forage system comprising of N-fertilized rye and ryegrass mixture.
$ Probability of difference value for comparisons between SR means within a FS by
period.
Standard errors for interaction means ranged from72.6 to 108.1.
Probability of difference value for comparisons between FS means within a SR by
period.








76

greater herbage disappearance in GL pastures than in GN pastures regardless of SR

treatment during P1 and P3 (Table 3.4). In P2, there was greater herbage disappearance

in low SR pastures than at high SR in the GL system (Table 3.4), a reverse of what

occurred during the other periods. Also in P2, there was greater herbage disappearance in

GN pastures than in GL pastures when SR was high, but there was no difference due to

FS when SR was low (Table 3.4).

The period effect shows that herbage disappearance decreased when pregraze

herbage mass in P2 decreased, then increased with pregraze herbage mass in P3. There

was correlation (r = 0.61; P = 0.001) between pregraze herbage mass and herbage

disappearance for all 1997 data. When the data were analyzed by SR, the correlation was

improved for high SR (r = 0.76; P = 0.001) but became weaker at low SR (r = 0.43; P =

0.04).

Regressions of pregraze herbage mass on herbage disappearance for 1996 showed

a linear relationship (P = 0.0001; Figure 3.1 [a]) but there was wide scatter in the data (r2

= 0.37; root mean square error [RSME] = 140.0). Analyzing the data at fixed levels of

SR did not improve the relationship. This was not surprising since SR did not affect

herbage disappearance during 1996. In 1997, regression analysis indicated a weak linear

relationship when all the data were considered (P = 0.0001; r2 = 0.37; RSME = 225.6); the

relationship was stronger when only high SR data were examined (Figure 3.1 [b]; P =

0.0001; r2 = 0.57; RSME = 245.0 ) and weaker for low SR data ( Figure 3.1 [c]; P =

0.036; r2 = 0.18; RSME = 149.2). Slopes were 0.52 and 0.21 for the high and low SR

data, respectively. Gibb et al. (1997) examined the decline in sward height due to grazing













1996 Data
HD = 218.6 + 0.23 pregraze HM
1000 r-038
800
S600
400
200
0
0 500 1000 1500 2000 2500
Pregraze HM (kg/ha)
(a)


High Stocking Rate 1997
HD = 62.4 + 0.51 pregraze HM
r' 0.57
1400
1200
1000 "
800 1
6 600 1 F lo,
I 400- -.
200
0
200 400 600 800 1000 1200 1400 1600 1800 2000
Pregraze HM (kg/ha)

(b)

Low Stocking Rate 1997
HD = 293.2 + 0.21 pregraze HM
1000 r2 -=0.18
900 ;----- -------------__ ___
800 ---------

600
500
400 ----
3WO
200
800 1000 1200 1400 1600 1800 2000 2200
Pregraze HM (kg/ha)


Figure 3.1. Regression relationships between pregraze herbage mass (HM) and
herbage disappearance (HD) for (a) all 1996 data combined, (b) high
stocking rate data for 1997, and (c) low stocking rate data for 1997.








78

and its relationships with pregraze pasture characteristics. They found that pastures with

a sward height of 5 cm before grazing consistently remained at a mean height between 4.5

and 4.8 cm, while height of taller swards (7 and 9 cm) declined. Parallels between herbage

disappearance responses in the present study and sward height decline demonstrated by

Gibb et al. (1997) seem likely, viz., low herbage availability results in reduced removal.


Herbage Allowance


In the 1996 winter grazing season, there was a period by SR interaction effect on

herbage allowance (P = 0.001). Similar to the herbage mass response, herbage allowance

was not different during the first two periods regardless of SR, then increased in P3 (Table

3.5). Low SR pastures had greater herbage allowance than high SR pastures, more so

during P3.


Table 3.5. Period by stocking rate (SR) interaction effect on herbage allowance during
winter 1996.

SR
Period
High Low
------------------ kg kg' ---------- P value
1 0.16 bT 0.55 b 0.0007
2 0.14 b 0.59 b 0.0001
3 0.38 a 1.27 a 0.0001



t Probability of difference value for comparisons between SR means within a period.
SMeans followed by the same letter within a column are not different (P > 0.05).








79

In the 1997, there was a FS by SR by CS interaction effect on herbage allowance

(P = 0.008). Regardless of FS or CS level, low SR pastures had greater herbage allowance

than high SR (Table 3.6). At high CS, there were no differences in herbage allowance

between FS when SR was high but GN pastures had greater herbage allowance than GL

pastures when SR was low (Table 3.6). At low CS on the other hand, there were no

differences between FS at either SR. It seems that the main cause of the interaction is the

high herbage allowance observed in low SR pastures on the GN system when cows were

fed at the high CS level. Recall that this was the same treatment that had high pregraze

herbage mass (Table 3.3).


Table 3.6. Forage system (FS) by stocking rate (SR) by concentrate supplement (CS)
interaction effect on herbage allowance during winter 1997.

CS

High Low
SR SR
Forage
System High Low High Low
-------- kg kg-' -------- P value --------- kg kg'' --------. P value

GL 0.26' 0.63 0.0003 0.28 0.82 0.0001
GN 0.32 1.04 0.0001 0.35 0.84 0.0001
P value' 0.3576 0.0001 0.2560 0.6539



t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover
mixture; GN = forage system comprising of N-fertilized rye and ryegrass mixture.
: Probability of difference value for comparisons between SR means within a FS by CS.
Standard errors for interaction means ranged from 0.03 to 0.05.
1 Probability of difference value for comparisons between FS means within a SR by CS.











There was also a period by FS interaction effect on herbage allowance (P = 0.001)

during the 1997 winter grazing season. In GL systems, herbage allowance was greatest

during P1, least during P2, and intermediate for P3 (Table 3.7). For GN systems, herbage

allowance was greater during P1 than for P2 and P3, which had similar herbage allowance

(Table 3.7). The GN pastures had greater herbage allowance than GL pastures during P1

and P2 but there were no differences due to FS in P3 (Table 3.7). Interpretation of these

data must be treated with caution since high SR pastures in the GL system were not

grazed during the latter 3 wk of P2. This allowed GL pastures to have similar herbage

allowance to GN pastures during P3, due to high average pregraze herbage mass for those

pastures (Table 3.2) after the long regrowth interval on the high SR treatment.


Table 3.7. Period by forage system interaction effect on herbage allowance during winter
1997.

Forage Systemt
Period
GL GN
--------------- kg kg-' ----------------- P value:

1 0.64 a 0.87 a 0.0001

2 0.35 c 0.55 b 0.0004
3 0.51 b 0.50 b 0.7824



t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover
mixture; GN = forage system comprising of N-fertilized rye and ryegrass mixture.
$ Probability of difference for comparisons between forage system means within period.
Means followed by the same letter within the same column are not different (P > 0.05).










Forage Nutritive Value

In vitro organic matter digestibility

During the 1996 winter grazing season, herbage in vitro digestible organic matter

(IVDOM) concentration was influenced by a period by SR interaction effect (P = 0.017).

At the high SR, IVDOM did not change with period while at low SR it was the same

during P1 and P2 but decreased in P3 (Table 3.8). Also, there were no differences due to

SR during the first two periods, but in P3, high SR had greater IVDOM than low SR

(Table 3.8). High SR treatments were grazed very closely throughout the experiment and

only in P3 was there sufficient residual forage in low SR pastures to allow accumulation of

mature herbage and lower IVDOM. Other researchers have reported increased pasture

digestibility at higher stocking rates (Fales et al., 1995) or when low herbage availability

treatments are achieved via intense grazing (Fisher et al., 1996; Holmes et al., 1992;

Hoogendoorn et al., 1992). The difference between high and low SR was attributed to a

lower proportion of green leaf, accumulation of senescent material, and greater average

age of plant tissues in leniently grazed swards. In vitro dry matter digestibility (IVDMD)

during spring in the first year of the Fales et al. (1995) study ranged from 694 g kg-' (low

SR) to 712 g kg-' (high SR). Differences of 30 to 60 g kg" were reported by Holmes et

al. (1992).

Herbage IVDOM concentration during the 1996 winter grazing season was also

affected by a period by CS interaction (P = 0.010). At high CS, IVDOM concentration

decreased from 759 g kg-' in P1 to 722 g kg' in P3, while at low CS, IVDOM increased

26 g kg'' from P1 to P3. The decrease in IVDOM on high CS pastures may be explained




Full Text
xml version 1.0 encoding UTF-8
REPORT xmlns http:www.fcla.edudlsmddaitss xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.fcla.edudlsmddaitssdaitssReport.xsd
INGEST IEID E2479RB1M_9C7J1C INGEST_TIME 2011-09-29T20:24:27Z PACKAGE AA00004705_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC
FILES



PAGE 1

FORAGE AND ANIMAL RESPONSES IN PASTURE-BASED DAIRY PRODUCTION SYSTEMS FOR LACTATING COWS BY BISOONDAT MACOON A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 1999

PAGE 2

To my wife, Yonette, for her love, patience, endless understanding, and moral support To my sons, Ron and Russ, for providing unconditional love and inspiration and giving final meaning to my life To my mother, Agnes, for her love and for instilling motivation for learning And to all those people, who stretched out a helping hand when I stumbled and fell as I traveled life's path to where I am today

PAGE 3

ACKNOWLEDGMENTS The author wishes to express his sincere appreciation and deep gratitude to Dr. Lynn E. Sollenberger, chairman of the supervisory committee, for his constant support outstanding guidance, and extraordinary patience prior to and throughout the doctoral program, for being an excellent mentor, and for his considerable contributions to the dissertation research and preparation of this manuscript. Much appreciation is extended to Dr. Charles R. Staples, co-chair of the supervisory committee for his diligent guidance and exceptional contribution to the dissertation research and also the preparation of this manuscript. Appreciation and sincere gratitude is extended to Dr. John E. Moore for his exceptional teaching, outstanding guidance, and for being a significant mentor throughout the author s graduate career. Appreciation and sincere gratitude is further extended to Dr. Kenneth M. Portier for his outstanding guidance with statistical matters and for serving on the supervisory committee and reviewing this manuscript. Special appreciation and sincere gratitude is also extended to Dr. Carrol G. Chambliss for his excellent guidance encouragement and support throughout the doctoral program and for serving on the supervisory committee and reviewing this manuscript. Special appreciation is extended to University-wide faculty members who always encouraged and supported the author's professional development. Prominent among these are Drs. Kenneth H. Quesenberry, David S. Wofford Kenneth L. Buhr, the late 111

PAGE 4

Edwin C. French III, Clarence B. Ammerman, and Peter E. Hildebrand. Sincere gratitude is extended to Dr. Jerry Bennett, Agronomy Department Chairman, and the administrative staff of the department for their help and support during the author's graduate program. The award of the teaching assistantship from the Agronomy Department, which made financial support for this doctoral program possible, is gratefully acknowledged The National Agricultural Research Institute of Guyana is gratefully recognized for its role in facilitating the author's graduate studies. Many people contributed to the successes of the author's program of study and their assistance is gratefully acknowledged. Special thanks to Richard Fethiere and Nancy Carter of the Forage Evaluation Support Laboratory for their friendship and help. The long hours spent in the field with Sid Jones and Dwight Thomas of the Forage Evaluation Field Laboratory were vital to the successful completion of the dissertation research and the author will always be grateful for their contribution. The help and support of John Funk, Pam Miles, and Nancy Wilkinson of the Animal Science Department and Jocelyn Jennings of the Dairy Science Department are gratefully acknowledged. The support of the staff of the Dairy Research Unit, including Farm Manager, Mr. Dale Hissem, Ms. Mary Russell, and Mr. James Lindsey, and the many people who worked specifically with the grazing study, including Ryan Richards, Shane Brooks, and Danny Tomlinson, is gratefully recognized. The help and friendship of Dr. Masaaki Hanada, a visiting scientist from Japan, is gratefully acknowledged. The author will always be grateful for the cooperative research responsibilities and special friendship shared with John Fike graduate student in the Dairy and Poultry Sciences Department. lV

PAGE 5

The author is grateful to former and current fellow students, Steve Welch, Renato Fontaneli, and Yoana Newman, who worked relentlessly to help on the author's research project, and also Elide Valencia, Marjatta Eilitta, Nadia Douglas, Brett Wade, Stuart Rymph, Alberta Chiteka, Liana Jank, Marcos Freire, Jean Thomas, Celina Johnson, and many others for their friendship and help during his graduate school life and wishes them the best in their careers. Gratitude is also extended to visiting scientists, Ricardo Reis and Dianelis Urbano for the sharing of ideas and their friendship. Special gratitude is extended to Dr. Leslie A. Simpson for his friendship and encouragement in pursuing the opportunity for doctoral studies and to Richard N. Cumberbatch and Godfrey A. Nurse for being friends and mentors. The efforts by Dr. Ronald W. Rice to help the Macoon family reach the United States of America and his special friendship, moral support, and help will forever be remembered and is recognized with deepest gratitude. Finally, the author wishes to express his most sincere appreciation, love, and gratitude to his wife, Y onette, for her love, endless understanding, and support throughout their life together and more so during the doctoral program, to his sons, Ron and Russ, for their unconditional love and for being the sources of his inspiration and to his mother, Agnes, for her love and for instilling the desire to seek knowledge. V

PAGE 6

TABLE OF CONTENTS ACKNOWLEDGMENTS....................................................................................... 111 LIST OF TABLES.................................................................................................. IX LIST OF FIGURES.................................. ............................................................. xvu ABSTRACT......... .... ... ........ ......... . ....... ............. ..................... .......................... XVlll CHAPTERS 1 INTRODUCTION................ ......... ....................................................... 1 2 LITERATURE REVIEW...................................................................... 8 Pasture-based Milk Production Systems................................................. 8 Economic Aspects of Pasture-based Milk Production....................... 9 Animal Responses to Management Variables.................................... 12 Forage Responses on Grazed-pasture Dairy Systems........................ 16 Plant-animal Interactions........................................................................ 21 Factors Influencing Intake of Grazing Animals................................. 22 Role of Grazing Time in the Plant-animal Interface................................. 34 Coat Color Relationships with Grazing Behavior and Animal Performance......................................................................... 39 Estimation oflntake on Pasture.............................................................. 42 3 GRAZING MANAGEMENT EFFECTS ON FORAGE PRODUCTION AND ANIMAL PERFORMANCE OF LACTATING DAIRY COWS ON SUBTROPICAL WINTER PASTURES....................... 53 Introduction.......................................................................................... 53 Materials and Methods........................................................ .................. 57 Pasture Variables............................................................................. 60 Plant-animal Interface Variables....................................... ............... 62 Animal Response Variables............................................................. 65 Vl

PAGE 7

4 5 Statistical Analysis... .................. .... ................................................ 66 Results and Discussion........................................................................... 69 Herbage Mass......................................................... ........................ 69 Herbage Disappearance.... ..... ......... ...... .... ..... . ................... ..... .... 74 Herbage Allowance .... ............ ............. ..... ........................................ 78 Forage Nutritive Value............... .......... ........... ................. .... ............ 81 Organic Matter Intake................. ..... ....... .... ........... .... ....... .... .... ..... 86 Milk Production per Cow........................... ...................................... 96 Milk Production per Hectare.. .. ............................ ............................ 101 4% Fat Corrected Milk Production per Cow.................................... 103 4% Fat Corrected Milk Production per Hectare.......... ..................... 104 Milk Composition.......... ..... ....................................................... .... 105 Animal Body Weight Changes......................... ................................ 113 Body Condition Score Changes.......... ............ .... ... ........................... 115 Blood Glucose Concentration........ .... .... .... .......... ........ .................... 116 Summary and Conclusions........................... ............................... .......... 117 GRAZING MANAGEMENT COAT COLOR AND SEASON EFFECTS ON GRAZING BEHAVIOR AND PERFORMANCE OF LACTATING DAIRY COWS ............ ........................ ..... ........ 122 Introduction.......... .......... .......... ... ............ .... ........... ...... ... ...... . . .... ... . 122 Materials and Methods..................................................... ........ .............. 125 Statistical Analysis........................................................... ... ............. 130 Results and Discussion. .... . .... ................. .... .... .... ......... ..................... 133 Responses to Grazing Management................................................. 133 Coat Color Effects on Grazing Behavior..................... .... .......... ...... 168 Coat Color Effects on Animal Responses......................................... 175 Summary and Conclusions...... .................................... ........... ............... 181 COMPARISON OF THREE TECHNIQUES FOR ESTIMATING FORAGE INTAKE OF LACTATING DAIRY COWS ON PASTURE ...... ...... . ...... ....... .. ...... ................ ............... .. .......... 187 Introduction...................................... ........................... ................. ........ 187 Materials and Methods...... ............................................... .................... 191 Statistical Analysis............................. ...... ...................... . ......... .... 194 Results and Discussion..... ................... ..... .................. .... ...................... 196 Forage DMI Data for 1996................................ ...... ................... .... 196 Forage DMI Data for 1997............................................ .... ............ 206 Summary and Conclusions..................................................................... 214 6 G E NERAL DISCUSSION AND SUMMARY.............................. ........ 219 Vll

PAGE 8

APPENDIX TABLES OF CLIMATOLOGICAL DATA VARIABLE LISTS RAW DATA AND PROBABILITY VALUES ASSOCIATED WITH RESPONSES REPORTED IN THE DISSERTATION... ..... 232 REFERENC ES................ .......... . ..... ......................... ... ..... .................... ..... ..... 291 BIOGRAPHICAL SKETCH... ..... ....... . . . ....... ........ ... .... ...... .................. .......... 305 Vlll

PAGE 9

LIST OF TABLES 3 1 Period by stocking rate (SR) interaction effect on pregraze herbage mass during winter 1996 ... ...... ... . . . .. . . ... . . . . . . . . . ... . . . . .. . . . ... . . ... .. 70 3 .2 Period by forage system (FS) by stocking rate (SR) interaction effect on pre graze herbage mass during winter 1997.. ................................. ..... ... 71 3 3 Forage system (FS) by stocking rate (SR) by concentrate supplement (CS) interaction effect on pregraze herbage mass during winter 1997..... ..... . 74 3 .4 Period by forage system (FS) by stocking rate (SR) interaction effect on herbage disappearance during winter 1997 .. . .... . .... ....... . ... .. . . . .. . . 7 5 3.5 Period by stocking rate (SR) interaction effect on herbage allowance during winter 1996... ......... . .................... ... ................... ....... .......... . 78 3 6 Forage system (FS) by stocking rate (SR) by concentrate supplement (CS) interaction effect on herbage allowance during winter 1997 ... .... . ... ... .. 79 3 7 Period by forage system interaction effect on herbage allowance during winter 1997 . . . . . . ... . . . . . .. .. ... .. . ... .. . ... ... ...... ... .. ..... ... .. ...... . .. . .. . 80 3 8 Period by stocking rate (SR) interaction effect on pasture in vitro digestible organic matter concentration during winter 1996..... ......... . .......... ...... 82 3.9 Period by stocking rate (SR) interaction effect on pasture neutral detergent fiber concentration during winter 1996. .. . . ... ... ..... .. ... . ... .... . ... ... . . . 85 3 10 Period by stocking rate (SR) interaction effect on forage organic matter intake (OMI) and total OMI during the 1996 winter grazing season....... 87 3 .11 Period by stocking rate (SR) interaction effect on average daily forage organic matter intake (OMI) and total OMI relative to cow body weight during the 1996 winter grazing season ... . . .... ... .... ... . ........... 88 l X

PAGE 10

3.12 Period by concentrate supplement (CS) interaction effect on forage organic matter intake (OMI) and total OMI during the 1996 winter grazing season.................................... .... ....... ...... ... ..... ....... .......... . .... 89 3.13 Period by concentrate supplement (CS) interaction effect on average daily forage organic matter intake (OMI) and total OMI, relative to cow body weight, during the 1996 winter grazing season....... ...................... 90 3 .14 Period by forage system interaction effect on forage organic matter intake (OMI) and total OMI during the 1997 winter grazing season................. 91 3 .15 Period effect on forage organic matter intake (OMI) and period by forage system interaction effect total OMI, both relative to cow body weight, during the 1997 winter grazing season.......... .... . ................................. 92 3.16 Forage system by stocking rate (SR) interaction trend on forage organic matter intake (OMI) and total OMI during the 1997 winter grazing season................................................................. .... ....... ........ ...... ........ 94 3.17 Period by stocking rate (SR) interaction effect on milk production per cow during winter 1997......................................................................... 97 3.18 Period by stocking rate (SR) interaction effect on milk production per hectare during winter 1997.............. .... ................................... .............. 102 3 .19 Period by stocking rate (SR) interaction effect on 4% fat corrected milk production per cow during winter 1997 .. .. .. . .. .. .. ... . . .. ... .. . . ... . ... .. 104 3.20 Period by stocking rate (SR) interaction effect on 4% fat corrected milk production per hectare during winter 1997 .. .. ... . . .. ..... .. .... . .... ... . .. .. 105 3 .21 Period by stocking rate (SR) interaction effect on milk fat concentration during winter 1997...... ...................................... .. . . . . . .. . . . . . . . . . . 106 3 .22 Period by stocking rate (SR) interaction effect on milk crude protein concentration during winter 1996.. .... .. ... ... .. .. ... .......... .... ....... ...... ... .... 108 3.23 Period by forage system interaction effect on milk urea nitrogen concentration during winter 1997............ ......... ......... ... .............. ... .. . 110 3.24 Forage system (FS) by stocking rate (SR) by concentrate supplement (CS) interaction effect on milk urea nitrogen concentration during winter 1997.... ................................................ ... ...................................... ....... 111 X

PAGE 11

3.25 Stocking rate (SR) by concentrate supplement (CS) interaction effect on blood glucose concentration during winter 1997 .. ........ ..... ...... .... ... .... 117 4.1 Season by forage system by stocking rate (SR) by concentrate supplement (CS) interaction effect on pre-graze herbage mass..... . .... ........... ..... ... . 134 4.2 Season by forage system by stocking rate (SR) by concentrate supplement (CS) interaction effect on herbage allowance.......... ......... ..... . ..... ........ 135 4.3 Season by forage system by stocking rate (SR) interaction effect on herbage in vitro digestible organic matter concentration... .... ...... ......... . 136 4.4 Season by forage system by stocking rate (SR) interaction effect on herbage neutral detergent fiber concentration.... .... ............ . ..... .... ........ 13 7 4.5 Stocking rate (SR) by concentrate supplement (CS) interaction effect on time cows spent grazing during daytime hours in summer 1996. ...... 139 4.6 Season by forage system by stocking rate (SR) interaction effect on total time cows spent grazing during a 24-h cycle... ......... .... .......... .............. 144 4 7 Season by concentrate supplement (CS) interaction effect on total time cows spent grazing during a 24-h cycle........ ............ .......... . ............... . 145 4 8 Temperature (maximum minimum and average) and solar radiation intensity recorded on pasture for observation days.. . .... . ........ . ..... ... 147 4 9 Season by forage system by stocking rate (SR) interaction effect on forage or g anic matter (OM) intake ....... ........ . ...... ... ..... .... .... .... . ............... .... 154 4.10 Season by forage system (FS) interaction effect on forage organic matter (OM) intake relative to animal body weight..... ......... . ........... ... . ....... ... 155 4 .11 Season by forage system by stocking rate (SR) interaction effect on total organic matter (OM) intake... ....... ............ .... . ......... ................ ........... 158 4.12 Season by stocking rate (SR) interaction trend on total organic matter (OM) intake relativ e to animal body weight... ............ ... ......... ..... ... .... 159 4 .13 Season by forage system (FS) interaction effect on total organic matter (OM) intak e relative to anim a l body weight...... ... .... .... ....... .... .......... . 160 X1

PAGE 12

4.14 Season by stocking rate (SR) interaction effect on average daily milk production . . .......................... : ... ....................... .......... ... .... ...... .... ... 160 4.15 Season by forage system (FS) interaction effect on average daily milk production ... ....................... ........ ... ......... .. .... ...... ....... .... .......... .. ..... .. . 161 4 16 Season by concentrate supplement (CS) interaction effect on average daily milk production ............... ............... ... .... ........ ... ... .......... .... ....... 162 4 17 Season by stocking rate (SR) interaction effect on animal body weight changes .. ............ ....... . ......... .... ...... ..... .............. ........... .. ......... ..... . 166 4.18 Forage system (FS) by concentrate supplementation (CS) interaction effect on animal body weight changes ........... ...... ........ .................. ..... 167 4 19 Temperature (maximum, minimum, and average) and solar radiation intensity recorded on pasture for coat-color study observation days ... ... 170 4.20 Season by coat color effects on daily forage organic matter (OM) intake for cows grazing summer and winter pastures ...... ... ... ........ .. ... ..... . ... 176 4.21 Season by coat color effects on daily total OM intake for cows grazing summer and winter pastures ..... ... ......... ... . ............. ................ ... ... ..... 176 4.22 Season by coat color effects on average daily milk production for cows grazing summer and winter pastures ......... ......... ............ .... .......... ... ... 178 4. 2 3 Season by coat color effects on 4% fat corrected milk production for d t tur cows g razmg summer an wm er pas es ........ ......... ..... ............ ...... . 179 4 .24 Season by coat color effects on average daily body weight changes for cows grazing summer and winter pastures ........ .... ... .............. ..... .... . 180 5 1 Period by concentrate supplement (CS) interaction effect on forage dry matter intake estimates predicted by the pulse dose marker technique in 1996 ...................... ...... .. .......... ............... ..... ..... ........ ....... 197 5 2 Period by stocking rate (SR) interaction trend on forage dry matter (DM) intake estimates predicted by the pulse dose marker technique in 1996 ... 198 5 3 Period by concentrate supplement (CS) interaction effect on deviation o f forage dry matter intake estimates predicted by the pul s e dose marker technique from base estimat e s in 1996 ......... .... ........ . .. . .. ....... .. ......... 199 XU

PAGE 13

5.4 Period by stocking rate (SR) interaction effect on deviation of forage dry matter intake estimates predicted by the pulse dose marker technique from base estimates in 1996. ... .. .. ... ... ... .. .. ... ... .... .. .... ... .. ... ......... ... .. 200 5.5 Period by stocking rate (SR) interaction effect on forage dry matter intake estimated by the herbage disappearance technique in 1996...... ....... .. .. . . 204 5.6 Period by forage system (FS) interaction effect on deviation of forage dry matter intake estimates predicted by herbage disappearance technique from base estimates in 1997............................... ...... ........ .. ... .... ......... 211 A-1 Climatological data starting from October 1995 to April 1997, i.e., from seeding of winter pastures in the first year to end of the study period..... 233 A-2 List of abbreviations used to describe response variables in appendix tables (in order of appearance)........................................................................ 234 A-3 Ingredient composition of concentrate supplement fed to animals in the winter 1996 and 1997 grazing studies......... ............... ... ............ ........... 236 A-4 Chemical composition of concentrate supplement fed to animals in the winter 1996 and 1997 grazing studies.............................. ...................... 23 7 A-5 Pregraze herbage mass (PRHM), herbage disappearance (HD) and herbage allowance (HA) for each sample time within each period in 1996 reported in Chapter 3................................... ........ ........ ................ 23 8 A-6 Forage in vitro digestible organic matter (IVDOM), crude protein (CP) and neutral detergent fiber (NDF) concentration for each sample time within each period during 1996 reported in Chapter 3 ......... ... ....... ....... 240 A7 Probability levels for tests of fixed effects of period (P) stocking rate (SR) and concentrate supplement (CS), and their interactions on response variables associated with 1996 herbage data reported in Chapter 3 ... .. .. . 241 A-8 Pregraze herbage mass (PRHM), herbage disappearance (HD) and herbage allowance (HA) for each sample time (week) within each period in 1997 reported in Chapter 3.................. ......................................... ...... . 242 A-9 Forage in vitro digestible organic matter (IVDOM), crude protein (CP) and neutral detergent fiber (NDF) concentration for each sample time (week) within each period during 1997 reported in Chapter 3.. ... . .... ... 246 Xlll

PAGE 14

AlO Probability levels for tests of fixed effects of forage system (FS) stocking rate (SR), concentrate supplement (CS), period (P), and their interactions on response variables associated with 1997 herbage data reported in Chapter 3 .. ... .. .. . . .. ... .... .. . .. ... .... . . ... . . . ... ...... ... . . ... . 250 A-11 Forage organic matter intake (FOMI) tota l organic matter intake (TOMI), FOMI relative to body weight (FOMIBW), and TOMI relative to bod y weight (TOMIBW) of cows during 1996 reported in Chap t er 3.. ....... ... 251 A-12 Probability levels for tests of fixed effects of period (P) stocking rate (SR), and concentrate supplement (CS) and their interactions on response variables associated with 1996 organic matter intake data reported in Chapter 3 ... ... . . .. ... . . ... .. ... ... ... .... . ........ ..... . ... ... . . .... ..... . . .... 252 A-13 Forage organic matter intake (FOMI) total organic matter intake ( TOMI), FOMI relative to body weight (FOMIBW) and TOMI relative to bod y weight (TOMIBW) of cows during 1997 reported in Chap t er 3.... ... . .... 253 A-14 Probability levels for tests of fixed effects of forage system (FS) stocking rate (SR) concentrate supplement (CS), period (P), and their interactions on response variables associated with 1997 organic matter intake data reported in Chapter 3... ... . . ..... ... . ... ..... ...... .... ... .... ......... 2 5 5 A-15 Calculated daily nutrient intake by cows in the winter 1997 grazing stud y for each forage system (FS) by stocking rate (SR ) by concentrate supplement (CS) treatment combination vs. NRC tabulated requirements. .................................................... .... ..... . . .... ........ . . . ..... 2 56 A-16 Anima l performance responses observed in 1996. . . . .... ........ ..... .... . ....... ... 2 5 8 A-17 Probability levels for tests of fixed effects of period (P) s t ocking rate ( SR), and concentrate supplement (CS), and their interaction s on response variables associated with 1996 animal performance d a ta report e d in Chapter 3 ..... ... ... . . ... . . ... . . . ... . . . .... . .... .. . ..... ...... ... ... . ... ... ... .. .. 2 59 A-18 Animal performance responses observed in 1997. ..... ................... .......... ... 26 0 A -19 Probability levels for tests of fixed effects of fora g e s y st e m (FS) stockin g rat e (SR), concentrate supplement (CS) period (P ), and their interactions on response variables associated with 1997 animal performance data reported in Chapter 3 . ...... ..... ... .... ...... .... . . ... ........ 26 1 XI V

PAGE 15

A-20 Milk composition and blood glucose concentration data obtained during 1996 reported in Chapter 3............. .... . ... ......................... .... ........ . . . 262 A-21 Probability levels for tests of fixed effects of period (P) stocking rate (SR ), and concentrate supplement (CS) and their interactions on response variables associated with 1996 milk composition and blood glucose concentration data reported in Chapter 3..... ... ....... .... . . ........ ..... ......... 264 A-22 Milk composition, milk urea N, and blood glucose concentration data obtained during 1997 reported in Chapter 3 .............. .... ..... . ... . ... ....... 265 A-23 Probability levels for tests of fixed effects of forage system (FS) stocking rate (SR), concentrate supplement (CS) period (P) and their interactions on response variables associated with 1997 m i lk composition, milk urea N and blood glucose concentrat i on data reported in Chapter 3............................. . ... ....... ....... ..... .......... ..... ...... 267 A-24 Program in SAS to compute new estimates of forage intake and total intake based on adjusted diet digestibility of mi x ed forage-concentrate supplement diet... ... ...... ......... ... ....... .......... . .......... .... ... ... . .... ...... 268 A-25 Program in SAS to compute parameters for fecal output calcu l ations based on chromium concentrations in fecal samples and time of fecal collection.... .... ........................ ....................... .... ..... .......... ... ........... .... 268 A-26 Pregraze herbage mass, postgraze herbage mass and herbage allowance data obtained on 1996 summer pastures reported in Chap t er 4..... ... .... . 2 69 A-27 Forage nutritive value organic matter intake bod y weigh t and animal performance data obtained in 1996 summer reported i n Chapter 4.. ...... 272 A-28 Probability levels for tests of fixed effects of forage system (F) stocking rate (SR) concentrate supplement ( CS) season (S) period wi t hin season (P[S]) and their interactions on pasture response v ari a bles reported in Chapter 4 . . . . ........ ..... ......... .............. ... ........ ........ . ... ... . 2 7 3 A-29 Probability levels for tests of fixed effects of forage system ( F ) stocking rate (SR) concentrate supplement (CS) season (S) p e riod wi t hin season (P[S]) and their interactions on animal response variables reported in Chapter 4 ......... ... . ..... . ..... ....... ........... ........... .... ....... ........ 274 A 30 Time spent grazing loafing (under and outside shade) and ea t in g concentrate supplement during 1996 summer ... .. ... ........... .... .......... . ... 275 xv

PAGE 16

A-31 Time spent grazing, loafing, and eating concentrate during 1997 winter....... 278 A-32 Probability levels for tests of fixed effects of forage system (F), stocking rate (SR), concentrate supplement (CS), season (S), and their interactions on animal grazing behavior reported in Chapter 4................ 280 A-33 Grazing behavior, organic matter intake, and animal performance of animals on the coat color study in summer 1996. ....... .. .. ......... ....... ....... 28 1 A-34 Grazing behavior, organic matter intake and animal performance of animals on the coat color study in winter 1997 ... ..... ...... ... .. ... ... .... . .... 284 A-35 Probability levels for tests of fixed effects of season (S), period within season (P[S]), coat color (CC), observation day (OBD) and their interactions on animal grazing behavior and animal performance reported in Chapter 4..................... ........... .............................. ............ 286 A-36 Forage dry matter intake predicted by different techniques during winter 1996 and the deviation of these estimates from the base estimate.... ... .. . 287 A-37 Forage dry matter intake predicted by different techniques during winter 1997 and the deviation of these estimates from the base estimate........... 28 8 A-38 Probability levels for tests of fixed effects of period (P) stocking rate (SR) and concentrate supplement (CS), and their interact i ons on forage dry matter intake estimated by different techniques during 1996 reported in Chapter 5............ ... ....... .... ....... .... ......................... .... 289 A-39 Probability levels for tests of fixed effects of forage s y stem (F) stocking rate (SR), concentrate supplement (CS), period (P), and their interactions on forage dry matter intake estimated by different techniques and their deviation from base estimates during 1997 reported in Chapter 5. .... ... ............. ... ......... ......................... .. ... . ... .. 290 XVl

PAGE 17

LIST OF FIGURES Figure 3.1 Regression relationships between pregraze herbage mass (HM) and herbage disappearance (HD) for (a) all 1996 data combined, (b) high stocking rate data for 1997, and ( c) low stocking rate data for 1997 ....... ...... .... .. 77 4.1 Average daily temperature (A VT; C) effects on daytime grazing (DG; a), nighttime grazing (NG; b), and total grazing time (TG; c) during summer................................................................................................. 150 4.2 Average solar radiation intensity (RAD; mol m2 s1 ) effects on daytime grazing (DG; a), nighttime grazing (NG; b), and total grazing time (TG; c) during summer ..................................................................... ... 4.3 Average solar radiation intensity (RAD) effects on time spent grazing during daytime by black (BHC) and white (WHC) hair coat cows during 151 summer................................................................................................. 172 4.4 Average solar radiation intensity (RAD) effects on time spent under shade by black (BHC) and white (WHC) hair coat cows during summer.......... 174 5 .1 Relationship between estimates of forage dry matter intake (DMI) predicted by the pulse dose marker technique (PDME) and the deviation of these estimates from the base estimates (DBE) in 1996...... ... .... ... .... ....... .. ...... 201 5.2 Relationship between estimates of forage dry matter intake (DMI) predicted by the herbage disappearance technique (HDE) and the deviation of these estimates from the base estimates (DBE) in 1996............ .............. 205 5.3 Relationship between estimates of forage dry matter intake (DMI) predicted by the pulse dose marker technique (PDME) and the deviation of these estimates from the base estimates (DBE) in 1997 .... ... ... ..... .. ... .... ..... ... ... 209 5.4 Relationship between estimates of forage dry matter intake (DMI) predicted by the herbage disappearance technique (HDE) and the deviation of these estimates from the base estimates (DBE) in 1997.......................... 213 xvn

PAGE 18

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy FORAGE AND ANIMAL RESPONSES IN PASTURE-BASED DAIRY PRODUCTION SYSTEMS FOR LACTATING COWS By Bisoondat Macoon December 1999 Chairman: Dr. Lynn E Sollenberger Major Department: Agronomy In response to greater volatility in milk prices and higher costs of production Florida s dairy producers have begun to consider use of grazed-pasture for their lacta t ing cows. Insufficient research information is available to provide comprehensive grazing recommendations During 1996 and 1997 research was conducted to quantify forage and animal production when lactating dairy cows grazed two cool-season forage systems ( FS ; N-fertilized rye [Seca/e cerea/e L.]-ryegrass [Lolium multiflorum L.] pastures vs. rye ryegrass-crimson clover [Trifolium incarnatum L.]-red clover [Trifolium prate ns e L.] pastures) at two stocking rates (SR; 5 vs. 2.5 cows ha1 ) and two concentrate supplementation levels (CS; 1 kg [as-fed] per 2 5 kg of milk vs. 1 kg per 3.5 kg) Additionally, studies conducted in summer 1996 and winter 1997 evaluated managemen t, season, and cows coat color effects on grazing behavior. A further study compared the xvm

PAGE 19

pulse dose marker and herbage disappearance techniques for estimating forage dry matter intake (DMI) vs. the energy requirement for animal performance method. Stocking rate was the key factor affecting forage production and animal performance. Greater herbage mass occurred at low than high SR (1390 vs. 1020 kg DM ha-1 ) resulting in greater intake (17 6 vs 15.8 kg OM d -1 ) and milk production per animal (23.5 vs. 20.l kg cow-1 d-1). Milk production per hectare was greater at high SR. Production based on high SR, however, may not be sustainable because of loss in body weight and condition. Animals fed at high CS had better energy balance and less body weight loss. Grass-N fertilizer systems were more adapted to intensive grazing than grass clover systems in this environment. Cows grazed for less time per day in summer (161 min) than winter (249 min) because of heat stress. In summer, animals on rhizoma peanut pastures grazed less (179 vs. 143 min) but achieved greater forage intake (14.9 vs. 11.0 kg OM d -1 ) and milk production (17.2 vs. 14.9 kg cow-1 d-1 ) than cows grazing bermudagrass. In winter, cows on low SR grazed longer (494 vs. 419 min d -1 ) and achieved greater forage intake (12.5 vs. 10.2 kg OM d -1 ) and milk production (20 0 vs 16.2 kg cow-1 d -1 ) than those on high SR. Cows with predominantly white coats grazed on average 13 min d -1 longer than those with predominantly black coats, regardless of season, resulting in higher intake (12.5 vs. 11.2 kg d-1 ) and greater milk production (11.5 vs. 8.0 kg cow-1 d -1 ) in summer but not in winter Forage DMI estimates obtained by the different techniques indicated that predictions by the herbage disappearance method more closely matched energy requirements compared to estimates by the pulse dose marker technique. XlX

PAGE 20

CHAPTER 1 INTRODUCTION Animal products contribute to the dietary energy requirements of humans, and protein from animal sources is especially important in human diets. The extent of this contribution varies widely throughout the world depending on geographical location, culture, and state of development (Henzell, 1983). Mille and milk products are major components of the contribution of animals to human food needs; milk is the major component of the dietary requirement of infants. Historically, the major source of nutrients for millcand meat-producing domestic herbivores, largely cattle, has been forages. Native grasslands and improved pastures still contribute the great majority of nutrients for cattle in many parts of the world, with additional nutrients coming from crop byproducts. Since the midto late 1950s, the dairy industry in the USA and other developed areas has adopted an approach to dairying that involves much greater use of concentrate feeds, greater reliance on stored forage and removal of dairy cattle from pastures. This came about in an era of advances in scientific knowledge that allowed for agricultural production to become more intensified. Since then, milk production per cow has increased greatly, largely due to increased grain consumption but also to improved cattle genetics and to the ability of animal nutritionists to formulate diets that more accurately and uniformly meet the nutritional requirements of 1

PAGE 21

2 the lactating cow. Total confinement systems of dairy production based on greater use of concentrate feeds and stored forages require high capital inputs for buildings and equipment, which, along with the cost of feedstuffs incur high production costs. Florida's dairy industry is an integral part of the state's agriculture with revenue from milk sales generating more than $400 million annually in the last several years (Florida Agricultural Statistics, 1999). Before recent legislation requiring use of animal wastes to grow crops, dairies in Florida typically comprised large herds housed on relatively small land areas and they grew little of their feed. Like the rest of the USA dairy industry since the midto late 1950s, most Florida dairy farms are managed based on high concentrate feed use, stored forage, and drylot feeding of total mixed rations (TMR) in total confinement. On these farms, pastures contribute little as a source of nutrition for dairy cows and are more used as exercise lots for dry cows and heifers. This system concentrates nutrients in a small area creating conditions where loss to the environment can occur. Thus, nutrient management has become a major focus of regulatory agencies and a major expense and constraint to profitability of producers Recent regulations require dairy farms to recycle nutrients from animal wastes into crop production. Changes in market conditions in the dairy industry (Elbehri and Ford, 1995) and growing pressure by regulatory agencies and environmental interests regarding nutrient management on dairy farms have led to a search for alternatives to total confinement systems. In recent years, dairying based on grazed pastures as sources of nutrients for lactating cows is once again being considered. Improved forages and forage management practices improved and cheaper fencing materials to support rotational grazing, reduced

PAGE 22

3 forage harvesting and feeding costs, possible improvements in herd health with animals off of concrete, lower capital outlay and reduced day to day costs for handling animal wastes, and an attempt to improve public perception of the dairy industry have been cited (Bernard and Chandler, 1994) as reasons for reverting to pasture-based dairy systems. In recent years, there has been a trend of adoption of pasture-based systems in temperate areas of the USA (Parker et al., 1992). Some Florida producers have begun to adopt similar systems during the last 5 yr. Insufficient information on pasture-based dairying in Florida, however, precludes recommendation of widespread use of grazed pastures for dairy cows. Research efforts are needed to address this void Studies in the northeastern USA have demonstrated that there are economic benefits from incorporating intensive grazing in the dairy production system; however, these benefits depend on the producers' managerial abilities (Elbehri and Ford, 1995). There is a need for targeted extension and research programs to improve feeding programs with grazing as well as the overall quality of pasture management. Fales et al. (1995) contended that stocking rate was the key management variable in determining productivity and profitability of grazing systems. It should be noted that in northeastern USA dairies, because of the high protein concentration and digestibility of temperate forages, there was little or no reduction in milk production when the system changed from feeding in confinement to one in which the forage component of the diet was grazed pasture (Fales et al., 1993). This situation may not be the same for the tropical and subtropical forage grasses grown in Florida because they generally contain more fiber and less protein and have lower digestibility than their temperate counterparts (Sollenberger

PAGE 23

4 and Chambliss, 1991). It is questionable whether or not high producing dairy animals grazing tropical grass pastures can achieve the level of nutrient intake required and cope with heat stress adequately to maintain a profitable milk production enterprise. On the other hand, Florida's climate allows for growing of cool-season forage crops during winter months when temperatures in temperate areas of the USA prohibit crop growth. This may be advantageous to Florida's producers by providing an opportunity for year round grazing management and perhaps decreasing dependence on grain consumption and stored forages, and further, reducing waste management concerns compared to their counterparts in temperate areas. Developing an information base on which to make grazing management decisions should include collection of data that help explain how animal responses are coupled to pasture production and environmental conditions and how these components interact at the plant-animal interface. It is well established that animal performance is largely determined by nutrient consumption (Mertens, 1994). Nutrient intake and subsequent animal performance of grazing animals in forage-based systems are dependent on forage and non-forage factors (Moore, 1994). Forage factors include quality-related characteristics and quantity, while non-forage factors include animal characteristics, environmental conditions, and supplemental feed management (Moore, 1980) Factors affecting forage intake may act through metabolic, physical, and/or behavioral control mechanisms (Hodgson, 1985). Moore (1983) suggested that these control mechanisms probably operate simultaneously, with one or the other being dominant depending on

PAGE 24

5 pasture characteristics. Hodgson (1985) indicated that the behavioral mechanism may override the others when herbage mass or herbage allowance are limiting. The behavioral mechanism expresses forage intake as the product of grazing time, bite weight, and biting rate. Bite weight and biting rate are influenced by animal and sward characteristics, but biting rate and grazing time are considered the primary compensatory responses of the animal to limitations in bite weight (Hodgson et al., 1994). These authors reported that grazing time increases in response to limitations in intake rate. Forbes et al. (1985) reported that there was evidence of differences in grazing time between breeds of cows and between animals of differing physiological state. In cattle, animals with higher energy requirements seem to spend more time grazing. Positive relationships between grazing time and milk yield have been reported in the tropics (Stobbs, 1970; Cowan, 1975). There also is evidence that black hair coat cows produce less milk than white-coated cows under conditions of heat stress (Hansen, 1990), likely because cows with black hair coat absorb more solar radiation than white hair coat cows. No studies of coat color effects on grazing behavior in lactating dairy cows were found in the literature, but there are anecdotal reports that dairy cows with predominantly white coats spent more time grazing than cows with black coats in the southeastern USA. Intake measurements may help to explain observed variation in animal outputs and can be related to pasture characteristics. Estimates of forage intake by grazing livestock are inherently difficult to obtain, and every method has unique advantages and disadvantages. Several practical approaches to estimating pasture intake were suggested by Moore (1996). These include estimates for individual animals as well as estimates for

PAGE 25

6 groups of animals, or a pasture. Comparisons among methods may aid researchers in choosing which is best suited for the objectives and conditions of particular experiments. Deriving from problems facing the dairy industry and the related scientific literature, a three-component research program was developed. The first component of the dissertation research addressed grazing management of cool-season forages during the winter months. The study was based on the null hypotheses that a) forage species does not affect pasture production or animal responses, b) neither pasture nor animal responses are a function of stocking rate imposed, and c) amount of concentrate fed does not affect animal performance nor pasture intake. A grazing trial using lactating Holstein cows (Bos taurus) was conducted to test these hypotheses. The objective was to quantify pasture and animal responses when lactating dairy cows graze two different cool-season forage mixtures (rye [Secale cerea/e L.] and ryegrass [Lolium multiflorum Lam.] mixed pastures vs. rye-ryegrass-crimson clover [Trifolium incarnatum L.]-red clover [T pratense L.] mixed pastures) at two stocking rates (5 vs. 2.5 cows per hectare) and two levels of concentrate supplementation (1 kg concentrate per 2.5 kg of milk produced vs. 1 kg concentrate per 3 5 kg of milk produced). A second component of the research examined grazing time of lactating cows on summer and winter pastures. This experiment was based on the null hypothesis that grazing time of lactating dairy cows is not influenced by pasture characteristics, rate of supplemental feeding, coat color, nor prevailing climatic conditions The overall objective was to examine grazing management variables, coat color and seasonal (summer vs. winter) effects on grazing time of lactating dairy cows

PAGE 26

7 A third component of the research compared methods of estimating forage intake of lactating dairy cows in both the summer and the winter grazing seasons. The three methods of intake estimation for animals grazing pasture suggested by Moore (1996) were studied viz., the animal performance method, the pulse dose marker method and the herbage disappearance method. The null hypothesis was that these three methods of estimating pasture intake are equally useful in determining intake of grazing dairy cows. The objective was to compare differences in estimates among the three methods which may help identify the most useful approach or combination of approaches for estimating pasture intake in pasture-based dairy production research As a result of these studies it is anticipated that applied and fundamental knowledge will be gained Specifically data will be available to inform producer choices of forage stocking rate and concentrate feeding rate in pasture-based systems. Additionally greater fundamental understanding will be gained of the pasture and animal factors affecting grazing behavior intake, and performance of lactating dairy cows.

PAGE 27

CHAPTER2 LITERATURE REVIEW Pasture-Based Milk Production Systems Historically, forages have been the major source of nutrients for cattle for both meat and milk production. Native grasslands and improved pastures still contribute the majority of nutrients for cattle in many parts of the world with aclditional nutrients coming from crop byproducts. The dairy industry in the USA and other developed countries has adopted an approach to dairying that involves much greater use of concentrate feeds greater reliance on stored forages, and removal of dairy cattle from pastures. This occurred during the 1950s an era when advances in scientific knowledge led the way toward more intensified agricultural production Since then milk production per cow has increased greatly largely due to increased grain consumption but also to improved cattle genetics and to the ability of animal nutritionists to formulate diets that more accurately and uniformly meet the nutritional requirements of the lactating cow (Mertens 1986). Present-day livestock production utilizes a substantial quantity of potential human food crops. Grain fed to livestock accounted for more than 40% of the world's production by the early 1980s (Henzell, 1983). Morley (1981) noted that pastures are available for much of the year in many parts of the world, with minimal costs in non renewable resources because they are harvested by the grazing animal Other sources o f 8

PAGE 28

9 livestock feed may not be available or are too costly in many regions of the world. Improved management of pastures emphasizing increased efficiency of pasture utilization can be the basis for improving human nutrition and welfare (Morley, 1981). Additionally, ever changing economic conditions (Elbehri and Ford, 1995) and increased environmental regulations have led dairymen to consider reverting from intensive to pasture-based systems. Economic Aspects of Pasture-Based Milk Production More economical milk production was listed among reasons why some dairy producers are reverting to utilization of pasture as a nutrient source (Bernard and Chandler, 1994). Elbehri and Ford (1995) cautioned that economic impacts of grazing based dairies have not been extensively researched. Additionally most of the reported studies used the partial cost approach which does not account for such factors as fixed costs, machinery costs, and labor requirement changes. They suggested that whole-farm economic analysis are needed in order to quantify the benefits of grazing-based systems. A comparative survey of Pennsylvania dairymen using or not using pasture-based systems was conducted to acquire empirical data on which to make decisions relative to adoption of intensive grazing systems (Parker et al., 1993). Of the dairymen using pasture-based systems, 74% ranked reduced feed costs as the most important reason for adopting grazing. Feed cost is estimated to account for between 50 and 60% of total operating costs (Elbehri and Ford, 1995). Parker et al. (1993) found that feed purchase costs were similar among systems, suggesting that pasture-based systems were not

PAGE 29

10 capturing the potential savings in feed costs that were available to them. There were no differences in average milk production between Holstein herds on grazed pastures compared to conventional confined systems (7878 vs. 8153 kg cow') in the surveyed area (Parker et al., 1993). Overall operating costs tended to be lower on the pasture-based systems. Tietz (1993) in an article summarizing research done by a University of Minnesota group reported that the pastured cows produced less milk than barn-fed cows in both years of the study but net returns favored the pasture systems because of cost savings associated with less feeding of stored feeds, use of facilities and equipment, manure disposal costs, and labor Miller and Schnitkey (1994) studied economic patterns and labor utilization in a comparison of intensive grazing vs. conventional systems in Ohio. They reported that total fixed cost per cow in the pasture system was 27 to 30% lower than in the conventional system. Returns above total cost were higher than for most major grain crops in the USA, hence grazing compared favorably to other cropping alternatives. These authors suggested that returns from intensive grazing systems were dependent on stocking rate (SR) Fales et al. (1995) found that on a per unit land area basis profitability increased with increased SR due to an increase in milk production per land area. On a per cow basis profitability was greatest at a low SR because of the value of silage associated with maintaining similar production levels at each SR in their study Parker et al. (1992) compared an intensive grazing system to a drylot feeding system using linked spreadsheet models and found that gross margin was $121 per cow higher on the grazing farm. Their analysis suggested that the average Pennsylvania dairy

PAGE 30

11 could reduce operating costs by $6,000 to $7,000 annually through intensive grazing, but overall income would not be improved if milk production per cow fell by more than 450 kg per lactation. Elbehri and Ford (1995) evaluated economic impact of intensive grazing for a representative Pennsylvania dairy farm using a simulation model. Under the assumption of equal milk production levels, annual net cash farm income of the intensively grazed farm was 14 to 25% higher compared to farms without intensive grazing systems. In terms of net income per cow, intensive grazing farms generated between $140 and $207 per year more than farms without intensive grazing depending on the forage scenario modeled It should be noted that the simulated economic benefits assumed above average managerial expertise to produce forage crops of good yield and quality and good herd management particularly in providing well-balanced nutrients and cost-effective feeds (Elbehri and Ford, 1995) Data from surveys conducted in Wisconsin in 1993 and 1995 (Jackson-Smith et al. 1996) showed that total returns to labor and management were, on average highest for fully intensive grazing operations ($16,034) less for semi-intensive grazing ($12 739) and considerably lower for non-intensive grazing and total confinement systems ($7 855 and $10 680). Total farm income was $69 ,379 for fully intensive graziers, $99 414 for semi-intensive graziers $105,368 for non-intensive graziers, and $146,061 for confinement operations. Despite the demonstrated potential to improve profitability of dairy farms producers are cautious to change because of their lack of confidence in the ability of pastures to provide high quality forage and the dearth of information describing grazing

PAGE 31

12 systems and ration programs necessary to maintain milk production (Parker et al. 1992) Jensen (1995) summarized the benefits of grazing vs. traditional dairy very well when she suggested that the economic advantage appears to be for producers now starting a dairy operation Start up costs for grazing dairies run close to $1, 900 per cow, less than half the $4 ,300 per cow initial costs associated with the traditional confined dairy (Jensen 1995). She also quoted a Florida producer Ron St. John, suggesting that grazing was an attractive way to expand with fewer costs and more favorable reaction from local residents Animal Responses to Management Variables Animal performance on pasture is likely coupled to pasture quantity and quality when all other variables are similar. Numerous management factors are involved in determining the supply and quality of forage. Major factors include forage species grazing frequency grazing intensity, season of year soil fertility and agronomic management. Concentrate supplementation fed to animals on pasture and its inherent associative effects (Moore, 1992) will also influence intake and subsequent performance of grazing animals. No recent reports in the USA comparing the effects of different forage systems on performance oflactating cows were found in the literature Moss and Lowe (1993) in subtropical Austral i a found that milk production per cow was not diff e rent between N fertilized ry e grass and ryegrass clover (Trifolium spp ) pasture systems In Florida, Baltensperger et al. (1986) compared responses from an N-fertilized rye-ryegrass mixture

PAGE 32

13 and a rye-ryegrass-clover mixture with no additional N that were rotationally grazed by steers. They reported that carrying capacity was not different between the two forage systems. Average daily gains were higher on the grass-clover pastures in the first two years of the study (0.8 vs. 0.6 and 0.8 vs. 0.5 kg d1 respectively) and were similar in the third year (0.9 kg d1 ; Baltensperger et al., 1986). Fales et al. (1995) contended that SR is a key management variable in determining productivity and profitably of grazing systems. Milk production responses to SR occur primarily through its effects on herbage allowance and intake. These authors suggest that SR has not been researched adequately for high producing dairy cows in the USA. The trend of milk production responses in several studies of SR effects in grazing dairy systems appears to be lower production per animal and higher production per land area with increasing SR (Davidson et al., 1997b; Fales et al., 1995; Kristensen, 1993; Moss and Lowe, 1993). Body weight (BW) changes were not as definitive, possibly because of confounding effects due to mobilization of body tissues by cows prioritizing milk production (Moe et al., 1970; 0rskov and Ryle, 1990). In a study conducted in Pennsylvania, daily milk production (25 kg cow1 ) was not different among cows grazing temperate pastures at three different SR, mainly because cows were managed to have similar production by feeding silage when pasture was limiting (Fales et al., 1995). Milk fat concentration, crude protein (CP), or somatic cell count (SCC) were not affected by SR treatments. Also, there were no SR effects on BW or body condition score (BCS) changes. Milk production per unit land area was greater at the high SR (Fales et al., 1995).

PAGE 33

14 Moss and Lowe (1993) studied grazing dairy systems integrating irrigated temperate pastures with rain-fed tropical pastures in subtropical Australia. They found that milk production per cow increased when SR decreased on N-fertilized ryegrass or ryegrass-clover pastures. In another Australian study in which tropical pastures were grazed at 2.0, 2.5, 3.0, or 3.5 cows ha1 fat corrected milk (FCM) production per cow decreased with increasing SR, but FCM yield per ha increased linearly with increasing SR (Davidson et al., 1985). Forage dry matter (DM) on offer was closely related to milk production in that study Body weight tended to decrease with increasing SR, and there were marked differences in BCS (data were not presented). More recently, Davidson et al. (1997b) showed that milk production per cow decreased with increasing SR while m i lk yield per unit land area had a positive linear relationship to SR. A study conducted in the Irish Republic demonstrated that increasing SR above levels that allowed for adequate forage supply resulted in decreased milk production (O'Brien et al., 1999). That study also concluded that supplementation in the presence of adequate availability of good quality forage was not economical. Concentrate feeding has been the basis of intensive dairy production in the USA. Pastures can potentially reduce costs associated with grain-based, stored-forage dairy systems but will require supplementation with concentrate for high producing dairy cows to achieve optimum milk production (Kolver and Muller 1998) Hoffman et al. (1993) investigated effects of three levels of concentrate supplementation on performance of lactating cows on rotationally stocked pastures. The treatments were 1) fixed levels of a control grain at 1 kg per 3 kg milk, or 2) 1 kg grain per 4 or 5 kg milk depending on

PAGE 34

15 quantity of pasture available, and 3) grain reformulated biweekly based on pasture quality at 1 kg per 4 or 5 kg milk depending on pasture availability. Average daily milk production was not different among ration levels (range= 23.2 to 24.7 kg cow1). Cows fed the control ration at the higher level gained more BW and had better BCS than those fed less ration. Berzaghi and Polan ( 1992) found that grazing lactating cows fed 5 7 kg d1 cracked com had higher daily milk production than those not given any supplemental feed (23.7 vs. 19 5 kg cow). Supplementation with 2.3 kg d 1 com silage DM had no effect on milk production, milk composition, BW changes, or BCS of Holstein cows grazing temperate pastures (Holden et al. 1995). Total dry matter intake (DMI) of cows in that study remained the same because forage intake was reduced with supplementation. Jones Endsley et al. (1997) showed that supply and digestion of nutrients in grazing dairy cows may be improved through an increase in the CP concentration of supplement or the amount of supplement offered. The effects of this on milk yield or milk components, however, may be small. Kolver and Muller (1998) compared nutrient intake and animal performance of high producing Holstein cows consuming pasture or total mixed ration (TMR). They concluded that although high nutrient intake from pasture can be achieved loss of body condition required to maintain high milk production necessitates supplemental energy and in some cases protein to achieve potential milk production A study in the UK evaluated the effect of sward characteristics, viz. tiller density of perennial rye grass (Lolium p e renne L.) and supplement type (based on high or low fiber concentration) on herbage intake and milk production of grazing dairy cows (Fisher et al. 1996). Sward, but not supplement type, affected herbage intake. Cows grazing swards

PAGE 35

16 with high tiller density had greater intake than those on low tiller density swards (14.5 vs. 11.6 kg DM d -1). There were no treatment effects on milk production, milk composition, BW, or BCS change, but the authors suggested that small increases in energy intake from sward and supplement effects may have been used primarily to ameliorate BW loss (Fisher et al., 1996). The treatment sward characteristics were achieved by manipulating grazing intensity as a prerequisite to the experiment, thus the relationship with SR. Studies using this approach, viz., altering grazing management to achieve desired sward characteristics, were also done in New Zealand. In one study (Holmes et al., 1992), swards were manipulated so that pregraze herbage mass (HM) was either high (5.1 Mg DM ha-1 ) or low (2.9 Mg DM ha-1), and pastures were grazed by lactating dairy cows offered a common daily herbage allowance. Cows grazing the low HM swards had higher milk production, milk fat, and milk protein because of higher forage quality due to less senescent material, lower concentrations of stem, more clover component, and higher digestibility than the high mass counterpart. In a second New Zealand study (Hoogendoorn et al., 1992), pastures were managed in the same manner to achieve low, medium and high HM swards but cows were now allowed a common daily allowance of green leaf DM (total DM allowance varied widely). Milk production and composition did not differ among treatments in this study. Forage Responses on Grazed-Pasture Dairy Systems Defoliation is the most important influence of the grazing animal on the pasture. Grazing management allows the manipulation of (and defoliation is characterized by)

PAGE 36

17 grazing frequency how often the sward is defoliated, grazing intensity how much of the sward is removed at defoliation, and timing the stage of plant development or time of season when defoliation is imposed. In addition, species of animal, prehension method, treading, excreta deposited on pastures, and even saliva deposited on plants during grazing are contributing factors that influence persistence, productivity, and botanical composition of the sward and the regrowth rate of plants following grazing (Matches, 1992). Few recent studies have been done in the USA to compare forage responses from pasture systems based on different forage species for dairying. Casler et al. (1998) noted that perennial cool-season grasses (used in pasture-based dairy systems) have historically been bred and evaluated under hay managements with mechanical harvesting. These researchers have begun development of a data base of forage yield and persistence responses of cool-season grass cultivars to management intensive grazing systems (Casler et al., 1998). Differences among and within species for herbage availability, apparent intake, and ground cover were demonstrated in their study. Many of the USA grazing studies with lactating dairy animals have been done in the Northeast, and have utilized pastures with forage species that were already acceptable for use in grazed-dairy systems (e.g., Fales et al., 1995; Hoffinan et al., 1993; Jones-Endsley et al., 1997; Kolver and Muller, 1998). It seems logical that forage species will affect pasture production and quality, but apparently, evaluation of forage species impact on pasture-based dairying are not deemed critically important for pastures based on temperate species in the northeast USA.

PAGE 37

18 A recent study in Missouri comparing responses from two ryegrass cultivars, 'Surrey' and 'Marshall' (Hafley, 1996), indicated that there was a tendency (p = 0.12) for Surrey to have less forage mass but there were no differences in forage nutritive value between the two cultivars. In Australia, Moss and Lowe (1993) reported more herbage production in N-fertilized rye grass pastures than in ryegrass-clover pastures (2.3 vs 1. 5 Mg DM ha-1 ) grazed by lactating dairy cows at SR of 5 or 10 cows ha-1 Average pasture digestibility (750 g kg-1 ) and CP (above 250 g kg-1 ) were not different between forage systems. Stocking rate is a major determinant of both animal and plant production (Fales et al., 1995; Kristensen, 1988; Le Due et al., 1979). In a review, Hart and Norton (1988) concluded that forage production usually decreases with increasing SR. With increased SR, there is less herbage available per animal, thus animals become less selective in choosing plant or plant parts (Matches, 1992). This likely leads to an increasing level of plant defoliation along with changes in sward morphology and composition (Matches et al., 1981 ). Maturity is the major factor affecting plant morphology and in determining forage quality (Nelson and Moser, 1994), so grazing management effect on plant morphology and maturity obviously will affect quality of the pasture sward. Increased SR led to increases in young leaves in continuously stocked perennial ryegrass pastures, and proportion of leaf in the canopy increased during regrowth following severe defoliation with rotational stocking (Parsons et al., 1988; Parsons and Penning, 1988). Younger leaves and high proportion of leaves has implications for improved forage nutritive value and greater herbage intake (Minson, 1982; Nelson and Moser, 1994). When pastures are

PAGE 38

19 underutilized due to lenient SR, animals tend to concentrate grazing on the shorter and less mature vegetation (Hodgson et al., 1984), likely resulting in areas of progressively more mature herbage in the sward, leading to a deterioration of overall pasture quality. There is evidence of decreases in herbage production with increasing SR in recent studies of grazed-pasture dairy systems (Fales et al., 1995; Hoogendoorn et al., 1992; Moss and Lowe, 1993). Fales et al. (1995) conducted a 2-yr grazing study with lactating dairy cows on pastures typical of many in the northeast USA. Temperate species orchardgrass (Dactylis glomerata L.), Kentucky bluegrass (Poa pratensis L.), and smooth bromegrass (Bromus inermis L.) were dominant along with an assortment of herbaceous weeds and very little clover. Pregraze HM was inversely related to SR in both years, a result of greater accumulation of herbage during the season at lower SR. Pasture growth rate (unit DM d1 ) tended to be directly related to SR in both years (Fales et al., 1995). They attributed the differences in pasture growth rates to the positive effects of higher grazing pressure on removal of old tissue and production of new growth as was discussed by Bircham and Hodgson (1984). Fales et al. (1995) suggested that herbage removal is an important consideration in pasture management because it reflects the efficiency of the system. Herbage rejected in one cycle is unlikely to be grazed in subsequent cycles, hence is wasted. Where SR is too low, herbage removal may not be optimal. Herbage nutritive value was positively related to higher SR, due to more complete removal ofleaftissue at higher grazing pressures during the first year of the study. Nutritive value differences due to SR occurred mainly in summer when CP and in vitro dry matter digestibility (IVDMD) were positively related and NDF concentration was negatively related to SR. In Australia,

PAGE 39

20 low SR pastures had greater forage available for grazing than high SR (2.4 vs. 1.2 Mg ha1 for N-fertilized ryegrass pastures and 1.6 vs. 0. 7 Mg ha1 for ryegrass-clover mixtures; Moss and Lowe, 1993). Forage nutritive value was not affected by SR in that study. Davidson et al. (1985) reported decreased forage DM on offer and increased CP concentration with increasing SR on tropical pastures grazed by lactating cows. In another more recent Australian study using lactating cows to graze kikuyu (Pennisetum c/andestinum Hochst.)-clover pastures, similar results were obtained, viz., pasture DM on offer was highest at the lowest SR and CP concentration increased with increasing SR (Davidson et al., 1997). On temperate pastures (mixed species dominated by perennial ryegrass) in Denmark, there were large differences in HM production between severe (high) and lax (low) SR treatments, with growth rates depressed significantly on the severely defoliated treatment (Kristensen, 1988). The high SR treatments in that study also had higher CP (262 vs. 229 g kg1), greater leaf concentration (856 vs. 730 g kg1), and lower NDF ( 413 vs 491 g kg), but OM digestibility (731 vs. 719 g kg 1 ) was similar between SR treatments. Although SR effects on the herbage production responses in the studies cited here are similar, Matches (1992) cautioned that forage species may respond differently to intensity of grazing and he indicated that no single description of plant response to grazing is applicable to all swards under all environments.

PAGE 40

21 Plant-Animal Interactions Production from domestic livestock is largely influenced by the feed intake of animals. Grazing systems are by definition dependent on forages; thus, any system of forage-based animal production is concerned with growth production, and management of plants (Forbes et al., 1985). Economically viable livestock production from forages depends largely on the quantity and quality of forage produced, the animal's capacity to harvest and utilize that forage efficiently, and on the producer's managerial ability given his resources (Forbes, 1988). Efficient grazing management systems require an understanding of the role of the different components in a forage-based livestock production system. Understanding the system as a whole rather than the system components in isolation is a requirement for successful livestock production on grazed pastures. Unfortunately, much of the forage-livestock research in the past focused on examining output of a particular system rather than interactions between the grazed forage and the grazing animal (Forbes, 1988). The study of the plant-animal interface involves understanding the mechanism whereby the components of the plant and animal systems interface or interact. The specific interactions of interest are 1) forage characteristics effects on the grazing animal (viz., effects on intake, digestibility, efficiency of nutrient utilization, and animal performance) and 2) how the act of grazing affects pasture growth and development, persistence composition and quality. Forbes (1988) suggested that investigating

PAGE 41

22 ingestive behavior of grazing ruminants is an integral part of the development of grazing systems. Factors Influencing Intake of Grazing Animals Generally factors influencing feed intake of the grazing animal can be categorized as forage, animal environmental, and management (including supplemental feed) effects (Forbes et al. 1985; Hodgson et al., 1994; Moore, 1994) Minson (1982) reported that the quantity of herbage eaten by the grazing animal depends on the availability of suitable forage, the physical and chemical nature of that forage and the nutrient requirements of the animal. Forbes et al. (1985) indicated that intake of grazing ruminants is controlled largely by the effects of diet composition principally diet digestibility on the rate of disappearance of material from the reticulo-rumen and by the effects of sward structure on ingestive behavior These two effects were discussed by Raymond (1969) as being intrinsic and extrinsic factors controlling voluntary herbage intake. Forbes (1988) reported that much work was done to develop stochastic simulation models that predict forage production and animal consumption but in many cases lack of suitable data has limited these efforts. Grazing animals spend much of their time obtaining food so it seems logical to assume that the forage being grazed will have large influences on intake. Long-term intake control may be influenced by energy balance of the animal (Moe and Tyrell 1973), but short-term intake of meals is probably controlled by a combination of plant structural attributes that affect rate of ingestion (Minson 1981; Moore and Sollenberger 1986), the effect of masticated forage on gut fill (Forbes 1996;

PAGE 42

23 Kennedy and Doyle, 1993), and social behavior and environmental factors affecting the appetite-satiety complex (Forbes, 1988). Baumont et al. (1990) concluded that feed intake control has to be considered a multiple-factor process based on evidence that showed sensory responses overriding satiety signals due to rumen fill. At a recent ASAS annual meeting, a special symposium focused on several complex models which had been proposed to explain and quantify regulation of intake and ruminal flows, and to predict voluntary feed consumption and animal performance based on mathematical synthesis mimicking in vivo performance (Allen, 1996; Forbes, 1996; Fisher, 1996). This demonstrates that active work is ongoing to continue elucidation of the intake and digestion process. Intake control mechanisms Factors affecting intake may act through metabolic, physical, and/or behavioral control mechanisms (Hodgson, 1985) Variations in digestion and rate of passage of forages in the digestive system of grazing animals may be determinants of, or responses to, variations of intake. The metabolic mechanism ( calculated as Intake [I] = digestible energy intake [DEi] / digestible energy concentration [DE]) responds to chemostatic sensory signals (Fisher, 1996) and assumes some upper limit on DEi. The DE concentration of the diet determines intake The physical or distension mechanism (I = gut fill / retention time) is controlled by mechanical sensors (receptors in the stomach wall sensitive to pressure; Allen, 1996) and assumes some upper limit on rumen fill. Retention time of particles in the rumen (or rate of passage from the rumen) determines intake after that limit is reached. When the behavioral mechanism (I = number of bites per day x bite

PAGE 43

24 weight; where number of bites per day = biting rate x grazing time) is acting, there are upper limits to both biting rate and bite weight. Compensating changes can occur in biting rate when bite weight is reduced, but how much this results in reducing intake variation depends on sward characteristics (Hodgson, 1994). In short swards, increase in biting rate is unlikely to balance the reduction in bite weight. Grazing time is usually extended in response to limitations in biting rate and bite weight, but this increase is seldom great enough to compensate for reductions in intake rate (Hodgson, 1985). These control mechanisms probably operate simultaneously, with one or the other being dominant depending on pasture characteristics (Moore, 1983). The metabolic mechanism applies to very high-quality forages (Waldo, 1986). In a rotationally stocked pasture, the metabolic mechanism can be acting when forage quantity is not limiting and when the animal has the opportunity the select high quality plants and plant parts As animals continue to graze, pasture quality may become limiting because the animals had earlier selected the more highly digestible components of the canopy but quantity is not yet limiting, so the distension mechanism becomes active. With further grazing, forage quantity may become limiting which causes the behavioral mechanism to act because bite weight is reduced due to limited forage availability. Thus, pasture conditions dictate which mechanism is operating and in turn the act of grazing affects pasture characteristics and their influence on the grazing animal. At the time when these ideas for intake control mechanisms on grazed pastures were being developed, conventional wisdom was that the intake control mechanisms switched from one to the other in response to the changing conditions, but recently Forbes (1996) suggested that it is more of an additive nature.

PAGE 44

25 Hodgson (1985) suggested that the behavioral mechanism may override the others when herbage availability is limiting Forage characteristics affecting intake Herbage intake responses to pasture characteristics are mediated by ingestive behaviors that change in response to changing sward conditions (Forbes et al., 1986). Forage factors affecting animal intake can be broadly classified as quantity and quality related characteristics. Quantity is generally expressed as HM, i.e., total amount of DM on pasture during grazing or as herbage allowance, amount of herbage available per animal, and depends on SR and accessibility of the canopy to the grazing animal. Quality related character of the pasture relates to chemical composition of the grazed herbage, physical characteristics as it relates to plant anatomy and morphology, and canopy structure viz. height, density botanical composition plant-part composition and dead vs. green tissue (Minson, 1981; 1982; Moore, 1994 ). Herbage mass can have a major effect on intake. Forage quantity has to be adequate to avoid restrictions to intake but pasture availability in excess of animal requirement leads to an accumulation of mature material with low digestibility and intake (Wilson and Minson, 1980). These factors have implications for managing pastures to ensure adequate forage availability. Hodgson (1985) reported that intake per bite or bite weight is the variable most directly affected by sward conditions, and normally falls sharply as HM declines The general result is a reduction in herbage intake when HM is limiting. The amount of herbage available to the grazing animal is a function of both HM and SR, expressed as grazing pressure ( animal B W per unit HM) or herbage allowance

PAGE 45

26 (the reciprocal of grazing pressure). High grazing pressure has been shown to decrease intake but pasture utilization was reportedly improved on tropical grass pastures (Adjei et al ., 1980). Many models of relationships between grazing pressure ( or herbage allowance) and animal performance (Adjei et al. 1980; Duble et at., 1971 McCartor and Rouquette 1977 ; Mott and Moore, 1985; Peterson et al., 1965; Stobbs 1977) show increased performance with increasing herbage allowance up to an optimum grazing pressure. Several studies have reported increased intake at low vs. high SR when dairy cows grazed temperate pastures (Hoogendoorn et al., 1992; Kristensen, 1988). Cows grazing tall fescue (Festuca arundinacea Schreb.) pastures had a linear increase in DM intake per grazing meal with increasing herbage allowances (Dougherty et al. 1992) Time spent grazing was related to herbage intake because even at the highest herbage allowance available DM was still limiting so it appeared that cows grazed up to a particular horizon then stopped In a second experiment conducted on the same field (Dougherty et al. 1992), herbage intake increased with increasing herbage allowance up to a point where there were no further increases ; herbage allowances this time covered a higher range and herbage availability was not limiting at the higher levels. The higher rates of intake were attributed to larger DM intake per bite in the second experiment. Sward height and density o f tillers are factors that contribute to HM on a given area of pasture. In temperate pastures there is a body of evidence supporting a positive relationship with these two factors and herbage intake (Dougherty et al., 1992; Fisher et al. 1996; Forbes 1988; Gibb et al., 1997; 1999 ; Hodgson, 1985; Kristensen, 1988). In fact Forbes (1988) reported that results from a number of studies in the UK indicate

PAGE 46

27 sward height is the primary factor influencing intake and, hence, production. This led to a series of grazing systems studies concerned with managing pastures based on sward height. Hodgson (1985) reported that depth of the leafy horizon of a sward appears to set an effective limit to the depth of a bite thus influencing bite weight. Additionally, Dougherty et al. (1992) reported that cattle stopped grazing after a particular horizon. Sward density, or number of tillers per unit area, seems to have a positive effect on herbage intake in temperate pastures, mainly because of larger bite weight (Dougherty et al., 1992). Hodgson (1985) stated that variations in bite weight appear to be attributable to variations in sward height for temperate pastures. In tropical pastures on the other hand, bite weight was found to decrease with increasing sward height (Forbes, 1988). The reason given to explain this contrast was generally lower bulk density with greater height in the tropical swards. Hodgson (1985) cautioned that there are apparent conflicts in the results of some of these field studies. Laca et al. ( 1992) found that even in homogeneous swards, both density and height are necessary to predict bite weight but reported that bite weight was more sensitive to sward height than to bulk density. Findings from a more recent study focused on tiller density characteristics as a main effect support a positive relationship between tiller density of a sward and herbage intake on temperate pastures (Fisher et al., 1996). Apart from being a major determinant of quality and quantity of forage consumed herbage allowance determines the opportunity for selection. In most practical conditions there is sward heterogeneity in both vertical and horizontal dimensions and with time (Hodgson et al. 1994). Particular characteristics of the sward may stimulate selective

PAGE 47

28 intake by the grazing animal conditioned by environment and plant factors. Selective grazing occurs when an excess of forage is available, and may be tempered by plant species composition of the sward and their canopy structure (Lascano, 1987; Sollenberger et al., 1987). The focus of many studies of sward canopy structure effects on selective grazing has been on vertical and horizontal distribution of plant components. In studies of vertical distribution, it has been shown in temperate pastures that there is little difference between composition of the diet and that of the upper strata of canopy within which the animals were known to be grazing (Hodgson et al., 1994). These authors caution, however, that it would be ridiculous to argue for nondiscriminatory selection being the general case since other studies have demonstrated that this is not so. Degree of selectivity may be due to difference in maturity of vegetation and its constituent species, morphological characteristics of particular plant species, and differences in grazing strategy between different animal species (Hodgson et al., 1994) Animals generally select leaf in preference to stem, green and young tissues in preference to dead or old tissues, particularly in mature pastures, or a specific plant species in preference to another (Minson 1981 ). Often, the selected plant or plant part is higher in CP, soluble carbohydrates and digestibility and lower in structural carbohydrates than the pasture as a whole. Animals seem to select for plants or plant parts that allow for increased voluntary intake (Minson and Wilson, 1994). Minson (1982) reported that this is often positively correlated with digestibility but may be more related to physical factors of the plant material and how it relates to rate of passage of indigestible residues through the rumen. Burns et al. (1991) reported that differences in DMI were due to differences in

PAGE 48

29 diet IVDMD rather than digesta kinetics. Hodgson et al. (1994) suggested that selection strategy was based on visual and olfactory/gustatory cues mediated by the effects of physical and structural characteristics of vegetation influencing ease and rate of intake. They reported that there is clear evidence that choice is strongly influenced by potential intake rate and these results appear to hold good for substantial ranges of variation in sward structure. Dairy cows grazing temperate swards with higher green leaf concentration, less dead material and higher organic matter digestibility did not have higher intake than cows on less digestible pastures in a New Zealand study (Holmes et al., 1992). This intake response is confounded, however, by herbage availability, thus opportunity for selection since the less digestible (more mature) pastures also had higher herbage allowance. In a second experiment with the same sward characteristics but with common herbage allowance, herbage intake was greater on swards that had greater concentration of green leaf DM and higher digestibility (Hoogendoorf et al. 1992). In a Danish study, Kristensen (1988) found that herbage intake was depressed by increasing herbage maturity due to lower nutritive value but in particular due to reduced green leaf and increased reproductive development. These observations of increased intake due to the presence of greater leaf concentrations may be related to shorter retention time in the rumen of leaf vs stem tissue even at similar digestibility, leading to higher intake as noted by Minson (1982)

PAGE 49

30 Animal characteristics affecting intake The animal itself is one of the factors affecting animal performance (hence nutrient intake) in forage-based livestock systems (Moore, 1994). Ingestive behavior and grazing selectivity can be influenced by interactions among the effects of variation in body size, mouth dimensions, and productive state within and between animal species, and by species-specific differences in behavior, some of which may relate to body shape and jaw size (Hodgson et al., 1994). Increased nutrient demand (as with lactating vs. non-lactating cows) will usually increase forage intake. Thus, both grazing time and herbage intake may be substantially higher in lactating than in non-lactating cows, and thin animals may have higher rates of intake than fat animals, though the relative magnitude of these effects may differ in different circumstances (Hodgson, 1985). Evidence to support these observations was recently demonstrated by Gibb et al. (1999). Generally, patterns of response in ingestive behavior or herbage intake to variations in sward characteristics appear to be similar for sheep and cattle and, within animal species, for different age and productive state (Hodgson, 1985). In each of the intake control mechanisms described earlier, there are animaland plant-dependent components that set limits to feed intake. The animal-dependent component of the metabolic mechanism is the DEi requirement of the animal, a function of species, physiological state, type, production situation, etc., and intake is regulated mainly by chemostatic feedback (Fisher, 1996; Forbes, 1996). In grazing situations, the herbage intake of productive animals is seldom affected by metabolic limits (Hodgson, 1985). In the distention mechanism, rumen fill is the animal factor, and limits are set by capacity of

PAGE 50

31 the reticulorumen (Allen, 1996; Fisher, 1996). It has been suggested that both cattle and sheep eat to a constant level of rumen fill but this does not appear to hold true for all ruminants (Forbes et al., 1985). It was demonstrated that rumen fill in sheep can be variable, responding to a number of factors including protein nutrition, and does not operate as a mechanism controlling intake at a preset level. Baumont et al. (1990) demonstrated that sensory responses induced by supplementary distribution of palatable hay is sufficient to override the satiety signals due to rumen fill in sheep. Limitations on number of bites per day is primarily an animal factor in the behavioral mechanism. Other animal-dependent factors that play a role in controlling herbage intake of grazing animals have to do with the animals requirement for rest, drinking, or carrying out social activities, and also the effect of environmental conditions on these activities (Forbes et al., 1985). These factors likely act upon time spent grazing. Concentrate supplementation effects on intake Supplemental feed has two important consequences when introduced into forage feeding systems; its direct influence on animal performance and its effects on/interactions with forage consumed (Mott and Moore, 1969). Raymond (1969) noted that understanding the biological basis of feed interactions, including the effect of other components of the total ration on amount of forage that the ruminant will eat, is important. Feeding supplemental concentrates in restricted amounts can result in an increase, a decrease, or no change in herbage intake (Caton and Dhuyvetter, 1997; Moore 1992; Raymond, 1969). When concentrates are fed as supplements, animal responses may not always be as expected. Deviations between expected and observed performance may

PAGE 51

32 be due to effects of concentrates upon voluntary intake of herbage (Moore, 1992). Also, there may be associative effects such that digestibility of the total diet is not the same as that calculated from the weighted intake and digestibilities of the components of the diet (Merchen and Bourquin, 1994). Associative effects between concentrates and forages can result in metabolizable energy (ME) concentrations of the mixed diet being higher or lower than the expected values (Dixon and Stockdale,1999; Moe, 1981; Moore, 1992). Herbage intake changes due to supplemental feed appear to be related to forage characteristics such as potential intake when fed alone, digestibility and CP concentration, and supplement characteristics such as amount, DE concentration, source (starch vs. fiber), and protein concentration and type (ruminally degradable vs. undegradable; Moore, 1992; 1994; Raymond, 1969). Generally, forage intake will decrease as increasing amounts of supplementary concentrates are fed. This decrease is more marked with forages of high digestibility (Moore, 1994; Raymond, 1969), when other nutrients are in balance with energy, and concentrates are fed in large amounts (Moore, 1994). In some cases, small amounts of concentrate may increase voluntary forage intake when forages are of low quality, especially when they have high ratios of total digestible nutrients (TDN) to CP (Caton and Dhuyvetter, 1997; Moore, 1994; Moore et al., 1995). Positive associative effects, where supplemental concentrate feeding increases forage intake, are usually due to provision of a limiting nutrient (e.g., N, P) in the supplement which is deficient in the forage (Dixon and Stockdale, 1999). Delagarde et al. (1999) demonstrated that supplementation with soybean meal increased herbage intake by way of improved ruminal digestion due to input ofN for dairy cows grazing perennial ryegrass

PAGE 52

33 pastures fertilized with low levels ofN. Supplementation with energy-based concentrates did not affect herbage intake in their study. Decreases in forage intake due to concentrate feeding is called substitution of forage with concentrate. When substitution occurs, animal response to supplemental energy concentrates may be lower than expected based upon calculations for the forage fed alone and the amount of concentrate being fed (Moore, 1994). Reductions in ruminal pH, often cited to be the cause of fiber digestion reduction, may not always explain reduced forage intake and digestibility associated with energy supplementation (Caton and Dhuyvetter, 1997). Jones-Endsley et al. (1997) fed mid-lactation Holstein cows grazing alfalfa (Medicago sativa L.)-orchardgrass pastures with supplement of different CP concentration (120 or 160 g kg1 on a DM basis) at 6.4 or 9.6 kg cow1 d1 Forage intake tended to increase when CP in supplement increased but was unaffected by amount of supplement. They concluded that supply and digestion of nutrients in grazing dairy cows may be improved through an increase in CP concentration of the supplement or the amount of concentrate offered. This did not, however, have much effect on milk production or composition in their study. In a Virginia study, lactating cows grazing permanent pastures with a supplement of 5.7 kg d1 cracked com had lower forage intake than those that were not given any supplement, but total intake was greater for the supplemented animals (Berzaghi and Polan, 1992). Feeding supplemental com silage to lactating cows that were intensively grazing primarily orchardgrass pastures demonstrated that each unit of com silage replaced 1.2 units of pasture, but total DMI was not different

PAGE 53

34 from those of animals that did not receive any com silage (Holden et al., 1995). Mueller et al. (1995) reported that, in general, the drop in forage intake that occurs with grain feeding is less than the increase in grain intake, so total DMI is increased. Substitution rates ( decrease of forage intake per unit of concentrate feed) in dairy cows vary, but in general, pasture DMI decreases about 0.5 to 0.9 units per unit of grain (Muller et al., 1995). Role of Grazing Time in the Plant-Animal Interface Behavioral limits to herbage intake may be the overriding intake control mechanism functioning in grazing animals (Hodgson, 1985). Intake per bite or bite weight is considered the dominant influence on herbage intake (Forbes et al., 1985; Hodgson, 1981; 1985; Moore and Sollenberger, 1986). Biting rate and grazing time are considered the primary compensating responses of the animal to limitations in bite weight. Hodgson et al. (1994) reported that the general rate of jaw movement (prehension, biting, and chewing) is remarkably constant. They suggested that variations in biting rate (bites min -1 ) reflect variations of the relative proportions of the three jaw movements to obtain a particular bite, and are therefore largely determined by the manipulation necessary to graze effectively in swards of different structure. Evidence suggests that on tall swards this can result in substantial reduction in biting rate but compensating changes which can occur in intake per bite and biting rate result in little variation on short-term herbage intake (Hodgson et al., 1994). On shorter swards, however, increase in biting rate does not balance reduced bite weight.

PAGE 54

35 Animals will extend grazing time in response to limitations in intake rate (Hodgson, 1981 ). It is believed, however, that there is a limit to grazing time extension in a given day so the degree of compensation is again limited (Hodgson 1985). Grazing time may increase as soon as sward conditions start to limit short-term rate of herbage intake. Hodgson (1994) surmised that there currently is no good basis for quantifying this relationship or for relating it directly to sward conditions. They suggested that although bite weight may be the primary animal response to variations in sward conditions, the value of studies of grazing behavior effects on herbage intake will ultimately depend on the effectiveness with which herbage intake predictions can be associated with adaptive changes in grazing time. Grazing time has two dimensions ; one in the context of the behavior of animals as a whole and the other as a variable influencing herbage intake. Research on grazing time as a component of grazing behavior has shown that in general domestic animals likely spend about one-third of the day actually grazing. Forbes et al. (1985) reported that this is influenced by animal characteristics and sward conditions and may range from approximately 4 to 13 h. There have been some reports of differences in grazing time between cattle breeds (Forbes et al. 1985). Recent studies in two Texas environments did not detect any differences in grazing time among various purebred and crossbred heifers (Forbes et al. 1998) sug g esting that breed differences depend on degree of adaptability of animals to environmental conditions. There is also evidence of differences in grazing time (and intake) due to different physiological state with lactating cows grazing longer than dry

PAGE 55

36 cows (Forbes et al., 1985; Gibb et al., 1999). Younger animals may graze longer than mature animals but the evidence for this is not conclusive (Forbes et al., 1985). Grazing time appears to increase when a decline in herbage availability limits intake per bite (Allden and Whittaker, 1970; Gibb et al., 1999; Popp et al., 1998). Gibb et al. (1999) demonstrated that this phenomena occurred for lactating as well as dry Holstein cows in response to varying sward heights grazed either continuously or rotationally, and was sufficient to compensate for reduced bite weight so that average daily herbage intake was similar among sward heights. In a 3-yr study, Popp et al. (1998) reported that cattle spent less time grazing as herbage availability increased in predominantly alfalfa pastures grazed either continuously or rotationally. The longer grazing time on the heavily stocked pastures was not sufficient to compensate for reduced intake in late season of the first year, but intake was similar in the subsequent years. Rate of intake (product of bite weight and biting rate) tended to be greater (p < 0.10) at the higher forage availability (Popp et al., 1998). Sward characteristics other than factors related to herbage availability also influence grazing time because of their influences on rate of intake, i.e., grazing time increases when rate of intake decreases (Forbes et al., 1985). Canopy structure and composition (Minson, 1981; 1982; Moore and Sollenberger, 1986), and green leaf and dead tissue fraction (Hodgson, 1985; Minson, 1981; 1982) have been shown to influence bite weight and bite rate. Whether or not extension of grazing time is sufficient to compensate for reduced intake rate depends on the particular situation (Gibb et al., 1999; Popp et al., 1998), but some evidence suggests that it is seldom great enough (Gibb et al., 1997; Hodgson, 1981 ). Gibb et al. ( 1997) suggested that increased grazing time as a

PAGE 56

37 response to reduced intake rate is limited by the requirement for ruminating and non grazing, non-ruminating activities, which is influenced by qualitative and quantitative aspects of the herbage ingested. Their study suggests that managing sward conditions to ensure maximum intake rate is the preferable option, since the cows' only effective strategy to compensate for any reduction in intake rate is to increase grazing time and given the limitations to grazing time extension, the amount of compensation required to balance daily intake may not be met. Not many reports of supplementation effects on grazing time were found. In one study, Krysl and Hess (1993) reported that cattle given supplemental feed spent less time grazing than those not receiving any supplemental feed. Grazing during a 24-h cycle is marked by periodicity. The evidence suggests that animals graze on four to five occasions during the day (Forbes et al. 1985). These occasions may not have a strict pattern, but the major periods in terms oflength of grazing occur at dawn mid-morning, mid-afternoon, and at dusk (Cowan, 1975; Gary et al., 1970; Gibb et al., 1997). The length of each period of grazing is determined largely by the rate of intake; i e. faster rate of feed ingestion results in quicker gut fill or achieving satiety and is thus dependent on sward conditions. Chilibroste et al. (1997) found that grazing time and DMI, were greater after longer periods of fasting and were reduced by the presence of indigestible material in the rumen. Numbers of grazing periods likely are dependent on rapidity with which the rumen is filled and subsequently emptied, and by environmental factors and length of the day because there is evidence of a marked influence of sunrise and sunset on initiation and cessation of grazing (For bes et al., 1985).

PAGE 57

38 Gary et al. (1970) reported that temperature was not correlated with the different grazing behavior on different days. It should be noted, however, that in their study (conducted in Pennsylvania) mean daily temperature was 19.3C with a 3.6 standard deviation which may not represent sufficient extremes in temperature to demonstrate any differences in grazing behavior due to effects of temperature. Night grazing makes up only a small proportion of total grazing time in temperate areas (Gary et al., 1970) but may increase with increasing daylength (Forbes et al., 1985). Night grazing can be a significant contributor to total grazing time in tropical regions (Cowan, 1975; Stobbs, 1970). Recent research with sheep demonstrated that there may be compensatory responses to restriction from grazing during the night (Jason et al., 1999) They found that sheep that were fasted during the night extended daytime grazing by fewer longer bouts compared to sheep that had continuous access to pasture, but total grazing time in a 24-h period was still shorter. Also, on pastures where forage availability was not limiting, overnight restricted sheep had larger intake rates mainly via larger bite weight and were able to counteract the reduction in total grazing time. In a another study that examined two durations of fasting (16.5 and 2.5 h) during the night before grazing, both DMI and grazing time for the first bout of grazing were greater with longer fasting but bite weight and biting rate were not affected by period of fasting (Chilibroste et al. 1997). It would have been interesting to see what responses would occur had the researchers included a restricted-during-the-day-graze-at-night treatment to explore the possibility of compensatory night grazing.

PAGE 58

39 Animals must make decisions whether to continue grazing at low rate of intake on certain swards at some stage. In conditions of severely limited feed intake due to the physical absence of feed or a lack of desire on the animals part to continue grazing the herbage on offer due to poor pasture characteristics, it may be advantageous for animals to reduce energy expenditure by reducing grazing activity (Forbes et al., 1985). There is evidence to suggest that when animals are moved from good pasture to poor pasture their energy expenditure is reduced (Young and Corbett, 1972), likely due to less grazing activity, though their energy intake will be less also. Gary et al. (1970) suggested, based on their observations, that there may be group facilitation of behavior, so the decision to stop grazing may be simultaneous. Coat Color Relationship with Grazing Behavior and Animal Performance Typical Florida weather conditions are considered adverse for high producing dairy cows. High temperature, solar radiation, and humidity are common, particularly during summer and can result in stress to dairy cattle (Collier et al., 1982). Decreased milk production (Becerril et al., 1991; 1993; Hanson, 1990), changes in milk composition (Becerril et al., 1993; Schleger, 1967), and poorer reproductive performance (King et al., 1988) can result from climatic stress. High temperature has been shown to depress growth, reduce production, and lower reproductive efficiency in several species of domestic animals (Campbell and Lasley, 1985). The principal cause of decreased milk secretion in hot weather is lowered feed consumption (Campbell and Lasley, 1985; Collier et al., 1982). Incident solar radiation seems to have overwhelming importance for the heat

PAGE 59

40 balance of animals (Walsberg et al., 1997) because it can directly increase body temperature. The heat load on cows' bodies from solar radiation is produced by absorption of light and associated heat on the animal surface exposed to sunlight. This along with other climatic factors can result in heat stress. Absorptivity, the ability of an object to absorb radiation, is a measure of the proportion of radiant energy that is absorbed when the object comes in contact with that energy. Textbook values for absorptivity for solar radiation of black coat cattle is 0.90 while for white coat cattle the value is 0.50. Thus, the amount of heat absorbed from solar radiation is directly related to hair coat color of cattle. Breed development in cattle used color patterns as a trade mark, but coat color relationships with production and reproduction were not always considered. Becerril et al. (1993) reported that evidence of studies investigating relationships between coat color and animal performance began surfacing in the late 1920s and early 1930s. In a study conducted during summer months, Hansen (1990) reported that a number of physiological variables including rectal temperature, skin surface temperature and respiration rates increased for Holstein cows kept in a dry lot with no shade provided vs. cows kept in a concrete-floored shed open on all four sides Moreover, the difference in magnitude of these responses in unshaded vs. shaded environments was greater for black coat cows than for white coat cows. Milk production tended (P = 0.106) to be influenced by a similar interaction. The depression in milk production caused by keeping cows in the unshaded environment was 1.5 kg cow1 d1 for white coat cows and 3.3 kg cow1 d "1 for black coat cows. These results suggest that coat color may influence

PAGE 60

41 resistance to heat stress in environments characterized by high solar radiation (Hansen 1990). Intensity of coat color was demonstrated to have strong relationships with milk production and milk fat production in nine commercial Australian Illawarra Shorthorn herds (Schleger 1967). Lighter colored animals produced higher milk yield Schleger (1967) also noted that this relationship tended to be stronger in lower producing herds. Analysis of data from records of first lactation dairy cows in Florida demonstrated a linear relationship between milk yield and percentage of white coat (Becerril et al. 1991; 1993); fat percentage declined with increasing percentage white coat but actual fat production tended to be higher in cows with more white coat (Becerril et al., 1993). Most cows for which the data were obtained were producing in management conditions designed to avoid heat and climatic stress; shade, fans, and sprinklers were provided. King et al. (1988) reported that coat color or seasonal effects did not affect milk production but white coat cows had better reproductive efficiency than black coat cows when freshened during summer months requiring fewer services per conception and having fewer open days. These data were from a commercial Holstein herd in Arizona The authors suggested that cooling facilities provided during the first 130 d oflactation may have removed any advantage white cows may have held (for milk production) during summer months None of the studies of coat color effects in dairy animals cited in this review reported feed intake data associated with the obs e rved performance levels nor w e re any of them conducted on pasture. Reports of studies examining coat color effects on grazing behavior were not found in the literature. There are anecdotal reports that white coat cows spent more time

PAGE 61

42 grazing than black coat cows in the southeastern USA. Godfrey and Hansen (1996) analyzed 27 years of records of Holstein cows on the Caribbean island of St. Croix (where the feeding system is based on grazing) to determine the influence of hair coat color and season on reproduction and lactation. The data indicated that there were seasonal patterns of reproduction and milk yield, likely related to forage availability, but no influence of hair coat color (Godfrey and Hansen, 1996). The authors suggested that with a feeding system based on grazing, nutritional influences may override the effect of coat color on milk yield Coat color is a fairly heritable trait (Becerril et al., 1991; 1993; 1996; King et al., 1988) should it be determined to be useful to select for favorable coat color where cattle are in hot environments Becerril et al (1993) found that small positive increases in heritability estimates occurred for production and reproductive traits when percent white coat was included in the model to estimate heritability but suggested that the differences may not be important. Estimation of Intake on Pasture Quantifying DMI in grazing systems is necessary for estimating nutrient consumption by grazing animals. Nutrient consumption is the product ofDMI and nutrient concentration of the D M consumed. Estimation of daily quantity of nutrient consumed on pasture is critical in coupling animal performance and pasture characteristics and in quantifying nutrient recycling in the soil-plant-animal system. Forage nutrient concentration may be estimated by accurate sampling and analysis of the grazed herbage and is routine in forage research. In grazing systems it is important but inherently difficult

PAGE 62

43 to quantify forage intake (Burns et al., 1994; Moore, 1996; Reeves et al., 1996). Many methods have been developed but choice of technique is likely determined by available resources and the experimental objectives. Techniques for estimating intake on pasture may be based on the use of internal or external markers, ingestive behavior, disappearance of herbage mass, prediction from forage characteristics, and animal performance (Moore, 1996). All of the commonly used techniques have unique advantages and disadvantages and results from all methods are only estimates of intake with an associated error that varies in magnitude (Burns et al., 1994; Moore, 1996). While none of the methods are completely adequate, it may be possible to obtain precise measurements of differences among pastures at specific times (Moore, 1996). Better accuracy is required, however, for them to be useful in quantitative applications (Burns et al., 1994). Moore (1996) categorizes the commonly used approaches to estimating forage intake by grazing animals based on suitability as estimates for a) individual animals, orb) groups of animals or a pasture. Approaches considered suitable for estimates of intake in individual animals are techniques based on the use of markers or on ingestive behavior, while estimates for groups of animals or a pasture are techniques based on disappearance of herbage mass, prediction from forage characteristics, or calculations of energy requirements for observed animal performance (Moore, 1996). A direct method of estimating intake is to weigh the animal before or after they eat (Bums et al., 1994). In grazing situations, this will have limited practicability, but according to Bums et al. (1994) it has been used successfully in studies where ingestive behavior is of interest and the duration of grazing can be restricted. Major limitations for

PAGE 63

44 expanded use ofthis technique are its short-term nature and the need to account for weight adjustments caused by defecation and urination (Burns et al., 1994). Intake of grazing animals has often been estimated by the difference in HM before and after grazing, i.e., disappearance of herbage mass. Daily herbage intake of the grazing animal is assumed to be herbage disappearance observed in a paddock divided by the product of number of animals and days grazed. This method is very simple and in theory is the most direct and accurate of all intake estimation methods because of its similarity to the method used for confined studies; i.e., intake is the difference between what is offered and refused. The technique is extremely labor intensive since forage HM has to be estimated both before and after grazing for rotationally stocked pastures or frequently for continuously stocked pastures. Also many sites have to be sampled to improve accuracy. Double sampling i e ., estimating HM at many sites by an indirect method (visual or mechanical) and clipping a percentage of the sites to measure actual HM, then using a regression relationship between clipped and indirect samples to determine pasture HM (Burns et al. 1989) is often used to ensure representative sampling The method may be too simplistic since it does not consider loss of HM due to trampling or removal by nonexperimental animals, feral animals as well as insects (Burns et al. 1994; Moore, 1996). Not accounting for growth during grazing may lead to underestimation of intake These problems can be minimized in rotationally stocked systems if the grazing period is kept short (1 to 3 d) or by placing cages in the pasture to prevent grazing in selected sample areas that will be used to estimate the growth rate in the period between sampling dates. The cage technique is used for continuously stocked

PAGE 64

45 pastures to measure herbage accumulation since before and after measurement is not possible. Well managed, uniform pastures with high growth rates may provide acceptable estimates of intake using the herbage disappearance method (Moore, 1996) Another situation where this technique may be useful is in studies of ingestive behavior where the interest is in herbage intake for a specific meal such as done by Dougherty et al. (1992) Moore (1996) feels that the HM disappearance method, although theoretically sound, is not reliable enough to be recommended for routine estimates of pasture intake. Indirect methods of estimating DMI of grazing animals have evolved because of the inherent difficulty of making direct determinations In this approach estimates of intake are made for individual animals grazing a pasture at a particular time (Moore 1996) These techniques estimate intake based on fecal output (FO) and the indigestibility of the diet, i.e ., forage intake is estimated by the equation: DMI (kg d -1 ) = 100 (FO [kg DM d -1]) / (100 -DM digestibility[%]) Intake and digestibility are not correlated highly across a wide range of forages so FO must vary independent of both intake and digestibility (Moore 1996) Thus, independent estimates of both FO and DM digestibility are required. Errors in either estimate will result in erroneous intake values. Because digestibility is in the denominator of the equation however errors in its determination will result in larger error in the estimation of intake than for errors in determining FO, which will only result in proportional errors for intake estimates. Fecal output may be obtained by collection of all defecation during the grazing period using a harness and collection bag apparatus (Burns et al. 1994). In theory this

PAGE 65

46 method is simple and straight forward. Rapid results are obtained and extensive laboratory facilities are not needed since only DM and ash determinations are required. Major disadvantages include significant reductions in animal performance, incomplete collection of feces, distortion of hind legs due to weight of feces in bag, high labor input for sampling and weighing, and it likely interferes with normal grazing behavior (Burns et al., 1994; Moore, 1996). Total fecal collection is generally not recommended, particularly with cattle, since determination of FO indirectly using inert markers offers opportunities to reduce negative effects on animal health. Estimation of FO using markers is based on the ratio between quantity of a marker dosed to an animal and its concentration in the feces [FO [g d-1 ] = (ug of marker administered)/ (ug of marker per g of feces)] (Burns et al., 1994). Diet digestibility data are then used to calculate intake. Daily administration of a known dose of an external marker (one or two doses per day) has been the classical approach (Burns et al., 1994; Moore, 1996). Chromic oxide ( chromium sesquioxide, Cr203 ) is the oldest known and has been used extensively (Carter et al., 1960; Moore, 1996; Pond et al., 1986). This technique is logistically very labor intensive, requiring animals be removed from pasture and restrained or the animal be trained to permit dosing through a rumen cannula. Grazing behavior, FO, and forage intake may be affected by dosing activities. Other approaches to administer chromic oxide have included dosing with chromic oxide impregnated paper feeding small quantities of the marker with some feedstuffs, and use of intraruminal controlled release device. Moore (1996) suggested that in all of these approaches, however, there may be incomplete recovery of the marker and/or diurnal

PAGE 66

47 variation in fecal chromic oxide concentration, both of which lead to errors in estimate of FO. An alternative approach is to use the pulse dose method suggested by Pond et al. (1986) in which the chromium is mordanted onto forage samples or esophageal extrusa, that have been extracted with neutral detergent reagent. This mordanted fiber is administered at the beginning of the measurement period. Frequent fecal collection is required for at least 96 h and each sample is analyzed for chromium concentration to characterize the "pulse" in marker concentrations found in the feces. A non-linear equation is used to describe the relationship between marker concentrations and time after dosing. With the characteristics of this excretion curve, it is possible to estimate rate of digesta passage, mean residence time, and digestive tract fill, as well as FO. This technique also has limitations, probably more than with daily dosing (Burns et al., 1994). Labor requirements are not reduced. While perhaps not as stressful as daily dosing, animals are disturbed every 2 to 3 h for fecal collections that may require rectal (grab) sampling. This may lead to interruptions of normal grazing behavior which may in turn result in misleading estimates of kinetic parameters. Sampling from recent defecations helps to alleviate this problem (Moore 1996) The large number of chemical analyses required to develop the marker excretion curve the complexity of modeling marker flow to calculate kinetic parameters, and proper interpretation of the resulting data add a degree of difficulty Additionally, kinetic properties of the dosed material must be similar to those of the digesta thus the marker has to be mordanted to the dietary component of interest, typically forage fiber. The mordanted fiber should have a particle size distribution

PAGE 67

48 similar to the digesta. Even though the list oflirnitations is long, Bums et al. (1994) reported successful use of this technique in their research. Another approach to estimating herbage intake by grazing animals is based on calculating, in retrospect, the amount of energy an animal would need to ingest daily to achieve the observed rate of performance (e.g., weight gain, milk production, etc.). Then DMI is estimated based on the intake of energy considered the requirement and the energy concentration of the diet (Moore, 1996; Reeves et al., 1996) The energy requirement for given performance levels of particular classes of animals may be obtained from tabulated values. Moore (1996) suggested that energy intake and forage energy concentration must be expressed as some measure of available energy rather than total or Gross Energy and must be expressed in the same units. He noted that Effective Feed Units, TDN, Digestible Energy (DE), Metabolizable Energy and Net Energy (NE) have all been used as expressions of available energy, and suggested that NE may be more appropriate to use in this calculation because of differences in efficiency of ME utilization among forages. Requirements of NE are partitioned into NE for maintenance (NErJ and for gain (NEg) in the NRC (1984) system based on efficiency of ME utilization and energy intake calculations have to be done separately for each (NRC, 1984). Separate calculations are not necessary for requirements of lactating dairy cows however, because it has been shown that they use DE or ME with similar degree of efficiency for maintenance, growth and lactation (NRC, 1988) In estimating energy requirements for grazing lactating dairy cows, adjustments have to be made for NE requirements related to activities such as actual

PAGE 68

49 grazing and walking since NRC (1988) only gives tabulated values for lactating cows in confined systems. Concentrations of forage NEm and NEg are calculated from ME concentration by NRC (1984) equations and ME is calculated from DE. Forage DE concentration may be estimated based on digestibility determinations. Accurate measurement of production responses such as body weight changes are critical to quantification of energy requirements. Weighing errors for grazing ruminants may be quite large and contribute to error of estimates of mean weights and liveweight changes (Moore, 1996). An additional disadvantage is that extraneous factors such as nutrient imbalances and environmental effects may affect conversion of NE to animal product. An advantage of this method is that the intake estimates reflect changes in forage quality and quantity integrated over reasonably long time periods. Additionally, this technique lends itself to mathematical simulation modeling (Moore, 1996). Intake estimation based on ingestive behavior is also possible, where DMI is the product of bite weight, biting rate, and grazing time. It is important to identify actual bites as separate from jaw movements related to prehension and swallowing. Biting rate can be recorded by observing or listening for the distinct sound of forage being severed, or by use of electronic devices designed to record biting activity (Forbes, 1988). Bite weight is best estimated by esophageal extrusa collected over measured short time periods and grazing time can be recorded by observation or using automatic devices (Forbes, 1988; Moore, 1996). This approach may provide useful data about a sward during short periods when the observations are made. This may not reflect, however, behavior throughout the day and behavior on one day may not reflect that of other days (Moore, 1996). Forbes (1988)

PAGE 69

50 suggested that the usefulness of this technique is limited to intake estimation for short periods of grazing such as studies of the influence of sward characteristics on ingestive behavior. Forage intake estimates can be predicted based on forage characteristics where DMI (as a function of body weight) is determined from regression coefficients and concentrations of various quality-related characteristics of forages. Multiple regression equations have been shown to provide better estimates than equations based on single "predictors" (Moore et al., 1994). This approach can provide acceptable estimates of potential intake. Frequent pasture sampling to represent the variation of forage quality within a pasture due to location and time will enhance the usefulness of the intake estimates. This approach may provide misleading results because estimates of intake predicted from laboratory analyses have higher relative error than do estimates of digestibility obtained in the same manner (Moore et al., 1994). Further, the method needs to incorporate effects on intake due to variation in forage availability, animal characteristics supplemental feeding, and environmental conditions for it to be taken seriously as an approach to estimating pasture intake. The importance of diet digestibility in estimating intake cannot be over emphasized. Perhaps more important than analyses of forage samples to determine their digestibility is obtaining samples that represent what the animals are consuming. Two basic approaches have been used to collect forage samples. One is a manual method in which the experimenter selects samples to represent what the animal is consuming based on observing the animals grazing behavior and the other method is to use surgically

PAGE 70

51 altered (rumen or esophageal cannula) animals (Burns et al. 1994). The first procedure is often criticized for being subjective while the second can lead to biased estimates if adequate sampling is not achieved. Once representative samples are collected, accuracy is dependent on the method used to estimate the digestibility of the diet. It appears that the two-stage in vitro bioassay is the preferred method and has the broadest application (Burns et al. 1994 ; Moore 1996) Moore (1996) cautioned that whatever laboratory method was used it should be calibrated against in vivo digestibility in order to improve the accuracy of the estimate. Internal markers natural plant constituents that are neither digested nor absorbed by the animal can be used to estimate forage digestibility by knowing the ratio of the marker in the diet and feces and calculated as digestibility(%)= 100 [100 (marker in diet/marker in feces)]. Substances evaluated as internal markers include silica lignin, fecal N chromogen indigestible NDF and acid soluble ash (Pond et al. 1986). Of these lignin has been the mos t widely used (Burns et al., 1994) but they all have limitations to their usefulness An infallible and totally dependable internal marker has not yet been found (Cochran et al. 1986). New methods for estimating intake are constantly evolving (Burns et al. 1994). Alkanes found in cuticular waxes have been investigated recently both as internal and e x ternal markers (Moore 1996) Use of alkanes may improve digestibility estimates (Burns et al., 1994) Reeves et al. (1996) reported that alkane t e chniques provided a direct and precis e estimate of pasture intake compared to estimates derived from herbag e disappearance or energy requirement calculations and can be obtained on a daily basis i f

PAGE 71

52 required Like other techniques there are short comings to the use of alkanes ( e.g. concentrations of odd-numbered alkanes may be too low in tropical forages; Moore, 1996) but ways to overcome some of these are being sought (Burns et al. 1994). Also controlled release devices have been developed for the delivery of Cr203 to overcome limitations related to frequent dosing and diurnal variations in Cr203 output (Burns et al. 1994 ) Several shortcomings have led to caution for the use of these release devices in a research setting The devices may be useful in the future if current problems can be sol v ed In the meantime new technologies to improve intake estimation must continuously be sought.

PAGE 72

CHAPTER3 GRAZING MANAGEMENT EFFECTS ON FORAGE PRODUCTION AND ANIMAL PERFORMANCE OF LACTATING DAIRY COWS ON SUBTROPICAL WINTER PASTURES Introduction Florida s dairy industry is an integral part of the state s agriculture with revenue from milk sales exceeding $400 million annually during the last several years. Like the rest of the USA dairy industry since the midto late 1950s, most Florida dairy farms gave up traditional pasture-based dairying and changed to management systems based on high concentrate feed use stored forage and drylot feeding of total mixed rations (TMR) in total confinement. With these intensive production systems, milk production per cow has increased greatly largely due to increased grain consumption but also to improved cattle genetics and to the ability of animal nutritionists to formulate diets that more accurately and uniformly meet the nutritional requirements of the lactating cow (Mertens, 1986). Total confinement systems of dairy production based on greater use of concentrate feeds and stored forages require high capital inputs for buildings and equipment which along with the cost of feedstuffs incur high production costs. Dairies in Florida are typically comprised of large herds housed on relatively small land areas that grow little of their feed. Pastures contribute little as a source of nutrition for dairy cows and are more used as exercise lots for dry cows and heifers. This system 53

PAGE 73

54 concentrates nutrients in a small area where loss to the environment can occur. Thus, nutrient management has become a major focus of regulatory agencies and a major expense and constraint to profitability of producers. Recent regulations require dairy farms to recycle nutrients from animal wastes into crop production or export from the farm. Fluctuating market conditions in the dairy industry (Elbehri and Ford, 1995) combined with growing pressure by regulatory agencies and environmental interests regarding nutrient management have led producers to search for alternatives to total confinement systems. Improved forages and forage management practices, improved and cheaper fencing materials to support rotational stocking, reduced forage harvesting and feeding costs possible improvements in herd health with animals off of concrete, lower capital outlay and reduced day to day costs for handling animal wastes, and improved public perception of the dairy industry (Bernard and Chandler, 1994) have made reverting to pasture-based dairy systems seem attractive. In recent years, significant numbers of dairy producers in temperate areas of the USA have adopted pasture-based systems (Parker et al., 1992). Florida producers have begun to adopt similar systems during the last 5 yr. Insufficient information on pasture-based dairying in Florida, however, precludes recommendation of widespread use of grazed pastures for dairy cows. Research efforts are needed to address this void. Studies in the northeastern USA have demonstrated that there are economic benefits from incorporating intensive grazing in the dairy production system, however, these benefits depend on the producers' managerial abilities (Elbehri and Ford, 1995).

PAGE 74

55 Milk production in the northeastern USA dairies was not greatly affected when the system changed from feeding in confinement to one in which the forage component of the diet was grazed pasture (Fales et al., 1993), likely because of the high protein concentration and digestibility of temperate forages This situation may not be the same for the tropical and subtropical forage grasses grown in Florida because they generally contain more fiber and less protein and have lower digestibilities than their temperate counterparts (Sollenberger and Chambliss, 1991). On the other hand, Florida's climate allows for growing of cool-season forage crops during winter months when temperatures in temperate areas of the USA prohibit crop growth. This may be advantageous to Florida's producers by providing an opportunity for year-round grazing and perhaps decreasing dependence on grain consumption and stored forages, and further, reducing waste management concerns compared to their counterparts in temperate areas. Development of an information base on which to make grazing management decisions for Florida's winter pasture systems should include data that help explain how animal responses are coupled to pasture production and how these components interact. It is well established that animal performance is largely determined by nutrient consumption (Mertens, 1994), which is influenced by pasture characteristics, environmental conditions and management factors. Baltensperger et al. (1986) reported that rye (Secale cereale L.), ryegrass (Lolium multiflorum Lam.), and clovers (Trifolium spp.) are valuable forages for winter pasture programs in Florida. The use of clover in mixed swards to supplement N fertilization in rye-ryegrass pastures in subtropical locations is beneficial (Baltensperger et al., 1986; Moss and Lowe, 1993) Forage

PAGE 75

56 availability or herbage allowance (Davidson et al. 1985 ; Dougherty et al 1992; Holmes et al., 1992) and pasture quality (Minson, 1982; Holmes et al., 1992; Hoogendoorf et al. 1992; Kristensen, 1988) are major determinants of limitations to intake and, hence, animal performance on pasture-based systems. Stocking rate (SR) is a key management variable in determining productivity and profitability of grazing systems (Fales et al., 1995) because it determines herbage allowance and is linked to herbage mass and pasture nutritive value responses (Davidson et al., 1985; Dougherty et al. 1992; Fisher et al., 1996) Although high nutrient intake from pasture may be possible, the high nutrient requirements of lactating dairy cows necessitate concentrate supplementation so that near potential milk production may be achieved (Kolver and Muller, 1998). To reduce production costs associated with intensive systems, concentrate supplementation studies on pasture-based systems need to focus on feeding levels to ensure maximum nutrient intake from pasture while reducing substitution of forage for concentrate. This research addressed grazing management of cool-season forages during the winter months in Florida. The objective was to quantify pasture and animal responses when lactating dairy cows grazed two different cool-season forage systems (FS; N fertilized rye and ryegrass mixed pastures vs rye-ryegrass-crimson clover [Trifolium incarnatum L.]-red clover [Trifolium pratense L.] mixed pastures) at two SR (5 vs. 2.5 cows per hectare) and two levels of concentrate supplementation (CS; 1 kg concentrate [as-fed] per 2.5 or 3.5 kg of milk produced).

PAGE 76

57 Materials and Methods This research was conducted during the 1995-1996 and 1996-1997 winter seasons at the University of Florida Dairy Research Unit at Hague, 18 km north of Gainesville FL (29 60' north lat.) The approximately 10-ha experimental area is mapped as a heterogenous mixture of Chipley fine sand (thermic, coated Aquic Quartzipsamments), less than 5% slope, Sparr fine sand (loamy, siliceous hyperthermic, Grossarenic Paleudults), and Tavares fine sand (hyperthermic, uncoated, Typic Quartzipsamments), 0 to 5% gradient. These soils are somewhat poorly to moderately well drained. Prior to initiation of summer grazing at the site in 1995, mean soil pH (1 :2 soil : deionized H2O ratio) was 5 9 and Mehlich-I (0 05 MHCL + 0.0125 MH2SO4 ) extractable P, K Mg, and Ca in the Apl horizon (0-to 15-cm depth) were 99, 26, 50, and 598 mg kg, respectively. Starting dates for the trials in the 2-yr study were 28 Jan 1996 and 13 Jan. 1997. Evaluations were conducted during three continuous 28-d periods (viz ., a frrst second and third period [Pl, P2, and P3]) each year, ending 22 April and 7 April in 1996 and 1997 respectively. Climatic data during months relevant to the study are presented in the Appendix (Table A-1) In 1996, rainfall for January and February were both much lower and in March more than twice that of the 70-yr average for Gainesville In 1997 rainfall in January was the same as the 70-yr average but, it was lower for the next two months. Rainfall totals for the November-December-January period prior to implementation of grazing (i.e ., coinciding with establishment phase of pastures) were 150 mm in the first

PAGE 77

58 year and 239 mm in the second year, compared to the 70-yr average of 195 mm. Average ambient temperatures during the study months were generally lower in 1996 and higher in 1997 compared to normal temperatures (Table A-1 ). In the fall before both winters (mid-October), 'Tifton 85' bermudagrass (Cynodon spp.) summer pastures were overseeded with a mixture of rye and ryegrass and 'Florigraze' rhizoma peanut (Arachis glabrata Benth.) summer pastures were seeded with a mixture of the two cool-season grasses along with crimson and red clovers. Both summer forages had been grazed previously using treatment arrays similar to those described for the present trial. All pastures received an initial application of 40 kg N and 40 kg K ha1 during establishment in both years. An additional 40 kg N ha1 were applied to all pastures when the trial period began each year. Subsequently, 40 kg N ha1 were applied to rye-ryegrass pastures at the beginning of each of the following two, 28-d grazing periods. In 1997, grass-clover pastures received one additional mid-season application of 40 kg N ha1 to ensure sufficient grass growth. Thus, the N-fertilized FS received a total of 160 kg N ha1 each winter growing season while in the 1996 and 1997 winter growing seasons pastures of grass-legume mixtures received 80 and 120 kg N ha, respectively. Treatments were two FS (N-fertilized rye-ryegrass mixture [GN] vs. rye-ryegrass clovers mixture [GL]), two SR (high and low SR; 5 vs. 2.5 cows per hectare), and two levels of CS (high and low CS; 1 kg concentrate per 2.5 or 3.5 kg milk produced) in a 2 x 2 x 2 factorial experiment. Each treatment was replicated twice in a completely randomized design so there were a total of 16 pastures (experimental units) in the

PAGE 78

59 experiment. During fall 1995, irrigation was unavailable and drought caused establishment failure of the clovers. Data from these pastures will not be reported; thus, in the first year of the study, only four treatments were evaluated (i.e., no FS factor, just a 2 x 2 factorial combination of the two levels each of SR and CS). Clover establishment was successful in the second year. Pasture sizes were 0.8 ha for the low SR treatment and 0.4 ha for the high SR treatment. Two lactating Holstein cows (Bos taurus) blocked by parity (viz., one primiparous and one multiparous cow) were assigned to each pasture during each of the three 28-d periods. All cows were more than 100 d in lactation at the beginning of the trial period in both years. A given pasture received the same treatment in each period, but cows were reassigned to different experimental units at the beginning of every period. Pastures were subdivided for rotational stocking so that cows were offered a fresh paddock every morning. Pastures for GN were subdivided into 22 paddocks and GL pastures into 29 paddocks to allow for 21-d and 28-d rest periods, respectively, between grazing. Cows were actually allowed two paddocks at a time to give them additional room for movement, so they were on the paddock that was grazed the day before and a fresh paddock during every 24-h period. Animals were milked in the morning and evening each day. Concentrate (four parts of a com [Zea mays L.]-soybean [Glycine max (L.) Merr.] meal-byproduct based formulation [120 g CP kg, 20 g fat kg , and 150 g NDF kg '] mixed with one part cotton [Gossypium hirsutum L.] seed; ingredient and chemical composition are presented in Appendix Tables A-3 and A-4) was fed to animals at the appropriate CS level after each milking. During 1996, animals were group fed in high or

PAGE 79

60 low CS groups at a loafing area adjacent to the milking parlor, but in 1997 cows were fed by experimental units via feed troughs in each pasture. The amount of concentrate per experimental group was adjusted twice weekly based on average milk production during the preceding 3to 4-d period. A minimum of 4.5 kg concentrate cow1 d "1 was fed. All pastures had water tubs fitted with float-control devices to ensure continuous drinking water availability. Pasture and animal responses to the treatments were measured during each period. The first 14 d of each period were considered an adaptation phase, and animal response data were recorded during the latter 14 d of the period. Pasture Variables Sampling for pregraze and postgraze herbage mass was done weekly in each period using a double sampling technique (Burns et al. 1989). Pregraze herbage mass samples were taken from a particular paddock the day before it was grazed and postgraze herbage mass samples were taken at the end of the 2 d grazing (since the rotational stocking scheme dictated that animals occupy a particular paddock for 2 d). Indirect estimates of herbage mass were done using a 0.25-m2 disk meter at 20 sites per paddock (sites were selected randomly but spatially arranged to represent the entire paddock) These indirect estimates were calibrated with direct estimates taken at the first and third sampling times during each period At these times direct samples were taken at three sites in each paddock to represent low intermediate and high d isk meter readings. Circular quadrats of similar size to the area covered by the disk meter (0.25 m2 ) were

PAGE 80

61 harvested using battery-powered hand clippers. Samples were clipped to a consistent stubble height of 3 cm that was below the expected grazing height, as suggested by Burns et al. (1989) Harvested samples were dried at 60C in a forced-air oven. Regression techniques were used to develop equations relating direct (harvested sample) and indirect (disk meter height) measures of herbage mass on pastures. Equations developed for a particular double sampling date (first and third week of each period) were used to estimate herbage mass for that date and for the indirect measures taken the following week (i.e., second and fourth week of the period respectively). Herbage removed from pasture during grazing (herbage disappearance) was the difference between pre graze and postgraze herbage mass. In 1996 samples to estimate nutritive value of the grazed portion of the pastures were taken twice in each period once each week during the latter 2 wk of the period. T he number of samples for nutritive value was doubled in 1997; samples were taken four times (once each week) during each of the three periods. Hand-plucked samples, selected to represent what the animals were consuming were composited from 15 to 20 random sites in the paddock that was to be grazed the following day. These samples were dried at 60C in a forced-air oven then ground in a Wiley mill to pass a I-mm stainless steel screen Samples for N analysis were digested using a modified aluminum block digestion procedure (Gallaher et al. 1975). Ammonia in the digestate was determined using semi automated colorimetry (Hambleton 1997). Crude protein (CP; dry matter [DM] basis) was calculated by multiplying N concentration by 6 25 Herbage in vitro organic matter digestibility (IVOMD) was determined using a modification of the two-stage procedure

PAGE 81

62 (Moore and Mott, 1974). Neutral detergent fiber (NDF) was determined using techniques outlined by Golding et al. (1985). Plant-Animal Interface Variables Herbage allowance (HA) was computed as the ratio of average available herbage (kg DM) to animal liveweight (kg) per hectare during the grazing period (Sollenberger and Moore, 1997). Average available herbage was calculated by summing pregraze and postgraze herbage mass then dividing the sum by two. Total organic matter intake (OMI) was computed based on fecal output (FO) and diet digestibility, viz., OMI = organic matter (OM) output of feces/ (1 [OM digestibility/100]). Fecal output was estimated using a pulse dose marker technique (Pond et al., 1986). Chromium-mordanted fiber was used as the external marker. Hand-plucked samples that represent the grazed portion of the pastures were used as the source of fiber for marking. The forage was dried at 60C in a forced-air oven then ground in a Wiley mill to pass a 2-mm stainless steel screen. The neutral detergent soluble component of the ground forage was extracted by boiling in detergent, and the fiber was washed and mordanted with chromium following recommended procedures (Uden et al., 1980) then packed into gelatin capsules. All study animals were dosed with the pulse markers (30 g mordanted fiber cow1 ) at approximately 1800 h on Day 24 of each period. The gelatin capsules were administered orally via balling gun. Fecal samples were obtained from rectal grab samples when animals were in the milking parlor or collected in the pasture by following the

PAGE 82

63 animals and grabbing a sample when they defecated. These samples were taken at dosing time (0 h) and approximately 12, 15, 18, 21, 24, 27, 36, 42, 48, 60, 72, and 84 h after dosing. Fecal samples were dried at 60C in a forced-air oven then ground in a Wiley mill to pass a 2-mm stainless steel screen. Marker concentration in the feces was assayed by atomic absorption spectrophotometry following the procedure outlined by Williams et al. (1962). All samples were analyzed in duplicate and analyses were repeated for samples where the difference of Cr concentration between duplicates exceeded 10%. The one-compartment model with time delay and gamma two age dependency (Pond et al., 1986; Pond et al., 1988) was used to describe the marker appearance curve. The model is: y = e-<.l.1 ci-tdD) / 0.59635 where y = concentration of marker, Ko = concentration of marker if instantaneously mixed in the compartment A1 = age dependent rate parameter, td = time delay, t = time after marker administration. The age dependent rate parameter assumes equal probability for escape of all particles from the rumen based on exponentially distributed residence times and the proportion of the dose remaining in the compartment at a specific time (Pond et al., 1988). Fecal output was then computed as:

PAGE 83

64 FO = (Marker dosed/ Ko)* A1 *24*0.59635 Total OMI was first calculated based on these estimates ofFO (called observed FO [FOO]) and IVOMD data of the respective pastures. Forage OMI was the difference between total OMI and the known amount of concentrate OM fed. Total diet digestibility data were then recalculated (called expected digestibility [EXPD]) to balance for the estimated proportion of forage and concentrate supplement OM consumed and their respective digestibilities, viz., EXPD = (forage OMI*forage digestibility + supplement OMI*supplement digestibility)/ total OMI. This EXPD was further adjusted to help account for differences in partial efficiency of utilization of Metabolizable Energy (ME) from concentrate and forage due to associative effects (Dixon and Stockdale, 1999; Moore, 1992) that may result from mixed forage-concentrate diets. This new calculation of total diet digestibility, called adjusted digestibility (AID), was obtained using an equation developed from a wide range of published data of ME concentrations showing deviation from the expected (based on calculations from the weighted intake and digestibilities of the forage and concentrate supplement components) when mixed diets are fed (Dr. John E. Moore, Emeritus Professor, Univ. of Florida, personal communication) The equation is: ADJ = 59.71 0.8948 EXPD + 0.01399 EXPD2 Using this adjusted digestibility value, a prediction of FO (FOP) from total OMI is computed thus: FOP = Total OMI (1 -ADJ)

PAGE 84

65 A new estimate of forage OMI is then recalculated via a series of iterations (given that concentrate OMI is fixed) to make the difference between FOO and FOP less than 0.01; thus recalculated total OMI will now be the sum of the new estimate of forage OMI and concentrate OMI. The SAS (SAS Institute Inc 1987) program used for these computations is shown in the Appendix (Table A-24) Animal Response Variables Milk yield was recorded at each milking during the latter 14 d of each period for each cow. Samples were taken for six consecutive milkings each week during the third and fourth week of each period and sent to an external laboratory (Southeast Dairy Laboratory, Inc., McDonough, GA) for analysis of milk fat, milk protein, and somatic cell count (SCC) Milk urea N was determined only for 1997 samples All animals were weighed after milking for three consecutive mornings at the beginning of the trial, at each change of period, and at the end of the trial to determine body weight (B W) changes. Average B W was the mean weight of the 3 d. Body condition scores (BCS) of all animals were evaluated using a five-point scale (where 1 = thin to 5 = obese; Wildman et al., 1982) also at the beginning of the trial, each change of period, and end of the trial. Blood samples to determine blood glucose concentrations were taken at the end of each period. Blood was collected via venipuncture of the coccygeal vein.

PAGE 85

66 Statistical Analysis Data to predict the marker appearance curve were analyzed using the NLIN procedure of SAS (SAS Institute Inc., 1982b ); iterative method used was Marquardt. The model fitted to the data was: Y = (l:Ko *L1 *T]*eL1 r) / 0.59635 where Y = Cr concentration in feces (ppm) Ko = concentration of marker if instantaneously mixed in the compartment, L1 = age dependent rate parameter, T = Time after marker administration time delay (i:). Starting values for the parameters were set at 100, 400, and 700 for Ko, 0.03, 0.05, and 0.08 for L1 and and 3, 5, and 7 for,: and bounds were set so that all of these parameters were> 0 (SAS program shown as Table A-25 in Appendix). Rate of passage (kg h "1 ) was then calculated as the product of L1 and 0.59635, retention time as the reciprocal of the average of the sum ofL1 and,: (viz., 2 / [L1 +,:]),and fill as g Cr administered in dose/ KoFecal output then was the product of fill and rate of passage in 24 h. Intake estimates based on these data were adjusted (described in previous section) prior to statistical analysis to make inferences about intake responses to experimental variables. All responses were analyzed by fitting mixed effects models (Littell et al., 1996) using the PROC MIXED procedure of SAS (SAS Institute Inc., 1992). Because the FS factor was lost in the first year of the study, data were analyzed separately for each year. For the 1996 data the model used was:

PAGE 86

67 YiJ1c = + P; +SJ+ (PS\+ C1c + (PC);1c + (SC)J1c + (PSC)iJ1c + eiJ1c where Yijk is the dependent variable is the overall mean P; is the period effect SJ is the SR effect (PS)iJ is the period by SR interaction C1c is the CS effect (PC);1c is the period by CS interaction (SC)J1c is the SR by CS interaction (PSC)iJk is the period by SR by CS interaction eiJlc is the error. In 1997, the model used was: Y ;11c, = + P; + F1 + (PF)iJ + S1c + (PS);1c + (FS)11c + (PFS)iJ1c + C1 + (PC);,+ (FC)JI + (SC)1c, + (PFC);p + (PSC)ilc/ + (PFSC)iJ1c1 + eiJ1c, where Y iJlcl is the dependent variable is the overall mean P ; is the period effect F1 is the FS effect (PF\ is the period by FS interaction S1c is the SR effect (PS);1c is the period by SR interaction (FS)11c is the FS by SR interaction

PAGE 87

68 (PFS)iik is the period by FS by SR interaction C, is the CS effect (PC)u is the period by CS interaction (FC)p is the FS by CS interaction (SC)k, is the SR by CS interaction (PFC)ii, is the period by FS by CS interaction (PSC)ikl is the period by SR by CS interaction (PFSC)iikl is the period by FS by SR by CS interaction eiikl is the error All effects were considered fixed except for the error term which was considered random. Period in both models was considered to be a repeated measure in time since it did not have a chance to be assigned randomly (Littell 1989) Subjects were the experimental units viz., each replication x FS x SR x CS combination. The REPEATED statement in the SAS program allowed for modeling the covariance structure within subjects. Options were used to specify three commonly used covariance structures to model vi z autoregressive order one compound symmetric and unstructured row-column variance covariance components (Littell et al. 1996; SAS Institute Inc. 1996). The covariance structure to assume in the model for final inference for each response variable was determined by first examining the Pr > IZI value to determine if it was within acceptable range(< 0.10) then examining the model-fit criteria computed by PROC MIXED Aikaike s Information Criterion and Schwarz' Bayesian Criterion. The covariance structures with values of the criteria closest to zero, as suggested by Littell et al. ( 1996)

PAGE 88

69 were accepted for final inferences. Means separation using probability of difference tests (PDIFF in SAS; SAS Institute Inc., 1987) was conducted only for effects in the respective models that were statistically significant (P < 0 05) or where there was a trend (P > 0 .05 but< 0.10). Means reported are least squares means (LSmeans) and were considered different at P < 0.05 unless otherwise stated. Regression and correlation techniques (PROC REG and PROC CORR procedures in SAS; SAS Institute Inc., 1982a; 1982b) were used to explore relationships between variables. Results and Discussion Herbage Mass In the 1996 winter grazing season, there was a trend toward a period by SR interaction effect on pregraze herbage mass (P = 0.076). The interaction was due mainly to the lack of SR differences in Pl (Table 3.1). This was expected, because some time is required before management treatments affect pregraze herbage mass. Pregraze herbage mass was similar during Pl and P2 within each SR, increased in P3, and was greater at low SR than at high SR in both P2 and P3 (Table 3 .1 ). In the 1997 winter grazing season, there was a period by FS by SR interaction on pregraze herbage mass (P = 0.003). During Pl, there were no differences between SR treatments within levels of FS, or vice-versa (Table 3.2). Again lack of differences during Pl was because pastures had been subjected to grazing treatments for only a short time.

PAGE 89

70 During P2, low SR pastures had greater pregraze herbage mass than high SR, regardless ofFS (Table 3.2). Also, GN pastures had greater pregraze herbage mass than GL pastures regardless of SR (Table 3.2). In P3, GL pastures had greater pregraze herbage mass than GN pastures when SR was high but there was no difference due to FS when SR was low. Also, pregraze herbage mass was greater for low SR than for high SR on GN pastures, but there was no difference due to SR in GL pastures (Table 3.2). After the first week of P2, animals were taken off the high SR treatments on GL pastures due to low forage availability, so the higher pregraze herbage mass on these pastures in P3 resulted from longer regrowth interval than for other pastures. Table 3 .1. Period by stocking rate (SR) interaction effect on pregraze herbage mass during winter 1996. Period 1 2 3 SR High Low -------------kg DM ha1 --------------760 bi 550 b 1320 a 1040 b 980 b 2080 a P valuet 0.1037 0.0030 0.0001 t Probability of difference value for comparisons between SR means within a period. t Means followed by the same letter within a column are not different (P > 0.05) Increased pregraze herbage mass during the last period of the trial in 1996 may be due to improved conditions for growth as the season progressed. Rainfall during the latter period of the study was higher than average and day length was increasing. Increased

PAGE 90

71 pregraze herbage mass in GL pas tures during P3 in 1997 likely was due to removal of cattle from high SR pastures during P2, and the a d ditional mid-season application of 40 kg Table 3.2. Period by forage system (FS) b y stocking rate (SR) interaction effect on pre graze herbage mass during winter 1 997. Period 1 2 3 Forage System t GL GN P value1 GL GN P value1 GL GN P value1 SR High Low -----kg DM ha-1 ---------1 640 1800 0 .1988 42 0 970 0.0002 1350 750 0.0 001 1590 1790 0.1153 970 1370 0.0015 1350 1420 0.5663 P value t 0.7018 0 9271 0.0002 0 0015 0.9504 0.0001 t GL = forage system comprising of rye ryegrass red clover and crimson clover mixture; GN = forage system comprising ofN-fertilized rye and ryegrass mixture t Probability of difference value for comparisons between SR means within a FS by period Standard errors for interaction means ranged from 86.9 to 117 .1. ,r Probability of difference value for comparisons between FS means within a SR by period.

PAGE 91

72 Nitrogen fertilization conferred an advantage to the GN system for herbage production in this study. Moss and Lowe (1993) demonstrated similar responses on Nfertilized ryegrass pastures vs. ryegrass-clover mixtures. Longer rest period on GL pastures did not seem to be as advantageous as N fertilization for herbage production. Many grazing studies have demonstrated similar responses to SR, viz., greater herbage mass at lenient SR (Davidson et al., 1985; Fales et al., 1995; Hoogendoorn et al., 1992; Moss and Lowe, 1993) The inverse relationship between pregraze herbage mass and SR found in the present study could be attributed to greater accumulation of herbage at low SR, which likely is linked to postgraze HM. In 1996, postgraze herbage mass was 370 kg DM ha' on the high SR compared to 820 kg DM ha on the low SR pastures (P = 0.001). In 1997, postgraze herbage mass on high SR pastures was 350 and 620 kg DM ha for GL and GN, respectively, compared to 640 (GL pastures) and 1000 kg DM ha (GN pastures) for low SR treatments (P = 0.0001; SR main effect). Herbage accumulation ( difference of pregraze herbage mass of the current cycle and postgraze herbage mass of the previous cycle) was 800 (high SR) vs. 1020 kg DM ha' (low SR) in 1996 and 520 (GL) and 250 kg DM ha' (GN) on high SR vs. 490 (GN) and 550 kg DM ha' (GL) for low SR pastures in 1997 (P = 0.0001 for FS main effect). Herbage accumulation results for GL pastures were confounded because high SR pastures were not grazed for most of P2 in 1997. In a New Zealand study on perennial ryegrass-white clover (Trifolium repens L.) pastures L'Huillier (1987) observed no differences in pregraze herbage mass when the grazing season started but greater pregraze herbage mass was associated with lower SR as the season progressed. Herbage accumulation for 28-d regrowth after grazing in that

PAGE 92

73 study responded in the same pattern as pregraze herbage mass. Fales et al. (1995) reported that greater herbage accumulation at lenient SR was responsible for greater pre graze herbage mass in their study. Pregraze herbage mass during the 1997 winter grazing season also was affected by an FS by SR by CS interaction (P = 0.029). At the high CS level, there was greater pregraze herbage mass at low than at high SR on GN pastures, but there was no difference due to SR in GL pastures {Table 3.3). Also at high CS, GN pastures had greater pregraze herbage mass than GL systems when SR was low, but there was no difference due to FS when SR was high (Table 3.3). At the low CS level, pregraze herbage mass at the low SR tended to be greater than that of the high SR in GL pastures and there was no difference due to SR in GN pastures {Table 3.3). There were no differences in pregraze herbage mass between FS, regardless of SR, when CS was low. Examination of these data suggests that the main reason for the interaction is the high value for pregraze herbage mass on the low SR treatment of GN system when fed at the high CS. It is likely that cows on this treatment, with forage availability not as limiting as at the high SR, were not grazing as much as cows fed the low CS at similar SR. Herbage accumulation, apart from being greater at the low SR treatment on GN pastures, tended to be greater (P = 0.06; 360 vs. 290 kg DM ha-1 ) on low CS treatments. Discussion of supplementation effects on pasture herbage production were not found in reports of studies involving supplementation of lactating cows on pasture.

PAGE 93

74 Table 3.3. Forage system (FS) by stocking rate (SR) by concentrate supplement (CS) interaction effect on pregraze herbage mass during winter 1997. cs High Low SR SR Forage Systemt High Low High Low -----kg DM ha1 -----P value i ------kg DM ha1 ------P value i GL 1090 1170 0.4841 1180 1430 0.0541 GN 1140 1690 0.0007 1210 1350 0.1959 P value1 0.6606 0.0011 0.8509 0.4620 t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover mixture; GN = forage system comprising ofN-fertilized rye and ryegrass mixture Probability of difference value for comparisons between SR means within a FS by CS. Standard errors for interaction means ranged from 74.1 to 82.8. Probability of difference value for comparisons between FS means within a SR by CS. Herbage Disappearance In the 1996 winter grazing season, herbage disappearance during grazing did not respond to SR or CS treatments (P > 0.10) but was influenced by a period effect (P = 0.0001). Herbage disappearance was 640 kg ha1 in Pl, decreased to 360 kg ha1 in P2, then increased back to 600 kg ha1 in P3. During the 1997 winter grazing season, there was a period by FS by SR interaction effect on herbage disappearance (P = 0.0076). Response patterns were similar for Pl and

PAGE 94

75 P3, when there was greater herbage disappearance at high SR than at low SR in GL pastures, but no difference due to SR in the GN system (Table 3.4). Also, there was Table 3.4. Period by forage system (FS) by stocking rate (SR) interaction effect on herbage disappearance during winter 1997. Period 1 2 3 Forage System t GL GN P value1 GL GN P value1 GL GN P value1 SR High Low ------------kg DM ha1 -----------1150 610 0.0001 190 470 0.0305 1040 580 0.0001 750 510 0 0201 460 470 0.9615 800 590 0.0388 P value i 0.0003 0.3156 0.0359 0.9711 0 0274 0 9804 t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover mixture; GN = forage system comprising ofN-fertilized rye and ryegrass mixture. t Probability of difference value for comparisons between SR means within a FS by period. Standard errors for interaction means ranged from72.6 to 108.1. 1 Probability of difference value for comparisons between FS means within a SR by period.

PAGE 95

76 greater herbage disappearance in GL pastures than in GN pastures regardless of SR treatment during Pl and P3 (Table 3.4). In P2, there was greater herbage disappearance in low SR pastures than at high SR in the GL system (Table 3.4), a reverse of what occurred during the other periods. Also in P2, there was greater herbage disappearance in GN pastures than in GL pastures when SR was high, but there was no difference due to FS when SR was low (Table 3.4). The period effect shows that herbage disappearance decreased when pregraze herbage mass in P2 decreased, then increased with pre graze herbage mass in P3. There was correlation (r = 0.61; P = 0.001) between pregraze herbage mass and herbage disappearance for all 1997 data. When the data were analyzed by SR, the correlation was improved for high SR (r = 0.76; P = 0.001) but became weaker at low SR (r = 0.43; P = 0.04). Regressions of pregraze herbage mass on herbage disappearance for 1996 showed a linear relationship (P = 0.0001; Figure 3.1 [a]) but there was wide scatter in the data (r2 = 0.37; root mean square error [RSME] = 140.0). Analyzing the data at fixed levels of SR did not improve the relationship. This was not surprising since SR did not affect herbage disappearance during 1996. In 1997, regression analysis indicated a weak linear relationship when all the data were considered (P = 0.0001; r2 = 0.37; RSME = 225.6); the relationship was stronger when only high SR data were examined (Figure 3.1 [b]; P = 0.0001; r2 = 0.57; RSME = 245.0) and weaker for low SR data ( Figure 3.1 [c]; P = 0.036; r2 = 0.18; RSME = 149 2). Slopes were 0.52 and 0.21 for the high and low SR data, respectively. Gibb et al. (1997) examined the decline in sward height due to grazing

PAGE 96

Figure 3.1. 77 1996 Data HD= 218.6 + 0.23 pregraze HM r2 -0.38 1000 ..----~---.---~----r----.---, 800 -f---4-----+------------'ii 600 .... Cl c 400 0 J: 200 0 0 500 1000 1500 2000 Pregraze HM (kg/ha) (a) High Stocking Rate 1997 1400 1200 'ii 1000 800 l 600 0 J: 400 200 0 HD = 62.4 + 0 .51 pregraze HM r2 -0.57 . .. -~ .,... - . L,..--9 2500 -~ .. -200 400 600 800 1000 1200 1400 1600 1800 2000 Pregraze HM (kg/ha) (b) ., 6 0 :z: (c) Low Stocking Rate 1997 1000 900 800 700 600 500 400 300 200 HD = 293.2 + 0.21 pregraze HM r1 = 0.18 .,. -. 800 1000 1200 1400 1600 1800 2000 2200 Pregraze HM (kg/ha) Regression relationships between pregraze herbage mass (HM) and herbage disappearance (HD) for (a) all 1996 data combined, (b) high stocking rate data for 1997, and ( c) low stocking rate data for 1997.

PAGE 97

78 and its relationships with pregraze pasture characteristics. They found that pastures with a sward height of 5 cm before grazing consistently remained at a mean height between 4.5 and 4.8 cm, while height of taller swards (7 and 9 cm) declined. Parallels between herbage disappearance responses in the present study and sward height decline demonstrated by Gibb et al. (1997) seem likely, viz., low herbage availability results in reduced removal. Herbage Allowance In the 1996 winter grazing season there was a period by SR interaction effect on herbage allowance (P = 0.001). Similar to the herbage mass response, herbage allowance was not different during the first two periods regardless of SR, then increased in P3 (Table 3.5). Low SR pastures had greater herbage allowance than high SR pastures more so during P3 Table 3 5. Period by stocking rate (SR) interaction effect on herbage allowance during winter 1996. Period 1 2 3 SR High Low --------------------kg kg-1 -------------------P valuet 0 16 bi 0.55 b 0.0007 0.14 b 0.38 a 0.59 b 1.27 a 0.0001 0.0001 t Probability of difference value for comparisons between SR means within a period. t M e ans followed by the same letter within a column are not different (P > 0 05)

PAGE 98

79 In the 1997, there was a FS by SR by CS interaction effect on herbage allowance (P = 0.008). Regardless ofFS or CS level, low SR pastures had greater herbage allowance than high SR (Table 3.6). At high CS, there were no differences in herbage allowance between FS when SR was high but GN pastures had greater herbage allowance than GL pastures when SR was low (Table 3.6). At low CS on the other hand, there were no differences between FS at either SR. It seems that the main cause of the interaction is the high herbage allowance observed in low SR pastures on the GN system when cows were fed at the high CS level. Recall that this was the same treatment that had high pregraze herbage mass (Table 3.3). Table 3 6. Forage system (FS) by stocking rate (SR) by concentrate supplement (CS) interaction effect on herbage allowance during winter 1997. Forage Systemt GL GN P value1 High SR High Low --------kg kg-I -------0.26 0.63 0.32 0.3576 1.04 0.0001 P value i 0.0003 0.0001 cs Low SR High Low ---------kg kg-I ---------0.28 0.82 0.35 0.2560 0.84 0.6539 P value i 0.0001 0.0001 t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover mixture; GN = forage system comprising ofN-fertilized rye and ryegrass mixture. t Probability of difference value for comparisons between SR means within a FS by CS. Standard errors for interaction means ranged from 0.03 to 0.05. Probability of difference value for comparisons between FS means within a SR by CS.

PAGE 99

80 There was also a period by FS interaction effect on herbage allowance (P = 0.001) during the 1997 winter grazing season In GL systems herbage allowance was greatest during Pl, least during P2, and intermediate for P3 (Table 3.7). For GN systems, herbage allowance was greater during Pl than for P2 and P3, which had similar herbage allowance (Table 3.7). The GN pastures had greater herbage allowance than GL pastures during Pl and P2 but there were no differences due to FS in P3 (Table 3.7). Interpretation of these data must be treated with caution since high SR pastures in the GL system were not grazed during the latter 3 wk of P2. This allowed GL pastures to have similar herbage allowance to GN pastures during P3, due to high average pregraze herbage mass for those pastures (Table 3.2) after the long regrowth interval on the high SR treatment. Table 3.7 Period by forage system interaction effect on herbage allowance during winter 1997. Period 1 2 3 Forage System t GL GN -------------------kg kg' -------------------P valuei 0.35 C 0.51 b 0 87 a 0.55 b 0.50 b 0.0001 0.0004 0.7824 t GL = forage system comprising of rye ryegrass, red clover, and crimson clover mixture; GN = forage system comprising ofN-fertilized rye and ryegrass mixture. t Probability of difference for comparisons between forage system means within period. Means followed by the same letter within the same column are not different (P > 0.05).

PAGE 100

81 Forage Nutritive Value In vitro organic matter digestibility During the 1996 winter grazing season, herbage in vitro digestible organic matter (IVDOM) concentration was influenced by a period by SR interaction effect (P = 0.017). At the high SR, IVDOM did not change with period while at low SR it was the same during Pl and P2 but decreased in P3 (Table 3.8). Also, there were no differences due to SR during the first two periods, but in P3, high SR had greater IVDOM than low SR (Table 3 8). High SR treatments were grazed very closely throughout the experiment and only in P3 was there sufficient residual forage in low SR pastures to allow accumulation of mature herbage and lower IVDOM Other researchers have reported increased pasture digestibility at higher stocking rates (Fales et al. 1995) or when low herbage availability treatments are achieved via intense grazing (Fisher et al., 1996 ; Holmes et al., 1992; Hoogendoorn et al. 1992). The difference between high and low SR was attributed to a lower proportion of green leaf, accumulation of senescent material, and greater average age of plant tissues in leniently grazed swards. In vitro dry matter digestibility (IVDMD) during spring in the first year of the Fales et al. (1995) study ranged from 694 g kg-1 (low SR) to 712 g kg-1 (high SR). Differences of 30 to 60 g kg -1 were reported by Holmes et al. (1992) Herbage IVDOM concentration during the 1996 winter grazing season was also affected by a period by CS interaction (P = 0.010). At high CS IVDOM concentration decreased from 759 g kg-1 in Pl to 722 g kg -1 in P3 while at low CS IVDOM increased 26 g kg -1 from Pl to P3. The decrease in IVDOM on high CS pastures may be explained

PAGE 101

82 by a greater proportion of mature herbage on those pastures than low CS because animals were not grazing as intensively in high CS pastures. Analysis of postgraze herbage mass data indicated that there was a trend (P = 0.079) for a CS effect; low CS pastures had less herbage remaining after grazing (530 kg ha-1 ) compared to high CS pastures (650 kg ha1 ) The low CS pastures were grazed more closely, resulting in less build up of mature herbage in the grazed portion of the canopy, thus leading to increased IVDOM as the grazing season progressed Table 3.8. Period by stocking rate (SR) interaction effect on pasture in vitro digestible organic matter concentration during winter 1996. Period 1 2 3 SR High Low -------------------g kg1 --------------------P valuet 740 at 765 a 761 a 733 a 746 a 700 b 0.6639 0.1183 0.0001 t Probability of difference value for comparisons between SR means within a period. t Means followed by the same letter within a column are not different (P > 0 05). Herbage IVDOM during the 1997 grazing season was affected by a FS by SR interaction (P = 0.001). In the GL system high SR pastures (782 g kg -1 ) had greater IVDOM than low SR (763 g kg .1 ) a SR effect similar to that described in 1996 but in the GN system, low SR pastures (782 g kg 1 ) had greater IVDOM than high SR (775 g kg 1 ) pastures There was no difference due to FS at high SR (782 vs. 775 g kg1 for GL and

PAGE 102

83 GN respectively) but GN pastures (782 g kg-1 ) had greater IVOMD than GL pastures (763 g kg-1 ) at low SR. The latter response reflects the longer regrowth interval on GL (28 d) than GN systems (21 d) a maturity effect that was significant only when postgraze herbage mass was greater on low SR pastures. It should be noted that although IVDOM concentration was affected by treatment these differences may not be large enough to impact subsequent animal performance and consequently may be of limited biological significance. Crude protein concentration During the 1996 winter grazing season, herbage CP concentration responded to a SR main effect (P = 0.025) and also a period main effect (P = 0.0001) The high SR pastures had greater CP concentration (300 g kg-1 ) than low SR (251 g kg -1 ) Similar responses were obtained in other studies of grazing dairy systems (Davidson et al. 1985b ; Fales et al. 1995 ; Hoogendoorf et al. 1992). Fales et al. (1995) reported CP ranging from 236 g kg-1 for a low SR (1 cow ha-1 ) to 273 g kg -1 at high SR (1.6 cow ha-1 ) in the first year of their study on temperate pastures. In the current study herbage CP concentration was greater during P2 (320 g kg-1 ) than for Pl (260 g kg-1 ) or P3 (246 g kg -1 ) ; CP for Pl and P3 were not different from each other. Values of CP for P2 are likely higher compared to P 1 because they represent herbage from a 21-d regrowth interval vs more mature herba g e. This was because staging of pastures had been done in mid December w hich was great e r than 1 mo prior to the beginning o f the study. Also it is lik e ly that the lower CP values for P3 compared to P2 may be a reflection of increasing stem and inflorescence density of rye and ryegrass during the latter period of the study.

PAGE 103

84 During the 1997 winter grazing season, there was a period by FS by SR interaction effect on herbage CP concentration (P = 0.010). These data will not be presented or discussed because the range of CP concentrations within period generally was small, CP concentration was always 209 g kg-1 or greater, and the few FS and SR effects that occurred were considered to be of limited biological significance. Neutral detergent fiber During the 1996 winter grazing season, there was a period by SR interaction effect on herbage neutral detergent fiber (NDF) concentration (P = 0.0001). At the high SR, NDF concentration was greater for Pl than for P2, and NDF for P3 was intermediate to and not different from, that of Pl and P2 (Table 3.9). At the low SR on the other hand, NDF concentration increased as the grazing season progressed (Table 3.9). This likely reflects an increase in the number of reproductive tillers at the lenient SR. Herbage NDF concentration was greater at high SR than at low SR during P 1 but was greater at low SR than at high SR during the subsequent periods (Table 3.9). It was observed that high SR pastures were grazed close to the stubble height of samples harvested, and also there were fewer ungrazed portions of the sward compared to low SR pastures. This minimized occurrence of senescent and mature plant material that would contribute to increased NDF During the 1997 winter grazing season, there was a period by FS by SR interaction effect on herbage NDF concentration (P = 0.019) There were no consistent effects of either SR or FS and the range ofNDF across the three-way interaction LSmeans was only 477 to 514 g kg -1

PAGE 104

85 Table 3.9. Period by stocking rate (SR) interaction effect on pasture neutral detergent fiber concentration during winter 1996. Period 1 2 3 SR High Low k -1 -------------------g g --------------------497 al 457 b 469 ab 449 C 490b 548 a P valuet 0.0115 0 0019 0 0001 t Probability of difference value for comparisons between SR means within a period. t Means followed by the same letter within a column are not different (P > 0.05) Means thus identified are different from the next higher mean in that column at P < 0.10. Fales et al. (1995) found greater NDF in low SR than high SR treatments (529 vs. 492 g kg 1 ) in the spring season during the first year of their study but these differences were not evident during the summer. In the second year of their study the NDF differences due to SR were not detected during spring, but NDF was greater at low SR (546 g kg1 ) than high SR (509 g kg1 ) during summer Kristensen (1988) reported greater crude fiber concentrations of swards grazed at lenient SR compared to more severe SR. As with IVDOM NDF data in the current study show valid statistical differences but likely lack biological importance because the magnitude of differences between treatment means may not be large enough to impact animal performance.

PAGE 105

86 Organic Matter Intake During the 1996 winter grazing season, a period by SR interaction affected both forage organic matter intake (OMI) and total OMI (P = 0 050 for both responses) Average daily forage OMI of cows grazing high SR pastures was the same during periods 1 and 3 and was greater during P2 (Table 3.10). At low SR, average daily forage OMI during Pl was similar to that of P2 but greater than that of P3, and P2 and P3 had similar daily forage OMI (Table 3 10). Forage OMI was greater at high than at low SR only during P2 (Table 3 .10). Average daily total OMI of cows grazing high SR pastures was highest during P2 and lowest during P3, with Pl being intermediate At low SR, daily total OMI was similar during Pl and P2, and both were greater than that for P3 (Table 3.10). As with forage OMI, daily total OMI was greater at high than at low SR only during P2. Average daily total OMI relative to cow BW essentially followed the same pattern of responses as did forage OMI except that at high SR, total OMI relative to BW for Pl was greater than that for P2 at P < 0.05 (Table 3.11), i e. these data were more sensitive to statistical tests than absolute estimates of intake. The high value for forage OMI on high SR pastures during P2, the source of the interaction effects may be misleading. It may reflect animals consuming more than their share of concentrate. Because cows were group fed by high and low CS groups during 1996 high SR cows may have compensated for low forage mass by increasing supplement intake compared to their low SR counterparts There is evidence to suggest that given an opportunity animals will att empt to rapidly increase intake on pasture if they are in a

PAGE 106

87 situation where intake was previously restricted (Chilibroste et al., 1997; Iason et al., 1999). It is likely that high SR animals, with less rumen fill if their intake on pasture was restricted, may have been eating the supplemental feed more rapidly than low SR animals. Because supplement was computed as being equal for both SR levels, forage OMI values would then be inflated. This experiment cannot provide such evidence, however, since the techniques used for estimating intake cannot discriminate between components of the diet. An additional indication that the forage intake value on that treatment is unlikely to be that high is suggested by the low pregraze herbage mass, low herbage disappearance, and low herbage allowance found during P2 in that year. Table 3.10. Period by stocking rate (SR) interaction effect on forage organic matter intake (OMI) and total OMI during the 1996 winter grazing season. Forage OMI Total OMI SR SR Period High Low High Low k .J d"1 -----g cow ------P valuet k .J d"1 -----g cow -----P valuet 1 11.06 ht 12.11 a 0.5390 19.52 b 20.58 a 0.5390 2 14.84 a 11.11 ab 0 .0332 22.49 a 18.76 a 0 0332 3 9 12 b 8 83 b 0 8643 16.11 C 15.82 b 0 8643 t Probability of difference for comparisons between SR means within period. t Means followed by the same letter within a column are not different (P > 0.05).

PAGE 107

88 Table 3.11 Period by stocking rate (SR) interaction effect on average daily forage organic matter intake (OMI) and total OMI relative to cow body weight, during the 1996 winter grazing season. Forage OMI Total OMI SR SR Period High Low High Low k 1 --------g g --------P value t k 1 --------g g --------P value t 1 21.2 bi 23.2 a 0.4918 37.4 b 39.4 a 0.5007 2 28.3 a 20.6 a 0.0116 43.1 a 35.2 a 0.0095 3 15.8 b 15.5 b 0.9049 28.2 C 28.0 b 0.9209 t Probability of difference for comparisons between SR means within period. t Means followed by the same letter within a column are not different (P > 0.05). The probability of difference between means thus identified and the next highest mean in that column is less than 0.10. Total OMI was also greater on high SR in P2 however suggesting that low SR animals may not have been consuming as much forage as they could. To rationalize how ingestive behavior of grazing animals may be functioning to account for this is unwarranted however due to the uncertainty of actual intake of supplement. Forage OMI and total OMI during the 1996 winter grazing season also responded to a period by CS interaction effect (P = 0.004 and P = 0.001 respectively). At high CS there was a consistent decrease in forage OMI as the season progressed, while at low CS forage OMI was greater for P2 than that for Pl and P3 (Table 3.12). Means for Pl and P3 were not different. Daily forage OMI was greater for high CS than for low CS only during the first period and there was a tendency for the reverse to occur during P2 i.e .,

PAGE 108

89 greater at low CS (Table 3.12). Daily total OMI followed the same patterns as with forage OMI relative to period effects at fixed levels of CS (Table 3.12) In terms of CS comparisons within each period, however, the patterns differed. There was greater total OMI at the high CS during periods 1 and 3 but no difference due to CS during P2 (Table 3.12). Analyses of 1996 winter forage and total OMI data adjusted relative to cow BW indicated that statistical inferences were the same as for the absolute values of intake (Table 3.13). Table 3 12. Period by concentrate supplement (CS) interaction effect on forage organic matter intake (OMI) and total OMI during the 1996 winter grazing season. Period 1 2 3 Forage OMI cs High Low k -1 d l -----g cow -----13.86 at 9.31 b 11.58 ab 14 37 a 9.18 b 8.77 b TotalOMI cs High Low P valuet k -I dl -----g cow -----0.0118 24.40 a 15. 70 b 0.1063 20.78 b 20.48 a 0.8098 18.04 b 13.89 b t Probability of difference for comparisons between SR means within period. P valuet 0.0001 0.8564 0.0204 t Means followed by the same letter within a column are not different (P > 0.05). The probability of difference between means thus identified and the next highest mean in that column is less than 0 10. Data presented earlier (Table 3 10) suggested that substitution effects were not occurring in the 1996 data so it is not surprising that there generally was greater total OMI when cows were fed at the higher CS level. Since total OMI is the sum of forage

PAGE 109

90 and supplement OMI and supplement level is fixed, total OMI changes only if forage OMI is affected by management treatments. An anomaly is seen during P2 where total OMI is not different between CS levels. As indicated earlier, uncertainty about supplement consumption distorts explanation of these results. The trend for lower forage OMI in P2 when the higher level of CS is fed may indicate a depression in forage OMI due to high concentrate. Herbage disappearance was also low during that period. Table 3.13. Period by concentrate supplement (CS) interaction effect on average daily forage organic matter intake (OMI) and total OMI, relative to cow body weight, during the 1996 winter grazing season. Forage OMI Total OMI cs cs Period High Low High Low k -1 -------g g ---------P valuet k -1 --------g g ---------P value t 1 26.7 at 17.8 b 0 0043 46.9a 29.9 b 0.0001 2 22.4 ab 26.6 a 0.1488 40.3 b 38.0 a 0.4234 3 16.0 b 15.3 b 0.8081 31.8 C 24.4 b 0.0163 t Probability of difference for comparisons between SR means within period. t Means followed by the same letter within a column are not different (P > 0.05). The probability of difference between means thus identified and the next highest mean in that column is less than 0.10. In the 1997 winter grazing season, there was a trend for a period by FS interaction effect (P = 0.072) on average daily forage OMI. The period effect was strong (P = 0.0001) with greatest forage OMI occurring during Pl for GL system and during Pl and

PAGE 110

91 P3 for GN (Table 3.14). There was greater daily forage OMI for GN pastures than for GL pastures during P3, but there were no differences due to FS in the previous two periods. Average daily total OMI was affected by a period by FS effect (P = 0 026). The pattern ofresponses was similar to that of forage OMI (Table 3.14). Also, there were no differences due to FS except during P3 when total OMI in GN pastures was higher than that for GL pastures (Table 3.14). Table 3.14. Period by forage system interaction effect on forage organic matter intake (OMI) and total OMI during the 1997 winter grazing season. Forage OMI TotalOMI Forage System t Forage System t Period GL GN GL GN k 1 d-1 -----g cow -----P valuel k -1 d 1 -----g cow -----P valuel 1 14.58 a 13.41 a 0 .3410 20.02 a 19.36 a 0.2552 2 9.47 b 9.03 b 0.7718 14.71 b 14.26 b 0.7701 3 8.91 b 11.51 a 0.0252 13.96 b 17.41 a1 0.0091 t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover mixture; GN = forage system comprising ofN-fertilized rye and ryegrass mixture. t Probability of difference for comparisons between forage systems means within period. Means followed by the same letter within a column are not different (P > 0.05). ,r The probability of difference between means thus identified and the next highest mean in that column is less than 0.10 During the 1997 winter grazing season, average daily forage OMI relative to cow BW was influenced by a period effect (P = 0.001). Average forage OMI relative to cow

PAGE 111

92 BW was greater for the first than for subsequent periods (Table 3.15), a pattern similar to that observed for herbage allowance (Table 3.7). Table 3.15. Period effect on forage organic matter intake (OMI) and period by forage system interaction effect total OMI both relative to cow body weight, during the 1997 winter grazing season. Forage OMI Total OMI Forage Systemt Period GL GN ----------------------------g kgl ---------------------------P valuei 1 25.9 a 37.6 a 36.9 a 0.8056 2 17.2 b 28.5 b 26.7b 0.5652 3 19.1 b 26.2 b 32.3 a1 0.0129 t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover mixture; GN = forage system comprising ofN-fertilized rye and ryegrass mixture. t Probability of difference for comparisons between forage systems means within period. Means followed by the same letter within a column are not different (P > 0.05). 1 The probability of difference between means thus identified and the next highest mean in that column is less than 0.10. Total OMI relative to cow BW during the 1997 winter grazing was influenced by a trend toward a period by FS interaction effect (P = 0.105). The period main effect was strong (P = 0.0001). In GL pastures, daily total OMI relative to cow BW was greater for Pl than for P2 or P3 (which were not different from each other; Table 3.15). In the GN system on the other hand, daily total OMI relative to cow BW decreased from Pl to P2 then increased during P3 to similar levels as Pl (Table 3.15).

PAGE 112

93 The pattern of intake responses in 1997 appears to be related to pregraze HM. Pregraze herbage mass was not different between FS during Pl, nor was forage OMI. The intake data for GL pastures during P2 were from low SR pastures only because animals were taken off high SR pastures during that period. As a result, forage OMis between FS during P2 were similar for the comparison GL low SR to the average of SR treatments on GN pastures Given that high SR treatments in GL pastures were not grazed in P2, there was a chance for herbage to accumulate so that pregraze herbage mass during P3 was not different compared to the low SR treatments of either FS. Greater forage OMI on GN pastures during P3 may be explained by greater intake on the low SR treatments compared to the GL counterpart. In a UK study, dairy cows grazing high tiller density swards (with greater pregraze HM) had 2.9 kg DM more daily herbage intake than those grazing low density swards with lower pregraze herbage mass (Fisher et al. 1996). Data for estimates of OMI relative to cow BW compared to absolute estimates of OMI appear to be more statistically sensitive to treatment effects Response patterns for OMI relative to cow BW were similar to those for absolute values but often showed statistical significance when absolute values showed only a trend. This suggests that converting estimates of intake to proportion of animal BW helps reduce variability in the data. Kolver and Muller (1998) detected statistical differences due to their experimental variables for DMI and OMI when using absolute values but did not find differences (P > 0 10) for CP or NDF intake When they analyzed the data as proportion of BW, however differences were detected (P < 0.02 and P < 0.01 for CP and NDF, respectively).

PAGE 113

94 There was also a trend for a FS by SR interaction effect on average daily total OMI (P = 0.072) during the 1997 winter grazing season and to a lesser extent on forage OMI (P = 0.107). This pattern of responses may have biological importance even though the statistical significance is borderline, so the data are presented. For both forage and total OMI there was no difference between FS at high SR, nor was there any difference between SR on GL pastures (Table 3.16). Forage OMI was greater at low than at high SR for GN systems, and marginally greater on GN pastures than on GL pastures when SR was low (Table 3.16). For total OMI, the response pattern was (Table 3.16). Table 3.16. Forage system by stocking rate (SR) interaction trend on forage organic matter intake (OMI) and total OMI during the 1997 winter grazing season. Forage OMI Total OMI Forage System t Forage System t SR GL GN GL GN k -1 -------g cow --------P valuet k -1 -------g cow --------P valuet High 11.44 10.18 0.2929 16.52 15.17 0.2805 Low 10.53 12.46 0 0662 16.47 18 67 0.0474 P value 0.4425 0 0345 0.9641 0.0043 t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover mixture; GN = forage system comprising ofN-fertilized rye and ryegrass mixture. t Probability of difference for comparisons between forage systems means within SR. Probability of difference for comparisons between SR means within forage systems. Lack of differences between FS at high SR is confounded by the fact that responses for GL pastures during P2 reflect intake data from low SR treatments only The

PAGE 114

95 greater forage and total OMI during P3 on GN compared to GL pastures when SR was low may be associated with the high pregraze herbage mass on high CS pastures. Although CS effects on forage intake were not detected in 1997, its effects on pregraze herbage mass suggest that high CS played a role in pasture production, viz., allowing accumulation of herbage mass likely due to lenient grazing. Additionally, examination of herbage allowance data (Table 3.6) shows that at low SR, herbage allowance was greater for GN than for GL pastures when cows were fed at the high CS level. These responses at the high CS level were apparently sufficient to override responses obtained at the low CS levels, thus explaining the greater intake at low SR for GN pastures. In 1997, cows were fed supplement by experimental unit so there was no opportunity for confounding results as occurred in 1996. Additionally, IVOMD of herbage from these pastures was greater than for other treatments. The combined effects of herbage allowance and forage digestibility may have led to the observed greater forage and total OMI. Hoogendoorn et al. (1992) found that daily DM intake increased when herbage allowance was increased, more so when herbage digestibility was greater. Estimates of intake in the present study fall within the range of intakes reported in the literature for lactating Holstein cows (Muller et al., 1995). Comparisons of calculated daily nutrient intake values for all FS by SR by CS treatment combinations with NRC tabulated requirements suggested that DM intake may have been inadequate at high SR, low CS treatments on GN pastures, but adequate levels of nutrients were provided by the experimental diet (Table A-15).

PAGE 115

96 Milk Production per Cow In the 1996 winter grazing season, milk production per cow was influenced by a CS main effect (P = 0.030). Cows fed at the high CS level had higher average daily milk production per cow (27.5 kg) than cows on the low CS treatment (23.7 kg). This response is likely related to effect of CS on total nutrient intake. Total OMI was generally greater for high CS than for low CS except in P2. Kolver and Muller (1998) demonstrated higher levels of milk production associated with greater nutrient intake. In the present study a larger proportion of the total intake on the high CS treatment, compared to the low CS treatment, is coming from the concentrate supplement component of the diet. Since the supplement has a higher concentration of energy, this further enhances the milk production capabilities of cows on high CS treatments. Muller et al. (1995) reported that when pastures are the major source of forage, energy may be the nutrient most limiting milk production. There were also trends for main effects of SR (P = 0.081) and period (P = 0 060) in the 1996 season Average daily milk production per cow was 24 2 kg at high SR and 26 9 kg at low SR Milk production decreased with period as the grazing season progressed, with daily average per cow of27.8, 25.0 and 23.9 kg for Pl, P2, and P3, respectively There was no FS or CS effect on milk production in 1997 Milk production per cow in the 1997 winter grazing season was influenced by a period by SR interaction (P = 0 001). Average daily milk production per cow was not different between SR levels

PAGE 116

97 during P 1 but was greater at low SR in the subsequent periods (Table 3 .17). Additionally, at high SR daily milk production per cow was greater during P 1 than for P2 and P3 (which had similar production), while at low SR, milk production remained the same as the season progressed (Table 3.17). The data showed that SR effects did not begin to limit nutrient intake due to restricted forage availability until the end of Pl, so milk production per cow became affected in P2 and P3. Pre graze herbage mass responses to grazing management were similar (viz., greater on low SR treatments), suggesting an important role of herbage mass in milk production from pasture-based systems. Additionally, forage and total OMI were greater when pregraze herbage mass was higher on low SR pastures. Table 3 .17. Period by stocking rate (SR) interaction effect on milk production per cow during winter 1997. Period 1 2 3 SR High Low k -I d-1 ---------------g cow ---------------19.4 at 13.9 b 14.5 b 20.5 a 19.4 a 20.1 a P valuet 0.2638 0.0004 0.0001 t Probability of difference value for comparisons between SR means within a period. t Means followed by the same letter within a column are not different (P > 0.05). Decreasing milk production as the season progressed was expected because all cows on the study were on the declining phase of the lactation curve (NRC, 1989). Cows

PAGE 117

98 grazing rotationally stocked temperate pastures in the northeast USA showed a rapid decline (25%; about twice the normal) during the first 8 wk of a 24-wk grazing season after which the rate of decline became more typical (Hoffman et al., 1993) These authors reported that higher producing cows tended to have a more rapid drop in milk production. In the first year of the present study milk production declined by about 10% from P 1 to P2 then by about 4% from P2 to P3 During the second year, milk production declined from Pl to P2 by about 16% but then increased by about 4% in P3. These data support the suggestion (Hoffman et al. 1993) that the decline in milk production is more rapid initially when animals are put on pasture, but the rate of decline in production tends to slow as the season progresses. Lack of decline at low SR during the second year may be linked to better forage availability and intake on these pastures. Concentrate supplementation for cows on grazed-pasture systems may or may not result in increased milk production. This is likely coupled to supplementation level and pasture quantity and quality characteristics (Hoffman et al 1993 ; Jones-Endsley et al. 1997; O Brien et al. 1999) Kolver and Muller (1996) demonstrated that although high nutrient intake may be achieved on pasture supplemental energy is necessary to achieve potential milk production of high producing cows Berzaghi and Polan (1992) reported that cows on pasture fed 5.7 kg d -1 cracked com had greater daily milk production than unsupplemented cows (23 7 vs 19.5 kg cow -1). Manyawu and Madzudzo (1995) found that Holstein cows grazing irrigated ryegrass pastures in Zimbabwe had higher milk production when fed a maize-cottonseed supplement compared to unsupplemented cows (15 7 vs. 14 3 kg cow-1 d-1). Herbage availability was limiting in their study. Milk

PAGE 118

99 production of Holstein cows grazing primarily orchardgrass (Dactylis glomerata L.) pastures and fed 1 kg grain DM per 4 kg milk did not change when supplemented with 2.3 kg d-1 of com silage, mainly because there was substitution of forage for com silage (Holden et al. 1994) Average daily milk production was 28.8 kg cow1 for com silage supplemented cows vs. 29.1 kg cow1 for unsupplemented cows. Hoffman et al. (1993) found no differences in daily milk production (range= 23.2 to 24.7 kg cow1 ) in a study investigating three levels of concentrate supplementation (viz. a control grain fed at 1 kg per 3 kg milk or 1 kg per 3 or 4 kg milk depending on quantity of pasture available, or a grain reformulated biweekly based on pasture quality and fed at 1 kg per 3 or 4 kg milk depending on pasture availability) on lactating cows grazing rotationally-stocked primarily orchardgrass pastures. O Brien et al. (1999) found that concentrate supplementation did not increase milk production in a 28-wk study when an adequate supply of herbage with good nutritive value was available. Mean daily milk production in that study was 21.7 kg cow1 during the first 7-wk period, dropping tol2.0 kg cow1 during the last 7-wk. In a study evaluating the effects of supplement CP concentration (120 or 160 g kg-1 ) and amount (6.4 or 9.6 kg) on lactating dairy cows grazing alfalfa (Medicago sativa L. )-orchardgrass pastures forage intake thus total intake increased with the higher CP supplement (Jones-Endsley et al. 1997). There was no change in total intake with amount of supplement fed because forage intake declined with increasing supplement. Milk production was not affected by the variation in total intake due to CP concentration but the authors contend the increased intake tended to enhance BCS and BW change (Jones-Endsley et al., 1997). This suggests that prioritization in partitioning

PAGE 119

100 nutrient intake for lactation vs. maintenance of body condition may be the reason why there were no CS effects in the present study's 1997 results. Milk production may be related to intake, but tissue turnover may also be an issue. Kolver and Muller (1998) suggested that animals already producing at a higher level may partition a greater proportion of nutrient intake toward milk production at the expense of body reserves. Stocking rate effects on milk production per cow were evident during both years of the study, more so during 1997. One possible explanation why there was only a trend for lower milk production per cow grazing high SR pastures compared to low SR in the first year of the study may be due to the CS effects. Since cows were group fed by high and low CS groups during that year, high SR cows may have been attempting to compensate for lower forage allowance by increasing supplement intake compared to their low SR counterparts. If this occurred, as suggested by OMI data earlier, then SR effects on milk production during the first year of the study were obscured by the supplement intake. The SR effects during the 1997 season could not have been affected by CS in this way because animals were fed by experimental units. Milk production per animal was approximately 30% (5.5 kg d"1 ) greater on low SR compared to high SR during the latter two periods of the 1997 study. The effects due to SR in the present study support the evidence of SR being a key grazing management variable, as has been reported by numerous studies (e.g., Davidson et al., 1985; 1997b; Fales et al., 1995; Kristensen, 1988; Moss and Lowe, 1993; O'Brien et al., 1999). In the Davidson et al. (1997b) study, average milk production per lactation was 20% less when SR was doubled.

PAGE 120

101 In the present study average daily milk production levels obtained in the first year (range= 23.9 to 27.8 kg cow d 1 ) were similar to values reported for studies on temperate pastures in the northeastern USA (Hoffman et al., 1993 ; Jones-Endsley et al ., 1997) the UK (Fisher et al. 1996), and in Denmark (Kristensen, 1988) Production dropped during the second year (range= 13.9 to 21.2 kg cow d 1 ) but was still within range of data reported for grazing studies on temperate forages ( e g. Holmes et al., 1992; Hoogendoorn et al. 1992; Manyawu and Madzudzo 1995 ; O Brien et al ., 1999) Milk Production per Hectare In the 1996 winter grazing season milk production per hectare was influenced by a SR main effect (P = 0.001) and a CS main effect (P = 0.040). Average daily production was greater on high SR pastures compared to low SR pastures (121.1 vs 67.3 kg ha1 ) and greater on the high CS treatment compared to low CS (101.5 vs 87. 0 kg ha1 ) Milk production per hectare during the 1997 winter grazing season was affected by a period by SR interaction effect (P = 0.001). Average daily production was greater at high than at low SR regardless of period (Table 3 .18). At high SR, milk production per hectare was highest during Pl, then subsequently declined but was similar in P2 and P3 whereas at low SR there were no differences among periods ( Table 3.18) As a result there was a g reater difference between SR levels during Pl compared to P2 and P3 (Table 3 18). There was no effect of CS nor were there any interactions involving CS on milk production per hectare during 1997 (P > 0 10)

PAGE 121

102 Table 3.18. Period by stocking rate (SR) interaction effect on milk production per hectare during winter 1997. Period 1 2 3 SR High Low k -1 d-1 ---------------g cow ----------------74.9b 72.7 b 51.2 a 48.4 a 50.3 a P valuet 0.0001 0.0001 0.0001 t Probability of difference value for comparisons between SR means within a period. t Means followed by the same letter within the same column are not different (P > 0.05). The CS effect on milk production per hectare during 1996 may be linked to the reduced SR effect and the possibility of excess consumption of supplement by cows on high SR treatments, as discussed earlier. The importance of SR as a grazing management variable is more clear when milk production per hectare is the response measured, as demonstrated by the results of both years of the present study. Similar to the results of the present study, other researchers (e g., Davidson et al., 1997b; Fales et al. 1995; Holmes et al., 1992) found a direct relationship between milk production per unit land area and SR. Fales et al. (1995) reported that when cows were fed supplemental silage to maintain similar milk production per cow on different SR, milk production per hectare was greater at higher SR. Even though high SR may support higher milk production per unit land area, at least in the short term, such systems may not be sustainable if they lead to poor pasture persistence or loss in BW and BCS, and low reproductive performance.

PAGE 122

103 4 % Fat Corrected Milk Production per Cow During the 1996 winter grazing season, fat corrected milk per cow responses to treatment effects were the same as for fresh milk production reported in the previous section, essentially because there was no effect of experimental treatments on milk fat concentration. The low SR had greater fat corrected milk than high SR (24.5 vs. 21.6 kg cow 1 d1 ; P = 0.043) and high CS had greater fat corrected milk than low CS treatments (24.5 vs. 21.6 kg cow1 d1 ; P = 0.041 ). Also, fat corrected milk production declined as the season progressed (24.9, 22.9, and 21.3 kg cow1 d1 for Pl, P2, and P3, respectively; P = 0.044). Average daily fat corrected milk per cow during the 1997 winter grazing season responses to experimental variables followed the same pattern as fresh milk production responses i.e. a period by SR interaction effect (P = 0.0023; Table 3.19). Correcting fresh milk production based on fat concentration takes into consideration the energy inputs required to achieve the observed production levels (NRC, 1988). Energy per unit of milk varies mainly with its fat concentration (Kleiber 1961 ) Given that there were only trends toward differences in fat composition of milk in this study however no changes to statistical inferences were achieved by correcting for milk fat. Similar results were obtained by Fales et al. (1995) in a study with Holstein cows grazing temperate pastures.

PAGE 123

104 Table 3 19 Period by stocking rate (SR) interaction effect on 4% fat corrected milk production per cow during winter 1997. Period 1 2 3 SR High Low k -I d-1 ---------------g cow ---------------13.7 b 13. 5 b 19 .2 a 18.l b 19.4 a P valuet 0 2019 0.0007 0.0001 t Probability of difference value for comparisons between SR means within a period t Means followed by the same letter within a column are not different (P > 0 05). 4% Fat Corrected Milk Production per Hectare In 1996 daily 4% fat corrected milk production per hectare was affected by both SR (P = 0.001) and CS (P = 0.044) main effects as well as a trend toward a period main effect (P = 0.073) The high SR pastures produced more fat corrected milk (108.2 kg ha' d"1 ) than low SR pastures (61.2 kg ha1 d"1 ) and high CS treatments produced more fat corrected milk (90.1 kg ha1 d 1> than low CS treatments (79.9 kg ha-1). Production of fat corrected milk decreased as the season progressed with 91.5 83.0 and 79.4 kg ha d 1 for Pl, P2, and P3 respecti v ely In the 1997 winter grazing season daily fat corrected milk production per hectare was influenced by a period by SR interaction (P = 0.001). The high SR pastures produced more fat corrected milk per hectare than low SR, regard l ess of period (Table 3.20). At high SR, daily fat corrected milk production was greatest during P 1 compared to P2 and

PAGE 124

105 P3 (which had similar production) but production was the same during all periods for low SR (Table 3.20). Table 3.20. Period by stocking rate (SR) interaction effect on 4% fat corrected milk production per hectare during winter 1997 Period 1 2 3 SR High Low k h -1 d-1 ----------------g a -----------------90.7 at 72.1 b 67.4 b 48.0 a 45.1 a 48.4 a P valuet 0.0001 0 0001 0.0001 t Probability of difference value for comparisons between SR means within a period. t Means followed by the same letter within a column are not different (P > 0.05). Milk Composition Milk fat concentration during the 1996 winter grazing season did not differ among experimental variables (P > 0.10). Values of milk fat concentration ranged from 33.2 to 34.6 g kg-1 In 1997, milk fat concentration was influenced by a trend toward a period by SR interaction effect (P = 0.071). Milk fat concentration was similar between SR treatments during Pl and P2 but was higher at low SR during P3 (Table 3.21). Also for high SR pastures milk fat concentration tended to be highest during P2, while it remained constant across periods at low SR (Table 3.21).

PAGE 125

106 Table 3.21. Period by stocking rate (SR) interaction effect on milk fat concentration during winter 1997 Period 1 2 3 SR High Low ------------------g kgI -------------------35.8 abi 35.8 a 38.0 a 35.2 b 35.7 a 37.6 a P value t 0.9912 0.0898 0.0495 t Probability of difference value for comparisons between SR means within a period. t Means followed by the same letter within a column are not different (P > 0.05) Milk fat composition is sensitive to changes in dietary carbohydrate. In a re v iew Nocek and Russell (1988) suggested that increasing concentrate regardless of carbohydrate source can cause a dramatic decline in milk fat percentage. Several studies have demonstrated decreased milk fat percentages when cereal-based concentrates were fed to grazing dairy cows (Arriga-Jordan and Holmes 1986 ; Kibon and Holmes 1987 ; Stockdale et al. 1987) High fiber diets tend to increase milk fa t percentage (Meijs 1986). In the present study there was greater forage OMI on low SR pastures which may explain the greater milk fat percentage on low SR pastures during P3 in 1997. Lack of concentrate supplement effect on milk fat concentration with pasture-fed dairy cows was found by other researchers (Hoffman et al. 1993; Holden et al. 1995 ; Kolver and Muller 1998 ; Manyawu and Madzudzo 1995). Hoffman et al. (1993) suggested that a possible e x planation for similar milk fat percentages among different levels of concentrate is that

PAGE 126

107 adequate fiber was consumed from pasture and from supplement. More than 50% of the diet of animals in the present study was from pasture. Also, the grain-based concentrate fed included 30% soybean hulls, and was mixed with 20% whole cottonseed as well which adds a substantial amount of fiber to the diet. In addition, NDF concentration of pastures did not vary substantially. These factors indicate that cows were receiving adequate fiber and that there may have been marginal variation in fiber concentration of diets consumed by animals on different treatments (Table A-15). Milk fat concentrations obtained in the present study were within the range obtained in several other studies (Hoffman et al. 1993; Holden et al. 1995; Kolver and Muller 1998). During the 1996 winter grazing season milk CP concentration was affected by a trend toward a period by SR interaction effect (P = 0 097). Average milk CP remained constant as the grazing season progressed when SR was high but increased after P 1 when SR was low (Table 3.22). As a result milk CP was similar between SR treatments during the first period but was higher at low SR compared to high SR in subsequent periods (Table 3.22). In the 1997 winter grazing season, there was a SR main effect on milk CP (P = 0 019) The low SR pastures had greater milk CP (323 g kg 1 ) than high SR pastures (312 g kg). Increased milk CP is associated with increasing microbial protein synthesis from carbohydrate intake which stimulates milk protein as well as lactose synthesis. In the rumen, carbohydrate is the major stimulant of microbial protein synthesis (Nocek and Russell, 1988). The present data do not indicate an obvious pattern of milk CP responses

PAGE 127

108 related to OMI in 1996 but low SR treatments had greater intake in 1997. There is the concern discussed earlier, however, that feeding management in 1996 may have resulted in confounding estimates of forage vs. supplement OMI. Table 3.22. Period by stocking rate (SR) interaction effect on milk crude protein concentration during winter 1996. Period 1 2 3 SR High Low k ) ------------------g g -------------------303 at 309 a 301 a 300b 331 a 327 a P valuet 0 7328 0.0593 0.0292 t Probability of difference value for comparisons between SR means within a period. t Means followed by the same letter within the same column are not different (P >0.05). Supplemental fat in the ration may depress milk protein synthesis (Hoffman et al. 1993 ). If high SR animals were consuming more than their fair share of supplement in 1996 as postulated then the cotton seed (fat) component of diet may have led to the reduction in milk CP observed in high SR pastures. The same argument cannot hold for the 1997 data however. Stobbs (1977) found curvilinear decreases in milk CP when cows grazed at increasingly higher stocking rates This response paralleled milk yield results and was attributed to lower intake of digestible energy. Treatments that had greater milk CP in the present study were the same treatments with greater milk yield per cow. A

PAGE 128

109 similar result was also obtained by Hoffman et al. (1993). Lack of milk CP differences during Pl is likely associated with no differences in intake or milk yield Davidson et al. (1997b) found that there was a linear decline in milk CP when concentration of dietary CP increased. In the present study, herbage CP was generally higher on high SR pastures after P 1 so the pattern of milk CP and pasture CP responses suggests an inverse relationship. Hoffman et al. (1993) indicated that high rumen digestible protein in the diet may lead to low milk CP if energy intake is inadequate Lower milk production on high SR pastures seems to be ample evidence that energy consumption was less than desirable. Lower milk CP with high CP diet was cited as being related to energy cost of excreting excess soluble N in the diet (Davidson et al. 1997b ). Milk CP concentrations in the present study were similar to those reported i n other studies (e.g. Davidson et al. 1997b ; Fales et al. 1995 ; Hoffman et al. 1993 ; Holden et al. 1995). Milk urea N ( data collected only during 1997) was influenced by a period by FS interaction (P = 0.004). During the first period milk urea N was higher for cows grazing GN pastures but there were no differences due to FS in subsequent periods (Table 3 23). Also on GL pastures milk urea N was lowest during Pl and similar during P2 and P3. On GN pastures however milk urea N increased from Pl to P2 then decreased in P3 (Table 3 23) There was also a FS by SR by CS interaction effect (P = 0.025) on milk urea N On GN pastures, milk urea N was greater at high SR than at low SR regardless of CS level while on GL pastures there was no SR effect at high CS but milk urea N was lower

PAGE 129

110 at high SR when CS was low (Table 3.24). Additionally, cows on GN pastures had greater milk urea N than those on GL pastures when grazing high SR treatments regardless of CS. There was no FS effect at low SR when CS was high but there was a trend (P = 0.059) for higher milk urea Non GL pastures when CS was low (Table 3.24). Table 3 23 Period by forage system interaction effect on milk urea nitrogen concentration during winter 1997. Period 1 2 3 Forage System t GL GN -------------------mg dL-1 --------------------P valuei 23.3 a 21.8 a 20 2 b 23 3 a 21.5 b 0 0002 0.9980 0.7101 t GL = forage system comprising of rye, ryegrass red clover and crimson clover mixture; GN = forage system comprising ofN-fertilized rye and ryegrass mixture. t Probability of difference for comparisons between forage system means within period Means followed by the same letter within a column are not different (P > 0.05). Urea is the primary form of excretory N in mammals. Broderick and Clayton (1997) demonstrated that milk urea N may serve as an index of inefficient utilization of dietary protein in the lactating dairy cow. It has long been known that inefficient N utilization by ruminants is reflected by blood urea N, and a strong linear relationship between milk and blood urea N has been found based on data from 35 conventional lactation trials (Broderick and Clayton 1997). These authors indicated that milk urea N concentrations below 14 mg dL-1 indicated insufficient CP per unit of dietary energy.

PAGE 130

111 Lean (1987) suggested that a BUN value> 27 is associated with excessive levels of rumen digestible protein. Using the equation developed by Broderick and Clayton (1997; milk urea N = 4.75 + 0.620 blood urea N), this translates to a milk urea N of about 21.5 mg dL-1 Based on these values, milk urea N data from the present study indicate that dietary protein was adequate and may have been above requirements in some treatments. Table 3.24. Forage system (FS) by stocking rate (SR) by concentrate supplement (CS) interaction effect on milk urea nitrogen concentration during winter 1997. cs High Low SR SR Forage Systemt High Low High Low -----mg dL 1 -----P value i ------mg dL1 ------P value i GL 19.7 19.l 0.6862 18.6 22.2 0.0374 GN 23.2 20.l 0.0496 24.0 19.4 0.0077 P value1 0.0443 0.432 0.0063 0.0585 t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover mixture; GN = forage system comprising ofN-fertilized rye and ryegrass mixture. t Probability of difference value for comparisons between SR means within a FS by CS. Standard errors of interaction means ranged from 0.9 to 1.1. 1 Probability of difference value for comparisons between FS means within a SR by CS The pattern of milk urea N responses obtained indicate a link to pasture CP concentration. During P 1, CP concentration and milk urea N were greater on GN pastures, indicating greater excretion ofN when CP concentration was higher.

PAGE 131

112 Additionally, pasture CP was greater on high SR treatments of GN pastures, which also had greater milk urea N values. Dietary CP concentrations had the strongest relationship with milk urea N concentrations among several factors tested (Broderick and Clayton, 1997). Inefficient utilization of N when ruminants graze pastures with high CP concentration is well documented (Holden et al., 1994; Kolver and Muller, 1998; Muller et al., 1995; Poppi and McLennan, 1995). Milk production also seemed to be linked with milk urea N responses. Average daily milk production per cow was greater at low SR vs. high SR. It should be noted that high SR treatments generally had greater milk urea N concentrations and lower milk production. According to Broderick and Clayton (1997), an increase in milk urea N with declining milk volume may be anticipated. Milk somatic cell count (SCC; 1,000 cells per milliliter of milk) was not affected by experimental variables during the 1996 winter grazing season. The SCC values ranged from 73 to 166. In 1997, milk SCC was affected by a trend toward a period by FS by SR interaction effect (P = 0.086). This trend was detected because there was a large magnitude in difference of SCC between GL (181) and GN (519) pastures at low SR during P3. Otherwise, SCC values ranged from 146 to 453 but no differences were detected among preplanned comparisons viz., between FS levels at fixed levels of SR and vice-versa, all within each period. Milk SCC is commonly used as a measure of milk quality. Values obtained in the present study were below the regulation maximum of 750. A SCC value above 300 may be considered abnormal and may be an indication of infection of the mammary system and may be caused by several factors, including stress. Because of the wide range of causes, it

PAGE 132

113 is difficult to pinpoint treatment-related effects on SCC differences in the present study. In studies conducted in the Northeastern USA, milk SCC was not different between pasture-fed and total mixed ration (TMR)-fed cows (Kolver and Muller, 1998), or between SR treatments (Fales et al., 1995). Animal Body Weight Changes In the 1996 winter grazing season, there was a SR main effect on average daily BW change of lactating cows on pasture (P = 0.049). Cows grazing high SR pastures had no change in B W while cows on low SR pastures gained an average of 0.40 kg cow d1 There was also a trend for a CS effect (P = 0.066) and a trend for a period effect (P = 0.053). Cows fed at the high CS level tended to gain more BW (0.38 kg cow1 d1 ) than cows on the low CS level (0.02 kg cow1 d1 ) Cows lost BW during the first period (0.14 kg cow1 d 1 ), then gained in the subsequent periods (0.25 and 0.48 kg cow1 d in P2 and P3, respectively). In the 1997 winter grazing season average daily BW change was influenced by a SR main effect (P = 0.002) and a period main effect (P = 0.0001). Cows on high SR pastures lost BW (0.48 kg cow1 d 1 ) while those on low SR pastures gained (0.21 kg cow1 d1 ) Cows lost BW in the first two periods of the season (0 53 and 0.49 kg cow1 d 1 respectively) but gained during the third period (0.62 kg cow d 1 ). There was no difference between the means of P 1 and P2, and both were less than the mean for P3 Cow BW changes may be associated with nutrient intake and milk production levels. Lactating dairy cows will mobilize body tissues when energy intake is insufficient

PAGE 133

114 to meet the demand for high levels of milk production (Moe et al., 1970; 0rskov and Ryle, 1990). High producing cows will tend also to partition a greater proportion of nutrient intake to milk production at the expense of body reserves (Kolver and Muller, 1998). In the present study, both milk production and B W gain were greater at low SR. The loss ofBW during the start of the study likely reflects a drop in nutrient intake when cows were removed from a confinement housing, TMR-fed environment and put on pasture. Stability in BW was achieved as the grazing season progressed. In 1996, OMI responses to SR do not appear to be linked to the BW responses, no doubt due to the discrepancies discussed earlier that may have occurred with supplemental feed intake. It does, however, tend to concur with the CS responses obtained. The 1997 data show agreement with milk production, BW changes, and OMI data with respect to SR effects, viz., greater intake on low SR pastures leading to greater animal performance. Kolver and Muller (1998) found that cows consuming an all-pasture diet suffered substantial BW losses (I.I kg d 1 ) compared to TMR-fed cows, but Holden et al. (1995) showed that there were no differences in BW changes for cows that received supplemental com silage (vs. no com silage) when grazing pasture and fed supplemental grain. Cows on pasture had lower total DMI compared to TMR-fed cows (Kolver and Muller, 1998). In the Holden et al. (1994) study, total DMI was not different because com silage substituted for pasture consumption. Also, loss ofBW has been observed when cows, usually on confined systems, were put on grazing trials (Jones-Endsley et al., 1997). Fales et al. (1995) did not find differences in BW changes due to SR in their study simply because cows were managed to attain similar levels of production via supplementation

PAGE 134

115 with silage. Davidson et al. (1985) found that cows lost more weight at higher SR, as with the results of the present study. Caution ought to be exercised with interpretation of BW data in lactating cows in the present study because of complexity regarding body tissue and milk production interactions and also because of the inherent difficulty (i.e. inability to obtain shrunk weights as would be done in trials with growing animals) in achieving BW measurements not confounded by rumen contents (Stuedemann and Matches 1989). Body Condition Score Changes In the 1996 winter grazing season, there were no differences in changes in body condition score (BCS; P > 0.10). Values for average changes ranged from a decrease in BCS of 0.08 at low CS to an increase of 0.13 at high CS; a decrease of 0.03 at high SR to an increase of 0.08 at low SR; and a decrease of0.09 during P2 to an increase by 0.14 during P3. In the 1997 winter grazing season, BCS change was influenced by a period effect (P = 0 001) and by a trend towards a forage effect (P = 0.064). Change in BCS was not quantified for P 1 but based on observations cows likely lost condition. During P2, cows lost BCS by an average of 0.34 units while in P3 they regained BCS by an average of 0 57. Cows grazing GL pastures had an increase in BCS averaging 0.26 while cows on GN pastures lost BCS by an average of 0.03. As with body weight changes, BCS likely reflect OMI interactions with each animal's achieved milk production, dependent on priority issues with partitioning of nutrients consumed

PAGE 135

116 Blood Glucose Concentration During the 1996 winter grazing season, animals' blood glucose concentration was not affected by any of the treatment variables (P > 0.10). Glucose concentrations of animals on the study ranged from 43.1 to 66.3 mg dL1 with an average of 56.3 mg dL1 Normal blood glucose concentrations are considered to be 50 to 60 mg dL1 with concentrations< 25 mg dL1 indicative of ketosis (Lean, 1987). The range of values obtained in this trial indicates no abnormal physiological conditions. During the 1997 winter grazing season, there was a period by FS by SR interaction effect on blood glucose concentration (P = 0.022). Values ranged from 59.3 to 68.6 mg dL1 and the only consistent response was that blood glucose was greater in P2 than in P3. Animals' blood glucose concentration during 1997 winter grazing season was also affected by a SR by CS interaction (P = 0.045). At high SR, there was no difference in glucose concentration due to CS effects (Table 3.25). Similarly, at high CS, there was no effect of SR level on blood glucose. Cows grazing low SR pastures at the low CS level had higher blood glucose concentration than cows on high SR pastures at the same CS (Table 3.25). Similarly, cows on low SR, low CS pastures had higher blood glucose concentration than cows grazing low SR, high CS pastures (Table 3 .25). Lowering of blood glucose is indicative of metabolic transitions associated with reduction of nutrient intake and loss ofBCS and BW (Kolver and Muller, 1998). Greater blood glucose concentration for cows at low SR when CS was low (Table 3.25) may be linked to intake because similar responses occurred for OMI, and may reflect better energy

PAGE 136

117 balance for animals on these treatments. Kolver and Muller (1998) found that blood glucose concentrations of grazing cows were lower than those fed TMR; DMI was higher in TMR-fed cows. Glucose concentrations obtained in the present study were similar to those (range= 59.9 to 65.4 mg dL-1 ) in the Kolver and Muller (1998) study. Table 3.25. Stocking rate (SR) by concentrate supplement (CS) interaction effect on blood glucose concentration during winter 1997. cs High Low P valuet SR High Low ------------mg d}"1 ------------64.3 63.1 0.4200 64.9 68.6 0.0400 P valuet 0.6835 0.0063 t Probability of difference value for comparisons between SR means within a CS. t Probability of difference value for comparisons between CS means within a SR. Summary and Conclusions The results of this 2-yr study suggest that achieving high pasture productivity and matching it with an appropriate SR are critical to successful management of pasture-based dairy operations during winter. Stocking rate was the key factor affecting herbage mass or accumulation, concurring with the evidence found by other researchers (Davidson et al., 1985; Fales et al., 1995; Fisher et al., 1996). Greater pregraze herbage mass occurred at lenient SR, and accounted for greater herbage disappearance and herbage allowance.

PAGE 137

118 Forage nutritive value tended to be poorer on those pastures where there was greater pregraze herbage mass, probably due to higher proportion of mature herbage in the grazed portion of the sward canopy compared to pastures with less pregraze herbage mass. Forage IVOMD an'd CP were greater and NDF was lower on low herbage mass, intensively grazed swards, maybe reflecting the positive effects of higher grazing pressure on more complete removal of old tissue during grazing allowing for production of new growth. Nevertheless, forage nutritive value was high among all treatments and magnitude of differences among IVOMD and NDF were small, suggesting that differences in their subsequent impact on animal performance may be negligible. Organic matter intake was enhanced when pastures had higher pregraze herbage mass ( and greater herbage allowance) as demonstrated by low SR treatments in the GN system during P3 in the 1997 data Intake data for 1996 may have been confounded if animals on high SR pastures possibly not consuming as much pasture as their low SR counterparts exhibited compensatory feed intake when offered concentrate supplement in a group feeding system. Concentrate feed intake, assumed to be the same for all animals on a particular CS treatment, could have been variable as a result of this Making inferences about SR effects on forage OMI from the 1996 data, then, is probably not advisable. This points out the need to take steps to ensure that animals are receiving specified supplemental feed allotments when intake estimations are a component of grazing studies. Feeding animals by experimental units, as was done in the second year of this study, is justified.

PAGE 138

119 Milk production per animal benefitted from higher pregraze herbage mass and herbage allowance on pastures. Even with the likely confounding effects due to CS in 1996 there was evidence of beneficial effects of greater pregraze herbage mass on milk production per cow at low SR. The greater production per animal at lenient SR is no doubt linked to the beneficial effects of high pre graze herbage mass on total nutrient intake. Blood glucose data suggest that there was less discrepancy between energy demand and supp l y when high pregraze herbage mass was associated with increased OMI. The increase in SR, when pastures were grazed intensively allowed for increased production per unit area of land in spite of a decrease in production per cow. This phenomena is well known (Davidson et al. 1997b; Fales et al. 1995 ; Kristensen 1988) In scenarios where land scarcity is a limitation to production high SR will be more profitable at least in the short term (Davidson et al. 1997b ; Fales et al., 1995). Loss of BW and BCS on the more intensively grazed treatments in the present study, however suggests that systems based on high SR may not be sustainable. Both BW and BCS changes were positive towards the end of the trial suggesting the need to explore whether body tissue mobilization is as drastic if animals are on pasture year-round compared to being kept in confined systems based on feeding TMR then moved to grazing studies. The evidence of underutilization of pastures on low SR treatments in the present study leading to decreases in forage nutritive value (albeit magnitude of differences was small) and the lower animal performance on high SR, suggests that the ideal SR, within the environmental and management conditions of this experiment may be intermediate to

PAGE 139

120 the 1 or 2 cows ha-1 used. A practical approach in pasture-based dairy systems may be to adjust SR based on herbage allowance or some measure of residual pasture herbage mass. Grass-N fertilizer instead of grass-clover systems seem more adapted to intensive grazing management for lactating dairy cows on sandy soils like those in this study. In the current economic environment N-fertilized cool-season grasses may be a more productive, more reliable option than grass-clover mixtures. The statistical evidence from the current study is not conclusive because legumes did not establish well and could not be included in 1996 and in 1997 herbage mass on high SR grass-clover pastures was too low for grazing during P2. These factors, however, do support the original thesis regarding the relative merits of grass-N and grass-clover systems on sandy soils Benefits to incremental increases of CS in this study may have been obscured by the effects of the other factors tested. High nutritive value of the cool-season pastures grazed may be a contributing factor. There was some indication that CS was playing a role in the responses obtained. For example, pregraze herbage mass was greater at the low SR on GN pastures when CS was high, but not when CS was low. This suggests that cows on high CS treatment were consuming less pasture when fed more supplement (substitution effect) in 1997 although this observation was not supported by OMI data. Also, there was greater milk production when cows on GN pastures were fed at high CS during Pl of 1997 i .e., when pregraze herbage mass and herbage allowance were not different among pastures, but similar responses were not detected in subsequent periods. Additionally blood glucose data indicate that energy demand and supply were more balanced at high CS compared to low CS. Because of the nutritional requirements of

PAGE 140

121 high producing dairy cows, it is unlikely that they can obtain sufficient energy to sustain potential milk production from pasture alone (Kolver and Muller, 1998), so supplemental feeding will remain an integral part of management of pasture-based dairy systems. Jones Endsley et al. (1997) found that supply and digestion of nutrients in grazing dairy cows may be improved with increasing amount of supplement fed but effects on milk yield may be small, which may be similar to the phenomena taking place in the present study. Milk urea N data suggest that inefficient utilization of dietary N was occurring in animals grazing pastures with higher CP concentrations. Supplemental feed with higher digestible energy concentrations (vs. being balanced for CP concentration) to allow better microbial synthesis of rumen degradable protein and reduce loss of N across the wall of the rumen (Holden et al., 1994; Hoover and Stokes, 1991) may warrant evaluation in the experimental conditions of the present study. Objectives of such studies would be to determine if nutrient intake and animal performance could be enhanced and N losses reduced Overall, the results of this study suggest that, given current management strategies for intensive grazing of cool-season pastures, successful winter grazing systems for lactating dairy cows on moderately-drained sandy soils in Florida will more likely be rotationally-stocked N-fertilized rye and ryegrass pastures than grass-clover mixtures. Matching these forage systems with appropriate stocking rates to ensure maximum forage intake and avoid large losses in body weight and condition is critical. Supplementation may be planned based on estimated energy intake from forages in order to achieve optimum milk production and ensure maintenance of body condition.

PAGE 141

CHAPTER4 GRAZING MANAGEMENT, COAT COLOR, AND SEASON EFFECTS ON GRAZING BEHAVIOR AND PERFORMANCE OF LACTATING DAIRY COWS Introduction Efficient grazing management systems require understanding the role of the different components in a forage-based livestock production system. Grazing recommendations should be based on data that couples animal performance to pasture production and environmental conditions. The interaction of these components at the plant-animal interface must be studied in order to develop successful management strategies for pasture-based ruminant production. Forbes (1988) suggested that investigating ingestive behavior of grazing ruminants is an integral part of the development of grazing systems. Nutrient intake and subsequent animal performance of grazing animals in forage based systems are dependent on forage and non-forage factors (Moore, 1994). Forage factors include quality-related characteristics and quantity, while non-forage factors include animal characteristics, environmental conditions, and supplemental feed management (Moore, 1980). Factors affecting forage intake may act through metabolic, physical, and/or behavioral control mechanisms (Hodgson, 1985). Moore (1983) suggested that these control mechanisms probably operate simultaneously, with one or the 122

PAGE 142

123 other being dominant depending on pasture characteristics. Hodgson (1985) indicated that the behavioral mechanism may override the others when herbage mass or herbage allowance are limiting. The behavioral mechanism expresses forage intake as the product of grazing time, bite weight, and biting rate. Bite weight and biting rate are influenced by animal and sward characteristics, but biting rate and grazing time are considered the primary compensatory responses of the animal to limitations in bite weight (Hodgson et al., 1994). Animals will extend grazing time in response to limitations in intake rate (Hodgson, 1981 ). It is believed, however, that there is a limit to grazing time extension in a given day so the degree of compensation is again limited (Hodgson, 1985). Grazing time may increase as soon as sward conditions start to limit short-term rate of herbage intake. Hodgson et al. (1994) surmised that there is currently no good basis for quantifying this relationship or for relating it directly to sward conditions. They suggested that although bite weight may be the primary animal response to variations in sward conditions, the value of studies of grazing behavior effects on herbage intake will ultimately depend on the effectiveness with which herbage intake predictions can be associated with adaptive changes in grazing time. Positive relationships between grazing time and milk yield have been reported in the tropics (Stobbs, 1970; Cowan, 1975). There also is evidence that black hair coat cows produce less milk than white hair coat cows under conditions of heat stress (Hansen, 1990), likely because cows with black hair coat absorb more solar radiation than white coat cows. No studies of coat color effects on grazing behavior in lactating dairy cows

PAGE 143

124 were found in the literature, but there are anecdotal reports that dairy cows with predominantly white coats spent more time grazing than cows with black coats in the southeastern USA. Coat color is a fairly heritable trait (Becerril et al., 1991; 1993; 1996; King et al., 1988) should it be determined to be useful to select for favorable coat color where cattle are in hot environments. Heat stress associated with high solar radiation in Florida can depress production of dairy cattle (Collier et al., 1982; Hansen, 1990). Additionally, intake of grazed herbage by dairy cows is invariably lower than can be achieved by total mixed ration feeding management. This reduced intake is attributed in part to a low intake rate per unit of time spent grazing and limitations to total time available for grazing in a day. Grazing time constraints may be further accentuated by the hot climate of Florida. Grazed pasture for dairy cows, however, is an alternative to traditional housing systems based on intensive feeding of grain and stored forages, which currently are less attractive because of economic and environmental regulation issues. Evaluation of the behavioral strategies available to the cow when grazing in varying management and climatic environments may provide insights to help maximize the contribution of grazed pastures to dairy production. This study characterized grazing behavior of lactating dairy cows on summer and winter pastures. The overall objective was to examine grazing management variables, coat color, and seasonal (summer vs. winter) effects on grazing time oflactating dairy cows. Specific objectives were i) to quantify forage system (FS), stocking rate (SR), and concentrate supplement (CS) effects on time spent grazing during a 24-h cycle, ii) to determine differences between time spent grazing between black coat cows and white coat

PAGE 144

125 cows, iii) to evaluate summer vs. winter effects on time spent grazing and relationship with ambient temperature and solar radiation, and iv) to examine relationships between grazing time responses, pasture characteristics, organic matter (OM) intake, and animal performance. Materials and Method This study was superimposed on grazing trials conducted during summer 1996 and winter 1996 1997 at the University of Florida Dairy Research Unit at Hague, 18 km north of Gainesville, FL (29 60' N lat.) Soils at the site were described previously (Chapter 3) and monthly climatic data are shown in the Appendix (Table A-1). Treatment arrays were the same in the summer and winter grazing studies. Winter management treatments were described previously (Chapter 3). In summer, FS were N fertilized 'Tifton 85' bermudagrass (Cynodon spp. [L.] Pers ) pastures comprising the grass-N (GN) system and rhizoma perennial peanut (Arachis glabrata Benth.) was the legume-based (GL) system These treatments parallel the rye (Secale cerea/e L.)-ryegrass (Lolium multiflorum L.) pastures and rye-ryegrass-red clover (Trifolium pratense L.) crimson clover (T incarnatum L.) pastures comprising the GL and GN systems, respectively, of the winter study. Stocking rates were adjusted to the herbage mass production potential of summer forages. For the GN system, which was expected to produce more herbage than GL, high SR was 10 cows ha1 and low SR was 7.5 cows ha1 The GL system had 7.5 or 5 cows ha1 for high SR and low SR, respectively. Concentrate supplement amount during summer was also adjusted higher compared to the winter study

PAGE 145

126 due to expected reduced intake resulting from heat stress and lower nutritive value of summer pastures compared to cool-season forages. Animals were offered 1 kg supplemental feed per 2 or 3 kg of milk produced daily for the high and low CS treatments, respectively. The same treatment was applied to the same experimental unit in summer as in winter, viz ., each replication-PS-SR-CS combination was represented on the same pasture. To achieve the different SR in GN pastures in summer six cows were assigned to the 0 8ha pastures for the low SR treatment and four cows were put on the 0.4-ha pastures for the high SR treatment. Similarly, four and three cows were assigned to the 0.8and 0.4ha pastures to achieve the low SR and high SR treatments, respectively, in the GL system. Cows were blocked by parity when assigned to pastures Paddock subdivisions and rest intervals were the same as described for the corresponding winter treatment array (Chapter 3) Grazing rotations, feeding and animal management were the same in summer as described for the 1996 -1997 winter study (Chapter 3) All GN summer pastures were fertilized when the growing season began in spring and at the beginning of each of three 28-d periods (P 1, P2, and P3) with 40 kg N ha -1 Thus GN pastures received a total of 160 kg N ha-1 during the summer growing season. The GL pastures did not receive N fertilizer. All cows were observed continuously for a 24-h duration. This was done on seven occasions during summer and six in winter. During daylight, the observer stood at a central position in the experimental field to allow vision of all pastures simultaneously. Time was recorded when animals started and stopped a particular activity. During

PAGE 146

127 darkness the observer walked to each paddock and, using a flashlight, recorded activity and number of animals engaged in the particular activity. It took at most 15 min to return to the same paddock at the extremes of the experimental site, thus, nighttime observation intervals were 15 min or less. Gary et al. (1970) demonstrated that observations at 15-min intervals were not different from continuous observations, and suggested that they provide a reliable sample of grazing activities that are continuous in nature, including grazing, eating supplementary feed, and loafing. In the present study, the time at which animals started or stopped eating concentrate, grazing, or loafing was recorded. In summer, distinction was made between loafing under or outside of shade. Shade was not provided during winter until the last period of the study and thus was not considered as a response parameter. Time spent for each activity was computed for daytime, nighttime, and total time in a 24-h period. Daytime (distinct from daylight) was considered to be the period from when animals returned to pastures after being milked in the morning to when they left pastures to go to the milking parlor in the afternoon. Similarly, nighttime was considered to be the period between returning to pasture from afternoon milking and leaving pastures for morning milking. Animals were milked at 0530 and 1700 h during winter and 0530 and 1730 h during summer. Ambient air temperature and incident solar radiation were recorded simultaneously with observations of animal activity each observation day at 15-min intervals during the daylight hours i e., when solar radiation was still detectable. Ambient temperature recording continued throughout the night but at 1-h intervals. Temperatures were recorded using a dry-bulb thermometer housed in a box and placed approximately 1.5 m

PAGE 147

128 above ground surface. Incident solar radiation was estimated by measuring photosynthetic photon flux density in 10-s integrating intervals using a LiCor light meter with an 1-m integrating bar For the coat color component of the study, pairs of animals with predominantly black or white hair color were selected based on pasture assignments in the companion study. Hair coat color was quantified using a Numonics model 1250 electronic planimeter (Numonics Corporation Landsdale, PA) Surface area covered by black hair coat was measured relative to total surface area from photographs of both left and right side-views of each cow. Percent black hair coat was the average of the two sides and percent white hair coat was 100 percent black coat. Analysis of variance found that proportion coat color did not differ between the two sides (P = 0.847). Becerril et al. (1991) reported that coat color percentage of the two sides were well correlated (0.96). Cows were considered predominantly black if they had more than 60% black coat. The criteria for selecting white coat cows was similar i.e., percent white coat greater than 60, but in instances was subjective and depended on how "white they appeared compared to the counterpart with which they were paired In summer cows considered black had percent black coat ranging from 62 to 96. White cows had a range of 62 to 100% white coat. In winter cows considered black had a percent black coat range of 66 to 90. For cows considered white during winter, the highest proportion white coat was 96% but there were four cows that did not fit the 60% criteria having only 38 42 46, and 58% white coat. Data from these cows were used because the cows were more white than the black coat counterpart with which they were paired.

PAGE 148

129 Animal activities were recorded for members of the selected pair in a similar procedure as was described earlier for observations on all animals. Observations of grazing time in response to coat color was done on six occasions each season. These observations were done on separate days to the observations for all animals during summer but both sets of observations were done concurrently during winter. The larger number of animals on summer pastures made it difficult to record separate observations on all animals and black coat cows-white coat cows pairs simultaneously. Also, observations for coat color effects on grazing time during summer was done only during daylight whereas in winter it was done throughout a 24-h period. Number of pairs studied depended on pasture assignments each 28-d period in the two seasons. On a given observation day, 11 or 12 color pairs were observed during summer, while in winter, it was six or eight pairs. Pregraze herbage mass, herbage allowance, forage nutritive value (in vitro organic matter digestibility [IVOMD], crude protein [CP], and neutral detergent fiber [NDF]), forage and total OM intake, milk production, and animal body weight (BW) changes were obtained as described in Chapter 3. Winter pasture sampling schedule was outlined in Chapter 3. During summer, HM sampling was done twice each period in the third and fourth week. Forage samples for laboratory analysis were collected every week during each period, i.e., four times per period. Values reported for IVOMD and CP represent data from all four sampling times of each period, while only thirdand fourth-week samples were analyzed for NDF.

PAGE 149

130 Statistical Analysis Data were analyzed by fitting mixed models (Littell et al., 1996) using the PROC MIXED procedure of SAS (SAS Institute Inc., 1992). To evaluate grazing time responses to management effects, the model used was: + Se; + Fi+ (SeF);i + St+ (SeS);t + (FS)ik + (SeFS)!it + C, + (SeC);1 + (FC)JI + (SC)kl + (SeFC)!i1 + (SeSC);k( + (SeFSC)!ikl + e!ikl where Y !ikl is the dependent variable yiikl = is the overall mean Se; is the season effect Fi is the FS effect (SeF)!i is the season by FS interaction St is the SR effect (SeS);t is the season by SR interaction (FS)ik is the FS by SR interaction (SeFS);ik is the season by FS by SR interaction C, is the CS effect (SeC);, is the season by CS interaction (FC)JI is the FS by CS interaction (SC)H is the SR by CS interaction (SeFC)!i, is the season by FS by CS interaction (SeSC);kf is the season by SR by CS interaction

PAGE 150

131 (SeFSC)!ikl is the season by FS by SR by CS interaction e!ikl is the error. Random effects were observation day (within each season) and the error term. All other effects were considered fixed. Subjects were the experimental units, viz., each replication by FS by SR by CS combination within each season. The model used to fit pasture and animal performance responses was similar to the model described for 1997 data in Chapter 3 but included the season effect and interaction of season with each other factor in that model. Coat color effects on grazing time were fitted to the model: = + Se;+ P(Se;)J + Ck+ (SeC);k + (CP(Se;)) Jk + T, + (TSe );1 + (TC)kl + (TP(Se;))p + (TCP(Se ;))Jk/ + e!ikl where Y !ikl is the dependent variable is the overall mean Se; is the season effect P(Se;)J is the period (within season) effect Ck is the color effect (SeC);k is the season by color interaction (CP(Se ; )) Jk is the color by period (within season) interaction T1 is the time effect (TSe);, is the time by season interaction (TC)kl is the time by color interaction (TP(Se;))p is the time by period (within season) interaction

PAGE 151

132 (TCP(Se;))1kl is the time by color by period (within season) interaction eiJkl is the error. Time and period are used in the model to account for the fact that observations were done twice in a time frame when the same pairs of animals were studied viz. a 28-d period. This was done because animals were reassigned to pastures at the beginning of each new period so that the same animals forming a pair did not recur in subsequent periods. Pair (within each period by season combination) its interactions with color and time and the error term were considered random effects. All other parameters of the model were considered fixed effects. Data for animal responses were fitted to a similar model except time as a factor was removed since these responses were estimated by period. Thus the model used was: yijlcl where Y iJkl is the dependent variable is the overall mean Se ; is the season effect P(Se;)1 is the period (within season) effect C1c is the color effect (SeC);1c is the season by color interaction (CP(Se;)) Jk is the color by period (within season) interaction eiJk is the error

PAGE 152

133 Pair (within each period by season combination), its interaction with color, and the error were considered random effects. Period (within season) was treated as a repeated measurement (Littell et al., 1996). Tables summarizing the raw data and the probability values for tests of fixed effects are presented in the Appendix (Tables A-26 to A-35) Means separation were conducted as described in Chapter 3 and responses were considered different at P < 0.10. Regression and correlation techniques (PROC REG and PROC CORR procedures in SAS; SAS Institute Inc. 1982a; 1982b) were used to model relationships between variables which appeared to be related. Results and Discussion Responses to Grazing Management Pasture-related variables Pregraze HM. There was a season by FS by SR by CS interaction (P = 0.0001) In summer, herbage mass was greater on GN pastures than GL pastures regardless of SR or CS level (Table 4.1). More pregraze HM was found on low SR treatments of GL pastures, but on GN pastures there was greater pregraze HM at high SR when CS was high but less at the low SR when CS was low Herbage mass during winter was greater or tended to be greater at low SR for the low CS treatment, but at high CS there was no effect of SR. During winter the GN system had greater herbage mass than GL only at high CS and low SR.

PAGE 153

134 Table 4.1. Season by forage system by stocking rate (SR) by concentrate supplement (CS) interaction effect on pre-graze herbage mass. Forage Systemt Summer 1996 GL GN P value1 Winter 1997 GL GN High CS SR High Low --kg DM ha-1 --2870 5820 0.0001 1080 1130 3460 5390 0.0001 P value1 0.7061 1170 1690 0 0001 P value i 0.0003 0.0075 0.4779 0.5354 Low CS SR High Low --kg DM ha-1 ---3030 5410 0.0001 1190 1200 0.9345 3490 6020 0.0001 1440 1350 0.4128 P value i 0.0046 0.0021 0.0386 0.1820 t GL = forage system comprising of perennial peanut in summer or rye, ryegrass, red clover, and crimson clover mixture in winter; GN = forage system comprising ofNfertilized 'Tifton 85' bermudagrass in summer or N-fertilized rye and ryegrass mixture in winter. t Probability of difference value for comparisons between SR means within a forage system by CS (within season). Standard errors of interaction means ranged from 80.0 to 160.1. ,i Probability of difference value for comparisons between forage system means within a SR by CS (within season). Herbage allowance. Similar to the responses obtained for pregraze herbage mass there was a season by FS by SR by CS interaction effect (P = 0.005) on herbage allowance. There was always greater herbage allowance on low SR pastures, regardless

PAGE 154

135 of season, FS or CS (Table 4.2). In summer, GN pastures had greater herbage allowance than GL pastures, while in winter herbage allowance generally was similar for both FS (Table 4.2). Table 4.2. Season by forage system by stocking rate (SR) by concentrate supplement (CS) interaction effect on herbage allowance. High CS Forage SR System t High Low ---kg kg' Summer 1996 GL GN P value1 Winter 1997 GL GN 0.58 1.00 0.0001 0.26 0.32 P value1 0.3239 1.08 1.23 0 0448 0.63 1.04 0.0001 P value i 0.0001 0.0015 0.0001 0.0001 Low CS SR High Low ----kg kg' ---0.60 0 96 0.0001 0.28 0 36 0 2026 1.05 1.27 0.0133 0.82 0.85 0.5996 P value i 0.0001 0.0004 0.0001 0.0003 t GL = forage system comprising of perennial peanut in summer or rye, ryegrass red clover and crimson clover mixture in winter; GN = forage system comprising ofNfertilized 'Tifton 85' bermudagrass in summer or N-fertilized rye and ryegrass mixture in winter t Probability of difference value for comparisons between SR means within a forage system by CS (within season). Standard errors of interaction means ranged from 0.03 to 0.05 1 Probability of difference value for comparisons between forage system means within a SR by CS (within season)

PAGE 155

136 Forage nutritive value. There was trend for a season by FS by SR interaction effect (P = 0.060) on pasture in vitro digestible organic matter concentration (IVDOM). During summer, IVDOM was consistently greater on GL than GN pastures, but in winter the differences that occurred were much smaller and were not consistent across SR (Table 4 3). Table 4.3. Season by forage system by stocking rate (SR) interaction effect on herbage in vitro digestible organic matter concentration Summer 1996 SR Forage System t High Low GL GN k I ------g g ------690 627 P value1 0.0001 697 627 0.0001 P value i 0.4373 0 9432 Winter 1997 SR High Low k 1 ------g g -------782 776 0.2641 763 782 0 0001 P value i 0.0013 0 1021 t GL = forage system comprising of perennial peanut in summer or rye, ryegrass red clover and crimson clover mixture in winter; GN = forage system comprising ofNfertilized Tifton 85' bermudagrass in summer or N-fertilized rye and ryegrass mixture in winter. t Probability of difference value for comparisons between SR means within a forage system by season. Standard errors of interaction means ranged from 2.8 to 8.4. ,r Probability of difference value for comparisons between forage system means within a SR by season. Herbage CP concentration was affected by a FS by SR by period (within season) interaction effect (P = 0 016). Examination of the data shows a trend (P = 0 059) for greater CP concentration on GL (range = 173 to 200 g kg -1 ) than GN (range = 127 to 197

PAGE 156

137 g kg1 ) pastures during the summer. Ignoring the period effect, interaction means ranged only from 158 to 189 g kg1 during summer and 235 to 281 g kg1 during winter (data not presented). Treatment differences were not consistent and are considered unlikely to influence animal performance significantly. Forage NDF was affected by a FS by SR by period (within season) interaction (P = 0.042). The role of period in the interaction was due mainly to Period 1 when there were no SR effects because treatments were just being imposed. As a result the NDF response will focus on the season by FS by SR interaction (P = 0.0001). Table 4.4. Season by forage system by stocking rate (SR) interaction effect on herbage neutral detergent fiber concentration. Forage Systemt GL GN P value1 Summer 1996 SR High Low k 1 ------g g ------P value t 497 459 0.0001 750 777 0.0405 0.0001 0.0001 Winter 1997 SR High Low k -I ------g g ------484 498 0.0602 500 482 0.0042 P value t 0.0429 0.0070 t GL = forage system comprising of perennial peanut in summer or rye, ryegrass, red clover, and crimson clover mixture in winter; GN = forage system comprising ofNfertilized 'Tifton 85' bermudagrass in summer or N-fertilized rye and ryegrass mixture in winter. t Probability of difference value for comparisons between SR means within a forage system by season. Standard errors of interaction means ranged from 6 5 to 17 .1. Probability of difference value for comparisons between forage system means within a SR by season.

PAGE 157

138 During summer, there was greater NDF on the GN system than on GL (Table 4.4). Statistical differences were detected between SR for summer and winter pastures, but the magnitude of these differences were small and may be unimportant biologically. Overall, nutritive value data indicate that there were large differences in IVOMD and NDF due to FS in summer pastures, indicating that perennial peanut has superior nutritive value compared to Tifton 85 bermudagrass. High NDF and poorer digestibility is expected in tropical grasses (Sollenberger and Chambliss, 1991 ), and perennial peanut has been demonstrated to have good nutritive value in many Florida studies. The results also demonstrate that with N fertilization, rotationally-stocked Tifton 85 bermudagrass can reach CP concentrations comparable to that for perennial peanut. Daytime grazing responses There was a season by FS by SR by CS interaction effect on time cows spent grazing during daytime hours (P = 0.002). There was a season main effect (P = 0.017) as cows spent less time grazing in daytime during summer (161 min) than in winter (249 min). Because of the four-way interaction and because there was a main effect of season, it was decided to analyze the data by season. On summer pastures, grazing time during daytime hours was influenced by FS main effects (P = 0.0001) and by a SR by CS interaction effect (P = 0.005). Cows grazed longer on GN (179 min) than on GL pastures (143 min). Cows fed at the high CS level spent less time grazing than those on low CS pastures regardless of SR, though this difference was less pronounced at high SR (Table 4.5). Also, cows grazed longer on high

PAGE 158

139 SR pastures than on low SR pastures when CS was high, but there were no differences between SR treatments when CS was low (Table 4.5). Table 4.5. Stocking rate (SR) by concentrate supplement (CS) interaction effect on time cows spent grazing during daytime hours in summer 1996 cs High Low P value? SR High Low -------------------min------------------158 172 0 0532 135 180 0.0001 P value t 0 0021 0.3036 t Probability of difference value for comparisons between SR means within a CS. t Probability of difference value for comparisons between CS means within a SR. Longer time spent grazing on GN summer pastures could possibly be related to greater pregraze HM and/or herbage allowance on those pastures compared to GL pastures. This would however be in contrast to the classical explanation that animals will extend grazing time as a compensatory mechanism when intake rates are reduced due to limiting forage availability (Hodgson 1981) Gibb et al. (1997) suggested that behavioral responses of cows may not be simply to differences in sward herbage mass, but may be related also to time spent searching by cows as they moved between fr equently vs infrequently grazed areas These authors implied that cows will spend time searching for less mature forage i.e ., more digestible when availability is not limiting. Herbage allowance data suggest that herbage availability was not limiting on GN pastures during

PAGE 159

140 summer in the present study. Biting rate and bite weight were not quantified, but observations while animals were grazing indicate that there were several jaw movements before an actual bite occurred on bermudagrass pastures. On perennial peanut swards, each jaw movement constituted one bite. Few, if any, prehensile movements occurred. Thus, if intake rate was faster on GL compared to GN pastures because of greater number of bites per jaw movement, then it is likely that grazing time required to achieve satiety on GL pastures was lower. Higher intake of digestible organic matter per unit DM, because of the greater herbage digestibility of GL pastures, may also have contributed to reduced grazing time observed on these pastures. In winter, the amount of daytime grazing was influenced by a FS by SR by CS interaction (P = 0.002). Cows spent a similar amount of time grazing regardless of CS level (P = 0.412; 252 min. for low vs. 245 min. for high) and at low CS there was no effect of SR or FS ( data not presented). At high CS, there were effects of SR and FS, but they were not consistent and could not be related to herbage mass. During winter, pregraze herbage mass and herbage allowance data suggest that forage availability was limiting on most pastures, thus minimizing treatment effects. Forbes et al. ( 1985) reported that in conditions of limited forage availability it may be advantageous for animals to reduce energy expenditure by reducing grazing activity. Dougherty et al. (1992) demonstrated that cows, when grazing swards with restricted herbage allowance, consumed herbage up to a particular horizon then stopped grazing, resulting in shorter grazing times.

PAGE 160

141 Nighttime grazing responses Time spent grazing during the period between returning to pastures from the milking parlor in the evening to leaving for milking next morning (nighttime) was affected by a season by FS by SR by CS interaction (P = 0 024). The main reason for the interaction effects was different grazing responses to management treatments within seasons, but overall mean grazing time during nighttime between seasons was not different (P = 0 .859). It was postulated that when grazing time during the day was limited by hot weather conditions during summer cows may compensate by grazing longer during the nighttime period; however, cows on summer pastures grazed an average of 194 min during nighttime while cows on winter pastures grazed for 191 min. These results suggest that cows may spend a fixed maximum amount of time grazing during nighttime across seasons. The absence of season effects may be due to a wide range in temperature and solar radiation conditions on sampling days within a season Data examining relationships between grazing time and temperature within a season will be described later. During the summer, there was a main effect ofFS (P = 0.001) and a trend for a CS main effect (P = 0.097) on time spent grazing during nighttime. Cows on GN pastures grazed for an average of 206 min compared to 183 min for cows on GL pastures. Rationale for these differences are the same as with daytime grazing, i.e. related to pasture characteristics and rates of intake (bite weight x biting rate). The trend for animals consuming the lower level of CS to graze longer than the high CS cows (200 vs. 189 min) suggests that cows will spend more time consuming pasture (when forage availability is not limiting) in an attempt to meet their energy requirements. Krysl and

PAGE 161

142 Hess (1993) also found that unsupplemented cattle grazed longer that those fed supplement when pastures were considered deficient in nutrients to meet cattle requirements Obviously, cows receiving a greater amount of CS will not need to spend as much time grazing as their low CS counterparts. Another rationale for this response to CS may be that cows will spend a specific maximum amount of time eating (for both grain supplement and pasture) during nighttime, and, since less time was spent eating the lower amount of grain ( data not presented) more time was available for consuming pasture. During winter there were main effects ofFS (P = 0.0001) and SR (P = 0.001) on nighttime grazing Cows grazed longer at night on ON compared to GL pastures (206 v s. 175 min, respectively) and longer on low SR (204 min) compared to high SR (178 min) pastures The behavioral responses seem to be related to forage availability in that time spent grazing was determined by the physical presence of herbage to graze Forage availability likely was insufficient on most treatments in the study and apparently animals stopped graz i ng when sward characteristics viz. lack of herbage in the grazing horizon made it difficult to continue. Lack of desire to graze on pastures with low herbage mass that had been fouled b y excreta during the day may also be a factor It should be noted that pregraze herbage mass when cows began grazing in the evening was even lower compared to that actually quantified since estimation of pregraze herbage mass was done prior to grazing of paddocks which began in the mornings. Grazing time in a 24-h cycle Total grazing time is the sum of daytime and nighttime grazing times Total grazing time was affected by a season by FS by SR interaction (P = 0.017) and also a

PAGE 162

143 season by CS interaction (P = 0.008). The three-way interaction occurred because cows grazed longer on GN pastures and there were no SR effects during summer, while in winter there was a SR effect on GN pastures but not on GL pastures and an FS effect at low SR but not at high SR (Table 4.6). Put another way the winter data show essentially that cows grazed longer on low SR, GN pastures compared to other treatment combinations, which had similar total grazing times. This was the same treatment with the greatest pregraze herbage mass and herbage allowance. The season by CS interaction occurred because during summer, cows grazed longer when fed at the low level of CS compared to the high level while there was no CS effect during winter (Table 4.7). These data also show that cows grazed longer during winter than summer. Total grazing time on winter pastures was on average more than 80 min longer than on summer pastures Because daytime and nighttime grazing essentially had the same pattern of animal behavior responses it is logical that total grazing time responded in the same manner. Explanation of the total grazing time response likewise would be similar to that already provided for day and night grazing times. Shade was provided during summer only Cows on GL pastures spent more time under shade than cows on GN pastures (291 and 262 min., respectively ; P = 0.0001). Also cows on high CS pastures spent more time under shade than cows on low CS pastures (289 vs. 265 min. ; P = 0 0001). Shade reduces stress due to solar radiation experienced by high-producing dairy cows in Florida conditions (Hansen 1990). These results suggest that time spent loafing under shade is inversely related to time spent grazing i.e. when cows were not grazing they loafed under shade thus cows on

PAGE 163

144 treatments that had shorter grazing time also spent more time under shade. Cows almost never sought shade at night. Table 4.6. Season by forage system by stocking rate (SR) interaction effect on total time cows spent grazing during a 24-h cycle. Forage System t GL GN Summer 1996 SR High Low --------mm --------P value t 325 383 0.8835 0.8492 P value 0 0001 327 386 0.0001 Winter 1997 SR High Low ---------mm--------P value t 420 419 0 9803 424 494 0.0001 0 7845 0 0001 t GL = forage system comprising of perennial peanut in summer or rye, ryegrass red clover, and crimson clover mixture in winter; GN = forage system comprising ofNfertilized 'Tifton 85' bermudagrass in summer or N-fertilized rye and ryegrass mixture in winter t Probability of difference value for comparisons between SR means within a forage system by season. Probability of difference value for comparisons between forage system means within a SR by season Examination of daytime grazing time responses as a percentage of total grazing time in a 24-h cycle may be instructive in highlighting the effects o f season on grazing behavior. Analysis of the data after daytime grazing was converted to percent of total grazing time indicates a season by FS by SR by CS inte r action (P = 0 001) Nighttime grazing as percent of total grazing time statistics were the same simply because nighttime grazing values were 100 minus daytime values. The interact ion data are not presented

PAGE 164

145 because the main highlight of these results is the role of season. Daytime grazing accounted for an average of 44% of total grazing time during summer, while in winter it was 57% of total grazing time. This difference should be considered in light of the fact that nighttime grazing was not different between the two seasons. Table 4.7 Season by concentrate supplement (CS) interaction effect on total time cows spent grazing during a 24-h cycle. cs High Low P value: Season Summer 1996 Winter 1997 -------------------min-------------------335 376 0.0001 438 440 0.8415 P value t 0 0018 0.0491 t Probability of difference value for comparisons between season means within a CS. Probability of difference value for comparisons between CS means within a season. These data suggest that pasture characteristics and seasonal differences play a substantial role in determining grazing time When forage availability is not limiting as with the summer pastures in this study then herbage digestibility seems to become an important factor Gibb et al. (1997) suggested that differences in behavioral responses of cows may not be due simply to herbage availability but to differences in spatial patterns of short, frequently grazed and tall, infrequently grazed patches in the paddock. Increasing proportion of the sward that is infrequently grazed may lead to an increase in search time as cows move between the frequently grazed areas. The winter data suggest that animals

PAGE 165

146 will stop grazing when forage availability becomes insufficient for them to continue, on the other hand. Even though there was relatively more forage available for grazing in summer, cows grazed longer during the winter months. This suggests that pasture characteristics and differences in weather conditions between seasons are linked together in their role in determining grazing time responses. Thus, evaluation of the grazing time relationship with climatic variables, specifically temperature and solar radiation, since these are associated with heat stress, was warranted. Grazing time relationship with temperature and solar radiation Weather conditions on observation days. Efforts were made to conduct observations of grazing behavior on days that represented the ranges of temperature and radiation that may be expected during the summer and winter grazing seasons in Florida (Table 4.8). The average daily temperature shown in parenthesis and obtained from records for Gainesville (collected by the University of Florida Agronomy Department) were used as a check to compare the data recorded in the field. Temperature recordings were not very different at the two locations. Sharp drops in radiation intensity are suggestive of cloudy days and/or rainfall. Climatological data for Gainesville indicate that rain fell on 12 Aug. 1996 (33 mm) and 25 Jan. 1997 (15 mm), the same days when low radiation was recorded. Conditions were also cloudy on 21 Sept. 1996, 22 Feb. 1997, and 29 Mar. 1997, but rainfall was not recorded for Gainesville on those days. The net radiation recorded for Gainesville is presented simply to show that estimates of radiation intensity in the study followed a similar pattern to those measured by weather station data logger equipment at a nearby location.

PAGE 166

147 Table 4.8. Temperature (maximum, minim u m, and average) and solar radiation intensity recor d ed on pasture for o b s ervation days. Temperature Solar radiation Observation Max. Min. Average Max. Average Nett D ate ----------------oc -------------1 -2 -1 --mo m s --MJm-2 Summer 1996 19 July 32.2 23.3 2 8 6 (27 7)t 1915 932 1 8.5 29 July 36.1 20.6 28.5 (26 9) 1844 963 14.4 12 Aug. 25.0 21.7 23.7 (22.5) 478 201 3.6 7 Sept. 32.2 21.1 27 .7 (26.7) 1871 820 15.0 14 Sept. 30.6 15.6 23. 7 (23.9) 1594 905 15.1 21 Sept. 28.3 1 8 .9 24.2 (25. 0 ) 1070 387 6.1 28 Sept. 31.1 20.0 25.9 (25.3) 1682 775 12.4 Winter 1997 25 Jan. 16.7 5.6 13.9(11. 1 ) 467 117 1.9 1 Feb. 21.1 7.2 16.1 (16.4) 1150 620 10.5 22 Feb. 25. 6 10. 0 18.6 (17.7) 1369 295 4.9 1 Mar. 28.3 17.8 24.2 (23. 1 ) 158 0 734 10.5 22 Mar. 27. 8 15. 0 22.2 (22.2) 1732 1027 16.1 29 Mar. 23.9 15. 0 20.6 (19.7) 571 227 3.4 t Average temperature (in parenthesis) and net solar radiation were obtained from data recorded by the University of Florida Agronomy Department in Gainesville. Examination of daily records for temperature and radiation for Gainesville suggests that conditions on observation days may be representative of the range of conditions expected during the periods when the grazing trials were conducted. The highest

PAGE 167

148 maximum ambient temperature of any day during the summer study period was 36.1 C, which was captured in the study (29 July; matched the highest maximum temperature record for July at that location based on records since 1903) and the lowest minimum was 12.2. Also the highest average daily temperature in summer was 29.2C and the lowest 21.1 C. The highest net radiation of any day during the summer study was 18.6 MJ m2 and the lowest was 3.6 MJ m; these extremes were obtained on the observation days. During the winter study period, the highest daily maximum temperature was 3 l .6C and the lowest minimum was -7 .2C. The highest average daily temperature during the study period was 24.2C and the lowest was 10.0C. The study captured a day with the highest average daily temperature. This is critical because the effects of heat stress on grazing behavior is the important focus. The highest net radiation of any day in the winter study period was 17.2 MJ m2 and the lowest was 1.9 MJ m2 The study captured days with the lowest and close to the highest net radiation. Temperature and solar radiation effects on grazing behavior. Regression analysis of average daily temperature vs. time spent grazing during daytime for combined data of both seasons shows that daytime grazing decreases with increasing temperature (P = 0 024; equation was: daytime grazing= 436 10.3 average daily temperature; r2 = 0.38; RSME = 62.9), but no relationship between daytime grazing and radiation intensity was detected (P = 0.191) Because of the differences in animal behavioral responses to factors other than climatic conditions and also because of an interest in responses to the range of climatic conditions within season, the data for each season were analyzed separately.

PAGE 168

149 In summer, daytime grazing decreased with increasing average daily temperature (P = 0.019; Figure 4.1 [a]). No relationship was found between daytime grazing and average daily temperature in winter (P = 0.933). Daytime grazing also decreased with increasing radiation intensity during summer (P = 0.020; Figure 4.2 [a]) but no relationship was detected between daytime grazing and radiation in winter (P = 0.502). Gary et al. (1970) could not find a relationship between grazing time and temperature in a study conducted in Pennsylvania during summer. They did not report the range of temperatures during the study period, but mean daily temperature was 19 .3 c, which was likely not sufficient to cause changes in grazing behavior due to heat stress During summer, there was a strong positive linear relationship between nighttime grazing and average daily temperature (P = 0 006; Figure 4.1 [b]) and also with radiation (P = 0 008; Figure 4 2 [b ]). In winter, nighttime grazing as with daytime grazing was not related with average daily temperature (P = 0.805) or radiation (P = 0.786). These responses suggest that, in summer, animals were attempting to compensate by grazing longer during nighttime on those days when heat stress induced by temperature and radiation restricted grazing during the day. This behavior was not detected when evaluating daytime grazing responses to management factors probably because a wide range of weather conditions were represented in data from each season. During summer grazing total grazing time in a 24-h cycle decreased with increasing temperature (P = 0.048; Figure 4.1 [c]) and with increasing radiation (P = 0.048; Figure 4.2 [ c ]). Total grazing time was not influenced by temperature (P = 0.863) or radiation (P = 0.781) during winter as expected since neither daytime grazing nor

PAGE 169

(a) (b) (c) 250 C E 200 Q) 150 g> 100 -~ C!) 50 c 240 I 220 200 180 C ~ 160 C9 140 23 23 c 400 I 380 g' 320 N 300 I! C!> 280 23 150 Daytime Grazing vs. Temperature DG = 784-24.1 AVT r2 = 0 70; RSME = 38.4 ----r-------24 25 26 27 Average daily temperature ----28 observed DG --predicted DG Nighttime Grazing vs. Temperature NG =-78 + 10.4AVT r2 = 0 .81; RSME = 12 4 --24 25 26 27 Average daily temperature 28 observed NG --predicted NG Total Grazing Time vs. Temperature TG = 705-13.6 AVT r2 = 0 57 ; RSME = 28. 6 -r---.... ----24 25 26 27 Average daily temperature -....__ 28 observed TG --predicted TG "' 29 I. 29 ' "' 29 Figure 4.1. Average daily temperature (AVT; C) effects on daytime grazing (DG; a), nighttime grazing (NG; b), and total grazing time (TG; c) during summer.

PAGE 170

(a) (b) (c) 151 Daytime Grazing vs. Radiation DG = 285 -0 18 RAD r2 = 0.69 ; RSME = 38. 8 c2~ .... =-.------,------.------r---, I 200 +---1---=-=-""'=::-------+---='----+---; !1~+----l------+-----;;;i:=--=-----',._-~ C) ~ 100 ----------+------+-------->-1 c, 50 +---+-----
PAGE 171

152 nighttime had showed any response These results indicate that the attempts to extend grazing during nighttime by cows grazing summer pastures was not sufficient to compensate for restricted daytime grazing due to heat stress factors If the theory that animals will spend a specific maximum amount of time grazing at night holds then this may be the reason why full compensation cannot be achieved for shorter daytime grazing due to heat stress. There was a positive linear relationship between amount of time cows spent under shade during summer and temperature (shade time [min]= 1141 + [54.5* average daily temperature] ; P = 0.042 ; r2 = 0.60; RSME = 109.4). A much stronger relationship was found between shade time and radiation (shade time= 93 + [0.52*radiation intensity]; P = 0.0001 ; r2 = 0.98 ; RSME = 26 8) as suggested by an examination of the regression diagnostic parameters viz. P r2, and RSME values and plots of the data points (not presented) This suggests cows seek shade more in response to radiation than ambient temperature. Hansen (1990) indicated that shade may help alleviate the depression in production of Holstein cows due to heat stress Multiple regression analysis of daytime and nighttime grazing and time spent under shade for the summer data in response to average daily temperature and radiation showed a reduction in error sums of squares and improvement of r2 values suggesting that the two predictor variables together may be more useful than a single predictor. The results indicate that daytime grazing response to climatic variables i n the conditions of this study during summer may be predicted by the following equation: daytime grazing = 610 (14.5*average daily temperature) (0 .11 *radiation intensity) (P = 0.029; r2 = 0.83; RSME

PAGE 172

153 = 32.3). Similarly nighttime grazing= -5 + (6.4*average daily temperature)+ (0.04*radiation intensity) (P = 0.003; r2 = 0.95; RSME = 7.2) and shade time= 394 + (13.4*average daily temperature)+ (0.45*radiation intensity) (P = 0 001; r2 = 0.99 ; RSME = 13. 3). The small number of data points (only seven) from which these equations were developed may reduce their usefulness for prediction of animal behavior responses to climatic environment. Research to obtain more data points while controlling the non climatic factors influencing grazing behavior is needed Lack of responses to temperature or radiation during the winter grazing season suggests that heat stress was not a factor influencing ingestive behavior on winter pastures. There was some variability among observation days for temperature and radiation intensity but this did not seem to influence time spent grazing. Grazing time in winter was longer than in summer ; obviously time spent grazing was not restricted by temperature or radiation but appeared to be determined mostly by pasture characteristics Grazing time relationship with organic matter intake and animal performance Forage organic matter intake (OMI). Forage OMI was affected by a season by FS by SR effect (P = 0.048). The interaction occurred because forage OMI was influenced by SR effects only on the GN system during winter while there were no SR effects on summer pastures; during summer OMI was greater on GL than GN pastures (Table 4.9).

PAGE 173

154 Table 4.9. Season by forage system by stocking rate (SR) interaction effect on forage organic matter (OM) intake. Forage Systemt GL GN Summer 1996 SR High Low -----kg OM d1 -----P value t 14.3 10.9 0.3474 0.9286 P value 0.0019 15.4 11.0 0.0001 Winter 1997 SR High Low -----kg OM d1 -----P value t 12.2 10.2 0.1430 10.5 12.5 0.0815 0.1465 0.0474 t GL = forage system comprising of perennial peanut in summer or rye, ryegrass, red clover, and crimson clover mixture in winter; GN = forage system comprising ofNfertilized 'Tifton 85' bermudagrass in summer or N-fertilized rye and ryegrass mixture in winter. t Probability of difference value for comparisons between SR means within a forage system by season. Probability of difference value for comparisons between forage system means within a SR by season. There was a season by FS effect (P = 0.0001) when forage OMI relative to cow BW responses were analyzed. Thus, the effect of SR detected with actual forage OMI estimates was lost when the data were converted relative to cow BW. Cows grazing GL summer pastures had greater forage OMI relative to cow BW than cows on GN pastures (Table 4.10). In winter, forage OMI was not different between FS. Forage OMI was greater on summer compared to winter pastures for the GL system, but on GN pastures there were no differences between seasons (Table 4.10).

PAGE 174

155 Table 4.10. Season by forage system (FS) interaction effect on forage organic matter (OM) intake relative to animal body weight. Forage System t GL GN Season Summer 1996 Winter 1997 -------------------g kg--------------------29 22 0.0001 20 21 0.5820 P value i 0.0001 0.5888 t GL = forage system comprising of perennial peanut in summer or rye, ryegrass, red clover, and crimson clover mixture in winter; GN = forage system comprising ofNfertilized 'Tifton 85' bermudagrass in summer or N-fertilized rye and ryegrass mixture in winter. t Probability of difference value for comparisons between season means within a FS. Probability of difference value for comparisons between FS means within a season. Earlier, it was shown that total grazing time in a 24-h cycle also responded to a season by FS by SR interaction effect. Comparison of grazing time (Table 4.6) and forage OMI (Table 4.9) data shows that in summer, forage OMI was greater on GN pastures but cows also spent less time grazing on these pastures. It was intimated earlier that cows may have been achieving satiety in a shorter time on GL pastures in summer possibly because of enhanced intake rates ( due to reduced number of jaw movements required to achieved each bite compared to animals on GN pastures). These pastures also had greater forage digestibility, and it is likely that these factors combined led to animals achieving greater forage OMI even though grazing time was shorter. Forage availability was not perceived as limiting on GN pastures at either SR. Field observations suggest that animals

PAGE 175

156 may have spent more time selecting for more digestible plants and plant parts in these conditions, in addition to the greater number of jaw movements per bite, resulting in slower intake rates. Thus, lower forage OMI on GN pastures was achieved in spite of animals spending more time grazing. Other studies have shown that forage intake may be reduced due to selective grazing by cattle when herbage available on tropical pasture is not limiting (Stobbs 1977). One option available to the grazing animal to compensate for reduction in bite weight and proportion of jaw movements that are effective bites is to extend grazing time and thus total number of bites (Hodgson et al., 1994). Gibb et al. (1997) found that increasing total grazing time was insufficient to compensate for lower forage intake due to reduced bite size and effective bites per jaw movement. They suggested that this limitation to grazing time compensation was as a consequence of requirements for rumination. On winter pastures on the other hand, greater forage OMI was achieved on the treatment where animals spent the greater time grazing. As alluded to earlier, magnitude of nutritive value differences of these pastures were not likely to be sufficient to cause drastic differences in animal performance. This would suggest logically that forage intake rates would not be affected. Also, Stobbs (1977) reported that there is less likely to be selective grazing on shorter, denser temperate pastures. The evidence seems to indicate that the observed greater forage OMI when animals spent more time grazing was because animals had more forage to graze on that treatment, while intake was obviously depressed when grazing time was shortened due to lack of forage on pastures. Earlier it was suggested that grazing time on winter pastures was determined by availability of forage for

PAGE 176

157 animals to graze. These forage OMI data seem to further support that notion. Dougherty et al. (1992) demonstrated that forage intake was increased when herbage availability was not limiting. Grazing time was the main component of ingestive behavior affected by limiting DM allowances (to the point where lack of forage caused cessation of grazing) in their study. The similarity of grazing time and forage OMI responses in the present study suggests that the same phenomena may be occurring. Total OMI. Total OMI responses to management treatments were similar to those obtained for forage OMI, viz, a season by FS by SR interactions (P = 0.024). Unlike, the responses for forage OMI, however, there was a CS main effect (P = 0.007) on total OMI At high CS, total OMI was 18.9 kg OM cow1 d1 while at low CS it was 16.6 kg OM cow1 d 1 The pattern of total OMI responses were essentially the same as for the forage OMI responses, viz ., FS effects during summer and greater intake at the low SR treatment on GN pastures in winter (Table 4.11 ). When these data were converted to reflect intake relative to cow BW, the CS main effect was detected (P = 0.004). Cows fed at the high CS level had total OMI relative to BW of 36 g kg1 compared to 32 g kg1 for cows fed the low CS rate. Also the three-way interaction of season with FS and SR became statistically weaker and instead there was a trend for a two-way interaction of season with SR (P = 0 085) and a strong interaction of season with FS (P = 0.0001). The trend for a season by SR interaction occurred because there was no effect of SR during summer but differences were detected in winter (Table 4.12). Likewise season by FS interactions occurred because during summer GL pastures had greater total OMI relative to cow BW than GN pastures but there were no effects of

PAGE 177

158 FS during winter (Table 4.13). There were differences in total OMI due to season regardless of SR treatments. Cows grazing summer pastures had greater total OMI than those on winter pastures (Table 4.12). Most of this however, may be attributed to the total OMI on GL pastures as suggested by the data in Table 4 .13. Table 4.11. Season by forage system by stocking rate (SR) interaction effect on total organic matter (OM) intake. Forage System t Summer 1996 SR High Low ----kg OM d-1 -----P value t GL GN 20 6 16.4 P value 0 0001 21.9 16 5 0.0001 0.2262 0.9111 Winter 1997 SR High Low -----kg OM d-1 -----17 7 15.2 0.1751 16.4 18.7 0.0403 P value t 0.3667 0.0019 t GL = forage system comprising of perennial peanut in summer or rye, ryegrass red clover and crimson clover mixture in winter ; GN = forage system comprising ofNfertilized Tifton 85' bermudagrass in summer or N-fertilized rye and ryegrass mixture in winter t Probability of difference value for comparisons between SR means within a forage system by season. Probability of difference value for comparisons between forage system means within a SR by season. These data including the responses to CS suggest that supplemental feeding did not cause measurable substitution effects (Moore 1994 ; Raymond 1969), i.e., forage OMI was link e d to ingestive behavior as described in the preceding section but the pattern of responses when the OM contribution from grain was included did not change.

PAGE 178

159 Animals grazed longer when fed at the low CS level during summer, suggesting that CS does play some role in determining the animals willingness to continue grazing after some level of nutrient intake has been achieved given the other factors especially heat stress associated with grazing behavior. Table 4 12. Season by stocking rate (SR) interaction trend on total organic matter (OM) intake relative to animal body weight. SR High Low P value i Season Summer 1996 Winter 1997 k I --------------------g g -------------------38 37 0 5302 29 33 0.0164 P value t 0 0001 0 0043 t Probability of difference value for comparisons between season means within a SR. t Probability of difference value for comparisons between SR means within a season. Milk production. Average daily milk production was affected by a strong season by SR interaction (P = 0 001) There were no differences in average daily milk production due to SR effects during summer but cows grazing low SR pastures had greater milk production than those on the high SR treatment in winter (Table 4.14) Average daily milk production was the same between seasons at high SR but was greater at the low SR treatment during winter than in summer. It would appear as though the main reason for the interaction is the high production on low SR pastures during winter.

PAGE 179

160 Table 4.13. Season by forage system (FS) interaction effect on total organic matter (OM) intake relative to animal body weight. Forage System t GL GN Season Summer 1996 Winter 1997 --------------------g kg-I --------------------42 33 0.0008 30 32 0.2465 P value t 0.0001 0.1028 t GL = forage system comprising of perennial peanut in summer or rye ryegrass red clover and crimson clover mixture in winter ; GN = forage system comprising ofNfertilized Tifton 85' bermudagrass in summer or N-fertilized rye and ryegrass mixture in winter. t Probability of difference value for comparisons between season means within a FS. Probability of difference value for comparisons between FS means within a season. Table 4.14. Season by stocking rate (SR) interaction effect on average daily milk production SR High Low P value t Season Summer 1996 Winter 1997 k .) d-1 ----------------g cow ---------------15.9 16.1 0.7028 16.2 20.0 0 0001 P value t 0.6580 0.0001 t Probability of difference value for comparisons between season means within a SR. t Probability of difference value for comparisons between SR means within a season.

PAGE 180

161 There was also a season by FS interaction effect (P = 0 001) on average daily milk production Cows grazing GL pastures during summer had greater milk production than cows on GN pastures (Table 4.15). In winter, there were no differences due to FS. As a result there was greater milk production on GN pastures in winter compared to summer but no differences due to season on GL pastures (Table 4.15) The main reason for the interaction was the low production on GN pastures during summer. Table 4.15. Season by forage system (FS) interaction effect on average daily milk production. Forage System t GL GN P value Season Summer 1996 Winter 1997 k -1 d-1 ----------------g cow ----------------17 2 14.9 0.0003 17.7 18.5 0.1861 P value t 0.4432 0 0001 t GL = forage system comprising of perennial peanut in summer or rye ryegrass red clove r, and crimson clover mixture in winter ; GN = forage system comprising o fNfertilized Tifton 85' bermudagrass in summer or N-fertilized rye and ryegrass mixture in winter. t Probability of difference value for comparisons between season means within a FS. Probability of difference value for comparisons between FS means within a season Milk production was also affected by a s e ason by CS interaction (P = 0 003). average daily production was lower when animals were fed at the lower rate of CS during summer but there were no effects of CS during winter (Table 4.16). This resulted in no

PAGE 181

162 difference between seasons at high CS but lower production at low CS in summer compared to winter (Table 4.16) Table 4.16. Season by concentrate supplement (CS) interaction effect on average daily milk production. cs High Low P value i Season Summer 1996 Winter 1997 k -1 d-1 ----------------g cow ---------------17.5 14.6 0.0001 18.2 17.9 0.6381 P valuet 0.2199 0.0001 t Probability of difference value for comparisons between season means within a CS. t Probability of difference value for comparisons between CS means within a season. The relationship between grazing behavior and milk production is complex. It seems evident that ingestive behavior was related to the animals' desire to achieve a particular nutrient intake requirement. Whether or not they achieve that requirement was evidently dependent on a number of factors, such as forage availability forage digestibility supplemental feeding, and restrictions to grazing due to heat stress factors These all played a complex role in determining how much time an animal spent grazing and subsequently the level of intake achieved. Animal performance ultimately depends on energy intake vs energy expenditure Evidence earlier in this chapter suggested that time spent grazing in summer pastures was dependent to a large extent on climatic conditions on one hand and pasture

PAGE 182

163 nutritive value (in this case a function of forage species) on the other. Heat stress was not found to influence grazing behavior in winter and differences in time spent grazing seemed associated with forage availability. Forage availability in summer was considered not limiting. This was regardless of SR and it was evident also that SR did not affect grazing time, forage or total OMI, and subsequently there were no differences in milk production due to SR effects in summer. In winter, SR was the critical management variable. The impact of SR resulted in lower pregraze herbage mass and herbage allowance on high SR pastures and, as a result, cows spent less time grazing high SR pastures probably because low forage availability caused cessation of grazing, similar to the findings of Dougherty et al. (1992). Forage OMI and total OMI were restricted as a result. On GN pastures, there was greater forage availability on the low SR treatment. Responses from the GL system are not included in this discussion because animals had to be removed during P2. Animals spent more time grazing on the low SR pastures in an effort to meet their energy requirements and were successful, as was evidenced by the greater forage and total OMI. This resulted in greater milk production on low SR pastures in winter. Stobbs (1977) reported increased milk production with increasing forage availability and attributed this to higher intake of digestible energy. Cows grazed less time on GL pastures but forage OMI was greater compared to GN pastures in summer Thus, the greater forage OMI was achieved at less energy expenditure ( due to less grazing time) on the GL system This is particularly critical given the heat stress conditions during summer, i.e., less time spent grazing and more time spent under shade should result in better animal performance (Hansen, 1990). It is not

PAGE 183

164 surprising, therefore, that greater milk production was achieved on GL pastures in summer. During summer, animals that were fed at the high CS levels spent less time grazing but had greater total OMI and subsequently greater milk production than animals fed at the low CS level. Krysl and Hess (1993) reported increased grazing time for unsupplemented vs. supplemented cattle. Similar effects were not detected in winter suggesting that the main effect of CS detected in the analyses was due mostly to the summer results It is clear from these data that milk production was determined by nutrient intake. Recent research demonstrated that milk production was related to OMI in a study where the highest forage OMI and milk production occurred at an intermediate SR but grazing time to achieve that intake level (compared to a lower level when SR was more lenient) was longer (Gibb et al., 1997). Grazing time was adjusted by animals to reflect forage availability, pasture nutritive value, supplemental feeding, and climatic conditions effects on ingestive behavior in the present study. This adjustment appeared to be linked to the animals' ability to achieve a particular level of intake given the limiting factors. Gibb et al. ( 1999) demonstrated this phenomena when cows compensated for reduction in intake rate (incurred by varying SR) by increasing grazing time, resulting in similar levels of intake in conditions where forage availability was still sufficient at the highest SR. In summer, longer grazing was required on GN pastures due to the pasture characteristics influence on ingestive behavior leading to greater energy expenditure but did not reflect better OMI. In winter, animals grazed longer when forage was available, resulting in greater OMI.

PAGE 184

165 Energy expenditure in grazing time was of little consequence in this situation because achieving higher OMI was more important. Also the evidence suggests that heat stress factors did not deter grazing during winter. Data from this study do not allow evaluation of within season weather conditions effects on animal performance. Body weight changes. Average daily BW changes were affected by a period (within season) by FS interaction (P = 0.015) and a trend for a period (within season) SR (P = 0.051 ). The period effect occurred because animals tended to lose weight early in the study then gained weight during the latter period. These data are not presented because the interactions with period are complex and may add little to explaining how BW changes may be linked with grazing behavior and intake. Examination of the season by SR (P = 0.0001) and the FS by CS (P = 0.048) interactions detected may provide a clearer explanation for BW responses. Results show that animals generally lost weight during summer regardless of SR, while in winter animals grazing high SR pastures lost weight but cows on low SR pastures gained weight (Table 4.17). Lactating dairy cows will prioritize milk at the expense of body tissue when intake requirements are not met (Kolver and Muller 1998; Moe et al. 1970) Data from the present study suggest that animals grazing summer pastures and high SR pastures in winter were not achieving OMI level sufficient to meet their energy requirements for the level of milk production attained and thus mobilized body tissue This has implications for management strategies to ensure adequa t e energy intake. Heat stress during summer may have been a large contributor to high energy for maintenance requirem e nts

PAGE 185

166 Table 4.17. Season by stocking rate (SR) interaction effect on animal body weight changes. SR High Low P value i Season Summer 1996 Winter 1997 ---------------------kg d 1 --------------------0.15 0.19 0.8231 0.41 0.21 0 0037 P valuet 0.1684 0.0238 t Probability of difference value for comparisons between season means within a SR. t Probability of difference value for comparisons between SR means within a season. Results for the FS by CS interaction indicate that BW changes were not influenced by CS effects when cows grazed GL pastures but cows on GN did not lose weight when fed at the higher CS level (Table 4.18) Weight losses were experienced on the GL pastures as well as the low CS treatments. The responses observed on GL pastures should be interpreted with caution because of the unplanned rest period on winter pastures. These data do indicate that higher CS levels may have been adequate to supplement cows on GN pastures. Body weight change in lactating dairy cows is a difficult response to link with grazing b e havior. T h e precision of weight measurements is compromised because lactating dairy cows cannot be fasted to achieve shrunk weights in order to eliminate the effect of rumen fill from feed and water. Also animals often are inc a pable of consuming sufficient energy to meet the high levels of milk production in early lactation and will

PAGE 186

167 mobilize body tissue to meet the demands (Moe et al., 1970; NRC, 1988) Later in lactation animals will tend to store body reserves for the subsequent pregnancy and lactation cycle (NRC 1988). Thus, animals body weight changes may be confounded by physiological state depending on stage of lactation and may not be a dependable reflection of energy intake based on ingestive behavior. Table 4.18 Forage system (FS) by concentrate supplementation (CS) interaction effect on animal body weight changes. Forage System t GL GN P value cs High Low k d' ----------------g cow ----------------0.21 0.01 0.2245 -0.07 -0 28 0 2364 P value i 0.4142 0.0492 t GL = forage system comprising of perennial peanut in summer or rye ryegrass, red clover and crimson clover mixture in winter ; GN = forage system comprising ofNfertilized 'Tifton 85' bermudagrass in summer or N-fertilized rye and ryegrass mixture in winter. t Probability of difference value for comparisons between CS means within a FS. Probability of difference value for comparisons between FS means within a CS Jones -E ndsley et al. (1997) found that whe n supply of en e r gy was adequate milk production was unaffected by increased supplementation but BW losses were minimized. Fisher et al. (1996) did not detect responses in cow BW to herbage intake but suggested that a small incr e ase in energy intake from sward and supplement may be used primarily t o ameliorate BW losses. Changes in BW data are necessary for quantifying energy

PAGE 187

168 requirements of animals based on observed performance. This is important for planning management strategies and for the development of simulation models to predict animal production. Coat Color Effects on Grazing Behavior Cows' coat color affected the amount of time spent grazing during daytime (P = 0.038), regardless of season, with cows of predominantly white coat grazing longer than cows with predominantly black coats (210 vs. 197 min). This observation was consistent even though time spent grazing fluctuated on the different observation days. There was a strong effect of season (P = 0.0001). Cows spent less time grazing in the daytime during summer compared to winter (154 vs. 253 min) regardless of coat color. Coat color did not have any effect on time spent grazing during nighttime (162 min for white coat cows vs. 159 min for black coat cows; P = 0.407). Comparisons for nighttime grazing between seasons are not valid because observations throughout the night were not done for all observation dates during summer. Total time spent grazing during a 24-h cycle was also affected by coat color (P = 0.049), with white coat cows grazing longer (372 min) than black coat cows (355 min) Black coat cows spent more time under shade (provided only in summer) during the day than white coat cows (320 vs. 300 min; P = 0.037). As with time spent grazing this observation was consistent even though time spent under shade fluctuated on different observation days. No studies of hair coat color effects on grazing behavior were found in the literature. There is evidence, however, that performance of lactating dairy cows with dark

PAGE 188

169 hair coats are more affected by heat stress conditions than cows with lighter coat color (Becerril et al., 1993; Hansen, 1990; King et al., 1988; Schleger, 1967). Behavioral responses relationships with climatic variables Fluctuations in grazing time seemed related to weather conditions (Table 4.19) specifically temperature and radiation intensity on observation days. Because of this observation investigation of quantitative relationships between these climatic variables and grazing behavior responses to each coat color seemed warranted. Regression analysis did not detect any relationship between temperature and daytime grazing of black coat cows in either summer (P = 0.274) or winter (P = 0.705). There was also no evidence of a relationship between temperature and daytime grazing of white coat cows in either summer (P = 0.191) or winter (P = 0.486) Also no evidence of a relationship between temperature and time spent under shade by black coat cows (P = 0.196) or white coat cows (P = 0.227) was found. It was shown earlier that during summer temperature and daytime grazing were inversely related across all cows. The reason for lack of relationships of grazing time and time spent under shade with temperature when the data are analyzed by coat color is not clear. It seems likely that the lesser number of data points with the coat color data vs. data from all cows resulted in lack of ability to detect statistical significance. Additionally, the regression approach in analyses of the coat color data did not allow for separation of other known sources of variation, viz., management treatments.

PAGE 189

170 Table 4.19. Temperature (maximum, minimum, and average) and solar radiation intensity recorded on pasture for coat-color study observation days Temperature Solar Radiation Observation Date Maximum Minimum Average Maximum Average -------------------oc ------------------1 -2 1 -----mo m s ---Summer 18 July 1996 2160 975 27 July 1996 36.1 23 3 29.3 1864 907 14 Sept. 1996 3 0.6 15.6 23 7 1594 905 20 Sept. 1996 2 8 .9 17.8 24 5 1882 763 21 Sept. 1996 28.3 18.9 24.2 1070 387 28 Sept. 1996 31.1 20.0 25.9 1682 775 Winter 25 Jan. 1997 16.7 5.6 14.0 467 117 1 Feb. 1997 21.1 7 2 16 1 1150 620 22 Feb. 1997 25.6 10.0 18.6 1369 295 1 Mar 1997 28.3 17.8 24.2 1580 734 22 Mar. 1997 27.8 15.0 22 2 1732 1027 29 Mar. 1997 23.9 15.0 20 6 571 22 7 There were trends for a relationship in summer between radiation and daytime grazing for both black coat cows (P = 0.119) and white coat cows (P = 0.080). In winter however there was no evidence of a relationship between radiation and black coat cows (P = 0.204) or white coat cows (P = 0.111). Fitting th e se data to a s e cond-order model improved the relationship (increased r2 values) between radiation intensity and daytime

PAGE 190

171 grazing for the swnmer data but there was no change in any of the other relationships investigated. Examination of the grazing time data and plots of grazing time vs. climatic variables indicated the possibility of an outlier in the swnmer data set. The highest grazing time occurred on the last observation day and did not seem to fit the pattern of responses. There were no differences in grazing time due to coat color on that day and temperature and radiation intensity were intermediate Grazing time results may have been distorted because fecal sampling occurred on the same day and likely resulted in animal behavior being disturbed by the sampling process. Given this it was decided to omit observations for that day from the analysis of the swnmer data Omission of the outlier did not change the relationship of temperature with any of the behavioral responses i e., there was still no evidence that temperature had any effect on grazing time of black coat cows or white coat cows. There was a trend toward a relationship between radiation and time spent grazing in the daytime by black coat cows (P = 0.083) and white coat cows (P = 0.068) cows Fitting the data to higher order models did not improve explanation of the results, i.e. r2 values remained the same Grazing time tended to decline with increasing radiation intensity (Figure 4.3) As noted earlier studies evaluating coat color effects on grazing behavior of lactating dairy cows were not found in the literature. Godfrey and Hansen (1996) observed that solar radiation may potentially be a greater source of heat stress to cows that graze extensively than for cattle managed intensi v ely in hot climates. Incident solar

PAGE 191

250 C: .E 200 Q) E i150 0) C: -~ 100 L. CJ 50 172 BH aytime grazmg = r2 = 0.69; RSME = 34.3 ---RAD ---. ------- 300 400 500 600 700 800 9001000 Solar Radiation Observed BHC grazing time Observed WHC grazing time -Er Predicted BHC grazing time Predicted WHC grazing time Figure 4.3. Average solar radiation intensity (RAD; mol m 2 s ') effects on time spent grazing during daytime by black (BHC) and white (WHC) hair coat cows during summer radiation was purported to have overwhelming importance for the heat balance of animals (Walsberg et al., 1997) because it can directly increase body temperature. The heat load on cows' bodies from solar radiation is produced by absorption of light and associated heat on the animal surface exposed to sunlight and, along with other climatic factors, can

PAGE 192

173 result in heat stress. Evidence from the present study supports the theory that heat stress may lead to reduced time spent grazing and that intensity of solar radiation is the major determining factor. Becerril et al. (1993) reported that hair coat color of cattle is directly related to the amount of heat absorbed from solar radiation. Since black hair coat color has higher absorptivity (Stewart, 1953), they are more likely to suffer greater effects of heat stress caused by solar radiation than white cows. Hansen (1990) reported that resistance to heat stress in environments characterized by high solar radiation is less in black compared to white coat cows. Rectal temperatures, body surface temperatures and respiration rates were higher in black compared to white coat animals in that study. Lack of grazing behavior relationships with climatic variables during winter suggests that heat stress was not a major limitation to winter grazing. It may also be an indication that solar radiation effects on body heat only results in heat stress to animals in hot environments. Shade was provided for summer grazing only. From the data with the outlier omitted, there was evidence of a linear relationship between radiation intensity and time spent under shade by black (P = 0.022) and white ( P = 0.018) animals. Fitting the data to higher order models did not improve explanation of these results, i.e., r2 values did not change. For both coat colors, time spent under shade increased with increasing radiation intensity (Figure 4.4). Hansen (1990) showed that providing shade reduced effects of heat stress of lactating dairy cows in a high solar radiation environment, more so for cows with black coats. These results suggest that animals suffering from heat stress will attempt to seek shade at the expense of meeting their nutrition requirements. This is demonstrated by

PAGE 193

174 450 400 .] BHC shade time= 26.9 + 0.447 RAD ,,,. -~ 350 E r2 = 0 .8 6; RSME = 48.3 -\/~/ ----Q) E IQ) "'C ca .c Cl) / 300 250 200 150 / / 7 / / /""-/// \ / / \ / / / WHC shade time= 55.8 + 0.456 RAD / / r2 = 0.88; RSME = 45.7 / / / / 100 I I I I I I I I I I I I 300 400 500 600 700 800 9001000 Solar Radiation Observed BHC shade time Observed WHC shade time -ePredicted BHC shade time Predicted WHC shade time Figure 4.4 Average solar radiation intensity (RAD; mol m 2 s 1 ) effects on time spent under shade by black (BHC) and white (WHC) hair coat cows durin g summer. their willingness to discontinue grazing. Additionally, with each additional increment of feed intake, heat production in the animal increases and, with reduced abi lity of cows to dissipate excessive heat by normal mechanisms it is not surprising that cows will seek shade during summer

PAGE 194

175 Quantitative coat color relationships with grazing behavior Given the effect of qualitative coat color on grazing behavior, it was decided to determine if quantitative relationships existed between percent black coat and grazing behavior responses which could perhaps contribute to explanation of grazing behavior results. Regressions of time spent under shade and grazing time as functions of percent black coat indicated no consistent relationships during summer as were observed for the analysis of the qualitative coat color effects. General lack of relationship between time under shade or grazing time and percentage black hair coat is not surprising because this regression approach does not allow for separation of other known sources of variation viz. the management treatments Coat Color Effects on Animal Responses Organic matter intake Forage OMI and total OMI were both influenced by trends for season by coat color interaction (P = 0 092 and 0 081, respectively). There were weak trends favoring white coat cows in forage (P = 0.208; Table 4.20) and total (P = 0.147; Table 4.21) OMI during summer Forage OMI was higher on winter compared to summer pastures for black-coated cows and tended to be higher in winter for white-coated cows (P = 0 117; Table 4.20). Total OMI was higher in winter regardless of coat color (Table 4.21).

PAGE 195

176 Table 4.20. Season by coat color effects on daily forage organic matter (OM) intake for cows grazing summer and winter pastures. Coat Color Black White P valuet Season Summer 1996 Winter 1997 -------------kg OM cow1 d 1 ---------------7.9 8.9 0.208 11.2 10.5 0.222 P valuet 0.004 0.117 t Probability value for comparisons between season means within coat color. t Probability value for comparisons between coat color means within season. Table 4.21. Season by coat color effects on daily total OMI for cows grazing summer and winter pastures. Coat Color Black White P valuet Season Summer 1996 Winter 1997 -------------kg OM cow' d' --------------11.2 12.5 0.147 16.8 16.3 0.295 P valuet 0.0002 0.0057 t Probability value for comparisons between season means within coat color. t Probability value for comparisons between coat color means within season. Seasonal differences in intake followed the pattern of time spent grazing, but the lack of coat color effects on intake reflect the variability associated with this measure and suggests that factors beyond coat color and its effect on grazing time are influencing intake The major management treatment affecting forage OMI in summer was FS while

PAGE 196

177 in winter it was SR. Supplementation with concentrate feed influenced total intake. These factors likely masked differences in intake that may be attributable to quantitative coat color differences. Regressions of forage OMI in response to percentage black hair coat showed a trend (P = 0.121) for lower intake with increasing percentage of black hair coat, but a relationship was detected in winter (P = 0.046). Forage intake in winter actually increased with increasing proportion of black on cows' hair coat (forage OMI = 8.23 + 0.044 percent black coat; r2 = 0.18; RSME = 2.51 ). When similar regressions were done for total OMI responses, trends were detected in both summer (P = 0.103) and winter (P = 0.096). There was a tendency for total intake to decrease with increasing proportion of black on cows' hair coat in summer (total OMI = 14.97 0.058 percent black coat; r2 = 0.27; RSME = 2.84) and to increase in winter (total OMI = 13.93 + 0.046 percent black coat; r2 = 0.13; RSME = 3.17). These responses are likely related to climatic influences in the different seasons in addition to the management treatment effects, which increases the complexity of linking grazing behavior to intake responses. No studies were found in the literature that quantified similar responses. Average daily milk production and body weight changes Average daily milk production was influenced by a season by coat color interaction (P = 0.021; Table 4.22). In summer, cows with black coats had lower daily milk production than w~te-coated cows, but in winter, milk production was not affected by coat color (Table 4.22). Milk production was higher in winter than in summer, regardless of coat color (Table 4.22). These results appear to be correlated with total intake (statistical analysis to establish correlation was not done because only four means were

PAGE 197

178 involved). Regressions of milk production response to percentage black on cow coats indicated a relationship in summer (P = 0.038) but not in winter (P = 0.908). Milk production in summer decreased with increasing proportion black on cows' hair coat (daily milk production= 13.9 0.082 percent black coat; r2 = 0.40; RSME = 2.97). Table 4.22. Season by coat color effects on average daily milk production for cows grazing summer and winter pastures. Coat Color Black White P valuet Season Summer 1996 Winter 1997 k -1 d-1 ----------------g cow ----------------8.0 11.5 0.0122 18.0 17.6 0.6421 P valuet 0.0001 0.0001 t Probability value for comparisons between season means within coat color. t Probability value for comparisons between coat color means within season. Season by coat color interaction also affected 4% fat corrected milk production (P = 0.014; Table 4.23). Similar to trends observed for average daily milk production, black cows produced less fat corrected milk than white cows during summer, but production levels between coat colors were not different in winter (Table 4.23). Also, fat corrected milk production was related to percentage black coat in summer (P = 0.025) but not in winter (P = 0.718). Cows' fat corrected milk production decreased with increasing proportion of black on cows' coats (fat corrected milk production= 12.88 -0.075 percent black coat; r2 = 0.44; RSME = 2.47).

PAGE 198

179 Table 4.23. Season by coat color effects on 4% fat corrected milk production for cows grazing summer and winter pastures. Coat Color Black White Season Summer 1996 Winter 1997 k -1 d-1 -----------------g cow -----------------7 6 10.5 0.0175 17. 1 16.3 0.3198 P value t 0 0001 0 0001 t Probability value for comparisons between season means within coat color. t Probability value for comparisons between coat color means within season. These results suggest that milk production is depressed in black coat cows in the heat stress conditions of summer but coat color does not affect production in winter probably because heat stress is not an issue in cool conditions Apparently even if solar radiation may be increasing body heat more in black than white cows in winter the cows normal mechanism for dissipating excessive heat is not hindered by environmental conditions. Other studies have demonstrated greater milk production in cows with predominantly white coats compared to those with predominantl y black coats (Becerril et al. 1993 ; Hansen 1990 ; King et al., 1988). Becerril et al. (1991) r e ported that white coa t color was linearly r elated to milk production similar to the pattern o f findings in the present study Godfrey and Hansen ( 1996) did not find diffe r ences in milk production due to coat color when cows grazed extensively in a hot environmen t. They suggested that this may have been due to overriding nutritional influences, however

PAGE 199

180 Daily body weight change was influenced by a season by coat color interaction (P = 0 043; Table 4.24). In summer, there were no differences in daily body weight changes between black or white coat cows (0.03 vs. -0 10 kg d1 respectively). In winter, all cows on the coat color study lost weight but black coat cows lost more weight (0.46 kg d"1 ) than white coat cows (0.09 kg d1). These black coat cows also had greater milk production than white coat cows in winter, suggesting that body tissue mobilization to support the higher milk production is occurring. Daily body weight changes were different between seasons for black cows, but not for white cows (Table 4.24). Table 4.24. Season by coat color effects on average daily body weight changes for cows grazing summer and winter pastures. Coat Color Black White P valuet Season Summer 1996 Winter 1997 k 1 d l -----------------g cow -----------------0.03 0.10 0.498 0.46 0.09 0.012 P valuet 0.025 0.966 t Probability value for comparisons between season means within coat color t Probability value for comparisons between coat color means within season. Greater BW loss partially explains higher production of black coat cows though there were no differences in total intake. General loss of body weight in winter indicates animals were performing above their intake of digestible energy. Given that black coat cows did not lose BW during summer there seems to be a phenomena occurring where cows when stressed, tend to be conservative with energy by reducing milk production and

PAGE 200

181 body tissue mobilization. In winter, when environmental stress was no longer perceived as a limitation to performance, milk production was higher even at the expense of body weight loss. Animal physiology studies to explore scientific basis for this occurrence may be warranted. No reports of body weight responses to coat color were found in the literature. Summary and Conclusions Grazing behavior responses in this study suggest that pasture characteristics, seasonal differences, and animal coat color characteristics influence grazing time of lactating dairy cows. The interaction of these factors in turn influenced pasture OMI, which subsequently affected milk production. The effect of pasture characteristics on grazing behavior was determined by imposing different management treatments. In summer, shorter grazing time on GL pastures seemed to be related to the higher digestibility and lower NDF concentration of perennial peanut compared to Tifton 85 bermudagrass Cows grazing perennial peanut achieved greater intake despite the shorter grazing time. This suggests that they were able to consume a greater amount of digestible energy at a lower energy expenditure for grazing compared to their counterparts grazing bermudagrass pastures. These differences in grazing time and forage intake may have been associated with ingestive behavior. Field observations suggest this may have been due to faster biting rate and less time spent in selection when grazing the shorter peanut canopy. Cows grazing bermudagrass pastures on the other hand seemed to have slower biting rates due to more prehensile jaw

PAGE 201

182 movements per bite. Also, more time may have been spent in searching for more digestible portions of the bermudagrass canopy to graze Forage allowance was not limiting in either SR treatment on bermudagrass so there likely was opportunity for selection. The ability of animals to achieve higher intake while spending less time grazing on perennial peanut pastures suggests that high digestibility, low fiber forage species are likely to result in greater production per animal for pasture-based dairy systems during summer in Florida. In winter cows grazed longer on low SR pastures and achieved higher forage OMI compared to animals on high SR treatments It appeared likely that shorter grazing time and depression of intake on high SR pastures was due to cessation of grazing because of lack of forage. Forage allowance on these pastures ranged from 0 28 to 0.36 kg forage per kg of cow liveweight quantities well below those thought needed to provide near maximum animal performance (Sollenberger and Moore, 1997). Dougherty et al. (1992) indicated that animals stopped grazing after they had removed forage above a particular horizon in the canopy. The same phenomena seemed to have occurred in this study. These results suggest that grazing management must ensure adequate availability of forage on pasture which entails moving cattle to new paddocks when forage becomes limiting Apparent lack of restriction in grazing time due to climatic factors implies that animals can expend more time grazing in winter, as long as there is adequate forage availability which may lead to increased forage intake and improved animal performance. This is assuming that animals had not reached the maximum limit on grazing time during a 24-h period Given the high quality of cool-season forages greater herbage allowance may allow for

PAGE 202

183 reductions in concentrate feed utilization and thus, lower production costs. Research may be necessary to determine the best management practice to improve intake via increased grazing time on winter pastures. Options include reducing stocking rates, thereby increasing herbage allowance, or keeping high stocking densities but moving animals to fresh paddocks as soon as a specific horizon in the sward has been grazed. These approaches should also seek to establish the upper limits to time spent grazing in these conditions. Night grazing times were not different between seasons when the entire range of sampling environments within a season were considered. When temperature and solar radiation effects on grazing time for individual sampling days were evaluated using regression, the response was different. Temperature and solar radiation were found to influence grazing in summer but not in winter. Daytime grazing in summer was shorter on days when temperature and solar radiation intensity were higher. On these days, there is evidence that animals attempted to compensate by grazing longer at night. This attempt to extend nighttime grazing was not sufficient to compensate for restricted daytime grazing. Cows also spent more time under shade on these days. It is likely that the influence of these climatic variables are limiting to animals' desire to graze only during the time of the year when heat stress is a problem. In winter, cows' normal mechanisms to dissipate excessive body heat apparently are not hindered by environmental conditions thus reducing incidences of heat stress. Nighttime grazing results suggest that there may be a possibility to overcome some of the limitation to increased grazing time at night by providing fresh paddocks in the

PAGE 203

184 evenmg. This practice eliminates the need for cows to graze paddocks that were contaminated with excreta during the day. Research options could include moving animals to fresh paddocks in the evenings instead of mornings, or moving to fresh paddocks both mornings and evenings. With lactating cows this does not involve substantially more labor because animals would be moved to new paddocks when they returned from each milking. The idea would be to improve forage intake by removing non-climatic restrictions to extension of grazing time. Offering fresh paddocks with unrestricted forage availability in the evening will also serve to test the theory of a fixed limit to nighttime grazing, both in winter and summer pastures Animals grazed longer when fed a lower amount of grain supplement in summer. This suggests that animals' willingness to continue grazing was associated with achieving some level of nutrient intake in conditions where forage availability was not limiting but heat stress seemed to be the major factor limiting time spent grazing. In winter, grazing time did not change in response to supplementation levels because animals continued to graze in order to maximize forage intake as long forage was still available on pasture Forage and total OMI data for winter suggest that there were no substitution effects of supplemental feed, perhaps indicating that cows were not getting sufficient forage, especially on high SR pastures. Cows with predominantly white coats grazed longer than cows with predominantly black coats regardless of season. The difference in grazing time was due mainly to daytime grazing since nighttime grazing was not different between coat colors. Temperature did not appear to influence the grazing behavior responses by the different

PAGE 204

185 color animals in either summer or winter. Solar radiation intensity, however, was found to be linearly related with time spent grazing by both black and white cows during summer, but not in winter. Solar radiation has great importance for heat balance of animals because of its direct effect on body temperature. Heat load on cows bodies from solar radiation is produced by absorption of light. Cows with predominantly black coats have almost twice the light absorptivity than cows with light coats (Stewart, 1953), hence their heat load will be greater and their resistance to heat stress will be less. The cows with lighter coats spent less time under shade, another indication that they were more resistant to heat stress than the cows with black coats. The lack of relationship between animals behavior due to coat color and solar radiation during winter may indicate that heat stress factors are not critically important during the cool season, likely because animals can quickly dissipate excess body heat caused by light absorption. The longer grazing time of cows with predominantly white coat was associated with a trend toward higher forage and total intake and with greater milk production in summer but not in winter. Quantitative relationships between proportion coat color and forage intake indicate that, in summer as proportion of black in the hair coat increased, forage intake and milk production decreased linearly but the opposite occurred in winter (even though cows with white coats were grazing longer) It was noted that in summer the white cows with higher milk production were losing body weight while black cows producing less milk did not lose weight. In winter when heat stress was no longer a problem milk was produced at the expense of body tissue This suggests that the more heat stressed animal may tend to conserve energy by reducing milk production and body

PAGE 205

186 tissue mobilization. Further studies examining physiological responses may be warranted to establish if this observation has scientific basis. In the conditions of this experiment, white coat cows demonstrated advantages over black coat cows in grazed-pasture systems for resistance to heat stress in hot environments characterized by high solar radiation Coat color is a fairly easily heritable trait (Becerril et al., 1991; 1996; King et al., 1998; Schleger, 1962) should producers using grazed-pasture systems decide to select for light coat color animals Hansen (1990) cautioned that it is not clear whether it would be useful to select for favorable coat color indicating the potential of white animals for sunburn. The author s personal experience in a tropical environment suggests that cattle with predominantly white coats ( even the white patches of animals with predominantly black coat) may be more susceptible to skin diseases and parasites Also the possibility that cows with black coat may have an ability to seemingly slow down energy expenditure may potentially confer some yet unknown benefit. The relationship of cows' grazing behavior to pasture characteristics, climatic conditions and animal coat color characteristics has offered options for developing management strateg i es to increase forage intake and possibly improve animal performance. The study served to underscore the importance of grazing time in studies of ingestive behavior. More importantly, it highlights the need to examine concurrently the other facets of ingestive behavior viz ., bite weight (intake per bite) and biting rate the product of which determines rate of intake in order to obtain a more complete understanding of factors determining forage intake of grazing animals.

PAGE 206

CHAPTERS COMPARISON OF THREE TECHNIQUES FOR ESTIMATING FORAGE INT AKE OF LACTATING DAIRY COWS ON PASTURE Introduction Production of lactating dairy cows is primarily dependent on the quantity and quality of feed consumed. Quantifying dry matter (DM) intake in grazing systems is necessary for the estimation of nutrient consumption by grazing animals. Nutrient consumption is the product ofDM intake (DMI) and nutrient concentration of the DM consumed. Estimation of daily quantity of nutrient consumed on pasture is critical in coupling animal performance and pasture characteristics and in quantifying nutrient recycling in the soil-plant-animal system. Forage nutrient concentration may be estimated by accurate sampling and analysis of the grazed herbage and is routine in forage research In grazing systems it is important but inherently difficult to quantify forage intake (Burns et al. 1994; Moore, 1996; Reeves et al., 1996). Many methods have been developed but choice of technique is likely determined by available resources and the experimental objectives. Techniques for estimating intake on pasture may be based on the use of internal or external markers ingestive behavior disappearance of herbage mass prediction from forage characteristics, and animal performance (Moore, 1996). All of the commonly used techniques have unique advantages and disadvantages and results from all 187

PAGE 207

188 methods are only estimates of intake with an associated error that varies in magnitude (Bums et al. 1994; Moore, 1996). While none of the methods are completely adequate, it may be possible to obtain precise measurements of differences among pastures at specific times (Moore, 1996) Better accuracy is required however, for them to be useful in quantitative applications (Bums et al., 1994). Moore (1996) categorizes the commonly used approaches to estimating forage intake by grazing animals based on suitability as estimates for a) individual animals orb) groups of animals or a pasture Approaches considered suitable for estimates of intake in individual animals are techniques based on the use of markers or on ingestive behavior, while estimates for groups of animals or a pasture are techniques based on disappearance of herbage mass, prediction from forage characteristics, or calculations of energy requirements for observed animal performance (Moore, 1996). Intake prediction of individual grazing animals is often based on the estimation of fecal output using indigestible markers in conjunction with herbage digestibility determined by an in vitro technique. The pulse dose marker technique (Pond et al., 1986) is recommended because it minimizes disturbance of animals since animals are dosed only once compared to twice daily in a steady state technique (Moore 1996). The technique is labor intensive with respect to preparing and administering marker, sampling forages and feces, and analyzing samples. There is a degree of difficulty in the use of this technique related to the large number of chemical analyses required to develop the marker excretion curve, the complexity of modeling marker flow to calculate kinetic parameters and proper interpretation of the resulting data. Additionally kinetic properties of the dosed material

PAGE 208

189 must be similar to those of the digesta, thus the marker has to be mordanted to dietary fiber. Reeves et al. (1996) suggested that errors associated with the use of in vitro digestibilities, including the effects of between-animal variation, diet composition, level of intake and physiological status are not accounted for by marker techniques to estimate fecal output. Moore ( 1996) noted that, at best, acceptable estimates of D MI using fecal output estimates are for only those periods of time during which the sampling process occurred. Extrapolation between or beyond those sampling times may provide inaccurate estimates for the entire experimental period. Despite the limitations, Burns et al. (1994) reported successful use of the pulse dose marker technique and Moore (1996) recommends it as the technique of choice for predicting intake by individual animals. Another approach to estimating herbage intake by grazing animals is based on calculating, in retrospect, the amount of energy an animal would need to ingest daily to achieve the observed rate of performance (e.g., weight gain, milk production, etc.) then estimating DMI based on the intake of energy considering the requirement and the energy concentration of the diet (Moore, 1996; Reeves et al., 1996). The energy requirement for given performance levels of particular classes of animals may be obtained from tabulated values. Accurate measurement of production responses such as body weight (BW) changes are critical to quantification of energy requirements. Weighing errors for grazing ruminants may be quite large and contribute to error of estimates of mean weights and liveweight changes (Moore, 1996). An additional disadvantage is that extraneous factors such as nutrient imbalances and environmental effects may affect conversion of net energy (NE) to animal product. An advantage of this method is that the intake estimates reflect

PAGE 209

190 changes in forage quality and quantity integrated over reasonably long time periods. Additionally, Moore (1996) noted that this technique lends itself to mathematical simulation modeling and suggested that it should be the technique of choice for intake prediction of groups of animals or a pasture. Intake of grazing animals has often been estimated by the difference in herbage mass before and after grazing, i.e., disappearance of herbage mass. This method is very simple and in theory is the most direct and accurate of all intake estimation methods because of its similarity to the method used for confined studies; i.e., intake is the difference between what is offered and refused. The technique is labor intensive since forage herbage mass has to be estimated both before and after grazing for rotationally stocked pastures or frequently for continuously grazed pastures. Also, many sites have to be sampled to improve accuracy by reducing errors due to within-paddock variability. Double sampling, i.e., estimating herbage mass at many sites by an indirect method (visual or mechanical) and clipping a percentage of the sites to estimate actual herbage mass, then using a regression relationship between clipped and indirect samples to determine pasture herbage mass (Burns et al., 1989), is often used to ensure representative sampling is accomplished. A major criticism of this approach is that it may be too simplistic because it does not consider loss of herbage mass due to trampling or removal by non-experimental animals; i.e., feral animals as well as insects (Burns et al., 1994; Moore, 1996). Estimates of intake using the herbage disappearance method may be acceptable on well managed, uniform pastures with high growth rates. It could be an acceptable alternative technique

PAGE 210

191 for prediction of intake for groups of animals or pastures in situations of rotational stocking and short grazing periods (Moore, 1996). This study evaluated three techniques for prediction of forage DMI of lactating dairy cows grazing cool-season pastures subject to varying management treatments. The techniques of choice recommended by Moore (1996) were studied, viz., the pulse dose marker method, animal performance method, and the herbage disappearance method. The null hypothesis was that these three methods of estimating pasture intake are equally useful in determining intake of grazing dairy cows. The objective was to compare estimates of forage DMI among the three methods in an attempt to identify the most useful approach or combination of approaches for estimating pasture intake of lactating cows. Materials and Methods The study was done in conjunction with a grazing management trial conducted during the 1995-1996 and 1996-1997 winter seasons at the University of Florida Dairy Research Unit. Details on location, site characteristics, climatic conditions during the study, treatment variables, and experimental procedures were described in Chapter 3. The procedure for prediction of forage organic matter intake (OMI) by the pulse dose marker technique was described in Chapter 3. Forage OMI estimates were converted to DM estimates by dividing the OMI by OM concentration of the forage. Forage DMI predictions based on herbage disappearance were calculated as the difference between pregraze and postgraze herbage mass. The procedure used in estimation of

PAGE 211

192 pregraze and postgraze herbage mass was described in Chapter 3. Because grazing periods were 1 d forage growth during the grazing period was no t quantified ('t Mannetje, 1978). Energy requirements were estimated by computing NE of lactation (NEL; Meal d -1 ) requirements for maintenance (NELM), for lactation (NELL) for BW changes (NELBW), for walking (NEL W), and for grazing activity (NELG) Total NEL requirement was obtained by summing all of these. Calculation ofNELM was done using the appropriate NRC equations based on BW and cows' parity, viz., NELM = 1.2 (0 080 BW 75) for first lactation cows NELM = 1.1 (0.080 BW 75 ) for second lactation cows, and NELM = 0.080 BW 75 for third or greater lactation cows (NRC, 1988). Estimates ofNEL requirements for lactation, NELL, were calculated based on daily milk production and milk fat concentration (NELL= kg milk per day* [ 0 3512 + (0.0962 *%milk fat)] ; NRC 1988) estimated from the latter 14 d of each 28-d study period, as described in Chapter 3. Average daily BW gain was charged a 5.12 Meal kg-1 NEL requirement while daily BW loss provided 4.92 Meal kg-1 to available energy, additional to that provided by feed intake (NRC, 1988). Energy requirements for walking were calculated using the estimate for horizontal walking of 0.62 cal kg-1 BW m-1 (AFRC 1993). The distance walked each day by animals in this study was 4.8 km (representing the average distance from pasture to milking parlor, 1 2 km, times four the number o f times they walked to or from the parlor). An equation developed by Rochinotti (1998) for estimating distance walked while grazing was first included to compute total distance walked but was omitted when it was realized that the additional distance computed was

PAGE 212

193 unreasonably large given the paddock sizes and that the equation was developed based on data from much larger paddocks. Calculation of the energy requirement for grazing activity was done using an equation suggested by Rochinotti (1998): NELG = 1.2 kcal grazing time (h) BW 75(divided by 1000 to convert to Meal). The energy requirement value used in this equation is the average of estimates made by Di Marco et al. ( 1996), viz., 1.85 kcal h1 (kg BW 75)"1 for regular pasture conditions (defined as a ryegrass [Lolium multiflorum L.] pasture with a herbage mass of 1480 kg ha1 height of 10.5 cm, digestible DM concentration of 610 g kg1 and crude protein [CP] concentration of 150 g kg 1), and 0.55 kcal h1 (kg BW 75)"1 for good pastures (defined as an oat [Avena sativa L.] pasture with a herbage mass of 2280 kg ha1 26.9 cm height, digestible DM concentration of 760 g kg1 and a CP concentration of 194 g kg-1). Grazing time was determined from a grazing behavior study (reported in Chapter 4). Based on data from that study, the values used in the computation were 8.25 h when animals grazed the low stocking rate (LSR) treatment of the N-fertilized forage system (GN) and 7 h when animals were on all other treatments Forage intake was estimated to be the amount of forage required to supply the NEL required to obtain the calculated total NEL requirements after accounting for NEL provided by the known amount of concentrate supplement fed to cows, i.e., total NEL minus NEL supplied by concentrate supplement. The NEL concentration of the supplement was computed by the equation: [NEL] = 0.0245 supplement digestible OM concentration+ 1.25 digestible ether extract (DEE)-0.12, derived from NRC (1988). The DEE concentration of the concentrate supplement was 0.06 g kg1 Forage NEL

PAGE 213

194 concentration was predicted by the equation: [NEL] = 0.0245 forage digestible OM concentration 0.12, omitting the DEE component of the equation since DEE concentration of forage is considered negligible. Comparisons of forage DM predictions using the three techniques were done using estimates predicted by calculated NEL requirements for the observed animal performance as the base estimate and comparing estimates predicted by the pulse dose marker technique and the herbage disappearance technique to this base estimate. The base estimate was subtracted from the estimates predicted by each of the other two techniques to determine the magnitude of deviation of their forage DMI estimates compared to the estimates predicted by calculated NEL requirements. Statistical Analysis The statistical model used to fit the data for prediction of the marker appearance curve was described in Chapter 3. Forage DMI estimates predicted by each technique were analyzed by fitting mixed effects models (analyzed separately for each year with the forage system (FS) effect omitted from the model for the 1996 data) as described in Chapter 3. These analyses allowed for inferences to be made about the effect of the experimental variables on forage DMI predicted by each technique and to observe whether the pattern of responses differed among estimates of the different techniques. The same models were also fitted to data representing deviation of estimates predicted by the pulse dose marker technique and the herbage disappearance technique from the base estimates Inferences from this analysis determined whether differences in predictions were affected

PAGE 214

195 by management treatments and/or level of forage DMI. Mean separation using probability of difference tests (PDIFF in SAS; SAS Institute Inc., 1987) was conducted only for effects in the respective models that were statistically significant (P < 0.05) or showed a trend (P < 0.10). Tables summarizing the raw data from this study and the probability values for tests of fixed effects are presented in the Appendix (Tables A-36 to A-39). Means reported are least square means and were different at P < 0.05 unless otherwise stated. Regression analysis (PROC REG procedures in SAS; SAS Institute Inc., 1982a; 1982b) was used to determine if relationships existed between the base estimates and estimates predicted by the pulse dose marker or herbage disappearance technique. The base estimate was the independent variable. The regressions were done first using the combined data for both years then for each year separately. Further, regressions were conducted on data sets within each treatment combination for which experimental variables influenced how much deviation there was between the base estimate and the intake prediction technique. Regressions were also done when other variables of interest appeared related to predictions of forage DMI. Results of these analyses were examined to determine if patterns existed that could help explain why a particular technique may over or underestimate forage DMI, compared to predictions based on energy requirements for observed animal performance, given specific conditions. The results are presented separately for each year of the study.

PAGE 215

196 Results and Discussion Forage DMI Data for 1996 Predictions based on NEL requirements for animal performance In 1996, forage DMI predicted by energy requirements based on animal performance was affected by a stocking rate (SR) effect (P = 0.049) and a trend for a concentrate supplement (CS) effect (P = 0.083). Forage intake predicted by animal performance was greater for cows grazing low SR (11.1 kg d1 ) compared to high SR (8.3 kg d1 ) pastures and cows fed the lower CS level had greater forage intake compared to cows on high CS (10.9 vs. 8.5 kg d1 respectively). These results conform to findings typically reported for grazing intensity and supplemental feeding effects on forage intake responses, i.e., increased intake at low stocking densities due to less limitation on forage availability (Dougherty et al., 1992; Fisher et al., 1996; Hoogendoorf et al., 1992; Kristensen, 1988) and reduced forage intake with increased intake of supplemental feed due to substitution of grain for forage (Berzaghi and Polan, 1992; Holden et al., 1995). Predictions by the pulse dose marker technique Forage DMI estimated by the pulse dose marker technique in 1996 was influenced by a period by CS interaction (P = 0.008) and also by a trend for a period by SR interaction (P = 0.066). The results indicate that forage DMI was greater at high CS compared to low CS during period 1 (P 1 ), tended to be lower at high CS compared to low CS during period 2 (P2), and was not different between CS levels during period 3 (P3; Table 5.1 ). At the high CS, forage DMI predicted by the pulse dose marker technique

PAGE 216

197 was greatest in Pl then consistently declined (P < 0.10) as the grazing season progressed. At the low CS level on the other hand, forage DMI predicted by the pulse dose marker technique was higher during P2 than during Pl and P3, with no difference between Pl and P3 (Table 5.1). Table 5.1. Period by concentrate supplement (CS) interaction effect on forage dry matter intake estimates predicted by the pulse dose marker technique in 1996. Period 1 2 3 cs High Low ------------kg d -1 ----------------15.5 al 12.6 ab 9.7 b 10.4 b 16.5 a 9.4 b P valuet 0.0320 0.0780 0.8769 t Probability of difference value for comparisons between CS means within a period t Means followed by the same letter within a column are not different (P < 0.05). Means thus indicated are different from the next higher mean in that column at P < 0.10. The trend for the period by SR interaction occurred because at high SR, forage DMI predicted by the pulse dose marker technique was greater for P2 than for Pl or P3 with no difference between Pl and P3, while at low SR forage DMI was greater in Pl than in P3 with P2 being intermediate but not different from either of the other two periods (Table 5.2). There was a trend for forage DMI to be higher at high SR than at low SR during P2 but there were no differences due to SR during the other periods (Table 5 2).

PAGE 217

198 Table 5.2. Period by stocking rate (SR) interaction trend on forage dry matter (DM) intake estimates predicted by the pulse dose marker technique in 1996. SR Period High Low P value t -----------kg d"1 ----------------1 12.3 bt 13.6 a 0.5055 2 16.7 a 12.4 ab 0.0626 3 9.9 b 9.2 b 0.7231 t Probability of difference value for comparisons between SR means within a period. t Means followed by the same letter within a column are not different (P < 0 05). Means thus indicated are different from the next higher mean in that column at P < 0.10. When estimates of the deviation of pulse dose marker technique predictions from the forage DMI base estimates data were analyzed period by CS (P = 0.001) as well as period by SR (P = 0.001) interactions were detected. The results show that at high CS the pulse dose marker technique overestimated forage DMI the most during Pl, then the magnitude of deviation declined with period as the grazing season progressed (Table 5.3 ) At low CS the pulse dose technique overestimation of forage DMI was greater for P2 than for Pl or P3. During Pl and P3 the technique actually underestimated forage DMI a t low CS Additionally, the pulse dose marker technique overestimated forage DMI when animals were fed at the high CS level compared to low CS during Pl, but there were no differences due to CS during P2 or P3 (Table 5.3).

PAGE 218

199 Table 5.3. Period by concentrate supplement (CS) interaction effect on deviation of forage dry matter intake estimates predicted by the pulse dose marker technique from base estimates in 1996. Period 1 2 3 cs High Low ------------kg d1 ----------------8.7 at 4.3 b -0.8 C -0.1 b 6.0 a 2.1 b P valuet 0.0023 0.4109 0.5461 t Probability of difference value for comparisons between CS means within a period. t Means followed by the same letter within a column are not different (P < 0.05). The period by SR interaction occurred because at high SR, the deviation of forage DMI predicted by the pulse dose marker technique from base estimates was greater for P2 than for Pl, and greater for Pl than P3, while at low SR the deviation in forage DMI was greater in Pl than in P2 or P3 the latter periods being not different (Table 5.4). The pulse dose marker technique overestimated forage DMI relative to the base estimate more at high SR compared to low SR during P2, and there was a marginal trend for greater underestimation at low SR compared to high SR during P3 (Table 5.4). Examination of these results comparing the forage DMI estimates pred i cted by the pulse dose marker technique and base estimates suggested that more overestimation tended to occur at the higher estimates of forage DMI. Regression analysis of all the 1996 data with estimates predicted by the pulse dose marker technique as the independent variable and deviation from base estimate as the dependent variable confirmed that the

PAGE 219

200 pulse dose marker technique was over estimating more when the predicted forage DMI was higher (Figure 5.1). Table 5.4. Period by stocking rate (SR) interaction effect on deviation of forage dry matter intake estimates predicted by the pulse dose marker technique from base estimates in 1996. SR Period High Low P value t ----------kg d"1 ----------------1 4.1 bi 4.5 a 0.8745 2 9.5 a 0 8 b 0.0025 3 0.3 C 3.3 b 0 1093 t Probability of difference value for comparisons between SR means within a period. t Means followed by the same letter within a column are not different (P < 0.05). Regression techniques could not establish a quantitative relationship (P = 0.293) for the 1996 data between forage DMI estimates predicted by NEL requirements vs those predicted by the pulse dose marker technique. This supports the earlier evidence that there was wide variation in the deviation of pulse dose marker technique estimates from base estimates among management treatments and period If the pulse dose marker technique had consistently over or underestimated forage DMI compared to the base estimates there would have been evidence of a quantitative relationship. Regressions were done on subsets of the data separately for each period each SR, and each CS because these factors featured among the causes for the observed variation. Except for

PAGE 220

201 15 DBE= 11.81 + 1.1711 PDME 10 ......... "'C 5 ..._ C) w 0 co 0 -5 -10 6 8 10 12 14 16 18 20 22 PDME (kg/d) Observed forage DMI difference _,.__ Predicted forage DMI difference Figure 5.1. Relationship between estimates of forage dry matter intake (DMI) predicted by the pulse dose marker technique (PDME) and the deviation of these estimates from the base estimates (DBE) in 1996. the Pl data which showed a trend toward a linear relationship (P = 0.090), all regressions indicated a lack ofrelationship (P values ranged from 0.313 to 0.951). The Pl data indicated that estimates predicted by the pulse dose marker technique tended to decrease with increasing base estimates (pulse dose estimate= 20.28 0.86466 base estimate; r2 = 0.40; RSME = 2.82). This further supports the evidence that there was overestimation of forage DMI when it was predicted by the pulse dose marker technique to be high, less overestimation as forage DMI dropped and an underestimation when intakes were low.

PAGE 221

202 There was wide scatter and lack of a discernable pattern in the other data subsets, likely caused by interactions. Analyses of further subsets comprising the interactions were done but are not reported because they may lack statistical validity due to the small number of data points involved in the analysis. Also, they did not change the inferences already made, viz., relationships indicating a pattern of overestimation when high forage DMI was predicted by the pulse dose marker technique. Several factors may be responsible for the observed large deviation of estimates predicted by the pulse dose marker technique from the base estimates at some treatments, and, perhaps more importantly, for the lack of an empirical relationship between the two. Moore (1996) cautioned that acceptable estimates ofDMI by the pulse dose marker technique are provided for only those periods of time when samples were obtained. It is possible that conditions may have varied at the time of sampling on some management treatments in the present study to an extent that extrapolation across the experimental period provided inaccurate estimates. Also, with this technique, total intake is estimated based on fecal output then forage intake is calculated based on the known amount of concentrate fed, thus quantifying correctly the amount of concentrate supplement intake is critical. During 1996, animals were group fed based on high or low CS treatment, allowing the possibility that some animals, ostensibly those that were on high SR treatments and presumably not grazing sufficiently to achieve rumen fill may have been consuming more than their share of the supplement. If that was the case, calculations for forage intake were based on incorrect supplement intake estimates in some instances, hence the large disparity in the data. Imprecise accounting for supplement intake,

PAGE 222

203 however, will also cause inaccurate estimates computed by the NEL requirement technique, so it is unlikely that supplement intake was the main factor causing poor estimates. Another possible cause for overestimation may be dosing errors. In some instances, animals may have chewed and/or spit out some of the mordanted fiber hence the amount of marker dosed was lower that used in computation. The lower marker dosage results in lower fecal marker concentration, thus overestimation of fecal output leading to overestimation of intake. Predictions by the herbage disappearance technique Forage DMI estimates predicted by the herbage disappearance technique in 1996 were affected by a period by SR interaction (P = 0.026). For high SR treatments, forage DMI was greater in Pl than P2 (Table 5.5). Forage DMI for P3 was intermediate but was not different from estimates for Pl or P2. For low SR pastures forage DMI was similar between Pl and P3 both of which were greater than forage DMI for P2 (Tab l e 5 5) Forage DMI was generally lower on high SR pastures compared to low SR; the statistical difference was marginal during P2 though intake on low SR was almost twice that for high SR pastures (Table 5 5) Deviation of forage DMI estimates predic t ed by the herbage disappearance technique from the base estimates in 1996 was affected by a trend for a period effect (P = 0 094). The herbage disappearance technique generally underestimat ed forage DMI compared to base estimates ; the magnitude of underestimation during P2 (4.3 kg d "1 ) being greater than that for Pl (0.09 kg d1 ) with P3 ( 2. 5 kg d 1 ) b e ing intermediate bu t not different from either Pl or P2 Examination of the data suggests that estimates

PAGE 223

204 predicted by the herbage disappearance technique are closer to the base estimates than estimates predicted by the pulse dose technique. Additionally, the greater underestimation that occurred in P2 is associated with the overall lower forage DMI predicted by the herbage disappearance technique during that period (Table 5.5) Table 5.5. Period by stocking rate (SR) interaction effect on forage dry matter intake estimated by the herbage disappearance technique in 1996. SR Period High Low P value t ------------kg d"1 ----------------1 5.9 at 11.2 a 0.0090 2 3.4 b 6.7 b 0.0684 3 4.7 ab 12.3 a 0.0012 t Probability of difference value for comparisons between SR means within a period. t Means followed by the same letter within a column are not different (P < 0.05). Regression analysis of all the 1996 data with estimates predicted by the herbage disappearance technique as the independent variable and deviation from base estimate as the dependent variable confirmed there was a linear relationship between forage DMI estimates predicted by the herbage disappearance technique and the deviations from the base estimates (P = 0.0001). As with the pulse dose marker technique, the deviation from base estimates increased with increasing forage DMI predicted by the herbage disappearance technique (Figure 5.2). The basic difference between the two techniques

PAGE 224

205 8 -6 4 2 32 C) 0 w CD -2 0 -4 -6 DBE= 7.74 + 0.7349 HDE r2 = 0.49; RSME = 2.87 .,..,.,.--"\ -\ J,~ _,,...i,..r v. - - -8 I I I I I I I . 2 4 6 8 10 12 14 16 18 HOE (kg/d) Observed forage DMI differences --Predicted forage DMI differences Figure 5.2 Relationship between estimates of forage dry matter intake (DMI) predicted by the herbage disappearance technique (HOE) and the deviation of these estimates from the base estimates (DBE) in 1996. was that the herbage disappearance method generally had lower forage DM predictions compared to the pulse dose marker technique. Regression techniques indicated that there was a marginal relationship (P = 0.111) between estimates predicted by the herbage disappearance technique and base es timates for all 1996 data. When analyses were conducted for subsets of the data representing each period, there was evidence of a relationship during P2 (P = 0.039 ; herbage disappearance

PAGE 225

206 estimate= 0.64 + 0.4713 base estimate; r2 = 0.54; RSME = 1.48), but not during Pl (P = 0.668) or P3 (P = 0.395). The pattern for a linear relationship between forage DMI estimates predicted by the herbage disappearance technique and base estimates is evidence that predictions by this technique are similar to the pattern in responses of calculated NEL requirements for observed animal performance. This is more evident during P2 when the magnitude of deviation from the base estimate was larger than in P 1 or P3. The lack of consistency in the response patterns probably reflects changes due to sward characteristics as the grazing season progressed. Pregraze herbage mass was lowest during P2 (Chapter 3). This may suggest that estimates by the herbage disappearance method may be less consistent when the sward has more forage, although, the estimates under these conditions are closer to the estimated NEL requirements The insinuation by these data is that estimates by the herbage disappearance technique are more precise when pasture herbage mass is low but may be more accurate when herbage mass is increased. Forage DMI Data for 1997 Predictions based on NEL requirements for animal performance Forage DMI estimates predicted based on NEL requirements to achieve the observed animal performance were affected by main effects of period (P = 0.0001), SR (P = 0.001), and CS (P = 0.003) in 1997. The forage DMI predicted for P3 (9.6 kg d'1 ) was greater than that for Pl (6.5 kg d '1 ) or P2 (6.4 kg d1). Estimates for Pl and P2 were not different from each other. Cows grazing high SR pastures had lower forage DMI (6.3 kg

PAGE 226

207 d-1 ) compared to low SR (8.7 kg d-1 ) treatments. Also, cows fed the higher CS rate had lower forage DMI (6.6 kg d -1 ) compared to animals on the low CS treatment (8.4 kg d-1). Examination of the herbage allowance data (reported in Chapter 3, Table 3 5) suggests that the period and SR effect on forage DM intake were associated with herbage allowance. Herbage allowance was greater on low SR compared to high SR pastures and was greater during P3 compared to Pl and P2. As noted with the 1996 data, these results conform to findings typically reported for grazing intensity effects on forage intake responses, i.e. higher intake when forage availability is increased (Dougherty et al., 1992; Fisher et al., 1996 ; Hoogendoorf et al., 1992; Kristensen 1988). The CS effect also concurs with the finding of other studies that suggest there is reduced forage intake with increased intake of supplemental feed, likely due to substitution of grain for forage (Berzaghi and Polan 1992; Holden et al. 1995 ; Reeves et al., 1996) Predictions by the pulse dose marker technique Forage DMI estimated by the pulse dose marker technique in 1997 was affected by a period main effect (P = 0 014) Estimates for Pl (15.2 kg d -1 ) were greater than those for P2 (10.3 kg d -1 ) or P3 (11.6 kg d-1 ) but estimates for P2 and P3 were not different from each other. When the data for deviation from base estimates were analyzed there was also a period effect (P = 0.0001) Comparisons of deviation from base estimate means among periods followed a similar pattern as the forage DMI estimates, with the pulse dose marker technique overestimating intake by 8.8 3.1, and 2 2 kg d -1 for Pl, P2 and P3, respectively. Comparison of these results suggested that larger overestimation compared to base estimates occurred when predicted forage DMI was higher. There were

PAGE 227

208 also trends for SR effects (P = 0 069) and a period by FS interaction (P = 0.058). Examination of the data for these trends showed that they matched marginal trends for a SR by FS interaction (P = 0 0122) and a period by FS interaction (P = 0.108) in the same manner, viz a tendency for larger overestimations when predicted forage DMI was higher. These data are not presented because their value in providing additional clarity in explaining these responses is minimal. Regression analysis of all the 1997 data with estimates predicted by the pulse dose marker technique as the independent variable and deviation from base estimate as the dependent variable confirmed there was a linear relationship between forage DMI estimates predicted by the pulse dose marker technique and the deviations from the base estimates (P = 0.0001) As with the 1996 results the deviation from base estimates increased with increasing forage DMI predicted estimates (Figure 5.3) Regression analysis of estimates predicted by the pulse dose technique in 1997 in relation to base estimates indicated no evidence of a r e lationship (P = 0 681 ). Analysis of subsets of the data to represent variables that caused variation among the deviation from base estimate responses did not change this inference. This lack of a quantitative relationship suggests that the pulse dose marker technique was not predicting forage DMI in the same pattern predicted by estimates ofNEL requirements Similar analysis of the combined data for both years also showed a lack of relationship (P = 0.676) further supporting this observation when an even larger data base was used to make inferences. Concentrate supplem e nt was fed to animals in pastures by e x perimental unit in 1997. Thus lack of relationship between pulse dose and base estimates could not be

PAGE 228

209 20 DBE= 7.19 + 0 587 P D ME 1 5 r2 = 0 69 ; RSME = 2.50 10 C) :: - w 5 co 0 0 ,I' 5 6 8 10 12 1 4 1 6 18 20 22 24 PDM E ( k g /d) Observed forage DMI difference Predicted forage DMI difference Figure 5.3. Relationship between estimates of forage dry matter intake (DMI) predicted by the pulse dose marker technique (PDME) and the deviation of these estimates from the base estimates (DBE) in 1997. attributed to incorrect supplement intake due to group feeding as may have been the case in 1996 It is possible that the 1997 measure of supplement intake may still be imprecise because of spillage incorrect weighing, or operator error Errors due to supplement intake, as noted earlier, will also feature in estimates of forage DMI predicted by computations based on NEL req u irements for observed animal performance. Thus a more likely explanation for the observed overestimation in P 1 and the lack of relationship between the pulse dose marker technique and NEL requirements seems to be dosing errors

PAGE 229

210 Predictions by the herbage disappearance technique In 1997, forage D MI estimates predicted by the herbage disappearance technique were affected by an SR effect (P = 0.0001). Cows grazing high SR pastures had lower forage DMI (5.4 kg d"1 ) compared to LSR (9.3 kg d1). This was similar to the results obtained when forage DMI was predicted based on NEL requirements but the magnitude of difference was larger in this case. This reflects both an overestimation at the higher forage intake prediction and underestimation at the lower prediction. There was also a period by FS interaction (P = 0.040). In both FS, forage DMI was estimated to be similar during Pl and P3; values that were lower than estimates for P2 in the grass-legume system (GL), but in GN pastures the estimates for Pl and P2 were not different, but P2 was different from P3 (Table 5.6). Greater forage DMI was estimated on GL compared to GN pastures during Pl and P3 but the opposite occurred during P2. These results must be interpreted with caution because animals grazing the high SR treatments on GL pastures had to be removed from pastures during P2 so the responses for GL systems may not reflect accurately the treatment performance. Analysis of the deviation from base estimates in relation to estimates predicted by the herbage disappearance technique indicated that there were main effects of period (P = 0.003) and CS (P = 0.018), and a marginal trend for a SR effect (P = 0.104). The results show that the herbage disappearance technique overestimated forage DMI by 1.7 kg d1 during Pl, which was statistically greater than the underestimations for P2 (0.7 kg d1 ) and P3 (1.2 kg d1). The means for P2 and P3 were not different from each other. Forage DMI for cows fed at the high CS rate was overestimated by 0.9 kg d1 while those

PAGE 230

211 on the low CS treatment was underestimated by 1.0 kg a-1 Also, forage DMI of cows grazing low SR pastures tended to be overestimated by 0.7 kg a -1 while for high SR pastures it was underestimated by 0.6 kg a-1 These values, while statistically different at the P = 0.10 level, are too small to be presented as realistically representing a deviation of intake estimates predicted by the herbage disappearance technique from the base estimates. The same may be said for all deviations from base estimates values found for the herbage disappearance technique in 1997. Table 5.6. Period by forage system (FS) interaction effect on deviation of forage dry matter intake estimates predicted by herbage disappearance technique from base estimates in 1997. Fst Period GL GN P valuel ------------kg d "1 ----------------1 9.1 a 7.3 ab 0.0221 2 4.4 b 6.3 b 0.0496 3 9.0 C 7.8 a 0.0882 t GL = forage system comprising of rye, ryegrass, red clover, and crimson clover mixture; GN = forage system comprising ofN-fertilized rye and ryegrass mixture. t Probability of difference value for comparisons between FS means within a period. Means followed by the same letter within a column are not different (P < 0.05). Unlike the 1996 results and the 1997 pulse dose marker data there was no clear pattern of overestimation or underestimation of forage intake by the herbage disappearance technique. Regression analysis with the predicted estimate as the

PAGE 231

212 independent variable and the deviation as the dependent variable indicates that there was a linear relationship (P = 0.001; Figure 5.4), however. Similar to the previous comparisons, there was a positive increase in the deviation from base estimates as estimates predicted by the technique increased. Examination of the data points indicate that there was about as much overestimation as there was underestimation (average deviation of all data points was 0.04 kg d -1). The low r2 value indicates a wide scatter among the data, but it should be noted that the magnitude of spread of these data from the prediction curve is much smaller than that found with the pulse dose marker technique in 1997 or any of the 1996 data. Regression techniques established that there was a positive linear relationship (P = 0.0001; herbage disappearance estimate = 3.21 + 0.5885 base estimate; r2 = 0.32; RSME = 2.13) between estimates predicted by the herbage disappearance technique and base estimates in 1997 Analysis of subsets of the data to represent variables that caused variation among deviation from base estimate responses did not change this inference; most of the regressions were statistically significant (P < 0.05) or showed a trend (P < 0 10). The pattern of these responses concur with the 1996 results. Analysis of the combined data for both years also indicated that estimates predicted by the herbage disappearance technique were positively related to the base estimates (P = 0.001; herbage disappearance estimate= 3.97 + 0.4335*base estimate; r2 = 0 16; RSME = 2 77).

PAGE 232

213 6 DBE= 3.43 + 0.4481 HDE 4 r2 = 0 24; RSME = 2 06 -2 "'C --C) 0 w co 0 -2 -4 -6 2 4 6 8 10 12 14 HOE (kg/d) Observed forage DMI differences Predicted forage DMI differences Figure 5.4 Relationship between estimates of forage dry matter intake (DMI) predicted by the herbage disappearance technique (HDE) and the deviation of these estimates from the base estimates (DBE) in 1997. Examination of the regression parameters (P, r2, and RSME values) suggests that the relationship was more consistent in 1997 compared to 1996. This was further evident when subsets of the data also showed the relationship. The magnitude of the deviations from base estimates were smaller in 1997 also, suggesting improved accuracy in the 1997 vs the 1996 data In 1996, sampling to estimate herbage disappearance was done twice during each 28-d period on alternate weeks. In 1997, sampling frequency was doubled; done four times per period (viz., every week). The results suggested that increasing the sampling frequency led to increased accuracy of the herbage mass data for calculating

PAGE 233

214 herbage disappearance. Burns et al. (1994) pointed out that a large number of samples are required to provide adequate estimates of the change in pasture herbage mass, indicating that the intensive sampling required to achieve the correct number of samples may be the most serious limitation of this technique. The data from the present study suggest that the sampling frequency in 1997 may have achieved a desirable estimate of change in pasture herbage mass. The sampling frequency in 1996, while it did not result in unreasonable deviations in forage DMI predicted by the herbage disappearance, could obviously have been improved. Moore ( 1996) suggested that use of the herbage disappearance technique for estimating forage DMI by groups of animals or a pasture was likely to be successful for only rotational stocking and short grazing periods. The "short" grazing period was defined by Burns et al. (1994) as 1 to 3 d. It was also indicated that success of this technique depended on using a cutting height that was low enough to include forage that may have been trampled (Burns et al., 1994). These conditions were met by the procedures used in this study, likely resulting in the apparent success of this technique in predicting forage DMI quite close to that estimated based on NEL requirements required for observed animal performance. Summary and Conclusions Forage DMI responses obtained by the different techniques compared in this 2-yr study indicate that predictions by the herbage disappearance technique more closely approximated the estimates calculated based on NEL requirements than did the estimates

PAGE 234

215 predicted by the pulse dose marker technique. It is not possible to directly measure fora g e intake, but estimates can be made by different techniques that may provide precise measurements of differences among pastures at different times Predictions of forage DMI using the technique that back calculates for NEL intake based on requirements for observed animal performance appear to give estimates that make biological sense. Estimates for forage DMI were greater when forage allowance was higher concurring with the conclusions from several studies (Dougherty et al ., 1992 ; Fisher et al., 1996 ; Hoogendoorf et al., 1992 ; Kristensen 1988) Also forage DMI was predicted to be higher by the NEL technique when animals were fed at the low CS level and vice versa indicating animals fed less supplement attempted to meet energy requirements by consuming more forage. Similar findings were reported when dairy cows were managed on grazed pasture systems (Berz.aghi and Polan 1992 ; Holden et al. 1995 ; Reeves et al. 1996) One criticism of the energy requirement technique is that i ntakes calculated are derived from generalized equations and this may not represent the intake of individual animals (Reeves et al., 1996). This limitation may be overcome i f the inferences are mad e for pastures with more than one animal representing sampling units as was done in the present study i.e. allowing for reduction in between-animal variation. Additionally precise estimates of supplement intake as well as forage digestibility (Moore 1996) are a requirement for successful use of this technique. Procedures to ensure adequate prediction of forage digestibility are well known (Moore 1996) Another serious limitation is accurate measurement of changes in live BW of animals with time especially

PAGE 235

216 those occurring over short periods of time. Because it was not possible to obtain shrunk BW measurements (Stuedemann and Matches, 1989) with lactating dairy cows, the BW changes measured may not be accurate, but the impact of this on subsequent intake predictions cannot be determined. An advantage of this technique is that it integrates intake responses over the period of study. The technique may be more precise in estimation because it directly relates to animal performance. This technique may be useful for planning efficient feeding management strategies that synchronize nutrient requirements with production levels. Estimates predicted by the pulse dose marker technique did not correlate well with the predictions based on energy requirements. The seemingly poor estimates when the predictions were high may have been caused by any number of factors, but they all point to the need for careful procedure and precision in preparing the mordanted fiber, in dosing, in obtaining samples, in chemical analysis, and in quantifying supplement intake. Additionally bearing in mind that the predictions represent estimates for the period in which samples were obtained, care should be taken to ensure conditions during the sampling process are representative of the overall conditions of the study. Despite the wide variation found in the present study, the pulse dose marker technique gave realistic estimates of forage DMI that were reflective of the animals grazing behavior (Chapter 4) and animal performance (Chapter 3). This technique may be more useful than the others in a research environment where digesta kinetics of consumed herbage may be critical to providing explanation of pasture characteristics and its impact on forage intake

PAGE 236

217 The herbage disappearance technique provided estimates that closely matched those obtained by predictions based on energy requiremen t s suggest i ng that under the conditions of this experiment, the technique may provide acceptable estimates of forage intake. The technique does not allow estimation of intake for individual cows therefore is more appropriate in settings where herd estimates of forage intake are required. Its usefulness may be in commercial operations where determination of the influence of pasture characteristics on intake is of concern Estimates of forage DMI by this technique are not hindered by group feeding management for concentrate supplement since knowledge of supplement intake does not feature in estimation of forage i ntake by this method. These results concur that the herbage disappearance technique is a suitable alternative for estimating forage intake for groups of animals on well managed rotationally stocked pastures with short grazing periods Based on these results, it may be concluded that predictions based on NEL requirements are useful for estimating forage DMI when evaluating effects of grazing management on forage intake of lactating dairy cows on grazed-pasture systems Precise estimates ofBW changes and supplement intake are required for the technique to be successful. The technique itself does not require substantial field work but detailed calculations are involved in arriving at estimates. The herbage disappearance technique provided equally suitable estimates ; the precision of these estimates appeared to improve when sampling to determine change in pasture herbage mass was increased. The frequent sampling required has often been a criticism for the use of the technique but these samples are required anyway to characterize pasture responses and to couple pasture

PAGE 237

218 characteristics with animal performance, so it should not be viewed as additional work The pulse dose marker technique estimates varied more but there is no way to determine that they are less acceptable than those of the other techniques. The v olume of work and the complexity of the procedures involved in using this technique substantiates the observation that choice of technique must be matched with resources and the research objectives.

PAGE 238

CHAPTER6 GENERAL DISCUSSION AND SUMMARY In recent years, Florida dairy producers have increased the quantity of home grown forage in cattle diets. This move has been driven in part by the need to recycle nutrients in cattle wastes and by greater attention to lowering feed costs. Some producers have adopted pasture-based dairy systems Because of the minor role of pastures in dairying over the last 30 yr, there is a relative dearth of information from which grazing recommendations for pasture-based dairy systems can be made Florida's subtropical climate allows for growth of tropical forage species during the warm months and temperate species during the cool season. These conditions afford opportunity for year round grazing systems for Florida producers, conferring an advantage compared to dairy producers in temperate states. As a consequence, management strategies for forage and animal production in different seasons need to be developed. The principal component of this dissertation research evaluated grazing management of cool-season, pasture-based systems for lactating dairy cows. The primary objective was to quantify pasture production and animal responses when Holstein (Bos taurus) cows grazed two different cool-season forage systems (FS; N-fertilized rye [Seca/e cerea/e L.] and ryegrass [Lolium multiflorum L.] mixed swards [GN] vs. rye ryegrass mixed with red clover [Trifolium pratense L.] and crimson clover [Trifolium 219

PAGE 239

220 incarnatum L.] swards [GL]) at two stocking rates (SR; 5 vs 2.5 cows ha') and two levels of concentrate supplementation (CS; 1 kg concentrate per 2.5 kg of milk produced vs. 1 kg concentrate per 3.5 kg of milk produced). A fundamental aim of this study was to couple forage production responses to observed animal performance by way of feed intake on pasture. In the first year of the study, drought conditions during establishment of pastures and unavailability of irrigation caused establishment failure of the clovers. Data from treatments including clover were omitted from the results reported, but it should be pointed out that the drought conditions affected establishment of rye and ryegrass to a lesser degree. During 1997, GN swards produced more herbage mass than GL swards overall. Additionally forage became scarce on the high SR treatments of the GL system after the initial 28-d grazing period, requiring animals be taken off those pastures for the latter 3 wk of the second period. These results suggest that N-fertilized, rotationally stocked, rye-ryegrass pastures on sandy soils in north Florida have a definite advantage compared to rye-ryegrass swards mixed with clovers. Herbage mass was generally affected by SR in both years. Pregraze herbage mass was greater at low SR compared to high SR. By the end of the study period, pregraze herbage mass was 40% greater on low SR pastures in 1996 and almost twice as great in 1997 compared to high SR ( on the GN system; the GL systems results were confounded because high SR treatments had not been grazed during the second period). Low SR allowed greater accumulation of herbage, which was linked to greater postgraze herbage

PAGE 240

221 mass on the low SR treatments. These results agree with the findings of studies evaluating SR effects on herbage production (Fales et al., 1995; L'Huillier, 1987). Concentrate supplement level played a role in interactions affecting herbage mass in 1997 The highlight of this interaction was that on low SR treatments, there was greater pregraze herbage mass on high CS compared to low CS pastures, suggesting tha t cows grazed less when fed more CS. More herbage remaining on the high CS compared to the low CS pastures allowed for greater herbage accumula t ion. This did not occur on high SR pastures because these pastures were grazed closely regardless of CS. In 1996 animals were group fed according to CS treatment. It was suspected that this led to some animals ostensibly those on high SR pastures where forage availability was l imi ted, consuming more than the allotted amount of supplement. These factors resulted in obfuscation of forage production responses to CS levels during 1996 Herbage disappearance was not affected by grazing management treatments in 1996 but was affected by SR by FS interaction in 1997 As d i scussed earlier the 1996 results may have been obscured by imprecise supplemental feeding managemen t. In 1997 more herbage was removed from high SR compared to low SR treatments on GL pasture except in the second period the period during which animals had to be taken off pasture There was a positive linear relationship between pregraze herbage mass and herbage disappearance more so at high SR in 1997, suggesting that more herbage will be remo v ed from a pasture if there was more at the initiation of grazing under the conditions of this experiment.

PAGE 241

222 Herbage allowance was mostly affected by SR, being greater at low SR compared to high SR. The difference was also reflective of herbage mass responses to management treatments; herbage mass was greater at low SR in both years. Differences in forage nutritive value were essentially determined by grazing interval and by accumulation of mature herbage on pasture High SR pastures were grazed very closely throughout the experiment and did not have as much residual forage as low SR pastures. Thus with less accumulation of mature herbage, high SR herbage had greater in vitro digestible organic matter (IVDOM) concentration and lower neutral detergent fiber (NDF), especially in the last period of the experiment. Also, GL pastures were grazed at 28-d intervals compared to GN pastures which were grazed every 21 d, thus herbage on GL pastures was more mature. Additionally, herbage crude protein (CP) concentrations were greater with less mature swards. A general decline in nutritive value in the latter period of the study was attributed to increasing stem and inflorescence density of rye and ryegrass, more so in low SR pastures. It should be noted that although laboratory estimates of nutritive value were affected by treatment in this study, these differences may not be large enough to impact animal performance greatly and are likely of limited biological importance. Forage organic matter intake (OMI) and total OMI were greater when pastures had higher pregraze herbage mass and herbage allowance at the more lenient SR. Limited herbage availability at high SR led to reduced forage and total intake The effects of CS on forage OMI is somewhat obscured because of imprecise estimates of supplement intake in 1996 and interactions with other management treatments in 1997

PAGE 242

223 Milk production per animal was greater when animals grazed pastures with higher pregraze herbage mass and herbage allowance, viz., low SR treatments. The greater milk production when herbage availability was not limiting is undoubtedly linked to the beneficial effects of herbage mass on nutrient intake. Blood glucose data indicated less discrepancy between energy demand and supply when high pregraze herbage mass was associated with increased OMI. Milk production per unit area of land was greater at high SR, reflecting opportunities for better economic returns using high SR in scenarios where land scarcity is a limitation. Loss of body weight (BW) and body condition score (BCS ) on the more intensively grazed treatments, however, suggests that systems based on high stocking rates may not be sustainable. Both BW and BCS changes were positive toward the end of the trial, suggesting that losses due to body tissue mobilization may not be as drastic if cows are kept on pastures year-round. The results for the primary objectives addressed in this component of the study suggest that, in the current economic environment, N-fertilized cool-season grasses may be a more productive more reliable option than grass-clover mixtures for intensive grazing management systems for lactating dairy cows on sandy soils in Florida. Stocking rate was demonstrated to be the most critical management variable affecting forage production, nutrient intake, and subsequent animal performance The role of CS in forage production and animal performance was not always clear but there was indication that there are beneficial effects to animal performance It is unlikely that high producing dairy cows could achieve potential milk production without concentrate supplementation. The

PAGE 243

224 challenge is to synchronize quantity and nutrient composition of supplement with forage nutritive value and quantity of available pasture. A second component of the dissertation research characterized grazing behavior of lactating cows on summer and winter pastures. Changes in time spent grazing by ruminants is often a compensatory mechanism to variation in short-term herbage intake as a result of pasture and animal characteristics and also climatic factors. Understanding the relationship between grazing time and pasture characteristics, animal traits, and climatic conditions, and how these are coupled to animal performance could provide insight to aid grazing management decisions. The main objective of this component of the research was to quantify grazing management variables, cows' hair coat color, and seasonal (summer vs. winter) effects on grazing time of lactating cows during 24-h grazing cycles. The study was imposed on ongoing winter ( described above) and summer grazing management trials. Treatment arrays were the same in summer as for winter. Summer pastures were 'Tifton 85' bermudagrass (Cynodon spp.) for the GN system and perennial peanut (Arachis glabrata Benth.) for the GL system. Stocking rates were adjusted to match herbage production potential of summer forages; high SR was 10 cows ha-1 on GN pastures and 7.5 cows ha-1 on GL pastures, while low SR was 7.5 and 5 cows ha-1 for GN and GL pastures, respectively. Concentrate supplementation rates in summer were 1 kg per 2 or 3 kg milk cow-1 d-1 for the high and low CS treatments, respectively. In summer cows generally grazed longer during daytime on GN (179 min) than on GL (143 min). It seems likely that the longer grazing time on GN pastures was because cows spent more time manipulating the canopy before taking a bite compared to GL

PAGE 244

225 swards in which most jaw movements appeared to be associated with a bite. Perennial peanut pastures had higher IVDOM and lower NDF concentrations than bermudagrass pastures. The cows grazing perennial peanut pastures achieved greater organic matter intake despite the shorter grazing time. In summer also, cows decreased time spent grazing when fed at the high CS level, suggesting that they stopped grazing after a particular level of intake was achieved. The willingness of cows to continue grazing in summer, even though forage availability was not perceived to be limiting, was tempered by heat stress factors. Grazing time was reduced and animals spent more time under shade on days when ambient temperature and solar radiation were high compared to cloudy days when solar radiation intensity was reduced. On days when heat stress caused marked reduction in daytime grazing, animals compensated by increasing time spent grazing during nighttime, but this increase was not sufficient to fully compensate and total grazing time was still less on hot compared to cooler days. In winter, cows tended to graze longer during daytime on pastures that had greater herbage mass and herbage allowance. The low SR-GN pastures had greater herbage mass and herbage allowance than other treatment combinations. Greater forage OMI was associated with the longer grazing time and with more herbage mass on these pastures. The shorter grazing time and depression of intake on high SR and GN pastures likely was due to cessation of grazing because oflow herbage mass. Total grazing time in a 24-h period followed the same pattern as daytime grazing in winter. Winter grazing time was not affected by temperature or solar radiation.

PAGE 245

226 Cows spent more time grazing during daytime in winter (249 min) than in summer (161 min). Nighttime grazing was not different between the two seasons (194 vs. 191 min for summer and winter grazing respectively) and as a result total grazing time in a 24-h period was greater in winter, with cows averaging 80 min more than in summer. Reduced grazing time in summer was caused in part by heat stress. During winter on the other hand, climatic conditions did not affect grazing time. The main cause of variation in grazing time and forage intake during winter was herbage mass on pasture. Lack of difference in nighttime grazing between seasons may be because cows have a fixed maximum amount of time they will spend grazing at night (tempered by their need for rest and other activities) and/or was likely caused by animals unwillingness to graze during darkness on paddocks fouled by their excreta during the day Within season milk production followed the pattern of OMI i.e. was greater when intake was greater. Total organic matter intake was greater in summer compared to winter but milk production was lower on the GN system likely because of lower digestible energy concentration ofbermudagrass. Cows on summer pastures generally lost BW while in winter cows with greater intake gained BW and those with lower intake lost BW Regardless of season cows with predominantly white hair coats spent more time grazing than cows with predominantly black hair coats. The difference was due mainly to daytime grazing since nighttime grazing was not different between coat colors Temperature did not influence differences in grazing behavior between animals of different coat colors. Solar radiation intensity, however, was inversely related with time spent grazing by both white coat and black coat cows during summer, but not in winter. This

PAGE 246

227 suggests that solar radiation is a critical factor in determining heat stress suffered by cows during hot seasons, but is not as critical when conditions are cool. The longer grazing time of white coat cows was associated with greater milk production in summer but not in winter. Forage OMI was higher on winter pastures for black cows but there were no differences between seasons for white cows. Total OMI and milk production were greater in winter regardless of coat color. Empirical relationships indicate that, in summer, as proportion of black in cows' hair coat increased, there was a linear decrease in total intake and milk production. Similar relationships were not found in winter. In summer, there were no differences in BW changes between coat colors. In winter, however, black coat cows lost more weight than white coat cows. The results from the animal behavior component of the research suggest that pastures that allow greater forage OMI in shorter grazing times, as obtained on perennial peanut pastures, are likely to increase production per animal during summer grazing in hot climates. Greater intake via longer nighttime grazing may be encouraged by providing fresh paddocks in the evening. In winter, increased forage intake will depend on management strategies to allow unrestricted forage availability. White coat cows have demonstrated an advantage compared to black coat cows relative to grazed-pasture systems in summer. Comparisons of techniques for estimating forage OMI was a third component of the dissertation research. Estimation of daily quantity of nutrients consumed on pasture is critical to understanding animal performance responses to pasture characteristics, but it is difficult to quantify forage intake of grazing animals. All of the commonly used

PAGE 247

228 techniques have unique advantages and disadvantages, and choice of technique is likely determined by available resources and experimental objectives. The study compared forage OMI estimates, obtained by the pulse dose marker and the herbage disappearance methods, relative to estimates determined by calculating the energy requirements based on animal performance. The aim was to identify the most useful approach or combination of approaches for estimating forage intake on pasture in pasture-based dairy research. Predictions based on the net energy of lactation (NEL) requirements for observed animal performance appear to give reliable estimates that make biological sense. Estimates for forage DMI were greater when forage allowance was higher. Also, forage DMI was predicted to be higher by the NEL technique when animals were fed at the low CS level and vice versa indicating animals fed less supplement attempted to meet energy requirements by consuming more forage These results suggest that predictions based on NEL requirements are useful for estimating forage DMI when evaluating effects of grazing management on forage intake of lactating dairy cows on grazed-pasture systems. Estimates predicted by the pulse dose marker technique did not correlate well with the predictions based on energy requirements. High estimates of forage OMI predicted by the pulse dose marker technique tended to be higher than estimates predicted by NEL requirements but the deviation was smaller at lower predictions This may have been caused by any number of factors, but they all point to the need for careful procedure and precision in preparing the mordanted fiber, in dosing, in obtaining samples, in chemical analysis, and in quantifying supplement intake. Additionally bearing in mind that the predictions represent estimates for the period in which samples were obtained, care should

PAGE 248

229 be taken to ensure conditions during the sampling process are representative of the overall conditions of the study. Despite the wide variation found in the present study, the pulse dose marker technique gave realistic estimates of forage DMI that were reflective of the animals grazing behavior and animal performance. This technique may be more useful than the others in a research environment where digesta kinetics of consumed herbage may be critical to providing explanation of pasture characteristics and its impact on forage intake. Forage intake estimates obtained by the herbage disappearance technique closely matched those obtained by predictions based on energy requirements, suggesting that under the conditions of this experiment, the technique may provide acceptable estimates of forage intake. The technique does not allow estimation of intake for individual cows, therefore, is more appropriate in settings where herd estimates of forage intake are required. Its usefulness may be in commercial operations where determination of pasture characteristics influence on intake is of concern. Estimates of forage DMI by this technique are not hindered by group feeding management for concentrate supplement, since knowledge of supplement intake does not feature in estimation of forage intake by this method. The results suggest that the herbage disappearance technique was a suitable alternative for estimating forage intake for groups of animals on well-managed, rotationally stocked pastures with 1-d grazing periods. The study showed that a substantial amount of work is required to obtain estimates of intake on pastures. Actual forage OMI cannot really be determined, but depending on the objectives of the experiment, each technique is capable of providing reasonably precise

PAGE 249

230 estimates of differences among pastures at specific times and was useful in understanding animal responses to pasture characteristics. Overall, this dissertation research provided a body of information on which to make recommendations for grazing management options in conditions similar to those of the study. It demonstrated that grass-N fertilizer systems likely are a more viable option for Florida producers for winter grazing than grass-clover swards. Stocking rate was identified as the most critical management variable and rates of approximately 1.5 cows ha1 appear to be appropriate under these conditions. Concentrate supplementation will be required by high-producing dairy cows, but the level of supplementation must be matched with nutrients supplied by the pasture. Further, it was demonstrated that in summer lactating dairy cows will benefit from pastures that allow them to obtain high forage intake in as short a grazing time as possible, such as obtained on perennial peanut pastures. Performance of animals grazing winter pastures can be improved if managed so that herbage availability does not limit intake. Estimating nutrient intake on pastures is critical to understanding pasture-animal relationships but the need to obtain such data must be justified, and techniques used to obtain estimates must be selected based on available resources and the experimental objectives. Several observations that may warrant further evaluation were identified. In winter grazing systems it is likely that management based on moving animals to new paddocks based on some estimate of residual herbage, rather than a fixed amount of time, will increase forage intake hence increase animal performance. Data are needed to substantiate and quantify optimal stocking rates and pasture carrying capacity. Further

PAGE 250

231 for both summer and winter grazing but more so for summer, providing fresh paddocks in the evening rather than in the morning, or both morning and evening, may potentially increase forage OMI intake likely via increased nighttime grazing. This hypothesis is based on the observation that animals may compensate during nighttime for reduced daytime grazing caused by heat stress and that they may avoid grazing paddocks fouled with their excreta during the day. A possible extension to such a study would be to determine the limits of nighttime grazing if not restricted by pasture conditions. It appears that cows suffering heat stress may operate on some sort of conservation mechanism related to energy partitioning to milk production and body maintenance, and, that this mechanism prioritizes milk production when stress factors are not present. It is well established that prioritization to milk production in high-producing cows occurs, but the possibility that a conservation mechanism exists, unless already known, warrants investigation. In the prediction of feed intake based on NEL requirements, measurements of cows' BW is critical. Many factors contribute to errors in BW estimates, especially in short-term studies. This may be minimized if a stable quantitative relationship was demonstrated between changes in body condition score and BW changes, so that estimates ofBW changes could be predicted from the body condition scores. This may be determined from large data sets of previously conducted trials, if they exist, and should investigate if there are variations among trials, etc.

PAGE 251

APPENDIX TABLES OF CLIMATOLOGICAL DATA, VARIABLE LISTS, RAW DATA AND PROBABILITY VALVES ASSOCIATED WITH RESPONSES REPORTED IN THE DISSERTATION

PAGE 252

233 Table A-1 Climatological data starting from October 1995 to April 1997, i.e., from seeding of winter pastures in the first year to end of the study period Rainfall t Average Ambient Air Temperature t 70-yr 1995 1996 1997 meant 1995 1996 1997 Normal ---------------mm------------------------------------oc --------------------Jan. 45 72 72 11.6 13.8 13.4 Feb. 29 36 94 13.4 16.4 14.3 Mar. 238 87 108 13.7 20.8 17.7 Apr. 68 182 77 18.5 18.9 20 6 May 40 90 24.3 24.1 June 171 173 25.4 26 7 July 217 204 26.9 27.4 Aug. 231 210 26.2 27.4 Sep 47 144 25.1 26.3 Oct. 142 133 93 22.7 20.9 22.0 Nov. 81 27 49 15.1 16 9 17.7 Dec. 24 140 74 11.9 13. 7 14.5 Annual 1337 1387 1388 Total t Data are for Gainesville as recorded by the Univ. of Florida Agronomy Department. t Seventy-year means are computed based on data recorded prior to 1990 Normals were calculated from 1961 1990 data.

PAGE 253

234 Table A-2. List of abbreviations used to describe response variables in appendix tables (in order of appearance). Abbreviation Description REP replication SR stocking rate cs concentrate supplement PRHM pregraze herbage mass HD herbage disappearance HA herbage allowance IVDOM in vitro digestible organic matter concentration CP crude protein concentration NDF neutral detergent fiber concentration p period FS forage system GN grass-N fertilizer forage system GL grass-legume forage systems FOMI forage organic matter intake (OMI) TOMI total OMI FOMIBW forage OMI relative to cow body weight TOMIBW total OMI relative to cow body weight MILK milk yield cow1 d -1 MLKHA milk yield ha1 d 1 ADG average daily gain BCSC change in body condition score MFAT milk fat concentration MCP milk crude protein concentration sec somatic cell count

PAGE 254

235 Table A-2 ----continued. Abbreviation Description BG blood glucose concentration MUN milk urea N concentration POHM postgraze herbage mass obsd observation day csam time spent eating concentrate in the morning grzd time spent grazing during daytime shd time spent loafing under shade during daytime noshd time spent loafing outside of shade during daytime cspm time spent eating concentrate in the evening grzn time spent grazing during nighttime shn time spent loafing under shade during nighttime noshn time spent loafing outside of shade during nighttime loafd time spent loafing during daytime loafn time spent loafing during nighttime tgraz total time spent grazing in a 24-h cycle bl cows with predominantly black hair coats wh cows with predominantly white hair coats NEL forage dry matter intake (DMI) predicted based on net energy of lactation requirements PDME forage DMI estimated by the pulse dose marker technique using chromium HDE forage DMI estimated by the herbage disappearance method DPDME deviation of PDME from NEL DHDE deviation ofHDE from NEL

PAGE 255

236 Table A-3. Ingredient composition of concentrate supplement fed to animals in the winter 1996 and 1997 grazing studies Ingredients Cornmeal Hominy Soybean hulls Soybean meal ( 48%) Whole cottonseed Dried cane molasses Mineral mix 1 t Mineral mix ui Calcium carbonate Mono-Dical phosphate Salt Trace mineralized salt Diabond Sodium bicarbonate Year 1996 1997 k 1 --------------g g ---------------402 240 72 200 40 10 10 4 4 8 10 353 239 96 198 50 25 13 13 13 t Composed of at least 550 g kg1 Dyna-Mate 7 g kg 1 Se, 4 g kg 1 CoSo4 19 g kg 1 CuSo4 25 g kg1 ZnSo4 7 g kg1 MnSo4 369 g kg 1 MgO,) 0.01 g kg 1 Cal, 1200 IU g1 vitamin A 700 IU g 1 vitamin D3 and 300 IU g 1 vitamin E. t Composed of at least 38 g k g1 N 105 g kg 1 Ca, 30 g kg 1 P 45 g kg 1 K 20 g kg 1 Mg, 74 g kg 1 Na, 11 g kg1 S 54 g kg1 Cl, 1525 ppm Mn, 1750 ppm Fe, 425 ppm Cu 1500 ppm Zn, 12 8 ppm I 49 ppm Co 24.2 IU g 1 vitamin A 35.2 IU g 1 vitamin D and 0 88 IU g1 vitamin E. Composed of at least 920 g kg 1 NaCl, 2.5 g kg 1 Mn, 2 g kg 1 Fe, 0 33 g kg 1 Cu, 0.07 g kg 1 I 0 05 g kg 1 Zn, and 0 0 2 5 g kg 1 Co.

PAGE 256

237 Table A-4. Chemical composition of concentrate supplement fed to animals in the win ter 1996 and 1997 grazing studies. Year Component 1996 1997 Dry matter g kg 1 904 914 NEL Meal kg1 ofDMt 1.90 1.89 NDF g kg1 ofDM 326 425 ADF g kg 1 of DM 233 277 CP g kg1 ofDM 156 180 Ca, g kg 1 ofDM 11.6 9.1 P, g kg1 ofDM 4 3 6.1 Mg, g kg 1 ofDM 3.4 3.4 K g kg 1 of DM 11.3 13.3 Na, g kg ofDM 6.4 9.3 S g kg of DM 1.9 2.0 Cl g kg 1 ofDM 2.6 8 2 Fe ,ppmofDM 537 355 Zn ,ppmofDM 121 159 Cu ,ppmofDM 29.8 33 Mn ,ppmofDM 65.4 66 t Calculated using NRC (1988) values for whole cottonseed.

PAGE 257

238 Table A-5 Pregraze herbage mass (PRHM), herbage disappearance (HD) and herbage allowance (HA) for each sample time within each period in 1996 reported in Chapter 3. Period Sample Pasture REPt Treatment Responses Time SR cs PRHM HD HA kg ha 1 ---------kg kg1 --1 1 1 1 Low Low 905 568 0 .41 1 1 2 1 Low High 1127 601 0 .61 1 1 3 2 Low High 1230 557 0 77 1 1 4 1 High High 977 706 0 23 1 1 5 2 High High 691 625 0.14 1 1 6 1 High Low 612 611 0 13 1 1 7 2 High Low 1072 823 0 24 1 1 8 2 Low Low 1428 829 0.78 1 2 1 1 Low Low 620 510 0.24 1 2 2 1 Low High 1333 668 0 73 1 2 3 2 Low High 517 443 0 24 1 2 4 1 High High 881 756 0 18 1 2 5 2 High High 818 627 0 19 1 2 6 1 High Low 541 541 0 .11 1 2 7 2 High Low 493 493 0 09 1 2 8 2 Low Low 1167 764 0 60 2 1 1 1 Low Low 835 243 0.53 2 1 2 1 Low High 1099 381 0 67 2 1 3 2 Low High 928 161 0.69 2 1 4 1 High H i gh 478 275 0 13 2 1 5 2 High H i gh 513 352 0 14 2 1 6 1 High Low 425 350 0.09 2 1 7 2 High Low 679 4 1 4 0 .18 2 1 8 2 Low Low 1016 188 0 63 2 2 1 1 Low Low 806 272 0 50 2 2 2 1 Low High 1681 528 1 04 2 2 3 2 Low High 923 248 0.65 2 2 4 1 High High 723 207 0 23 2 2 5 2 High High 723 422 0 .21 2 2 6 1 High Low 547 429 0 12 2 2 7 2 High Low 654 531 0 15 2 2 8 2 Low Low 1163 402 0 65 2 3 1 1 Low Low 793 417 0 44 2 3 2 1 Low High 844 496 0.44 2 3 3 2 Low High 659 478 0 34 2 3 4 1 High High 595 526 0.13 2 3 5 2 High High 653 425 0.18 2 3 6 1 High Low 320 320 0 06 2 3 7 2 High Low 288 288 0 06 2 3 8 2 Low Low 1036 618 0 49 t See Table A-2 for description of abbreviations.

PAGE 258

239 TableA-5. --continued. Period Sample Pasture REP 1 Treatment Responses Time SR cs PRHM HD HA kgha-1---kg kg-1 3 1 1 1 Low Low 1736 657 1 02 3 1 2 1 Low High 2528 1416 1 20 3 1 3 2 Low High 1808 720 1 09 3 1 4 1 High High 1462 645 0.43 3 1 5 2 High High 1387 728 0 37 3 1 6 1 High Low 1076 579 0 28 3 1 7 2 High Low 1038 504 0 25 3 1 8 2 Low Low 1657 591 1 06 3 2 1 1 Low Low 166 1 532 1 .01 3 2 2 1 Low High 1680 605 0 .91 3 2 3 2 Low High 1425 437 0 9 1 3 2 4 1 High High 1218 580 0 35 3 2 5 2 High High 1241 611 0 34 3 2 6 1 High Low 850 424 0 22 3 2 7 2 High Low 1001 533 0.23 3 2 8 2 Low Low 1646 317 1 16 3 3 1 1 Low Low 2576 843 1 56 3 3 2 1 Low High 2693 833 1 50 3 3 3 2 Low High 2973 497 2 05 3 3 4 1 High High 1844 441 0.62 3 3 5 2 High High 1558 28 0.56 3 3 6 1 High Low 1377 328 0.43 3 3 7 2 High Low 1823 778 0 46 3 3 8 2 Low Low 2608 672 1 77 t See Table A-2 for description of abbreviations

PAGE 259

240 Table A-6. F o rage in vitro d igestible o rganic matter (IVDOM), crude protein (C P ) and neutral d etergent fi ber (NDF) concentration for each sample time within each period during 1 9 96 rep o rted in Chapter 3. Period Sampl e Pasture RE P t Treatment Responses Time S R CL IVDOM CP NDF k -1 -------g g ----------1 1 1 1 Low Low 656 233 491 1 1 2 1 Low High 760 243 453 1 1 3 2 Low High 783 285 454 1 1 4 1 High High 765 221 491 1 1 5 2 High High 729 293 503 1 1 6 1 Hig h Low 759 316 500 1 1 7 2 High Low 707 262 506 1 1 8 2 L o w Low 732 225 474 2 1 1 1 Low Low 791 335 415 2 1 2 1 Low High 743 296 448 2 1 3 2 Low High 726 300 457 2 1 4 1 Hig h High 758 311 468 2 1 5 2 High H igh 771 330 453 2 1 6 1 Hig h Low 7 8 9 360 431 2 1 7 2 High Low 753 334 454 2 1 8 2 Low Low 751 277 494 2 2 1 1 L o w Low 749 278 548 2 2 2 1 L o w High 733 312 513 2 2 3 2 L o w High 743 322 512 2 2 4 1 High High 782 355 453 2 2 5 2 High High 734 313 495 2 2 6 1 High Low 752 372 461 2 2 7 2 High Low 780 357 455 2 2 8 2 Low Low 732 270 537 3 1 1 1 Low Low 747 273 480 3 1 2 1 L o w High 715 250 521 3 1 3 2 Low High 691 230 565 3 1 4 1 High High 763 336 474 3 1 5 2 High High 764 335 464 3 1 6 1 High Low 772 347 428 3 1 7 2 High Low 742 334 450 3 1 8 2 L o w Low 716 243 524 t See Table A-2 for description of abbreviations.

PAGE 260

241 Table A-6. ----continued. Period Sample Pasture REP t Treatment Responses Time SR CL IVDOM CP NDF k -1 -----------g g ------------3 2 1 1 Low Low 685 154 565 3 2 2 1 Low High 690 163 537 3 2 3 2 Low High 648 168 597 3 2 4 1 High High 744 231 484 3 2 5 2 High High 757 247 501 3 2 6 1 High Low 787 216 484 3 2 7 2 High Low 760 231 465 3 2 8 2 Low Low 709 178 557 t See Table A-2 for description of abbreviations Table A-7. Probability levels for tests of fixed effects of period (P) stocking rate (SR) and concentrate supplement (CS), and their interactions on response variables associated with 1996 herbage data reported in Chapter 3 Source of Response Variable Variation PRHMt HD HA IVDOM CP NDF SR 0 0047 0.4838 0.0004 0.0201 0.0246 0.0326 cs 0 1732 0.9837 0.2529 0.5771 0 8958 0.6045 SR*CS 0.6632 0.8786 0.8929 0.5392 0.3047 0.2676 p 0.0001 0.0001 0.0001 0 0177 0.0001 0.0001 P*SR 0.0762 0.2707 0 0004 0.0171 0.3184 0.0001 P*CS 0 7551 0.7538 0 9923 0.0099 0 9803 0.0995 P*SR*CS 0.9738 0.3466 0.7645 0.1157 0.4463 0.5657 t See Table A-2 for description of abbreviations

PAGE 261

242 Table A-8. Pregraze herbage mass (PRHM) herbage disappearance (HD) and herbage allowance (HA) for each sample time (week) within each period in 1997 reported in Chapter 3. Period Week Pasture REP 1 Treatment Reseonses FS SRCL PRHM HD HA --kg ha 1 --kg kg1 -1 1 1 1 GN Low Low 1996 495 1.43 1 1 2 1 GN Low High 1727 319 1 16 1 1 3 2 GN Low High 2165 690 1 .31 1 1 4 1 GN High High 2121 673 0.62 1 1 5 2 GN High High 2084 489 0 65 1 1 6 1 GN High Low 2159 784 0.68 1 1 7 2 GN High Low 1908 440 0 70 1 1 8 2 GN Low Low 1482 135 1.11 1 1 9 1 GL High High 2313 1 877 0 .51 1 1 10 1 GL Low High 1231 692 0 63 1 1 11 2 GL High High 1249 983 0 28 1 1 12 2 GL Low High 1291 685 0 70 1 1 13 1 GL High Low 1499 1157 0 33 1 1 14 1 GL Low Low 1428 850 0 73 1 1 15 2 GL High Low 1160 875 0 26 1 1 16 2 GL Low Low 1208 753 0 62 1 2 1 1 GN Low Low 1601 220 1 22 1 2 2 1 GN Low High 2034 526 1 32 1 2 3 2 GN Low High 1933 299 1 28 1 2 4 1 GN High High 1877 442 0 .58 1 2 5 2 GN High High 1990 528 0 .61 1 2 6 1 GN High Low 1865 483 0 63 1 2 7 2 GN High Low 1958 490 0 .71 1 2 8 2 GN Low Low 1125 118 0 84 1 2 9 1 GL High High 1956 1454 0.46 1 2 10 1 GL Low High 1119 730 0 53 1 2 11 2 GL High High 1428 982 0 34 1 2 12 2 GL Low High 1725 922 0 93 1 2 13 1 GL High Low 1618 1191 0 37 1 2 14 1 GL Low Low 1903 1 024 1 .01 1 2 15 2 GL High Low 1855 1 457 0 40 1 2 16 2 GL Low Low 1451 611 0 85 1 3 1 1 GN Low Low 1938 522 1 37 1 3 2 1 GN Low High 2302 580 1 50 1 3 3 2 GN Low High 2012 532 1.26 1 3 4 1 GN High High 1788 422 0.55 1 3 5 2 GN High High 1368 408 0 .41 1 3 6 1 GN High Low 2031 750 0 64 1 3 7 2 GN High Low 2153 836 0 72 1 3 8 2 GN Low Low 1079 90 0 .81 t See Table A-2 for description of abbreviations

PAGE 262

243 Table A-8 ----continued. Period Week Pasture REP 1 Treatment Reseonses FS SR CL PRHM HD HA --kg ha 1 --kg kg1 -1 3 9 1 GL High High 1983 1016 0 55 1 3 10 1 GL Low High 1502 788 0 78 1 3 11 2 GL High High 1355 826 0 34 1 3 12 2 GL Low High 1916 618 1 18 1 3 13 1 GL High Low 2009 1169 0 52 1 3 14 1 GL Low Low 2110 665 1 29 1 3 15 2 GL High Low 1809 1339 0.41 1 3 16 2 GL Low Low 1602 645 0 95 1 4 1 1 GN Low Low 1662 780 1 04 1 4 2 1 GN Low High 1766 727 1.04 1 4 3 2 GN Low High 1958 941 1.07 1 4 4 1 GN High High 1618 729 0.44 1 4 5 2 GN High High 1418 717 0.37 1 4 6 1 GN High Low 1444 977 0 37 1 4 7 2 GN High Low 1584 950 0 46 1 4 8 2 GN Low Low 1880 863 1 14 1 4 9 1 GL High High 1725 1045 0 45 1 4 10 1 GL Low High 1174 591 0 62 1 4 11 2 GL High High 1174 802 0 28 1 4 12 2 GL Low High 1505 678 0 86 1 4 13 1 GL High Low 1291 854 0 .31 1 4 14 1 GL Low Low 1762 635 1 05 1 4 15 2 GL High Low 1931 1445 0.43 1 4 16 2 GL Low Low 2335 892 1.40 2 1 1 1 GN Low Low 1514 685 0 83 2 1 2 1 GN Low High 1732 813 1.00 2 1 3 2 GN Low High 1941 886 1.11 2 1 4 1 GN High High 1314 583 0 38 2 1 5 2 GN High High 1514 926 0.43 2 1 6 1 GN High Low 1584 935 0.41 2 1 7 2 GN High Low 1348 625 0 38 2 1 8 2 GN Low Low 861 340 0 50 2 1 9 1 GL High High 306 80 0 10 2 1 10 1 GL Low High 732 320 0.45 2 1 11 2 GL High High 431 246 0.12 2 1 12 2 GL Low High 490 288 0 26 2 1 13 1 GL High Low 350 173 0 10 2 1 14 1 GL Low Low 541 242 0 35 2 1 15 2 GL High Low 365 66 0 12 2 1 16 2 GL Low Low 843 568 0.43 2 2 1 1 GN Low Low 1505 451 0 .91 2 2 2 1 GN Low High 1348 294 0 .91 2 2 3 2 GN Low High 1253 371 0.79 2 2 4 1 GN High High 695 348 0.19 2 2 5 2 GN High High 625 316 0.19 2 2 6 1 GN High Low 765 478 0.19 2 2 7 2 GN High Low 826 373 0 23 2 2 8 2 GN Low Low 617 194 0 38 t See Tabl e A-2 for description of abbreviations.

PAGE 263

244 Table A-8 ----continued Period Week Pasture REP 1 Treatment Reseonses FS SR CL PRHM HD HA --kg ha 1 -----kg kg1 -2 2 10 1 GL Low High 608 341 0 34 2 2 12 2 GL Low High 938 615 0 47 2 2 14 1 GL Low Low 1166 640 0 70 2 2 16 2 GL Low Low 1431 637 0.85 2 3 1 1 GN Low Low 1598 600 0 92 2 3 2 1 GN Low High 1265 511 0 76 2 3 3 2 GN Low High 1841 695 1 .11 2 3 4 1 GN High High 640 219 0 19 2 3 5 2 GN High High 876 122 0 34 2 3 6 1 GN High Low 508 241 0 14 2 3 7 2 GN High Low 855 229 0 27 2 3 8 2 GN Low Low 1362 422 0 84 2 3 10 1 GL Low High 1005 487 0 60 2 3 12 2 GL Low High 1471 477 0 .91 2 3 14 1 GL Low Low 1364 550 0 90 2 3 16 2 GL Low Low 1136 470 0 69 2 4 1 1 GN Low Low 1265 466 0 73 2 4 2 1 GN Low High 1654 604 1.02 2 4 3 2 GN Low High 1195 126 0 84 2 4 4 1 GN High High 799 308 0 24 2 4 5 2 GN High High 660 394 0 19 2 4 6 1 GN High Low 772 300 0 23 2 4 7 2 GN High Low 820 342 0 24 2 4 8 2 GN Low Low 667 241 0.40 2 4 10 1 GL Low High 1035 385 0 66 2 4 12 2 GL Low High 858 403 0 49 2 4 14 1 GL Low Low 934 440 0 59 2 4 16 2 GL Low Low 1071 537 0 6 1 3 1 1 1 GN Low Low 1262 322 0 78 3 1 2 1 GN Low High 1634 544 1 00 3 1 3 2 GN Low High 1109 617 0 60 3 1 4 1 GN High High 555 401 0 14 3 1 5 2 GN High High 336 261 0 08 3 1 6 1 GN High Low 577 328 0 17 3 1 7 2 GN High Low 402 350 0 08 3 1 8 2 GN Low Low 475 132 0 2 9 3 1 9 1 GL High High 1237 973 0 2 8 3 1 10 1 GL Low High 7 4 5 638 0 33 3 1 11 2 GL High High 1001 862 0 22 3 1 12 2 GL Low High 879 626 0.40 3 1 13 1 GL High Low 1320 952 0.30 3 1 14 1 GL Low Low 1276 761 0 66 3 1 15 2 GL High Low 1423 1 149 0 32 3 1 1 6 2 GL Low Low 1224 846 0 .61 3 2 1 1 G N Low Low 1612 318 1 0 3 3 2 2 1 GN Low High 1459 535 0 87 3 2 3 2 GN Low High 1874 361 1 27 t See Tabl e A-2 for de sc ri ption o f abbr e viation s

PAGE 264

245 Table A-8 ---continued Period Week Pasture REP 1 Treatment Reseonses FS SR CL PRHM HD HA --kg ha 1 ----kg kg1 --3 2 4 1 GN High High 759 550 0.19 3 2 5 2 GN High High 686 375 0 20 3 2 6 1 GN High Low 664 486 0.17 3 2 7 2 GN High Low 919 474 0 25 3 2 8 2 GN Low Low 1597 516 0.96 3 2 9 1 GL High High 1391 981 0.33 3 2 10 1 GL Low High 700 467 0 36 3 2 11 2 GL High High 1090 784 0 27 3 2 12 2 GL Low High 1250 746 0 .61 3 2 13 1 GL High Low 1288 805 0 .31 3 2 14 1 GL Low Low 1832 868 1 04 3 2 15 2 GL High Low 1570 1045 0.40 3 2 16 2 GL Low Low 1333 463 0 84 3 3 1 1 GN Low Low 1295 528 0.73 3 3 2 1 GN Low High 1776 953 0 95 3 3 3 2 GN Low High 1251 399 0 79 3 3 4 1 GN High High 770 750 0.16 3 3 5 2 GN High High 691 481 0.18 3 3 6 1 GN High Low 775 649 0 18 3 3 7 2 GN High Low 779 590 0 18 3 3 8 2 GN Low Low 1192 679 0.61 3 3 9 1 GL High High 1215 805 0.30 3 3 10 1 GL Low High 1312 927 0.66 3 3 11 2 GL High High 1071 879 0.25 3 3 12 2 GL Low High 1769 891 0.92 3 3 13 1 GL High Low 1396 992 0.32 3 3 14 1 GL Low Low 1956 1039 1 06 3 3 15 2 GL High Low 1649 1220 0 39 3 3 16 2 GL Low Low 1529 702 0 90 3 4 1 1 GN Low Low 1506 845 0 76 3 4 2 1 GN Low High 1781 775 1.02 3 4 3 2 GN Low High 1580 756 0.90 3 4 4 1 GN High High 932 813 0 .21 3 4 5 2 GN High High 937 797 0 .21 3 4 6 1 GN High Low 1089 914 0 25 3 4 7 2 GN High Low 956 880 0 19 3 4 8 2 GN Low Low 1285 821 0 63 3 4 9 1 GL High High 1402 1114 0 .31 3 4 10 1 GL Low High 1679 967 0 93 3 4 11 2 GL High High 1071 943 0 23 3 4 12 2 GL Low High 1101 826 0 .48 3 4 13 1 GL High Low 1757 1482 0 36 3 4 14 1 GL Low Low 1655 1238 0.77 3 4 15 2 GL High Low 1739 1310 0 .41 3 4 16 2 GL Low Low 1516 754 0.87 t See Table A-2 for description of abbreviations.

PAGE 265

246 Table A-9. Forage in vitro digestible organic matter (IVDOM), crude protein (CP) and neutral detergent fiber (NDF) concentration for each sample time (week) within each period during 1997 reported in Chapter 3. Period Week Pasture REP1 Treatment Reseonses FS SR CL IVDOM CP NDF g kg 1 1 1 1 GN Low Low 546 220 574 1 1 2 1 GN Low High 594 215 548 1 1 3 2 GN Low High 640 249 495 1 1 4 1 GN High High 651 259 508 1 1 5 2 GN High High 591 223 575 1 1 6 1 GN High Low 617 236 543 1 1 7 2 GN High Low 622 226 541 1 1 8 2 GN Low Low 668 258 514 1 1 9 1 GL High High 692 236 486 1 1 10 1 GL Low High 694 216 498 1 1 11 2 GL High High 595 153 552 1 1 12 2 GL Low High 607 210 527 1 1 13 1 GL High Low 639 228 493 1 1 14 1 GL Low Low 608 198 533 1 1 15 2 GL High Low 629 242 489 1 1 16 2 GL Low Low 617 206 530 1 2 1 1 GN Low Low 681 249 484 1 2 2 1 GN Low High 759 231 457 1 2 3 2 GN Low High 642 229 524 1 2 4 1 GN High High 717 273 482 1 2 5 2 GN High High 688 248 495 1 2 6 1 GN High Low 684 253 514 1 2 7 2 GN High Low 654 231 518 1 2 8 2 GN Low Low 735 276 475 1 2 9 1 GL High High 696 227 492 1 2 10 1 GL Low High 734 230 495 1 2 11 2 GL High High 756 219 493 1 2 12 2 GL Low High 689 194 508 1 2 13 1 GL High Low 708 221 502 1 2 14 1 GL Low Low 709 233 504 1 2 15 2 GL High Low 766 224 419 1 2 16 2 GL Low Low 636 196 504 1 3 1 1 GN Low Low 715 250 462 1 3 2 1 GN Low High 679 258 449 1 3 3 2 GN Low High 696 260 469 1 3 4 1 GN High High 587 519 1 3 5 2 GN High High 722 272 461 1 3 6 1 GN High Low 718 239 479 1 3 7 2 GN High Low 687 257 482 1 3 8 2 GN Low Low 692 235 482 t See Table A-2 for description of abbreviations.

PAGE 266

247 Table A-9. ----continued. Period Week Pasture REP1 Treatment Responses FS SR Cl IVDOM CP NDF ---g kg 1 3 9 1 Gl High High 710 229 480 1 3 10 1 Gl low High 676 187 497 1 3 11 2 Gl High High 638 192 513 1 3 12 2 Gl Low High 703 198 492 1 3 13 1 GL High low 659 187 505 1 3 14 1 GL low low 640 225 491 1 3 15 2 Gl High low 674 203 487 1 3 16 2 Gl Low Low 681 219 496 1 4 1 1 GN low low 691 237 451 1 4 2 1 GN low High 683 259 459 1 4 3 2 GN low High 679 250 463 1 4 4 1 GN High High 717 278 456 2 1 1 1 GN low low 750 280 467 2 1 2 1 GN Low High 629 263 511 2 1 3 2 GN low High 720 278 438 2 1 4 1 GN High High 660 290 493 2 1 5 2 GN High High 673 243 491 2 1 6 1 GN High low 683 258 494 2 1 7 2 GN High Low 675 270 525 2 1 8 2 GN low Low 738 338 431 2 1 9 1 Gl High High 745 300 473 2 1 10 1 GL Low High 755 258 470 2 1 11 2 GL High High 758 270 476 2 1 12 2 GL low High 728 276 501 2 1 13 1 Gl High Low 717 255 507 2 1 14 1 Gl low Low 705 267 497 2 1 15 2 Gl High Low 747 306 451 2 1 16 2 Gl Low Low 673 242 512 2 2 1 1 GN Low low 732 307 468 2 2 2 1 GN low High 698 129 461 2 2 3 2 GN low High 721 296 480 2 2 4 1 GN High High 700 318 513 2 2 5 2 GN High High 731 331 483 2 2 6 1 GN High low 749 334 471 2 2 7 2 GN High low 752 198 497 2 2 8 2 GN low low 760 158 467 2 2 10 1 Gl low High 648 254 529 2 2 12 2 Gl low High 676 169 550 2 2 14 1 GL Low Low 658 256 507 2 2 16 2 Gl Low low 712 271 510 2 3 1 1 GN low low 678 278 502 2 3 2 1 GN low High 630 234 555 2 3 3 2 GN Low High 688 269 509 2 3 4 1 GN High High 729 339 476 t See Table A-2 for description of abbreviations.

PAGE 267

248 Table A-9. ----continued Period Week Pasture REP1 Treatment Responses FS SR CL IVDOM CP NDF g kg -2 3 5 2 GN High High 764 330 462 2 3 6 1 GN High Low 699 294 512 2 3 7 2 GN High Low 718 295 506 2 3 8 2 GN Low Low 689 253 507 2 3 10 1 GL Low High 676 275 535 2 3 12 2 GL Low High 641 263 530 2 3 14 1 GL Low Low 648 297 471 2 3 16 2 GL Low Low 639 274 556 2 4 1 1 GN Low Low 718 252 464 2 4 2 1 GN Low High 767 226 442 2 4 3 2 GN Low High 707 261 467 2 4 4 1 GN High High 664 279 548 2 4 5 2 GN High High 658 250 560 2 4 6 1 GN High Low 682 266 546 2 4 7 2 GN High Low 688 263 528 2 4 8 2 GN Low Low 699 267 501 2 4 10 1 GL Low High 686 271 502 2 4 12 2 GL Low High 659 261 515 2 4 14 1 GL Low Low 626 273 509 2 4 16 2 GL Low Low 637 220 528 3 1 1 1 GN Low Low 759 198 459 3 1 2 1 GN Low High 722 243 470 3 1 3 2 GN Low High 738 266 475 3 1 4 1 GN High High 726 356 468 3 1 5 2 GN High High 727 341 470 3 1 6 1 GN High Low 712 370 462 3 1 7 2 GN High Low 693 364 467 3 1 8 2 GN Low Low 677 324 475 3 1 9 1 GL High High 602 258 557 3 1 10 1 GL Low High 657 287 520 3 1 11 2 GL High High 600 230 521 3 1 12 2 GL Low High 688 290 472 3 1 13 1 GL High Low 648 238 454 3 1 14 1 GL Low Low 609 249 506 3 1 15 2 GL High Low 659 292 397 3 1 16 2 GL Low Low 650 250 506 3 2 1 1 GN Low Low 660 262 495 3 2 2 1 GN Low High 655 251 458 3 2 3 2 GN Low High 635 233 491 3 2 4 1 GN High High 688 329 475 3 2 5 2 GN High High 691 345 489 3 2 6 1 GN High Low 697 321 467 3 2 7 2 GN High Low 694 293 487 3 2 8 2 GN Low Low 719 334 443 t S ee T abl e A 2 for d e scription o f abbreviations.

PAGE 268

249 Table A-9. ----continued. Period Week Pasture REP 1 Treatment Responses FS SR CL IVDOM CP NDF --g kg 1 ----3 2 9 1 GL High High 685 252 481 3 2 10 1 GL Low High 690 284 478 3 2 11 2 GL High High 727 277 494 3 2 12 2 GL Low High 625 242 485 3 2 13 1 GL High Low 727 211 499 3 2 14 1 GL Low Low 695 228 452 3 2 15 2 GL High Low 702 230 479 3 2 16 2 GL Low Low 632 234 473 3 3 1 1 GN Low Low 677 228 502 3 3 2 1 GN Low High 697 181 507 3 3 3 2 GN Low High 667 246 495 3 3 4 1 GN High High 707 327 526 3 3 5 2 GN High High 689 288 561 3 3 6 1 GN High Low 705 297 528 3 3 7 2 GN High Low 713 281 505 3 3 8 2 GN Low Low 720 260 489 3 3 9 1 GL High High 721 263 488 3 3 10 1 GL Low High 642 264 484 3 3 11 2 GL High High 651 236 496 3 3 12 2 GL Low High 657 237 488 3 3 13 1 GL High Low 681 221 478 3 3 14 1 GL Low Low 673 218 433 3 3 15 2 GL High Low 704 228 470 3 3 16 2 GL Low Low 670 222 456 3 4 1 1 GN Low Low 651 200 475 3 4 2 1 GN Low High 680 195 475 3 4 3 2 GN Low High 654 188 489 3 4 4 1 GN High High 741 306 443 3 4 5 2 GN High High 709 285 461 3 4 6 1 GN High Low 620 252 457 3 4 7 2 GN High Low 650 242 451 3 4 8 2 GN Low Low 657 240 473 3 4 9 1 GL High High 634 233 467 3 4 10 1 GL Low High 671 197 481 3 4 11 2 GL High High 620 221 514 3 4 12 2 GL Low High 641 201 496 3 4 13 1 GL High Low 690 173 468 3 4 14 1 GL Low Low 653 203 468 3 4 15 2 GL High Low 619 209 481 3 4 16 2 GL Low Low 646 223 456 t See Table A-2 for description of abbreviations.

PAGE 269

250 Table A-10. Probability levels for tests of fixed effects of forage system ( FS) stocking rate (SR) concentrate supplement (CS) period (P) and their interactions on response variables associated with 1997 herbage data reported in Chapter 3 Source of Response Variable Variation PRHMt HD HA IVDOM CP NDF FS 0.0441 0 0047 0.0010 0.0492 0.0052 0 7215 SR 0.0014 0.1635 0.0001 0.0593 0.0105 0 9019 FS *SR 0.1214 0.4395 0 0291 0 0016 0 0741 0 0116 cs 0.6846 0 7594 0.6949 0 9313 0.6686 0 3079 FS*CS 0.0188 0.4504 0.0111 0.4051 0.8740 0.2198 SR*CS 0.3024 0.4644 0.6817 0.7437 0.1246 0.7544 FS*SR*CS 0.0285 0.2828 0.0075 0.0482 0 2114 0.4087 p 0.0001 0.0001 0.0001 0.3260 0.0001 0.0052 P*FS 0 0001 0.0001 0.0002 0 9391 0 0127 0.9726 P*SR 0 0002 0.0005 0.7651 0.6051 0.1407 0.7336 P*FS*SR 0 0031 0.0076 0.4185 0.1567 0.0097 0.0192 P*CS 0.3710 0.6379 0.4454 0 7851 0.8573 0.1419 P*FS*CS 0 5822 0.5864 0.4593 0.9846 0.3215 0.6799 P*SR*CS 0 9004 0.6216 0 7960 0 5644 0 7949 0.6007 P*FS*SR*CS 0 7361 0.8029 0 9691 0.9796 0.8789 0.8481 t See Table A-2 for description of abbreviations

PAGE 270

251 Table A 11. Forage organic matter intake (F O MI), total organic matter intake (TOMI), F O MI relative to bo dy weight (F O MI B W), and T O MI re l ative to body weight (T O MIBW) of c o ws d uring 1996 reported in Chapter 3. Per i od Pas t ure REPt Co w I D T r ea tm e nt Respon s es SR cs FO MI T O MI F O MI B W TOMIBW -----kg d-1 ---------g kg-1 BW ------1 1 1 5599 Low L o w 6.25 12.64 10.23 20.69 1 1 1 5608 L o w L o w 10.20 16.59 16.64 27.07 1 2 1 534 8 Low H i gh 1 8.35 28.89 32.83 51.69 1 2 1 5659 Low High 1 4.35 24.89 27.07 46.96 1 3 2 54 8 3 Low High 17 .25 27.79 33.49 53.95 1 3 2 5661 Low High 13.10 23.64 27.57 49.75 1 4 1 5 6 1 5 High High 15.05 25.59 24.91 42.35 1 4 1 5664 High High 14.05 24.59 28.27 49.48 1 5 2 5591 H i gh High 10.05 20.59 20.26 41.51 1 5 2 5643 High High 10.35 20.89 17.75 35.83 1 6 1 2509 High L o w 7.10 13.49 17.42 33.10 1 6 1 5624 High L o w 1 3.15 19.54 23.13 34.37 1 7 2 5639 High L o w 10.30 16.69 18.49 29.96 1 7 2 5653 High Low 9.25 15.64 16.92 28.61 1 8 2 5636 Low Low 9.70 16 09 19.49 32.33 1 8 2 5654 Low L o w 10.20 16.59 18.83 30.62 2 1 1 534 8 Low L o w 11.45 17.56 20.40 31.29 2 1 1 5591 Low L o w 9.45 15.56 18.44 30.37 2 2 1 5624 Low High 12. 60 21.80 21.52 37.24 2 2 1 5664 Low High 1 1 20 20.40 22.25 40.52 2 3 2 5627 Low H i gh 9.85 19.05 19.03 36.81 2 3 2 5665 Low High 7.70 16.90 16 68 36 .62 2 4 1 5639 H i gh High 16.75 25 95 29.25 45.31 2 4 1 5661 H i gh High 1 6.95 26.15 34.97 53.95 2 5 2 5375 High High 8.15 17.35 15.37 32.72 2 5 2 5677 High High 7.90 17.10 17.39 37.64 2 6 1 5643 High Low 22.55 28.66 39.53 50.25 2 6 1 5654 High Low 16. 50 22.61 30.78 42.18 2 7 2 5436 High L o w 14.20 20.31 25.01 35.78 2 7 2 5672 High Low 17.15 23.26 35.99 48.82 2 8 2 5362 Low Low 13.95 20.06 21.06 30.28 2 8 2 5673 Low L o w 14.05 20.16 27.38 39.29 t See Table A-2 for description of abbreviations.

PAGE 271

252 Table A-11. ----continued Period Pasture REP 1 Cow ID Treatment Responses SR cs FOMI TOMI FOMIBW TOMIBW ---kg d"1 ----------g kg1 BW -----3 1 1 5643 Low Low 11.00 16.12 18.56 27.20 3 1 1 5670 Low Low 9 95 15.07 19.44 29.44 3 2 1 5362 Low High 11.95 20.81 17.63 30.70 3 2 1 5591 Low High 7 65 16 .51 14.31 30.88 3 3 2 5638 Low High 8 .45 17 .31 15.49 31.72 3 3 2 5669 Low High 5.15 14.01 9.95 27.07 3 4 1 5627 High High 8.80 17.66 16.95 34.01 3 4 1 5659 High High 12.20 21.06 22.83 39.40 3 5 2 5483 High High 8.20 17 06 15.18 31.58 3 5 2 5654 High High 9.10 17.96 16.44 32.44 3 6 1 5486 High Low 11.75 16 .87 22.04 31. 64 3 6 1 5599 High Low 6 .35 11.47 10.47 18.91 3 7 2 5390 High Low 7.85 12 .97 12.30 20. 32 3 7 2 5566 High Low 8.10 13.22 13.23 21. 59 3 8 2 2509 Low Low 7.10 12.22 16.19 27. 87 3 8 2 5611 Low Low 7.10 12.22 12.05 20. 74 t See Table A-2 for description of abbreviations. Table A-12. Probability levels for tests of fixed effects of period (P) stocking rate (SR), and concentrate supplement (CS), and their interactions on response variables associated with 1996 organic matter intake data reported in Chapter 3. Source of Response Variable Variation FORMit TOMI FOMIBW TOMIBW SR 0.4481 0.4481 0.3699 0.3387 cs 0.5713 0.0204 0.4140 0.0099 SR*CS 0.4998 0.4998 0.4224 0.3816 p 0.0016 0.0002 0.0001 0.0001 P*SR 0.0496 0.0496 0.0181 0.0208 P*CS 0.0036 0.0009 0.0027 0.0015 P*SR*CS 0.5875 0.5875 0.2137 0.0883 t See Table A-2 for description of abbreviations.

PAGE 272

253 Table A-13. Forage organic matter intake (FOMI) total organic matter intake (TOMI) FOMI relative to body weight (FOMIBW), and TOMI relative to body weight (TOMIBW) of cows during 1997 reported in Chapter 3. Period Pasture REP1 Cow ID Treatment Res12onses FS SR cs FOMI TOMI FOMIBW TOMIBW -------kg d 1 ------g kg1 BW -----1 1 1 1753 GN Low Low 12.55 17.80 23 .37 33 15 1 1 1 5775 GN Low Low 12 50 17 .75 28.21 40.05 1 2 1 2048 GN Low High 12.35 19.79 20 63 33 06 1 3 2 5757 GN Low High 13.10 20 74 26.61 42.13 1 4 1 5434 GN High High 10.10 17.02 15.64 26.36 1 4 1 5748 GN High High 10.95 17.87 21. 64 35 32 1 5 2 5508 GN High High 16.05 24.16 25 79 38.82 1 5 2 5728 GN High High 15 .35 23.46 30 03 45 89 1 6 1 5574 GN High Low 11.40 15 70 21. 58 29 72 1 7 2 5649 GN High Low 13.10 17.12 26.37 34.46 1 7 2 2647 GN High Low 11.75 15.77 25.45 34 16 1 8 2 5675 GN Low Low 15.65 20 02 30.63 39 19 1 8 2 2601 GN Low Low 18.40 22.77 36 20 44 80 1 9 1 2238 GL High High 19 15 26 98 31.80 44.80 1 10 1 5159 GL Low High 14.65 22.29 23.47 35 .71 1 10 1 5710 GL Low High 13.60 21. 24 26 .75 41.78 1 11 2 5303 GL High High 11.60 18.32 19.01 30 02 1 11 2 5743 GL High High 9.00 15 72 18 .61 32 50 1 12 2 5370 GL Low High 11.45 17.85 20 15 31.41 1 12 2 5734 GL Low High 14.40 20 .80 27.77 40 .11 1 13 1 5534 GL High Low 16.75 22 16 26 .87 35 .55 1 13 1 5732 GL High Low 12.35 17.76 25 .86 37.19 1 14 1 5556 GL Low Low 14.73 19.66 26.39 35. 22 1 15 2 5562 GL High Low 18 90 23 95 30 .83 39.07 1 15 2 5691 GL High Low 18.38 23 .43 35 .89 45 75 1 16 2 5650 GL Low Low 16 95 23 .03 31. 27 42.48 1 16 2 2653 GL Low Low 12 23 18.31 22 82 34 17 2 1 1 5159 GN Low Low 8.90 14.27 14 23 22 .81 2 1 1 5728 GN Low Low 12 95 18 32 25 70 36 36 2 2 1 5650 GN Low High 11. 75 18 07 21.49 33 04 2 2 1 5684 GN Low High 9 .85 16 17 19.16 31.45 2 3 2 5303 GN Low High 9.70 17 06 16.26 28 59 2 3 2 5768 GN Low High 7.15 14 .51 14.90 30.23 2 4 1 5562 GN High High 8 .85 13 54 15 04 23 .01 2 4 1 5734 GN High High 8.93 13 62 17 .84 27.22 2 5 2 5370 GN High High 7.05 11.23 12 .85 20 .47 2 5 2 5775 GN High High 9.63 13 .81 22.67 32 52 2 6 1 2245 GN High Low 7.68 12.09 13.02 20 49 2 6 1 2653 GN High Low 7 .38 11.79 14 57 23 28 2 7 2 2048 GN High Low 6 .25 10 .55 11.02 18.61 2 7 2 5715 GN High Low 10.60 14 90 20.18 28 .36 t See Table A 2 for description of abbreviations

PAGE 273

254 Table A-13. ----continued. Period Pasture REP I Cow ID Treatment Resr2onses FS SR cs FOMI TOMI FOMIBW TOMIBW ------kg d"1 ------------g kg1 BW -----2 8 2 5534 GN Low Low 10.23 15.40 16 69 25.13 2 8 2 5748 GN Low Low 7 63 12 80 15 .71 26 37 2 10 1 1753 GL Low High 7 38 14 22 14.32 27 60 2 10 1 5691 GL Low High 11. 38 18 22 22.47 35 98 2 12 2 5508 GL Low High 11.03 18 .31 17.97 29 83 2 12 2 5732 GL Low High 7.68 14 96 16.44 32.02 2 14 1 5649 GL Low Low 6.63 11. 04 13 62 22 68 2 14 1 5743 GL Low Low 7.63 12 04 15.87 25 05 2 16 2 2238 GL Low Low 9.55 13 73 16 33 23.48 2 16 2 2647 GL Low Low 10. 78 14.96 23.49 32.61 3 1 1 5508 GN Low Low 8.95 14.40 14.47 23.29 3 1 1 2601 GN Low Low 10. 35 15 80 20 09 30.66 3 2 1 2238 GN Low High 8.33 16.68 14 17 28 38 3 2 1 2653 GN Low High 7.55 15 90 15 .01 31.61 3 3 2 5556 GN Low High 8.78 16. 10 15.42 28.29 3 3 2 5743 GN Low High 28 80 36.12 57 88 72 .59 3 4 1 5675 GN High High 11. 98 17.39 23 62 34 29 3 4 1 5691 GN High High 10.05 15.46 20 03 30 82 3 5 2 1753 GN High High 8 00 13.05 15 74 25 68 3 5 2 5710 GN High High 9.43 14.48 18.43 28.30 3 6 1 5650 GN High Low 8.60 12 58 15.96 23.34 3 6 1 2647 GN High Low 10. 88 14 86 23.55 32.18 3 7 2 5303 GN High Low 8.38 12 36 13 72 20 24 3 7 2 5771 GN High Low 9.75 13.73 20.28 28.56 3 8 2 5434 GN Low Low 21.75 26.52 35.03 42 .71 3 9 1 2245 GL High High 7.23 12.36 12 82 21.92 3 9 1 5684 GL High High 7.93 13.06 15 38 25.34 3 10 1 5562 GL Low High 10.05 16.65 16 90 28.00 3 10 1 5775 GL Low High 6 65 13.25 15 52 30.92 3 11 2 5649 GL High High 9 05 13.46 18.84 28 02 3 11 2 5715 GL High High 12 95 17 36 23.77 31.87 3 12 2 5534 GL Low High 8 00 14 80 12.60 23 32 3 12 2 5728 GL Low High 7.00 13 80 13 70 27 00 3 13 1 5159 GL High Low 8.28 12.46 13 37 20.12 3 13 1 5734 GL High Low 10 90 15 08 21.49 29 74 3 14 1 2048 GL Low Low 9.05 13 98 15.75 24 33 3 14 1 5768 GL Low Low 10.85 15 78 21.50 31. 28 3 15 2 5370 GL High Low 10.05 14.03 17 99 25 12 3 15 2 5748 GL High Low 7 70 11.68 15 55 23 59 3 16 2 5574 GL Low Low 9.88 14.21 17 86 25.69 3 16 2 5757 GL Low Low 7.00 11. 33 14 13 22.87 t See Table A-2 for description of abbreviations

PAGE 274

255 Table A-14. Probability levels for tests of fixed effects of forage system (FS), stocking rate (SR), concentrate supplement (CS), period (P), and their interactions on response variables associated with 1997 organic matter intake data reported in Chapter 3. Source of Response Variable Variation FOMit TOMI FOMIBW TOMIBW FS 0.6247 0.4985 0.4713 0.3824 SR 0.3833 0.0520 0.4661 0.0924 FS*SR 0.1067 0.0721 0.1778 0.1618 cs 0.5436 0.1316 0.5268 0.1875 FS*CS 0.6228 0.4478 0.6669 0.5242 SR*CS 0.7253 0.9589 0 7897 0 .8941 FS*SR*CS 0.5293 0.5125 0.7519 0.7986 p 0.0001 0.0001 0.0002 0.0001 P*FS 0.0717 0.0263 0.1539 0.1049 P*SR 0.6378 0.3039 0.8461 0.7101 P*FS*SR 0.4767 0.3887 0.6389 0.6601 P*CS 0.7443 0.9666 0.3766 0.4930 P*FS*CS 0.9347 0.6611 0.9532 0.8452 P*SR*CS 0.9579 0.8302 0.9898 0.9347 P*FS*SR*CS 0.4226 0.4089 0.3335 0.3381 t See Table A-2 for description of abbreviations.

PAGE 275

Table A-15. Calculated daily nutrient intake by cows in the winter 1997 grazing study for each forage system (FS) by stocking r ate (SR) by concentrate supplement (CS) treatment combination vs. NRC tabulated requirements Treatment Componen t FSt SR CS Feed DM NEL NDF ADP CP Ca P Mg K Na S Cl F e Zn Cu Mn -------------_!g d -1 ---------------------------------____g d 1 -----------------------------m_g_d1 ---------1 Forage 12.4 22.2 6.0 3.7 3.1 80 51 43 247 1 12 2 3708 235 111 618 !High Supplement 6 0 11.4 2.6 1.7 1.1 55 37 20 80 56 12 49 2137 957 199 397 L----------Total ___ l8.4 _33. 6 __ 8.6 __ 5.4 __ 4.2 135 ___ 87 __ 64 327 ___ 57 __ 24 ___ 52 5845 1192 310 1015 Hight ___________________________________________________________________________________________________________________ I Forage 13.3 24.0 6.5 4 0 3.3 87 55 47 266 1 13 3 3993 253 120 666 I Low Supplement 4.7 8.8 2.0 1.3 0 8 42 28 16 62 43 9 38 1654 741 154 308 _J _____ Total 18.0 32.8 8.4 5.3 4.2 129 83 62 328 45 23 41 5647 994 274 973 GL I Forage 11.3 20.3 5.5 3.4 2.8 73 46 39 225 1 11 2 3378 214 101 563 1High Supplement 6.9 13. 1 2.9 1.9 1.2 63 42 24 92 64 14 57 2460 1102 229 457 L_ ___________ Total __ _18. 2 33.4 __ 8.4 __ 5.3 __ 4.1 __ 136 ___ 88 __ 63 317 __ 66 __ 25 __ 59 5838 1316 330 1020 Lowi. ___________________________________________________________________________________________________________________ I Forage 11.9 21.3 5.8 3 6 3 0 77 49 42 237 1 12 2 3558 225 107 593 I Low Supplement 4.9 9 3 2.1 1.4 0 9 45 30 17 65 46 10 40 1743 781 162 324 _J _____ Total 16.8 30 6 7.8 4 9 3 8 122 79 58 303 47 22 43 5301 1006 269 917 Reguirement 16 23. 3 4.5 3.4 2 2 84 54 32 144 29 32 40 800 640 160 640 t See Table A-2 for description of abbreviations. ; Calculations based on estimates ofDM intake; supplement values were calculated from feed composition data, and NEL, NDF, and CP from forages were computed from average of data obtained in the study while all other nutrients were calculated from NRC tables of feed composition. Tabular values from NRC requirements for a 500 kg cow producing 20 kg of 4% fat corrected milk and gaining 0 275 kg d 1 ; assuming intake to be 32 g kg of body weight. N V, 0\

PAGE 276

Table A-15. ----continued Treatment Component FSt SR CS Feed DM NEL NDF ADF CP Ca P Mg K Na S Cl Fe Zn Cu Mn -------------!g d 1 --------------------------------J d-1 -----------------------------m_g_p.-1 --------1 Forage 11.6* 20.9 5 6 3.5 2.9 75 48 41 232 1 12 2 3840 220 104 580 1High Supplement 5 .7 10.8 2.4 1.6 1.0 52 35 19 76 53 11 47 2034 911 189 378 L_ ___________ Total ___ 17. 3 31.7 ___ 8 1 ___ 5 1 __ 3 9 __ 128 ___ 83 ___ 60 __ 308 ___ 54 ___ 23 ___ 49_5514_1131 __ 293 __ 958 Highl, ______________ -----------------------------------------------------------------------------------------------------1 Forage 10 .7 19.3 5.2 3 2 2.7 70 44 38 215 1 11 2 3219 204 97 537 I Low Supplement 4 2 7.9 1.8 1.2 0 8 38 25 14 55 39 8 34 1480 663 138 275 _J _____ Total_ 14.9 27.2 7.0 4.4 3.4 10.8 _69 _52 270 40 _19 _36 4699 867 234 812 GN Forage 13.1 23.6 6.3 3.9 3.3 85 54 46 262 1 13 3 3927 249 118 655 1High Supplement 7.4 14.0 3.1 2.1 1.3 67 45 25 99 69 15 61 2631 1178 245 489 l_ ___________ Total ___ 20 5 37.6 __ 9 5 ____ 6 __ 4 6 __ 153 ___ 99 ___ 71 __ 360 ___ 70 ___ 28 ___ 63 6558 1427 __ 362 1144 Lowt ___________________________________________________________________________________________________________________ I Forage 14.7 26.5 7.1 4.4 3 .7 96 60 52 294 1 15 3 4416 280 132 736 I Low Supplement 5.1 9.6 2.2 1.4 0.9 46 31 17 67 47 10 4 1 1796 805 167 334 _J _____ Total_ 19.8 36.1 9.3 5 8 4 6 142 _91 _69 362 49 _25 44 6212 1084 299 1070 Reguirement 16 23.3 4.5 3.4 2.2 84 54 32 144 29 32 40 800 640 160 640 t See Table A-2 for description of abbreviations. i Calculations based on estimates ofDM intake; supplement values were calculated from feed composition dat a, and NEL, NDF, and CP from forages were computed from average of data obta ined in the study while all othe r nutrients were calculated from NRC tables of feed composition. Tabular values from NRC requirements for a 500 kg cow producing 20 kg of 4% fat corrected milk and gaining 0.275 kg d 1 ; assuming intake to be 32 g kg- of body weight. N V, ....J

PAGE 277

25 8 Ta b le A -16. Animal perform an ce responses observed in 1996. Period Pasture REPt Treatment Responses SR CL MILK MLKHA ADG BCSC --------------kg d'1 ---------------1 1 1 Low Low 26.09 65.22 -0.27 t 1 2 1 Low High 34.91 87.28 -0.28 1 3 2 Low High 31.19 77.98 0.11 1 4 1 High High 30.79 153.97 -0.11 1 5 2 High High 25.09 125.45 -0.08 1 6 1 High Low 22.66 113.30 -0 25 1 7 2 High Low 25.22 126.10 0.24 1 8 2 Low Low 26.65 66.63 -0.47 2 1 1 Low Low 26.26 65.64 0.60 -0 .2 2 2 1 Low High 29.66 74.16 0.99 0 2 3 2 Low High 27.59 68.98 0.36 2 4 1 High High 24.39 121.97 0.73 0 2 5 2 High High 20.80 1 04.02 -0 23 2 6 1 High L o w 23.46 1 17.29 0.14 0.2 2 7 2 High Low 22.94 114.70 0 84 2 8 2 Low Low 24.67 61.67 0.53 3 1 1 Low Low 26.56 66.39 1.51 -0.15 3 2 1 Low High 22.51 56.28 0.51 0.35 3 3 2 Low High 26.64 66.60 1.18 0.15 3 4 1 High High 29.30 146.52 0.42 0.2 3 5 2 High High 26.88 134 38 0.9 0 0.35 3 6 1 High Low 23.36 116.81 -0.62 0 .2 3 7 2 High Low 15.85 79.24 -0.02 -0 .2 3 8 2 Low Low 20.24 50.60 -0.02 0.65 t See Table A-2 for description of abbreviations. t A period ( ) indicates missing data.

PAGE 278

259 Table A-17. Probability levels for tests of fixed effects of period (P) stocking rate (SR) and concentrate supplement (CS), and their interactions on response variables associated with 1996 animal performance data reported in Chapter 3 Source of Response Variable Variation MILKt MLKHA ADG BCSC SR 0.0808 0.0004 0.0486 0.4431 cs 0.0299 0 0397 0.0662 0.0086 SR*CS 0.9068 0 3327 0.2514 0.8789 p 0.0601 0.1327 0.0528 i P*SR 0.3444 0.5840 0.1696 P*CS 0.3444 0.2558 0.5898 P*SR*CS 0.1600 0.1510 0.3622 t See Table A-2 for description of abbreviations t A period(.) indicates probability value not computed due to missing or unbalanced data.

PAGE 279

260 Table A-18. Animal performance responses observed in 1997 Period Pasture REP 1 Treatment Res12onses FS SR CL MILK MLKHA ADG BCSC 1 1 1 GN Low Low 20 .25 50 62 0.45 i 1 2 1 GN Low High 19 93 49.82 -0 62 1 3 2 GN Low High 22 .21 55 54 -0 23 1 4 1 GN High High 19 73 98 63 -1. 79 1 5 2 GN High High 22 .87 114 35 -0 28 1 6 1 GN High Low 16 08 80.42 -0 92 1 7 2 GN High Low 16 94 84 68 -0 26 1 8 2 GN Low Low 18 .65 46.62 -0 .11 1 9 1 GL High High 21. 23 106 17 0 30 1 10 1 GL Low High 22 08 55 20 0 18 1 11 2 GL High High 17 96 89 .81 -1.00 1 12 2 GL Low High 17 94 44. 86 -0 58 1 13 1 GL High Low 20 68 103.41 -0 99 1 14 1 GL Low Low 18 57 46. 43 -0.95 1 15 2 GL High Low 19 .81 99 07 -0 35 1 16 2 GL Low Low 24.20 60 49 0 12 2 1 1 GN Low Low 21.79 54.48 -0 07 -0 50 2 2 1 GN Low High 18 04 45. 09 0.35 0 00 2 3 2 GN Low High 20.99 52.49 0 95 -0 35 2 4 1 GN High High 14.49 72 46 -0 96 -0 .85 2 5 2 GN High High 11.58 57 89 -1.01 -0 20 2 6 1 GN High Low 17 .31 86 57 -1. 94 0 65 2 7 2 GN High Low 16 26 81. 32 -0 63 -0 50 2 8 2 GN Low Low 20 57 51. 42 0 15 0 .85 2 10 1 GL Low High 19 47 48 69 -0 55 0 00 2 12 2 GL Low High 19 42 48. 54 0 24 0 35 2 14 1 GL Low Low 16.86 42. 16 -0 16 0 00 2 16 2 GL Low Low 17 70 44. 26 -0 .41 -0 35 3 1 1 GN Low Low 21.86 54 64 0 63 0.70 3 2 1 GN Low High 23 37 58 .43 1 36 0 55 3 3 2 GN Low High 21. 24 53 10 0 72 0 65 3 4 1 GN High High 15 .71 78 53 -0 28 0 15 3 5 2 GN High High 15 05 75.24 0.48 0 00 3 6 1 GN High Low 14 13 70 63 0 13 0 55 3 7 2 GN High Low 16 04 80 18 0.39 0 50 3 8 2 GN Low Low 19 55 48. 88 0 .61 0 35 3 9 1 GL High High 14 66 73 30 0 .31 0 30 3 10 1 GL Low High 18 .85 47 13 1 56 0 85 3 11 2 GL High High 13.42 67 08 0.54 0 50 3 12 2 GL Low High 18 10 45 25 0 .88 0.85 3 13 1 GL High Low 16.46 82 32 0.45 0.30 3 14 1 GL Low Low 19.60 48 99 1 34 1 15 3 15 2 GL High Low 10 89 54 46 1 15 1 20 3 16 2 GL Low Low 18 39 45 98 0 56 0 50 t Se e Table A2 for description of abbr e viations. t A p eriod(.) indi c ate s mis si ng data.

PAGE 280

261 Table A-19. Probability levels for tests of fixed effects of forage system (FS), stocking rate (SR) concentrate supplement (CS) period (P) and their interactions on response variables associated with 1997 animal performance data reported in Chapter 3 Source of Response Variable Variation MILK t MLKHA ADG BCSC FS 0.1587 0.2852 0 9063 0 0639 SR 0.0002 0.0001 0.0016 0.1309 FS*SR 0.4683 0.7407 0.7936 1.0000 cs 0 7607 0.9696 0.9338 0.7414 FS*CS 0.4376 0.2443 0.5657 0 5101 SR*CS 0.9363 0.9069 0.2119 0.1157 FS*SR*CS 0.9620 0.5698 0.7885 0.8029 p 0 0015 0 0002 0 0001 0.0003 P FS 0.1208 0.1144 0.1800 0 9342 P*SR 0.0097 0 0004 0.1354 0 6529 P *F S*SR 0.7570 0.6658 0.9783 t P*CS 0.3434 0.2515 0.8814 0.3075 P*FS*CS 0.0958 0.1968 0.3676 0.4682 P*SR*CS 0.4693 0.2547 0.8195 0 9015 P*FS*SR*CS 0.3862 0.1771 0.6786 t See Table A 2 for description of abbreviations. t A period(.) indicates probability value not computed due to missing or unbalanced data

PAGE 281

262 Table A-20. Milk composition and blood glucose concentration data obtained during 1996 reported in Chapter 3 Period PastureREP 1 Cow ID Treatment Res12onses FS SR CS MFAT MCP sec BG -----g kg 1 ------1,000 mL1 mgdL1 1 1 1 5608 GN Low Low 38.5 31.0 73 53 8 1 1 1 5599 GN Low Low 37 0 30 6 34 55 6 1 2 1 5659 GN Low High 30 9 30 9 22 55 1 1 2 1 5348 GN Low High 31.4 28 5 29 51.1 1 3 2 5483 GN Low High 31. 6 28 5 54 52.5 1 3 2 5661 GN Low High 32 3 31. 0 34 66 3 1 4 1 5615 GN High High 31. 8 28 0 26 60.7 1 4 1 5664 GN High High 25 7 29 8 38 60.9 1 5 2 5591 GN High High 38 6 34 4 67 58.1 1 5 2 5643 GN High High 36 4 27.7 189 57 8 1 6 1 2509 GN High Low 35.4 30 3 83 49.6 1 6 1 5624 GN High Low 35.5 30 7 181 50 0 1 7 2 5653 GN High Low 33.6 32 1 247 56 8 1 7 2 5639 GN High Low 32 2 29.5 120 55 3 1 8 2 5636 GN Low Low 29.6 27 0 375 50 9 1 8 2 5654 GN Low Low 33.4 32.2 71 59 9 2 1 1 5348 GN Low Low 33 8 29 7 103 51. 1 2 1 1 5591 GN Low Low 39.7 36.1 99 t 2 2 1 5664 GN Low High 30 3 31.4 46 63 6 2 2 1 5624 GN Low High 36.6 36.4 118 43 1 2 3 2 5627 GN Low High 34 8 29 0 63 63.6 2 3 2 5665 GN Low High 37.7 33.1 85 62.5 2 4 1 5639 GN High High 27 6 32.4 93 58.3 2 4 1 5661 GN High High 32.8 31. 6 33 59.9 2 5 2 5677 GN High High 40.4 35 7 179 2 5 2 5375 GN High High 31.2 28.0 78 60 9 2 6 1 5643 GN High Low 34.2 28 5 102 66.3 2 6 1 5654 GN High Low 36 8 31. 9 90 61.7 2 7 2 5436 GN High Low 31.4 28.4 42 59 8 2 7 2 5672 GN High Low 33.8 31.0 135 44.0 2 8 2 5362 GN Low Low 36.1 35 5 15 61.6 2 8 2 5673 GN Low Low 33 9 33 7 248 48 7 2 9 1 5636 GL High High 29 5 27 5 227 45.3 2 9 1 5676 GL High High 38 3 33 7 135 55 7 2 10 1 2509 GL Low High 36 5 33 3 95 57 2 2 10 1 5611 GL Low High 35.4 32 7 1272 46 7 2 11 2 5653 GL High High 39.4 33 3 168 51.5 2 11 2 5486 GL High High 35 8 28 3 43 48 2 2 12 2 5599 GL Low High 36 7 31.4 30 59 3 2 12 2 5390 GL Low High 32.1 27 6 54 59 0 t See Table A-2 for description of abbreviations t A period(.) ind i cates missing data

PAGE 282

263 Table A-20 ----continued Period PastureREP 1 Cow ID Treatment Res12onses FS SR CS MFAT MCP sec BG -----g kg 1 ------1,000 mL 1 mgdL1 2 13 1 5659 GL High Low 34.4 30.6 22 36 1 2 13 1 5615 GL High Low 32.5 27.7 34 35.1 2 14 1 5566 GL Low Low 35 3 33.8 87 2 14 1 5608 GL Low Low 37 7 32.0 49 38.3 2 15 2 5670 GL High Low 35 2 32.2 35 2 15 2 5638 GL High Low 34 3 31. 1 2657 2 16 2 5669 GL Low Low 40 3 37 3 25 62 0 2 16 2 5483 GL Low Low 31. 7 29.9 71 31. 1 3 1 1 5670 GN Low Low 32 7 32 7 79 50.0 3 1 1 5643 GN Low Low 35.4 29 0 55 57.9 3 2 1 5362 GN Low High 32 0 33.9 16 53 7 3 2 1 5591 GN Low High 37 0 35 9 55 59 2 3 3 2 5669 GN Low High 36 1 33 8 24 58 7 3 3 2 5638 GN Low High 31. 9 30 6 319 51. 9 3 4 1 5627 GN High High 33 3 29 1 48 56 1 3 4 1 5659 GN High High 29 2 29.8 19 50 3 3 5 2 5654 GN H i gh High 3 1. 3 33 0 54 48 2 3 5 2 5483 GN High High 29.6 28 8 58 58 6 3 6 1 5486 GN High Low 31. 0 27 9 30 56 3 3 6 1 5599 GN High Low 36 0 28 1 34 56 8 3 7 2 5566 GN High Low 37 5 37.4 70 58 6 3 7 2 5390 GN High Low 27 9 26 6 70 62 8 3 8 2 5611 GN Low Low 30 9 34.3 1 532 59 2 3 8 2 2509 GN Low Low 32.4 31. 5 92 49 8 3 9 1 5608 GL High High 36 1 30 .4 63 52 5 3 9 1 5673 GL High High 33 3 32.4 689 61.2 3 10 1 5436 GL Low High 30 0 29.1 82 57 7 3 10 1 5653 GL Low High 34 1 31. 1 166 53 5 3 11 2 5624 GL High High 34.6 29 7 172 52 1 3 11 2 5672 GL High High 33.3 31.0 46 57 0 3 12 2 5677 GL Low High 38.4 31. 9 118 54 9 3 12 2 5636 GL Low High 28 9 26 8 299 56 7 3 13 1 5664 GL High Low 3 1 7 27 6 47 56 2 3 13 1 5348 GL High Low 34 9 29 3 56 55.6 3 14 1 5661 GL Low Low 36 0 29 .2 31 55.2 3 14 1 5639 GL Low Low 35.4 3 2. 1 71 55.6 3 15 2 5665 GL High Low 32 9 30 0 98 55 1 3 15 2 5375 GL High Low 33.8 29.0 39 63 0 3 16 2 5676 GL Low Low 37 6 31.6 141 52.6 3 16 2 5615 GL Low Low 3 1. 3 26 9 32 53 9 t See Tabl e A 2 for d es cription o f a bb re vi a ti o ns. t A period ( .) ind i ca t e s missing data.

PAGE 283

264 Table A-21. Probability levels for tests of fixed effects of period (P) stocking rate (SR) and concentrate supplement (CS), and their interactions on response variables associated with 1996 milk composition and blood glucose concentration data reported in Chapter 3. Source of Response Variable Variation MFATt MCP sec BG SR 0.5299 0 0885 0.3800 0.4595 cs 0.4135 0.7423 0.2291 0.3738 SR*CS 0.7436 0.7162 0.3704 0.6855 p 0.2904 0.0479 0.6937 0.6447 P*SR 0.5345 0.0972 0.2831 0 8412 P*CS 0.8212 0.5225 0.6510 0.3338 P*SR*CS 0.4887 0.2180 0.5906 0.3345 t See Table A-2 for description of abbreviations.

PAGE 284

265 Table A-22. Milk composition, milk urea N, and blood glucose concentration data obtained during 1997 reported in Chapter 3. Period PastureREP 1 Cow ID Treatment Res12onses FS SR cs MFAT MCP sec MUN BG -----g kg1 -----1,000 mL1 --mg dL1 --1 1 1 1753 GN Low Low 32 8 29 2 562 14 97 t 1 1 1 5775 GN Low Low 38 8 33 0 33 18.93 1 2 1 2048 GN Low High 41.8 31. 1 49 20. 14 1 2 1 5771 GN Low High 36 5 34.4 105 18 77 1 3 2 2245 GN Low High 36 1 29.8 74 18.48 1 3 2 5757 GN Low High 37 9 31. 1 27 19 20 1 4 1 5748 GN High High 34.8 29 8 38 24.35 1 4 1 5434 GN High High 37 9 29 2 85 22 56 1 5 2 5728 GN High High 36.5 33 8 125 22.51 1 5 2 5508 GN High High 30.8 28.8 52 18.13 1 6 1 5574 GN High Low 39 7 30 7 55 24.73 1 6 1 5684 GN High Low 39 1 36 7 130 19 90 1 7 2 2647 GN High Low 36.5 27 9 105 24.10 1 7 2 5649 GN High Low 34.5 31. 3 882 19.55 1 8 2 2601 GN Low Low 35.5 33 1 280 17 .48 1 8 2 5675 GN Low Low 32.1 30.5 42 19.82 1 9 1 5768 GL High High 33.2 31. 3 155 16.75 1 9 1 2238 GL High High 37.4 30 2 62 19 .01 1 10 1 5710 GL Low High 34.3 34 3 337 11.80 1 10 1 5159 GL Low High 31. 5 29 2 360 16 86 1 11 2 5743 GL High High 39.5 33.7 382 13.79 1 11 2 5303 GL High High 30.9 29 0 672 16.35 1 12 2 5734 GL Low High 33.2 30 3 47 16 58 1 12 2 5370 GL Low High 38 0 38.8 1128 12 15 1 13 1 5732 GL High Low 34 8 30 5 486 14.56 1 13 1 5534 GL High Low 33 8 27 5 225 15.43 1 14 1 5715 GL Low Low 35.1 32.4 65 18.20 1 14 1 5556 GL Low Low 35 7 32 5 268 18 .80 1 15 2 5691 GL High Low 36.9 30 8 197 17 70 1 15 2 5562 GL High Low 36 0 30.4 55 22. 08 1 16 2 5650 GL Low Low 40.2 33.8 101 16.81 1 16 2 2653 GL Low Low 33 0 29.0 282 19.83 2 1 1 5728 GN Low Low 33 6 34.8 55 23.00 77.63 2 1 1 5159 GN Low Low 32.3 29.7 617 21. 82 58.29 2 2 1 5684 GN Low High 39.4 40.8 149 20.52 62.76 2 2 1 5650 GN Low High 40 1 33 6 49 21. 67 71.73 2 3 2 5768 GN Low High 36 3 33.3 663 20. 30 70.87 2 3 2 5303 GN Low High 31. 1 30.8 282 23.05 64.13 2 4 1 5562 GN High High 42 7 30 8 73 25.40 63 66 2 4 1 5734 GN High High 35 5 31.3 89 25.40 73.32 2 5 2 5775 GN High High 39 3 33 6 49 24.27 68 69 2 5 2 5370 GN High High 39 9 36.5 619 21.14 67 19 2 6 1 2245 GN High Low 35 5 29.0 87 24.63 68 .61 2 6 1 2653 GN High Low 32.4 28 2 263 25. 40 67.68 t See Table A-2 for description of abbreviations. t A period(. ) indicates missing data.

PAGE 285

266 Table A-22 ----continued Period PastureREPf Cow ID Treatment Res12onses FS SR cs MFAT MCP sec MUN BG -----g kg1 -----1,000 mL 1 --mg dL 1 ---2 7 2 5715 GN High Low 35.7 31. 8 79 25. 38 70 .82 2 7 2 2048 GN High Low 43.0 30 3 40 25. 40 67 88 2 8 2 5748 GN Low Low 33.4 30.8 36 23.93 66 35 2 8 2 5534 GN Low Low 34.4 29.2 194 20. 98 56.74 2 10 1 5691 GL Low High 37 0 32.8 198 25. 05 63.39 2 10 1 1753 GL Low High 33 0 27 6 604 19.56 64.08 2 12 2 5508 GL Low H i gh 32 3 29.8 61 22 62 60 .59 2 12 2 5732 GL Low H i gh 37 8 33 8 807 20.30 78.73 2 14 1 5649 GL Low Low 36.2 32.7 965 24.47 71.87 2 14 1 5743 GL Low Low 37.5 32.5 335 25 19 71. 22 2 16 2 2647 GL Low Low 37.1 30 2 84 24 69 2 16 2 2238 GL Low Low 39.6 30.8 163 24.29 65 30 3 1 1 5508 GN Low Low 30.8 30 1 59 17.98 65.10 3 1 1 2601 GN Low Low 36.9 34 5 197 17 89 3 2 1 2238 GN Low High 38.7 31.3 76 21. 77 67.28 3 2 1 2653 GN Low High 33.6 31. 0 223 20.43 62 09 3 3 2 5743 GN Low High 37.2 33.6 422 19.39 59.34 3 3 2 5556 GN Low High 36.4 34.0 943 17 .91 3 4 1 5675 GN High High 33.5 32.3 44 25.40 3 4 1 5691 GN High H i gh 37 9 31. 7 296 25.40 59 07 3 5 2 5710 GN High H i gh 35 5 31. 7 143 23.83 3 5 2 1753 GN High H i gh 29 1 25. 3 572 19.57 61.53 3 6 1 2647 GN High Low 35 5 30 3 129 24.99 56 .01 3 6 1 5650 GN High Low 42.0 32 7 113 23 33 60 49 3 7 2 5303 GN High Low 28 5 29.6 298 25.40 58 .91 3 7 2 5771 GN High Low 38 3 34.6 197 24.95 3 8 2 5434 GN Low Low 42 1 31. 3 157 24.81 3 8 2 5732 GN Low Low 38 5 36 8 2073 10.68 74 90 3 9 1 5684 GL High H i gh 33.7 36 7 179 20.98 60 20 3 9 1 2245 GL High H i gh 34 4 28. 6 77 23.34 61. 50 3 10 1 5562 GL Low H i gh 39 8 31.3 159 25.40 59.42 3 10 1 5775 GL Low H i gh 38 9 34 8 49 20 25 60 53 3 11 2 5649 GL High High 35.1 31. 9 933 21.77 63.14 3 11 2 5715 GL High H i gh 38 1 31. 8 143 25.40 68.85 3 12 2 5534 GL Low High 36 8 30 7 617 16.78 58 39 3 12 2 5728 GL Low High 34 8 33 0 45 21.37 69 60 3 13 1 5 1 59 GL High Low 33.5 29 8 653 19 02 57 15 3 13 1 5734 GL High Low 36 7 30.2 118 19.86 65.79 3 14 1 2048 GL Low Low 40 3 30.6 38 24.09 67 79 3 14 1 5768 GL Low Low 37 7 32 5 431 21. 74 66 73 3 15 2 5748 GL High Low 36 5 32 6 72 24 59 60 .71 3 15 2 5370 GL High Low 35 3 34 8 1452 15.53 58 2 1 3 16 2 5757 GL Low Low 36 5 30 0 2 6 23 45 3 16 2 5574 GL Low Low 42 7 37 7 81 25.40 t S ee T able A 2 for description of abbreviations t A p eriod(.) ind i c a t e s missing data

PAGE 286

267 Table A-23 Probability levels for tests of fixed effects of forage system (FS), stocking rate (SR), concentrate supplement (CS) period (P), and their interactions on response variables associated with 1997 milk composition milk urea N, and blood glucose concentration data reported in Chapter 3. Source of Response Variable Variation MFATt MCP sec MUN BG FS 0.7421 0 6495 0.3035 0.0609 0 6913 SR 0.8175 0 0186 0.9186 0 0117 0.0182 FS *SR 0.7841 0.8773 0.2040 0.0060 0 8452 cs 0.8625 0.1962 0 8271 0.3211 0.2557 FS*CS 0.3520 0 8266 0.5847 0.3599 0.9782 SR*CS 0.7624 0.7946 0.6363 0.2712 0.0448 FS*SR*CS 0.1363 0.5093 0 3119 0.0252 0 5388 p 0 6208 0.5926 0.4497 0.0001 0.0036 P*FS 0.3666 0 5509 0.3459 0.0098 0 7736 P*SR 0.0706 0.9826 0.9855 0.55 2 5 0.0274 P*FS*SR 0.5986 0.3421 0 0858 0 1852 0.0217 P*CS 0.4092 0 3329 0.8891 0.5402 0 3463 P*FS*CS 0 6650 0.4265 0.3762 0 2899 0 2076 P*SR*CS 0.5984 0.9533 0 9271 0.7439 0.0503 P*FS*SR*CS 0.5013 0 9900 0 5413 0.3891 0 2652 t See Table A-2 for description of abbreviations.

PAGE 287

268 Table A-24. Program in SAS to compute new estimates of forage intake and total intake based on adjusted diet digestibility of mixed forage-concentrate supplement diet. OPTIONS LS=l44 PSs62: DATA NAME: INFILE 'FILENAME.TXT': INPUT PER past cows FORAGE s SR s cs s OMI FOO IVOMO SUPINTAK SUPOIG: FRGDIG IVOMD/100: DO FRGINTAK l TO 40 BY .05 UNTIL (DIFF < .01): TOTINTAK = FRGINTAK + SUPINTAK: EXPDIG (FRGINTAK*FRGDIG + SUPINTAK*SUPDIG)/TOTINTAK*lOO: ADJDIG (59.71 -.B94B*EXPDIG + .01399*EXPDIG**2)/100: FOP s TOTINTAK*(l-ADJDIG): DIFF FOO-FOP: END: PROC PRINT: RUN: Table A-25. Program in SAS to compute parameters for fecal output calculations based on chromium concentrations in fecal samples and time of fecal collection. OPTIONS LS 75 PS 62: DATA NAME: INFILE 'FILENAME.TXT': INPUT PER PAST ANIMAL TIME CR: Y = CR: PROC SORT: BY PER PAST ANIMAL: PROC NLIN ITER=50 CONVERGENCE.00001 METHOD-MARQUARDT: BY PER PAST ANIMAL: PARMS KOlOO 400 700 Ll.03 .05 .OB TAU 3 5 7 : BOUNDS KO>O. Ll>O. TAU>O: T = TIME-TAU: IF T
PAGE 288

269 Table A-26 Pregraze herbage mass, postgraze herbage mass and herbage allowance data obtained on 1996 summer pastures reported in Chapter 4. Period week Pasture REP 1 Treatment Res12onses FS SR CL PRHM POHM HA ------kg ha1 -------kg kg-1 1 1 1 1 GN Low Low 5297 4313 1.13 1 1 2 1 GN Low High 4557 4402 1.24 1 1 3 2 GN Low H i gh 4510 4392 1 .11 1 1 4 1 GN High High 4136 3966 0.92 1 1 5 2 GN High High 4199 3877 0.83 1 1 6 1 GN High Low 5001 4848 0.91 1 1 7 2 GN High Low 4970 4828 0 95 1 1 8 2 GN Low Low 1 1 9 1 GL High High 2765 1615 0 60 1 1 10 1 GL Low High 3376 2704 1.22 1 1 11 2 GL High High 3421 3274 0.93 1 1 12 2 GL Low High 3476 2939 1 26 1 1 13 1 GL High Low 3175 2218 0 .70 1 1 14 1 GL Low Low 3203 2939 1.32 1 1 15 2 GL High Low 3467 3022 0.91 1 1 16 2 GL Low Low 3266 2486 1.05 1 2 1 1 GN Low Low 5360 4749 1.19 1 2 2 1 GN Low High 5406 4343 1.34 1 2 3 2 GN Low High 5110 4898 1.25 1 2 4 1 GN High High 5336 4501 1.12 1 2 5 2 GN High High 5554 4799 1 .07 1 2 6 1 GN High Low 5492 4422 0.92 1 2 7 2 GN High Low 5180 4789 0 96 1 2 8 2 GN Low Low 1 2 9 1 GL High High 2701 1465 0.57 1 2 10 1 GL Low High 3549 2754 1.26 1 2 11 2 GL High High 3093 2185 0.73 1 2 12 2 GL Low High 3661 2620 1.24 1 2 13 1 GL High Low 3239 1900 0.67 1 2 14 1 GL Low Low 3695 2821 1.40 1 2 15 2 GL High Low 3777 2185 0 .83 1 2 16 2 GL Low Low 3868 2587 1.18 2 1 1 1 GN Low Low 7907 4577 1 .61 2 1 2 1 GN Low High 6935 3686 1 .54 2 1 3 2 GN Low High 6180 3611 1 29 2 1 4 1 GN High High 7370 4169 1.05 2 1 5 2 GN High High 6500 3761 0.91 2 1 6 1 GN High Low 6896 3976 1.14 2 1 7 2 GN High Low 6884 3922 1.16 2 1 8 2 GN Low Low t See Table A -2 for description of abbreviations

PAGE 289

270 Table A-26 ----continued. Period week Pasture REP 1 Treatment Responses FS SR CL PRHM POHM HA -------kg ha1 -------kg kg-1 2 1 9 1 GL High High 3706 1522 0.68 2 1 10 1 GL Low High 4051 2115 1.13 2 1 11 2 GL High High 3543 1392 0.67 2 1 12 2 GL Low High 5134 2813 1.66 2 1 13 1 GL High Low 3017 1116 0 53 2 1 14 1 GL Low Low 3960 2144 1 03 2 1 15 2 GL High Low 3289 1087 0.64 2 1 16 2 GL Low Low 3905 1898 1 .21 2 2 1 1 GN Low Low 7191 3718 1.41 2 2 2 1 GN Low High 7370 3568 1.59 2 2 3 2 GN Low High 5182 3439 1 14 2 2 4 1 GN High High 6692 3804 0 96 2 2 5 2 GN High High 7075 4072 0.99 2 2 6 1 GN High Low 5822 3782 1 00 2 2 7 2 GN High Low 5489 2505 0 86 2 2 8 2 GN Low Low 2 2 9 1 GL High High 2817 1087 0 .51 2 2 10 1 GL Low High 3398 1768 0 95 2 2 11 2 GL High High 2600 841 0.47 2 2 12 2 GL Low High 3320 1792 1.06 2 2 13 1 GL High Low 3089 1145 0 54 2 2 14 1 GL Low Low 3887 1869 0.97 2 2 15 2 GL High Low 2727 943 0.53 2 2 16 2 GL Low Low 4087 1956 1.26 3 1 1 1 GN Low Low 6179 4211 1 38 3 1 2 1 GN Low High 5521 3709 1 22 3 1 3 2 GN Low High 4643 3618 0 95 3 1 4 1 GN High High 5059 4685 1 03 3 1 5 2 GN High High 5521 4238 0.94 3 1 6 1 GN High Low 6006 4421 1 12 3 1 7 2 GN High Low 4274 4019 0.86 3 1 8 2 GN Low Low 3 1 9 1 GL High High 2502 595 0.37 3 1 10 1 GL Low High 3124 1622 0 80 3 1 11 2 GL High High 2604 736 0.47 3 1 12 2 GL Low High 2982 1358 0 78 3 1 13 1 GL High Low 2298 285 0 37 3 1 14 1 GL Low Low 3104 1411 0 76 3 1 15 2 GL High Low 3226 905 0.49 3 1 16 2 GL Low Low 3410 1355 0.91 3 2 1 1 GN Low Low 4170 3481 1 02 3 2 2 1 GN Low High 4863 4047 1 .18 3 2 3 2 GN Low High 4435 3308 0 89 3 2 4 1 GN High High 5660 4476 1 08 t See Table A 2 for description of abbreviations.

PAGE 290

271 Table A-26 --continued Per i od week Pasture REP 1 Treatment Responses FS SR CL PRHM POHM HA -------kg ha 1 ----kg kg-1 3 2 5 2 GN High High 6792 4530 1 09 3 2 6 1 GN High Low 5302 3673 0 97 3 2 7 2 GN High Low 3569 2861 0 67 3 2 8 2 GN Low Low 3 2 9 1 GL High High 2410 947 0.40 3 2 10 1 GL Low High 2522 1608 0 70 3 2 11 2 GL High High 2287 1678 0 56 3 2 12 2 GL Low High 2896 1892 0 87 3 2 13 1 GL High Low 2574 905 0 50 3 2 14 1 GL Low Low 2542 1313 0 65 3 2 15 2 GL High Low 2471 919 0.40 3 2 16 2 GL Low Low 2920 1467 0 84 t S ee Tabl e A -2 for descript ion of abbr e viations

PAGE 291

272 Table A-27 Forage nutritive value, organic matter intake body weight and animal performance data obtained in 1996 summer reported in Chapter 4. Period Pasture REP 1 Treatment Res12onses FS SR CL IVOMDCP NDF FOMI TOMI BW MILK ADG ------g kg"1 ----------kg d"1 ---kg --kg d "1 -1 1 1 GN Low Low 610 127 799 7.2 11.2 568 15.4 -1.14 1 2 1 GN Low High 619 117 797 3.5 10.7 484 20.1 -0 79 1 3 2 GN Low High 599 136 804 1 6 8 9 533 15 2 -1.11 1 4 1 GN High High 599 165 697 5 9 13.8 438 18.7 -0.44 1 5 2 GN High High 640 158 789 5 6 12 4 484 16.1 -0.36 1 6 1 GN High Low 615 152 785 6 7 10.9 539 15 5 -0.90 1 7 2 GN High Low 603 160 785 9 7 14 7 518 19.5 -0.61 1 9 1 GL High High 704 198 489 12.5 20.6 484 21.7 -1.48 1 10 1 GL Low High 711 179 443 10.4 18 0 500 20 0 -1. 14 1 11 2 GL High High 704 204 467 8.5 16.2 480 18.5 -0 96 1 12 2 GL Low High 698 190 449 8.4 18 0 509 23 0 -0 75 1 13 1 GL High Low 713 205 432 12 1 17.6 515 20 4 -0 60 1 14 1 GL Low Low 704 189 472 12.8 18 1 467 14.4 0 94 1 15 2 GL High Low 704 195 448 11. 0 15 1 477 17.5 0 73 1 16 2 GL Low Low 683 187 464 10.3 16 0 549 23 7 -0.41 2 1 1 GN Low Low 629 147 742 7.8 12.6 517 16 9 -0.03 2 2 1 GN Low High 640 146 781 10.6 17 6 460 19.0 0.42 2 3 2 GN Low High 636 155 787 7.7 15.5 505 18 6 0 65 2 4 1 GN High High 649 188 755 5 2 12.1 549 13 8 0.57 2 5 2 GN High High 645 179 672 7 1 14 7 562 17.8 0 57 2 6 1 GN High Low 653 167 778 9 3 13 5 478 13.7 0.04 2 7 2 GN High Low 642 154 738 9.4 13.4 464 15.3 0.32 2 9 1 GL High High 685 172 477 7 8 15.6 515 17.4 -0 04 2 10 1 GL Low High 705 173 450 10 2 18.3 545 19 5 0.01 2 11 2 GL High High 672 173 518 7 8 17.5 489 20.4 -0.15 2 12 2 GL Low High 681 182 476 7 5 17.1 480 20.1 -0 .41 2 13 1 GL High Low 664 178 555 12.3 17.8 521 18 6 0 13 2 14 1 GL Low Low 694 170 474 16 2 20.3 594 17 6 -0.31 2 15 2 GL High Low 651 171 593 9 6 14.3 458 14 9 -0.11 2 16 2 GL Low Low 687 169 455 8.4 13.3 478 13 4 0.10 3 1 1 GN Low Low 649 190 768 9.4 11.8 501 8.1 -0. 12 3 2 1 GN Low High 631 195 775 9 2 15 7 506 15.4 -0 17 3 3 2 GN Low High 629 202 757 8.4 15 3 580 18.0 0.49 3 4 1 GN High High 622 184 752 4 3 11. 4 471 15.6 0.41 3 5 2 GN High High 639 188 747 7 8 13 6 521 13.9 0.58 3 6 1 GN High Low 617 173 754 7 7 10 2 465 11.7 0.15 3 7 2 GN High Low 611 176 745 9 8 13.1 481 11.1 0 23 3 9 1 GL High High 687 194 548 14. 2 21. 1 564 17 1 0 13 3 10 1 GL Low High 717 183 464 15.7 22.8 591 18 6 0 70 3 11 2 GL High High 694 189 486 9.6 16 7 472 16.2 0 .11 3 12 2 GL Low High 678 176 445 10 5 17.0 554 15.8 0 69 3 13 1 GL High Low 697 209 483 12 2 15.8 461 12 5 -0 07 3 14 1 GL Low Low 715 193 469 15 4 20.0 596 9 6 0 .88 3 15 2 GL High Low 702 189 468 14 3 18.5 560 14 0 0 23 3 16 2 GL Low Low 697 170 451 16 9 22.0 521 18.0 0 66 t S e e T a bl e A-2 for descrip tion of abbrevia ti ons

PAGE 292

273 Table A-28. Probability levels for tests of fixed effects of forage system (F), stocking rate (SR), concentrate supplement (CS), season (S), period within season (P[S]) and their interactions on pasture response variables reported in Chapter 4. Source of Response Variable Variation PRHM t HA IVDOM CP NDF FS 0.0001 0.0001 0 0001 0.7231 0.0001 SR 0.0006 0.0001 0 7436 0.0281 0.5180 FS*SR 0.2678 0.5848 0 2865 0.2445 0.0549 cs 0.2577 0.8234 0.7622 0.5985 0.8536 FS*CS 0.1640 0.1093 0.7965 0.7939 0.2894 SR*CS 0.1444 0.9951 0.7678 0 .2 964 0.4733 FS*SR*CS 0.1910 0.1953 0.2554 0.3625 0.0604 s 0.0001 0.0001 0.0001 0.0001 0.0001 S*FS 0.6875 0.0029 0.0001 0 0002 0.0001 S*SR 0.0030 0.0014 0 .2 918 0.3959 0.6704 S*FS*SR 0.4881 0.0002 0.0596 0.3524 0.0001 S*CS 0.1058 0.7875 0.7560 0.9312 0.2420 S*FS*CS 0 0055 0.0412 0.7548 0.6463 0.6694 S*SR*CS 0.0001 0.6033 0.6595 0.4843 0.3773 S*FS*SR*CS 0.0001 0.0053 0.6741 0.6326 0.4002 P(S) 0.0001 0.0001 0.5375 0.0001 0.0271 P(S)*FS 0.0000 0 0001 0.1301 0.0050 0.0216 P(S)*SR 0.0001 0.0520 0.7333 0.2391 0.4015 P(S)*FS*SR 0.0409 0.6914 0.2034 0.0164 0.0417 P(S)*CS 0.3101 0 7207 0.9041 0.9724 0.2210 P(S)*FS*CS 0.2946 0.4188 0 9963 0.6030 0.4804 P(S)*SR*CS 0.1096 0.8083 0.8225 0.9688 0.1041 P(S)*FS*SR *CS 0.6246 0.9838 0.9312 0.9803 0.8266 t See Table A-2 for description of abbreviations

PAGE 293

274 Table A-29. Probability levels for tests of fixed effects of forage system (F), stocking rate (SR), concentrate supplement (CS), season (S), period within season (P[S]), and their interactions on animal response variables reported in Chapter 4. Source of Response Variable Variation FOMJt TOMI MILK ADG FS 0.0184 0.0064 0.1734 0.6205 SR 0.3387 0.0740 0.0022 0.0021 FS*SR 0.3527 0.3300 0.6223 0.2493 cs 0 5027 0.0073 0.0091 0.5236 FS*CS 0.5614 0.4106 0.5322 0.0481 SR*CS 0.9965 0.7964 0.5198 0.2008 FS*SR*CS 0.9625 0.9542 0.7760 0.5699 s 0.0031 0 0003 0.0001 0.8611 S*FS 0.0002 0.0001 0.0005 0.8304 S*SR 0.9532 0.3801 0.0002 0.0001 S*FS*SR 0.0484 0.0235 0.6256 0.1037 S*CS 0.8183 0.1344 0.0034 0.5582 S*FS*CS 0.7305 0 6581 0.7140 0.2675 S*SR*CS 0.6194 0.8447 0.2784 0.2168 S*FS*SR*CS 0.3208 0.2619 0.6369 0.9782 P(S) 0.0001 0.0001 0.0001 0.0001 P(S)*FS 0.0233 0.0084 0.1441 0.0 l 51 P(S)*SR 0.2522 0.1092 0.0172 0.0510 P(S)*FS*SR 0.6881 0.4161 0.1342 0.3096 P(S)*CS 0.8959 0.9442 0.0845 0.7204 P(S)*FS*CS 0.8979 0.7780 0.1012 0.5623 P(S)*SR*CS 0 7184 0.6154 0.6157 0.9331 P(S)*FS*SR *CS 0.7909 0.7229 0.3925 0 8662 t See Table A-2 for description of abbreviations.

PAGE 294

275 Table A-30 Time spent grazing, loafing (under and outside shade) and eating concentrate supplement during 1996 summer. Period obsdt Pasture REP Treatment Res~onses FS SR cs csam grzd shd noshd cs~m grzn shnnoshn minutes 1 1 1 1 GN Low Low 26 73 432 1 17 225 0 291 1 1 2 1 GN Low High 31 71 411 16 24 263 0 243 1 1 3 2 GN Low High 29 64 436 2 23 250 7 247 1 1 4 1 GN High High 32 71 427 0 23 190 0 307 1 1 5 2 GN High High 41 37 444 8 26 172 34 287 1 1 6 1 GN High Low 21 90 420 0 15 222 0 288 1 1 7 2 GN High Low 20 79 433 0 15 229 39 241 1 1 9 1 GL High High 30 75 426 0 21 207 8 291 1 1 10 1 GL Low High 33 68 430 0 17 177 29 304 1 1 11 2 GL High High 42 51 440 0 35 227 33 233 1 1 12 2 GL Low High 40 44 448 2 28 212 31 274 1 1 13 1 GL High Low 23 87 424 0 21 212 35 280 1 1 14 1 GL Low Low 21 88 428 0 22 249 13 265 1 1 15 2 GL High Low 19 104 414 0 18 165 67 272 1 1 16 2 GL Low Low 21 89 428 0 16 256 18 260 1 2 1 1 GN Low Low 15 138 400 0 14 210 3 321 1 2 2 1 GN Low High 23 108 408 12 17 221 9 301 1 2 3 2 GN Low High 22 95 426 7 18 245 3 279 1 2 4 1 GN High High 21 126 397 0 21 252 0 265 1 2 5 2 GN High High 26 108 407 0 20 301 0 207 1 2 6 1 GN High Low 19 114 404 0 10 304 13 204 1 2 7 2 GN High Low 21 89 423 0 13 259 0 254 1 2 9 1 GL High High 29 75 435 0 25 192 47 266 1 2 10 1 GL Low High 22 69 448 0 23 193 18 295 1 2 11 2 GL High High 28 70 438 0 31 186 1 310 1 2 12 2 GL Low High 23 62 447 0 18 174 19 317 1 2 13 1 GL High Low 15 101 413 0 19 263 1 241 1 2 14 1 GL Low Low 15 92 414 6 16 164 58 284 1 2 15 2 GL High Low 20 159 348 0 16 217 29 261 1 2 16 2 GL Low Low 19 95 414 0 15 197 0 310 2 3 1 1 GN Low Low 14 310 0 223 12 108 0 392 2 3 2 1 GN Low High 12 205 84 245 13 130 4 367 2 3 3 2 GN Low High 18 205 0 324 16 117 7 378 2 3 4 1 GN High High 13 312 0 227 1 3 150 0 343 2 3 5 2 GN High High 15 238 0 284 17 146 0 361 2 3 6 1 GN High Low 15 210 0 312 16 151 0 358 2 3 7 2 GN High Low 11 266 0 253 13 199 0 313 2 3 9 1 GL High High 19 297 0 240 19 106 0 381 2 3 10 1 GL Low High 18 184 13 340 20 133 0 367 2 3 11 2 GL High High 18 245 9 285 20 228 0 273 2 3 12 2 GL Low High 15 187 0 325 20 203 0 305 2 3 13 1 GL High Low 11 284 0 233 15 161 0 353 2 3 14 1 GL Low Low 12 276 0 240 15 181 0 333 2 3 15 2 GL High Low 14 237 0 277 15 152 0 362 2 3 16 2 GL Low Low 13 187 0 329 15 191 0 323 t See Table A-2 for description of abbreviations.

PAGE 295

276 Table A-30. ----continued. Period obsdf Pasture REP Treatment Res12onses FS SR cs csam grzd shd noshd cs12m grzn shnnoshn ----------minutes 2 4 1 1 GN Low Low 13 161 321 0 9 244 0 305 2 4 2 1 GN Low High 13 135 341 0 11 248 0 293 2 4 3 2 GN Low High 9 140 335 0 12 224 0 314 2 4 4 1 GN High High 19 128 326 2 15 244 0 282 2 4 5 2 GN High High 14 133 324 0 13 192 0 336 2 4 6 1 GN High Low 9 166 291 0 11 179 0 352 2 4 7 2 GN High Low 10 178 278 0 11 235 0 312 2 4 9 1 GL High High 15 114 341 0 15 196 0 335 2 4 10 1 GL Low High 13 64 392 0 12 208 0 324 2 4 11 2 GL High High 15 84 377 0 15 191 0 338 2 4 12 2 GL Low High 10 87 367 0 12 187 0 346 2 4 13 1 GL High Low 8 119 337 0 13 226 14 292 2 4 14 1 GL Low Low 7 105 351 0 13 279 0 254 2 4 15 2 GL High Low 9 96 359 0 21 164 0 362 2 4 16 2 GL Low Low 11 81 372 0 20 170 0 361 3 5 1 1 GN Low Low 10 194 305 16 13 229 0 313 3 5 2 1 GN Low High 12 160 347 4 14 220 0 318 3 5 3 2 GN Low High 11 142 339 29 14 204 0 330 3 5 4 1 GN High High 16 142 334 24 15 186 0 336 3 5 5 2 GN High High 12 156 330 8 12 210 0 308 3 5 6 1 GN High Low 9 177 272 48 10 199 0 320 3 5 7 2 GN High Low 10 163 305 30 8 226 0 289 3 5 9 1 GL High High 14 151 348 0 14 171 0 349 3 5 10 1 GL Low H i gh 14 135 346 18 13 192 0 328 3 5 11 2 GL High High 15 149 348 0 13 185 0 334 3 5 12 2 GL Low High 13 144 353 1 13 179 0 334 3 5 13 1 GL High Low 10 152 348 0 13 169 8 335 3 5 14 1 GL Low Low 11 154 344 0 12 168 0 344 3 5 15 2 GL High Low 10 137 343 18 11 128 0 384 3 5 16 2 GL Low Low 10 150 346 0 11 193 0 318 3 6 1 1 GN Low Low 28 309 60 59 13 249 0 274 3 6 2 1 GN Low High 20 210 118 94 14 167 0 355 3 6 3 2 GN Low High 19 197 113 112 13 156 0 359 3 6 4 1 GN High High 19 224 87 106 15 143 0 368 3 6 5 2 GN High High 12 223 132 46 8 165 0 352 3 6 6 1 GN High Low 12 260 61 99 7 188 0 332 3 6 7 2 GN High Low 10 254 67 83 9 183 0 328 3 6 9 1 GL High High 21 175 123 115 22 140 0 382 3 6 10 1 GL Low High 21 143 190 80 21 164 0 358 3 6 11 2 GL High High 13 221 175 11 17 142 0 385 3 6 12 2 GL Low High 13 156 155 96 12 168 0 342 3 6 13 1 GL High Low 14 193 140 73 10 161 0 352 3 6 14 1 GL Low Low 14 221 119 66 9 184 0 332 3 6 15 2 GL High Low 12 173 112 120 9 129 0 385 3 6 16 2 GL Low Low 13 176 128 101 11 176 0 338 t See Table A 2 for description of abbreviations

PAGE 296

277 Table A-30. ----continued Period obsdf Pasture REP Treatment Res12onses FS SR cs csam grzd shd noshd cs12m grzn shnnoshn --------------------------minutes 3 7 1 1 GN Low Low 10 272 241 42 8 198 0 351 3 7 2 1 GN Low High 9 200 268 86 13 213 0 329 3 7 3 2 GN Low High 10 186 300 66 11 198 0 343 3 7 4 1 GN High High 10 245 303 0 13 187 0 348 3 7 5 2 GN High High 14 220 321 0 12 152 0 370 3 7 6 1 GN High Low 10 289 247 7 8 255 0 270 3 7 7 2 GN High Low 14 310 222 4 9 211 0 316 3 7 9 1 GL High High 25 191 302 38 14 160 0 368 3 7 10 1 GL Low High 24 153 371 8 12 216 0 314 3 7 11 2 GL High High 24 155 341 35 16 124 0 398 3 7 12 2 GL Low High 24 157 355 17 10 179 0 348 3 7 13 1 GL High Low 22 163 355 10 10 146 0 380 3 7 14 1 GL Low Low 12 211 312 5 9 193 0 329 3 7 15 2 GL High Low 14 167 357 3 9 132 0 390 3 7 16 2 GL Low Low 13 214 281 33 10 182 0 340 t See Table A-2 for description of abbreviations.

PAGE 297

278 Table A-31. Time spent grazing loafing and eating concentrate during 1997 winter Period obsd1 Pasture REP Treatment Res12onses FS SR cs csam grzd loafd cs12m grzn l oafn minutes 1 1 1 1 GN Low Low 11 337 101 18 149 533 1 1 2 1 GN Low High 16 284 145 16 240 443 1 1 3 2 GN Low High 19 300 124 17 212 470 1 1 4 1 GN High High 17 198 227 17 214 465 1 1 5 2 GN High High 15 232 186 18 130 545 1 1 6 1 GN High Low 7 253 174 20 167 505 1 1 7 2 GN High Low 13 300 115 11 159 520 1 1 8 2 GN Low Low 14 236 178 47 151 491 1 1 9 1 GL High High 17 231 189 13 172 509 1 1 10 1 GL Low High 15 192 229 13 167 514 1 1 11 2 GL High High 21 287 124 13 120 560 1 1 12 2 GL Low High 13 250 168 14 220 458 1 1 13 1 GL High Low 12 251 167 13 142 535 1 1 14 1 GL Low Low 12 229 188 11 173 504 1 1 15 2 GL High Low 11 264 154 11 153 523 1 1 16 2 GL Low Low 11 283 133 14 156 517 1 2 1 1 GN Low Low 10 282 148 15 187 470 1 2 2 1 GN Low High 11 319 108 14 213 442 1 2 3 2 GN Low High 10 238 188 13 207 448 1 2 4 1 GN High High 15 200 217 15 183 468 1 2 5 2 GN High High 13 220 196 15 147 500 1 2 6 1 GN High Low 11 214 202 10 172 481 1 2 7 2 GN High Low 12 240 175 11 152 497 1 2 8 2 GN Low Low 11 282 133 8 252 399 1 2 9 1 GL High High 6 247 166 14 149 501 1 2 10 1 GL Low High 5 184 231 12 194 456 1 2 11 2 GL High High 7 294 120 14 121 525 1 2 12 2 GL Low High 17 238 166 12 192 456 1 2 13 1 GL High Low 12 288 122 11 138 508 1 2 14 1 GL Low Low 16 226 181 10 151 496 1 2 15 2 GL High Low 17 221 185 10 160 488 1 2 16 2 GL Low Low 12 302 110 9 184 464 2 3 1 1 GN Low Low 13 252 205 14 370 280 2 3 2 1 GN Low High 15 279 176 16 306 341 2 3 3 2 GN Low High 15 330 124 15 286 359 2 3 4 1 GN High High 15 221 231 8 260 388 2 3 5 2 GN High High 16 217 232 8 246 401 2 3 6 1 GN High Low 14 264 185 8 285 361 2 3 7 2 GN High Low 13 293 156 7 287 359 2 3 8 2 GN Low Low 12 299 150 8 349 296 2 3 10 1 GL Low High 15 228 223 8 281 365 2 3 12 2 GL Low High 13 188 260 9 262 379 2 3 14 1 GL Low Low 13 257 190 8 207 437 2 3 16 2 GL Low Low 13 179 267 8 253 390 t See Table A-2 for description of abbrevi a tions

PAGE 298

279 Table A-31 -----continued. Period obsdt Pasture REP Treatment Res12onses FS SR cs csam grzd loafd cs12m grzn loafn -----------------------minutes ---2 4 1 1 GN Low Low 13 220 242 9 217 472 2 4 2 1 GN Low High 14 373 87 7 221 468 2 4 3 2 GN Low High 11 261 199 11 243 442 2 4 4 1 GN High High 8 182 278 4 215 474 2 4 5 2 GN High High 7 235 223 6 258 429 2 4 6 1 GN High Low 7 323 135 6 212 473 2 4 7 2 GN High Low 6 227 228 5 157 527 2 4 8 2 GN Low Low 7 241 212 7 209 473 2 4 10 1 GL Low High 7 287 152 11 296 387 2 4 12 2 GL Low High 10 251 188 9 280 403 2 4 14 1 GL Low Low 6 326 119 11 227 452 3 4 16 2 GL Low Low 5 362 91 6 191 492 3 5 1 1 GN Low Low 10 321 67 13 187 462 3 5 2 1 GN Low High 8 307 0 11 199 449 3 5 3 2 GN Low High 12 283 0 12 174 471 3 5 4 1 GN High High 11 330 127 9 190 455 3 5 5 2 GN High High 7 335 8 11 184 461 3 5 6 1 GN High Low 6 315 33 11 141 504 3 5 7 2 GN High Low 10 260 81 7 200 441 3 5 8 2 GN Low Low 7 185 87 7 228 415 3 5 9 1 GL High High 11 294 79 17 99 543 3 5 10 1 GL Low High 10 263 36 8 119 522 3 5 11 2 GL High High 5 343 132 14 105 538 3 5 12 2 GL Low High 6 271 93 9 95 539 3 5 13 1 GL High Low 5 298 75 11 113 528 3 5 14 1 GL Low Low 6 300 0 8 165 472 3 5 15 2 GL High Low 11 287 38 9 168 475 3 5 16 2 GL Low Low 9 291 8 11 106 531 3 6 1 1 GN Low Low 0 249 141 10 216 481 3 6 2 1 GN Low High 0 255 139 11 177 518 3 6 3 2 GN Low High 0 217 182 14 169 522 3 6 4 1 GN High High 0 78 194 12 187 503 3 6 5 2 GN High High 0 114 126 9 145 545 3 6 6 1 GN High Low 0 143 163 8 153 537 3 6 7 2 GN High Low 0 78 156 8 145 542 3 6 8 2 GN Low Low 0 212 150 4 145 544 3 6 9 1 GL High High 0 206 208 14 153 534 3 6 10 1 GL Low High 0 116 170 9 126 551 3 6 11 2 GL High High 0 152 152 15 129 555 3 6 12 2 GL Low High 0 123 126 11 144 535 3 6 13 1 GL High Low 0 184 71 13 132 551 3 6 14 1 GL Low Low 0 171 145 10 149 532 3 6 15 2 GL High Low 0 134 152 9 143 539 3 6 16 2 GL Low Low 0 189 148 10 131 550 t See Table A-2 for description of abbreviations.

PAGE 299

280 Table A-32. Probability levels for tests of fixed effects of forage system (F), stocking rate (SR), concentrate supplement (CS), season (S), and their interactions on animal grazing behavior reported in Chapter 4. Source of Response Variable Variation Combined Season Summer Winter grzdt grzn tgraz grzd grzn tgraz grzd grzn tgraz FS 0.001 0.001 0 001 0 .001 0 001 0.001 0.674 0 001 0 004 SR 0 555 0 001 0.004 0 185 0 130 0.798 0.139 0 001 0.001 cs 0.001 0.561 0.003 0 001 0.097 0 001 0.4 1 2 0.438 0.852 FS*SR 0.000 0.655 0.014 0.140 0.224 0.963 0.001 0.570 0 003 FS*CS 0 840 0 598 0 631 0 119 0.483 0 156 0.461 0 950 0.588 SR*CS 0 .101 0.437 0 .511 0.005 0.878 0.109 0.921 0.357 0 639 FS*SR*CS 0 009 0 766 0 100 0 645 0.151 0.472 0.002 0 080 0.133 s 0 017 0.859 0.023 ----sFs 0 002 0.356 0.105 --S*SR 0.044 0.074 0 011 ----scs 0.026 0 088 0 008 -----S*FS*SR 0 021 0.227 0 017 ----sFscs 0 134 0 .663 0.183 ---ssRcs 0.142 0 .571 0.163 ----S*FS*SR*CS 0 002 0.024 0.464 --t See Table A-2 for description of abbreviations.

PAGE 300

281 Table A 33. Grazing behavior, organic matter intake and animal performance of animals on the coat color study in summer 1996. pa i r Coat osso t # Color ResQonses csam cs~m grzd grzn shd shn noshd noshnFOMI TOMI MILK ADG --------------minutes ---------------kg d "1 1 1 wh 11 12 108 101 405 0 0 16 10 7 14 7 17.1 -1.20 1 1 bl 13 12 104 116 406 0 0 0 1 2 wh 13 14 79 123 369 0 0 4 1 2 bl 15 15 61 109 386 0 0 18 1 3 wh 19 18 101 111 358 0 0 0 1 3 bl 19 18 71 111 388 0 0 0 1 4 wh 23 22 73 112 377 0 0 0 1 4 bl 23 22 46 112 404 0 0 0 5 8 9 7 9 3 -0.18 1 5 wh 17 24 149 41 310 0 0 65 8 1 12.1 10. 7 -0. 39 1 5 bl 22 24 34 77 420 0 0 29 1 6 wh 27 27 79 90 372 15 6 0 1 6 bl 18 22 71 95 395 15 0 0 7 3 10. 0 7 7 -0. 32 1 7 wh 21 14 50 103 381 19 0 0 1 7 bl 21 18 57 101 374 17 0 0 8 5 11. 2 8 1 -0. 35 1 8 wh 12 20 93 117 346 0 0 0 6 2 8.2 8 7 -0.47 1 8 bl 12 11 52 126 387 0 0 0 1 9 wh 1 3 11 116 79 321 0 0 47 13. 7 19.4 23 2 -0.41 1 9 bl 40 11 70 103 340 0 0 23 2 1 wh 17 6 93 124 422 4 59 0 10 7 14 7 17. 1 -1. 20 2 1 bl 1 7 6 99 128 456 0 18 0 2 2 wh 12 13 133 93 418 0 0 5 13. 7 19. 4 23 2 -0.41 2 2 bl 12 13 102 79 449 0 0 18 2 3 wh 16 19 122 114 413 0 37 0 2 3 bl 16 19 68 114 441 0 63 0 2 4 wh 17 16 164 113 334 0 72 0 2 4 bl 17 18 113 111 413 0 44 0 2 5 wh 18 20 47 105 431 0 90 0 2 5 bl 18 21 99 104 427 0 42 0 5 8 9 7 9 3 -0.18 2 6 wh 19 19 88 85 475 12 0 0 8 1 12. 1 10. 7 -0. 39 2 6 bl 19 18 103 98 460 0 0 0 2 7 wh 18 12 133 98 418 0 0 0 2 7 bl 18 15 109 95 442 0 0 0 7 3 10. 0 7 7 -0.32 2 8 wh 26 12 75 98 458 0 0 0 2 8 bl 9 12 57 87 473 11 20 0 8 5 11.2 8 1 -0.35 2 9 wh 13 11 133 98 418 0 0 0 6 2 8 2 8.7 -0.47 2 9 bl 13 11 118 98 433 0 0 0 3 1 wh 11 14 176 273 336 0 0 265 3 1 bl 11 14 178 235 334 0 0 303 6.9 10 1 8.3 -0.02 3 2 wh 9 14 139 214 322 0 50 320 12. 9 19. 8 17. 7 0.49 3 2 bl 11 14 139 207 345 0 26 327 3 3 wh 11 12 187 201 308 0 0 317 5.8 8.7 8.1 0 29 3 3 bl 11 12 137 218 353 0 5 300 t See Table A-2 fo r description of abbreviations.

PAGE 301

282 Table A-33. -----continued pair Coat osso t # Color Res12onses csam cs12m grzd grzn shd shn noshd noshnFOMI TOMI MILK ADG ---------------------------minutes ------------------------kg d "1 3 4 wh 8 10 193 202 257 0 48 317 3 4 bl 10 10 163 195 300 0 33 323 9 5 12. 0 11.3 0.15 3 5 wh 9 8 236 190 255 0 7 325 6.3 7.9 4 9 -0.14 3 5 bl 9 8 156 201 258 0 84 314 3 6 wh 14 13 135 194 331 0 33 326 11.4 14. 9 9 3 0.18 3 6 bl 17 13 120 161 339 0 37 359 3 7 wh 15 13 131 192 345 0 21 327 7 3 10.8 10.0 -0.19 3 7 bl 15 13 179 156 318 0 0 363 6.4 10.0 6.1 0.08 3 8 wh 12 13 145 82 349 0 5 431 3 8 bl 15 13 130 217 366 0 0 296 6 7 9.9 6 3 0.21 3 9 wh 10 12 133 181 366 0 0 331 3 9 bl 10 12 172 174 327 0 0 338 8 9 11. 2 4 6 0 44 3 10 wh 13 11 163 172 332 0 0 340 3 10 bl 13 11 84 75 358 0 53 437 4 1 wh 11 13 152 44 330 0 0 0 4 1 bl 11 13 220 44 262 0 0 0 6 9 10. 1 8.3 -0.02 4 2 wh 9 14 157 41 286 0 35 0 12. 9 1 9 8 17.7 0.49 4 2 bl 7 15 145 40 327 0 7 0 4 3 wh 12 11 227 43 195 0 105 0 5 8 8.7 8 1 0 .29 4 3 bl 16 11 119 43 348 0 56 0 4 4 wh 8 7 301 79 146 0 79 0 4 4 bl 8 7 287 79 218 0 22 0 9 5 12.0 11. 3 0.15 4 5 wh 12 11 262 36 248 0 0 0 6 3 7.9 4.9 -0.14 4 5 bl 8 9 161 38 268 0 85 0 4 6 wh 15 14 101 46 371 0 0 0 11.4 1 4 9 9 3 0 .18 4 6 bl 15 14 82 46 390 0 0 0 4 7 wh 13 16 129 46 346 0 0 0 7 3 10.8 10. 0 -0.19 4 7 bl 13 16 136 46 339 0 0 0 6 4 10. 0 6 1 0 08 4 8 wh 12 11 132 52 368 0 15 0 4 8 bl 12 11 147 52 368 0 0 0 6 7 9.9 6 3 0 .21 4 9 wh 7 8 147 56 370 0 0 0 4 9 bl 10 9 188 55 326 0 0 0 8 9 1 1 2 4.6 0.44 4 10 wh 15 9 177 55 330 0 0 0 4 10 bl 15 9 152 55 355 0 0 0 5 1 wh 20 14 172 171 112 0 138 351 5 1 bl 20 14 189 191 125 0 108 331 6 9 10.1 8.3 -0.02 5 2 wh 19 14 230 158 100 0 92 356 12. 9 19. 8 17. 7 0.49 5 2 bl 19 12 180 152 125 0 118 364 5 3 wh 8 7 240 148 164 0 0 370 5 8 8 7 8 1 0 .29 5 3 bl 8 10 57 158 201 0 146 357 5 4 wh 12 7 288 226 5 0 128 294 5 4 bl 12 7 232 149 117 0 72 371 9 5 12. 0 11. 3 0 .15 5 5 wh 10 9 249 128 27 0 128 383 6 3 7 9 4 9 -0.14 5 5 bl 10 9 208 129 86 0 110 382 t See Table A-2 for description of abbreviations.

PAGE 302

283 Table A-33. ---con tinued. pa i r Coat osso t # Color Res12onses csam cs~m grzd grzn shd shn noshd noshnFOMI TOMI MILK ADG minutes ------------------------kg d "1 5 6 wh 21 21 164 188 141 0 108 334 11.4 14. 9 9 3 0 .18 5 6 bl 21 21 158 161 240 0 15 361 5 7 wh 13 17 232 118 175 0 0 409 7.3 10. 8 10. 0 -0.19 5 7 bl 13 17 237 117 170 0 0 410 6.4 10. 0 6 1 0.08 5 8 wh 13 13 187 117 155 0 65 392 5 8 bl 13 13 166 147 159 0 82 362 6 7 9.9 6 3 0 .21 5 9 wh 14 8 170 220 161 0 74 297 5 9 bl 14 11 320 128 85 0 0 386 8.9 1 1 2 4.6 0.44 5 10 wh 12 9 209 144 109 0 87 370 5 10 bl 12 9 204 96 111 0 90 418 6 1 wh 8 13 185 180 129 0 241 362 6 1 bl 8 13 289 168 140 0 126 374 6 9 10. 1 8.3 -0.02 6 2 wh 12 11 189 170 279 0 82 372 12. 9 19.8 17. 7 0.49 6 2 bl 12 11 119 232 314 0 1 1 7 310 6 3 wh 14 12 213 158 328 0 0 364 5 8 8.7 8 1 0 .29 6 3 bl 14 12 220 153 321 0 0 369 6 4 wh 10 8 372 251 157 0 1 4 276 6 4 bl 10 8 234 261 302 0 7 265 9 5 12. 0 1 1 3 0 .15 6 5 wh 11 9 267 190 272 0 0 336 6 3 7 9 4 9 -0.14 6 5 bl 11 9 294 192 245 0 0 334 6 6 wh 24 12 171 207 360 0 0 323 11. 4 14. 9 9 3 0.18 6 6 bl 24 12 130 140 401 0 0 390 6 7 wh 24 18 192 147 329 0 10 372 7 3 10.8 10.0 -0.1 9 6 7 bl 24 16 202 131 298 0 3 1 390 6 4 10.0 6 1 0.08 6 8 wh 24 10 161 129 340 0 28 398 6 8 bl 24 10 208 258 321 0 0 269 6 7 9 9 6.3 0.21 6 9 wh 12 9 170 185 301 120 58 217 6 9 bl 12 9 275 277 254 120 0 125 8 9 11. 2 4 6 0 44 6 10 wh 14 9 198 134 329 0 0 388 6 10 bl 14 9 132 95 385 0 10 427 t See Table A-2 for description of abbre vi ations.

PAGE 303

284 Table A-34. Grazing behavior organic matter intake, and animal performance of animals on the coat color study in winter 1997. Pair Coat osso t # Color Res12onses csam csem grzd grzn loafd loafn FOMI TOMI milk ADG -------------------minutes ---------------------kg d "1 -1 1 wh 17 18 311 129 120 553 12.6 17 8 23 1 -0 66 1 1 bl 4 18 362 169 82 513 12 5 17.8 17.4 -0.25 1 2 wh 16 17 223 240 202 439 11. 0 17.9 19 7 -1.54 1 2 bl 18 17 172 187 251 492 10.1 17.0 19 8 -2 04 1 3 wh 15 18 209 119 209 555 16.1 24.2 26. 5 0 09 1 3 bl 15 18 255 140 163 534 15.4 23.5 19.3 -0.64 1 4 wh 7 20 241 149 185 523 14.6 -0.28 1 4 bl 7 20 264 185 162 487 11.4 15 7 17 5 -1. 56 1 5 wh 13 11 325 173 90 506 11. 8 15 8 15.6 -0 17 1 5 bl 13 11 275 145 140 534 13.1 17 1 18.3 -0 36 1 6 wh 18 13 228 187 190 494 19 3 -0 14 1 6 bl 15 13 233 157 188 524 19 2 27 0 23.1 -0.45 1 7 wh 28 13 290 120 113 560 9 0 15.7 19.5 -0 28 1 7 bl 14 13 283 120 134 560 11. 6 18.3 16.4 -1.72 1 8 wh 13 14 249 167 169 511 11. 5 17 9 13 9 -0.48 1 8 bl 13 14 251 272 167 406 14 4 20 8 22.0 -0 69 2 1 wh 10 15 293 212 137 444 12 6 17 8 23.1 -0 66 2 1 bl 10 15 270 161 160 495 12.5 17 8 17.4 -0 .25 2 2 wh 15 15 226 214 191 436 11. 0 17.9 19 7 -1. 54 2 2 bl 15 15 174 151 243 499 10 1 17 0 19.8 -2.04 2 3 wh 13 13 196 130 221 520 16.1 24 2 26.5 0 09 2 3 bl 13 17 245 165 172 481 15.4 23 5 19 3 -0.64 2 4 wh 11 7 237 166 180 489 14 6 -0.28 2 4 bl 11 12 192 178 225 472 11.4 15.7 17.5 -1.56 2 5 wh 12 9 245 172 170 479 11.8 15 8 15 6 -0 17 2 5 bl 12 12 235 133 180 515 13 1 17 1 18.3 -0.36 2 6 wh 8 14 258 191 154 459 19.3 -0 14 2 6 bl 4 14 237 107 179 543 19 2 27 0 23.1 0.45 2 7 wh 7 14 359 116 55 529 9 0 15 7 19 5 -0.28 2 7 bl 8 13 229 126 184 520 1 1.6 18 3 16.4 -1.72 2 8 wh 17 11 190 217 214 432 11.5 17 9 13.9 -0.48 2 8 bl 17 13 285 167 119 480 14.4 20.8 22.0 -0.69 3 1 wh 15 17 322 261 131 382 7 2 14.5 20 7 1.15 3 1 bl 15 13 337 311 116 336 9 7 17 1 21.3 0 75 3 2 wh 16 8 214 247 235 400 7 1 11. 2 9 4 -0 94 3 2 bl 16 8 220 244 229 403 9 6 13 8 13 8 -1.08 3 3 wh 14 8 268 285 181 361 7 4 11. 8 17 3 -1.97 3 3 bl 14 8 260 285 189 36 1 7.7 12 1 17.3 1 .91 3 4 wh 13 8 209 303 243 343 7.4 14 2 19.6 -0 90 3 4 bl 16 8 246 260 203 386 11. 4 18 2 19.4 -0 19 3 5 wh 12 8 258 207 189 437 7 6 12 0 19.4 0 05 3 5 bl 14 8 255 207 190 437 6 6 11. 0 14.4 0.38 3 6 wh 13 8 187 278 259 366 10.8 15 0 15.9 -0 04 3 6 bl 13 8 171 229 275 415 9 6 13 7 19 6 -0 .80 t See Table A-2 for d e scription of abbreviation s.

PAGE 304

285 Table A-34. --continued Pair Coat osso t # Color Res12onses csam csem grzd grzn loafd loafn FOMI TOMI milk ADG -----------------minutes ----kg d"1 4 1 wh 11 15 261 216 199 465 7.2 14 5 20. 7 1.15 4 1 bl 11 7 260 271 200 418 9.7 17 1 21. 3 0 75 4 2 wh 6 6 225 293 234 393 7 1 11. 2 9 4 -0.94 4 2 bl 8 6 245 222 212 464 9 6 13 8 13 8 -1.08 4 3 wh 7 4 360 234 98 453 7.4 11.8 17.3 -1. 97 4 3 bl 7 9 285 190 173 492 7 7 12 1 17 3 1 .91 4 4 wh 7 11 313 268 126 415 7.4 14 2 19 6 -0.90 4 4 bl 6 11 262 325 178 358 11.4 18 2 19.4 -0 19 4 5 wh 3 11 339 206 110 473 7 6 12 0 19.4 0 05 4 5 bl 10 11 313 248 129 431 6.6 11. 0 14.4 -0.38 4 6 wh 5 8 366 187 87 494 10 8 15 0 15 9 -0.04 4 6 bl 5 5 358 195 95 489 9 6 13 7 19 6 -0 80 5 1 wh 8 11 339 210 0 438 7 6 15 9 24.6 1.74 5 1 bl 8 11 275 188 0 460 8 3 16.7 22 1 0 98 5 2 wh 14 9 320 157 133 487 12 0 17 4 15.3 -0.41 5 2 bl 7 9 340 222 120 422 10.1 15 5 16 1 -0 14 5 3 wh 7 11 331 204 11 441 8.0 13.1 16 3 0.41 5 3 bl 7 11 340 164 5 481 9.4 14 5 13 8 0.54 5 4 wh 7 7 167 257 62 386 18 7 1.43 5 4 bl 7 7 203 198 111 445 21.8 26 5 20 5 -0.20 5 5 wh 11 18 299 98 72 543 7 9 13 1 11.7 -0 66 5 5 bl 11 15 289 101 86 543 7 2 12 4 17.7 0.04 5 6 wh 5 14 350 106 127 537 13.0 17.4 13.5 1 14 5 6 bl 5 14 336 105 138 538 9 1 13 5 13 3 -0 .05 6 1 wh 9 294 184 100 513 7 6 15 9 24 6 1.74 6 1 bl 13 216 170 178 523 8 3 16.7 22.1 0.98 6 2 wh 12 84 193 189 497 12 0 17.4 15.3 -0.41 6 2 bl 12 73 181 200 509 10 1 15 5 16.1 -0 14 6 3 wh 9 131 152 109 538 8.0 13 1 16 3 0.41 6 3 bl 9 97 138 143 552 9.4 14 5 13 8 0 54 6 4 wh 3 238 145 124 545 18.7 1.43 6 4 bl 6 186 144 176 543 21. 8 26 5 20.5 -0 20 6 5 wh 14 188 155 226 532 7 9 13.1 11. 7 -0.66 6 5 bl 14 224 152 190 535 7.2 12 4 17 7 0.04 6 6 wh 16 167 152 137 531 13 0 17.4 13 5 1.14 6 6 bl 14 137 106 167 579 9.1 13 5 13 3 -0.05 t See T a ble A2 for description of abbreviations.

PAGE 305

286 Table A-35 Probability levels for tests of fixed effects of season (S) period within season (P[S]) coat color (CC) observation day (OBD) and their interactions on animal grazing behavior and animal performance reported in Chapter 4. Source of Response Variable Variation grzd t grzn tgraz shd FOMI TOMI MILK ADG s 0 0001 0 0001 0.0001 0 0106 0 0004 0.0001 0 1575 P(S) 0 0001 0 0001 0 0001 0 0001 0.1120 0 1234 0.2320 0 0035 cc 0 0383 0.4066 0.0493 0 0374 0 6762 0.4526 0.0663 0.3114 s cc 0.6689 0.4683 0 9579 0 0924 0 .0812 0 0213 0 0429 CC*P(S) 0 9718 0 9399 0 9792 0.9679 0 7839 0 7154 0 .9581 0.8184 OBD 0.3472 0 0001 0.0011 0 0161 S OBD 0 0068 0 0290 0.1828 -CC*OBD 0 5305 0 9953 0.5826 0 9576 -P*OBD(S) 0 0001 0 0001 0.0001 0 0001 CC*P*OBD(S ) 0.4647 0 1435 0 0338 0 8536 ---t See Table A-2 for description of abbreviations

PAGE 306

287 Table A-36 Forage dry matter intake pred i cted by different techniques during winter 1996 and the deviation of these est imates from the base estimate. Period Pasture REPt Treatment Responses SR cs NEL PDME HOE DPDME DHDE ---------------k g cow1 d -1 1 1 1 Low Low 12.86 8.97 9.80 -3.89 -3.06 1 2 1 Low High 7 72 17 97 11. 53 10 25 3.81 1 3 2 Low High 6 77 16 75 9.10 9.98 2 .33 1 4 1 High High 6.54 15.94 6 65 9 39 0 10 1 5 2 High High 5 95 11. 28 5 69 5 33 -0 26 1 6 1 High Low 8.15 11. 15 5.24 3 00 -2 92 1 7 2 High Low 11.89 10.71 5 98 -1. 19 -5 .91 1 8 2 Low Low 9 .33 10 84 14.48 1 .51 5 15 2 1 1 Low Low 12 65 11. 58 5 65 -1.07 -7 00 2 2 1 Low High 12 02 13.23 8 52 1.21 -3 .51 2 3 2 Low High 9.08 9.65 5.38 0.58 -3 70 2 4 1 High High 8.13 18.64 3 05 10 .51 -5.08 2 5 2 High High 4 .05 8 82 3.63 4 77 -0.42 2 6 1 High Low 9.75 21.66 3 33 11.90 -6 .42 2 7 2 High Low 6 .65 17 57 3.74 10.93 -2.9 1 2 8 2 Low Low 13 00 15 33 7 32 2.33 -5 68 3 1 1 Low Low 18.02 11.29 12 32 -6.73 -5.70 3 2 1 Low High 9.39 10.54 17 30 1.15 7.9 1 3 3 2 Low High 12.96 7 22 10 03 -5 74 -2.93 3 4 1 High High 9.78 11.56 5 05 1 78 -4 73 3 5 2 High High 10.04 9.47 4 1 4 -0.56 -5 90 3 6 1 High Low 9.39 9 85 4 03 0.46 -5 .36 3 7 2 High Low 9 02 8 72 5 50 -0 .31 -3.52 3 8 2 Low Low 9 .57 7.68 9 57 -1.89 0.0 1 t See Table A-2 for description of abbreviations.

PAGE 307

288 Table A-37. Forage dry matter intake predicted by different techniques during winter 1997 and the deviation of these estimates from the base estimate Period Pasture REPt Treatment Reseonses FS SR cs NEL PDME HOE DPDME DHDE kg cow 1 d 1 1 1 1 GN Low Low 7 .07 13.57 9 .17 6 50 2 10 1 2 1 GN Low High 6 .68 13.44 9 .78 6 77 3.10 1 3 2 GN Low High 5 .84 14 .20 1 1 .19 8 36 5.35 1 4 1 GN High High 2 .34 11.49 5 .15 9 15 2 .81 1 5 2 GN High High 5.92 17.06 4.87 11. 14 -1. 05 1 6 1 GN High Low 4 .92 12 .36 6.81 7 43 1.89 1 7 2 GN High Low 7 .61 13.48 6 18 5 .87 -1.43 1 8 2 GN Low Low 8.38 18.38 5.48 10.00 -2.90 1 9 1 GL High High 6 .71 20.94 9 .30 14 23 2 59 1 10 1 GL Low High 7.38 15.32 9.66 7.94 2 28 1 11 2 GL High High 3.71 11.17 6 .19 7.46 2 48 1 12 2 GL Low High 5 .14 14 .03 10 .01 8.89 4.87 1 13 1 GL High Low 6.07 15 .83 7 54 9 76 1.47 1 14 1 GL Low Low 8 18 16 .02 10 .95 7 .84 2.77 1 15 2 GL High Low 8 18 20.33 8 82 12.15 0 64 1 16 2 GL Low Low 10 24 15.80 10 .00 5 56 -0 24 2 1 1 GN Low Low 8.84 1 2 12 1 0 .01 3 .29 1 1 7 2 2 1 GN Low High 8.05 11.97 1 0 .10 3 93 2 05 2 3 2 GN Low High 9.08 9.33 9.45 0.25 0 37 2 4 1 GN High High 5.01 9.80 3 .31 4.79 -1. 70 2 5 2 GN High High 3.61 9.22 3 .99 5 .61 0 39 2 6 1 GN High Low 3.31 8 31 4 .44 5 00 1 13 2 7 2 GN High Low 7 .08 9.33 3 .57 2.25 -3.52 2 8 2 GN Low Low 9 .37 9.78 5.44 0.42 -3 93 2 10 1 GL Low High 4.93 10.34 5 .29 5.42 0 36 2 12 2 GL Low High 5 .71 1 0.32 6 .15 4.61 0 44 2 14 1 GL Low Low 7 .98 7.87 6.46 -0.10 1 52 2 16 2 GL Low Low 8.25 11. 17 7 .63 2 92 -0 62 3 1 1 GN Low Low 11. 16 10 77 9 .15 -0 39 -2.01 3 2 1 GN Low High 10 62 8 .80 12 .76 -1. 82 2.13 3 3 2 GN Low High 9 .21 20.83 9.70 11. 62 0.49 3 4 1 GN High High 5.79 12 .30 5 .71 6 .51 -0 08 3 5 2 GN High High 7 88 9.74 4 .35 1 86 -3 54 3 6 1 GN High Low 7 33 10 .83 5.40 3 50 -1. 93 3 7 2 GN High Low 9.95 10.04 5.21 0 08 -4 74 3 8 2 GN Low Low 11. 42 23.69 9 .76 12.27 -1. 66 t See Table A 2 for description of abbreviations

PAGE 308

289 Table A-37. ----continued Period Pasture REPT Treatment Reseonses FS SR cs NEL PDME HOE DPDME DHDE kg cow 1 d 1 3 9 1 GL High High 5.97 8.38 6.68 2.42 0.71 3 10 1 GL Low High 11. 52 9.22 10 34 -2 30 -1.17 3 11 2 GL High High 8 55 12 23 5 98 3 68 -2.57 3 12 2 GL Low High 9.25 8.31 10 65 -0.94 1.39 3 13 1 GL High Low 10.30 10 56 7.29 0.26 -3.01 3 14 1 GL Low Low 13.45 10.94 13.47 -2.51 0.01 3 15 2 GL High Low 9.98 9.79 8.14 -0.19 -1.83 3 16 2 GL Low Low 11. 25 9.36 9.53 -1.89 -1. 72 t See Table A-2 for description of abbreviations. Table A-38. Probability levels for tests of fixed effects of period (P) stocking rate (SR), and concentrate supplement (CS) and their interactions on forage dry matter intake estimated by different techniques during 1996 reported in Chapter 5 Source of Response Variable Variation NELt PDME HDE DPDME DHDE SR 0 0485 0.4577 0.0146 0.0864 0.1323 cs 0.0833 0.7594 0.8533 0.1907 0 1325 SR*CS 0.5882 0.4784 0.8897 0.3868 0.5960 p 0.2099 0.0048 0.0005 0 0001 0.0935 P*SR 0.4082 0.0664 0 0260 0 0006 0.1998 P*CS 0.5387 0 0081 0 5438 0.0003 0.9527 P*SR*CS 0.7632 0.3768 0.3380 0.6573 0.4714 t See Table A-2 for description of abbreviations

PAGE 309

290 Table A-39. Probability levels for tests of fixed effects of forage system (F ), stocking rate (SR), concentrate supplement (CS), period (P), and their interactions on forage dry matter intake estimated by different techniques and their deviation from base estimates during 1997 reported in Chapter 5 Source of Response Variable Variation NEO PDME HDE DPDME DHDE FS 0.8182 0.6589 0.2174 0.5695 0.4763 SR 0.0005 0.4255 0.0001 0.0685 0.1040 FS*SR 0.8418 0.1224 0.2943 0.1168 0.5516 cs 0.0029 0.5239 0.8628 0.1025 0.0176 FS*CS 0.2908 0.8703 0.0736 0.6860 0.5770 SR*CS 0.8235 0.4870 0.1055 0.4102 0.3098 FS*SR*CS 0.7748 0.6001 0.1288 0.5009 0.3725 p 0.0001 0.0142 0.0007 0.0002 0 .0031 P*FS 0.1105 0.1078 0 0399 0.0597 0.9117 P*SR 0.2530 0. 5639 0.1521 0.5814 0 .7193 P*FS*SR 0.9886 0.5497 0 9025 0.5329 0.9232 P*CS 0.9018 0.9461 0.9257 0.9788 0 8790 P*FS*CS 0.8869 0 9532 0.9566 0.9223 0.9396 P*SR*CS 0.9150 0.9487 0.8082 0.8883 0.7873 P*FS*SR*CS 0.6035 0.7780 0.4226 0.6060 0.8608 t See Table A-2 for description of abbreviations.

PAGE 310

REFERENCES Adjei M.B. P. Mislevy, and C Y. Ward. 1980 Response of tropical grasses to stocking rate. Agron J 72: 863-868. AFRC. 1993. Energy and protein requirements of ruminants. Agricultural and Food Research Council. CAB International Wallingford, U .K. Allden W.G., and I.A. McD. Whittaker. 1970. The determinants of herbage intake by grazing sheep: The interrelationships of factors influencing herbage intake and availability. Aust. J Agric Res. 21 : 755-766 Allen, M.S. 1996. Physical constraints on voluntary intake of forages by ruminants. J. Anim. Sci. 74:3063-3075. Arriga-Jordan, C.M. and W. Holmes. 1986. The effect of concentrate supplementation on high-yielding dairy cows under two systems of grazing. J. Agric. Sci. (Camb ) 107:453-461. Baltensperger D.D. C.S. Jones, W.R. Ocumpaugh K.A. Albrecht and G.O. Mott. 1986. Comparisons of winter pasture systems for Florida. p. 59-62. In Proc. Beef Catt l e Short Course, 351 \ 7-9 May, 1986. Anim. Sci. Dept., IFAS, Univ. of Florida Gainesville. Baumont, R. N. Seguier, and J.P. Dulphy. 1990. Rumen fill forage palatability and alimentary behavior in sheep J. of Agric. Prod. 115:277-284. Becerril, C.M., M. Campos, C.J. Wilcox and P J Hansen. 1991. Effects of white coat color percentage on milk and fat yields of Holstein cows. J. Dairy Sci. 74(Suppl.1):288. (Abstr.) Becerril, C.M., C.J Wilcox and V.M. Guerrero. 1996 Holstein white coat color and performance: Phenotypic, genetic and environmental correlat i ons. Brazilian J. of Genetics. 19 : 587-591. 291

PAGE 311

292 Becerril, C.M., C.J. Wilcox, T.J. Lawlor, G.R. Wiggans, and D.W. Webb. 1993. Effects of percentage of white coat color on Holstein production and reproduction in a subtropical environment. J. Dairy Sci. 76:2286-2291. Bernard, J., and P. Chandler. 1994. Use of pasture as nutrient source for lactating dairy cows reviewed. Feedstuffs, June 1994:11-14. Berzaghi, P ., and C.E. Polan. 1992. Digesta passage in grazing lactating cows fed with or without com. J. Dairy. Sci. 75(Suppl. 1):223. (Abstr.) Bircharn, J.S., and J. Hodgson. 1984. The effect of change in herbage mass on herbage growth and senescence in mixed swards. Grass and Forage Sci. 38:323-331. Broderick, G.A., and M.K. Clayton 1997. A statistical evaluation of animal and nutritional factors influencing concentrations of milk urea nitrogen. J. Dairy Sci. 80:2964-2971. Bums, J.C., H. Leppke, and D.S. Fisher. 1989. The relationship of herbage mass and characteristics to animal responses in grazing experiments. p. 7-19. In G.C. Marten (ed.) Grazing research: Design, methodology, and analysis. Crop Sci. Soc. Amer. Special Pub. No. 16. Crop Sci. Soc. Amer., Inc., Madison, WI. Bums, J.C., Pond, K.R., and D.S. Fisher. 1991. Effects of grass species on grazing steers. II. Dry matter intake and digesta kinetics. J. Anim. Sci. 69: 1199-1204. Bums, J.C., Pond, K.R., and D.S. Fisher. 1994. Measurement of forage intake. p. 494532 In G.C. Fahey, M. Collins, D.R. Mertens, and L.E. Moser (eds.) Forage quality, evaluation, and utilization. ASA, CSSA, SSSA, Madison, WI. Campbell, J.R., and J.F. Lasley. 1985. The science of animals that serve humanity. Chpt. 13. Ecology and environmental physiology. McGraw Hill, New York. Carter, J.F., D.W. Bolin, and D. Erickson. 1960. The evaluation of forages by the agronomic "difference" method and the chromogen-chromic oxide "indicator" technique. North Dakota Agricultural College Agric. Exp. Stn. Bull. 426. Casler, M.D., D.J. Undersander, C. Fredricks, D.K. Combs, and J.D. Reed. 1998. An on farm test of perennial grass varieties under management intensive grazing. J. Prod. Agric. 11 :92-99 Caton, J.S., and D.V. Dhuyvetter. 1997. Influence of energy supplementation on grazing ruminants: Requirements and responses. J. Anim. Sci. 75:533-542.

PAGE 312

293 Chilibroste, P ., S. Tamminga, and H. Boer. 1997. Effects of length of grazing session, rumen fill and starvation time before grazing on dry-matter intake, ingestive behavior and dry-matter rumen pool sizes of grazing lactating dairy cows. Grass and Forage Sci. 52:249-257. Cochran, R.C., D.C. Adams, J.D. Wallace, and M.L. Galyean. 1986. Predicting digestibility of different diets with internal markers: Evaluation of four potential markers. J. Anim. Sci. 63:1476-1483. Collier, R.J., D K. Beede, W.W. Thatcher, L.A. Israel, and C.J. Wilcox. 1982. Influences of environment and its modification on dairy animal health and production J. Dairy Sci. 65:2213-2227. Cowan, R.T. 1975. Grazing time and pattern of grazing Friesian cows on tropical grass legume pasture. Aust. J. Exp. Anim. Hush. 15:32-37. Davidson, T.M., R.T. Cowan, and R.K. Shepherd. 1985a. Milk production from cows grazing tropical grass pastures. 2. Effects of stocking rate and level of nitrogen fertilizer on milk yield and pasture-milk yield relationships. Aust. J. Exp. Agric. 25:515-523. Davidson, T.M., R.T. Cowan, R.K. Shepherd, and P. Martin. 1985b. Milk production from cows grazing tropical grass pastures. 1. Effects of stocking rate and level of nitrogen fertilizer on the pasture and diet. Aust. J. Exp. Agric. 25:555-514. Davidson, T M., P.J. Frampton W.N.Orr, B.A. Silver, P. Martin, and B. McLachlan. 1997a. An evaluation of kikuyu-clover pastures as a dairy production system. 1. Pasture and diet. Trop. Grassl. 31: 1-14. Davidson, T.M., P.J. Frampton, W.N. Orr, B.A. Silver, and D. Williams. 1997b. An evaluation ofkikuyu-clover pastures as a dairy production system. 2. Milk production and systems comparison. Trop Grassl. 31:15-23. de Carvalho, J.H. 1976. Plant age and its effect upon forage quality. M.S. thesis, Univ. of Florida Gainesville. Di Marco, O.N., M.S. Aello, and D.G. Mendez. 1996. Energy expenditure of cattle grazing on pastures oflow and high availability. Anim. Sci. 63:45-50. Dixon, R.M., and C.R. Stockdale. 1999. Associative effects between forages and grains: consequences for feed utilization. Aust. J. of Agric. Res. 50:757-773.

PAGE 313

294 Dougherty, C.T ., N W. Bradley L.M. Laurialt, J.E. Arias and P.L. Cornelius. 1992. Allowance-intake relations of cattle grazing vegetative tall fescue Grass and Forage Sci. 47:211-219. Duble, R.L. J A. Lancaster, and E.C. Holt. 1971. Forage characteristics limiting animal performance on warm-season perennial grasses Agron J. 63:795-798. Elbehri A ., S A. Ford. 1995 Economic analysis of major dairy forage systems in Pennsylvania: The role of intensive grazing. J. Prod Agric. 8 : 501-507 Fales S.L., L.D. Muller S.A. Ford M. O'Sullivan R.J. Hoover L.A. Holden L.E. Lanyon, and D.R. Buckmaster. 1995 Stocking rate affects production and profitability in a rotationally grazed pasture system J. Prod. Agric. 8:88-96 Fales S.L., L.D. Muller M. O'Sullivan L.E. Lanyon R.J. Hoover, and L.A Ho l den. 1993. Intensive grazing of high-producing Holstein cows: milk production, forage utilization and profit potential at three stocking rates. p. 1309-1310 In M.J. Baker, J.R. Cush, and L.R. Humphreys (eds.). Proc. Int. Grassl. Congress, 171 \ Palmers ton North New Zealand. 8-21 Feb. 1993. Keeling and Mundy, Ltd., Palmerston North NZ. Fisher D.S. 1996. Modeling ruminant feed intake with protein, chemostatic, and distension feedbacks. J Anim. Sci. 74:3076-3081. Fisher, G E.J ., A.M Dowdeswell and G. Perrott 1996. The effects of sward characteristics and supplement type on the herbage intake and milk production of summer-calving cows. Grass and Forage Science 51: 121-130. Florida Agricultural Statistics. 1999. Florida agricultural statistics: Poultry and livestock June 1999. Florida Agricultural Statistics Service Orlando FL. Forbes J.M. 1996. Integration of regulatory signals controlling forage intake in ruminants J. Anim. Sci 74:3029-3035. Forbes, T.D.A 1988. Researching the plant-animal interface: The investigation of ingestive behavior in grazing animals J. Anim. Sci. 66:2369-2379. Forbes T.D A. F.M Rouquette, and J.W. Holloway 1998 Comparisons among Tuli, Brahman, and Angus-sired heifers: Intake digesta kinetics and grazing behavior J Anim. Sci. 76 : 220-227.

PAGE 314

295 Forbes, T.D.A., E.M. Smith, R.B. Razor, C .T. Dougherty, V.G. Allen, L.L. Edinger, J.E. Moore, and F.M. Rouquette, Jr. 1985. The plant-animal interface. p. 95-116 In V.H. Watson and C.M. Wells Jr. (eds.) Simulation of forage and beef production in the southern region. Southern Coop Series Bull. # 308. Gallaher R.N. C.O. Weldon, and J.G. Futral. 1975. An aluminum block digestor for plant and soil analysis. Soil. Sci. Soc. Amer. Proc. 39:803-806. Gary, L.A., G.W. Sherritt and E.W. Hale. 1970. Behavior of Charolais cattle on pasture J. Anim. Sci. 30:203-206. Gibb M.J., C.A. Huckle, R. Nuthall, and A.J. Rook 1999. The effect of physiological state (lactating or dry) and sward surface on grazing behavior and intake by dairy cows. App. Anim. Behavior. Sci. 63:269-287. Gibb, M.J. C.A. Huckle, R. Nuthall, and A.J. Rook. 1997. Effect of sward surface height on intake and grazing behavior by lactating Holstein Friesian cows. Grass and Forage Sci. 52 : 309-321. Godfrey, R.W. and P.J. Hansen. 1996. Reproduction and milk yield of Holstein cows in the US Virgin Islands as influenced by time of year and coat color. Arch. Latinoam. Prod. Anim. 4 : 31-44. Golding E.J. M.F. Carter and J.E. Moore. 1985 Modification of the neutral detergent fiber procedure for hays. J. Dairy Sci. 68:2732-2736. Hafley, J.L. 1996. Comparison of Marshall and Surrey ryegrass for continuous and rotational grazing. J. Anim. Sci. 74:2269-2275. Hambleton, L.B. 1977. Semiautomated method for simultaneous determination of phosphorus, calcium, and crude protein in animal feeds. J. Assoc. Off. Anal. Chem. 60:845-854 Hansen, P.J. 1990. Effects of coat color on physiological responses to solar radiation in Holsteins. Veterinary Record. 127:333-334. Hart, R.H and B.E. Norton. 1988. Grazing management and vegetation response. p. 493525. In P .T Tueller (ed.) Vegetation science applications for rangeland analysis and management. Kluwer Academic Puhl. Dordrecht. Henzell, E.F. 1983 Contribution of forages to worldwide food production: Now and in the future. p. 42-47 In J A. Smith and V.W Hays (ed.) Proc. Int. Grassl. Cong. 14th Lexington KY. 15-24 June 1981. Westview Press, Boulder CO.

PAGE 315

296 Hodgson, J. 1981. Variations in the surface characteristics of the sward and the short-term rate of herbage intake by calves and lamb. Grass and Forage Sci. 36:49-57. Hodgson, J. 1985. The control of herbage intake in the grazing ruminant. Proc. Nutr. Soc. 44:339-346. Hodgson, J., D.A. Clark, and R.J. Mitchell. 1994. Foraging behavior in grazing animals and its impact on plant communities. p. 796-827. In G.C. Fahey, M. Collins, D.R. Mertens, and L.E. Moser (eds ) Forage quality, evaluation, and utilization. ASA CSSA, SSSA Madison, WI. Hoffman, K., L.D. Muller, S.L. Fales and L.A. Holden. 1993. Quality evaluation and concentrate supplementation of rotational pasture grazed by lactating cows J. Dairy Sci. 76:2651-2663. Holden, L.A., L.D. Muller, T. Lykos, and T. W. Casso. 1995. Effect of com silage supplementation on intake and milk production in cows grazing grass pastures. J. Dairy Sci. 78:154-160. Holden, L.A ., L.D Muller, G.A. Varga, and P.J. Hillard. 1994. Ruminal digestion and duodenal nutrient flows in dairy cows consuming grass as pasture, hay, or silage. J. Dairy Sci. 77:3034-3042. Holmes, C.W., C.J. Hoogendoorf, M.P.Ryan, and A.C.P. Chu. 1992. Some effects of herbage composition, as influenced by previous grazing management, on milk production by cows grazing ryegrass/white clover pastures. 1. Milk production in early spring: effects of different regrowth intervals during the preceding winter period. Grass and Forage Sci. 47:309-315. Hoogendoorn, C.J., C.W. Holmes, and A.C.P. Chu 1992. Some effects of herbage composition, as influenced by previous grazing management, on milk production by cows grazing ryegrass/white clover pastures. 2. Milk production in late spring/summer: effects of grazing intensity during the preceding spring period. Grass and Forage Sci 47:316-325. Hoover, W.H., and S.R. Stokes. 1991. Balancing carbohydrates and proteins for optimum rumen microbial yield. J. Dairy Sci. 74:3630-3644. Iason, G.R., A.R. Mantecon, D.A, Sim, J. Gonzalez, E. Foreman, F.F. Bermudez, and D.A. Elston. 1999. Can grazing sheep compensate for a daily foraging time constraint? J. Anim. Ecol. 68:87-93.

PAGE 316

297 Jackson-Smith D. B. Barham M. Nevius and R. Klemme. 1996 Grazing in dairyland : The use and performance of management intensive rotational grazing among Wisconsin dairy farms. Agric. Tech. and Family Farm Inst. College of Agric. and Life Sciences Univ of Wisconsin Madison. Technical report #5. Jensen K.C. 1995. Grazing trend changes feed company opportunities. Feedstuffs July 1995:12-13. Jones-Endsley, J.M. M.J. Ceveca, and T.R. Johnson. 1997. Effects of dietary supplementation on nutrient digestion and milk yield of intensively grazed lactating dairy cows. J. Dairy Sci. 80:3283-3292. Kennedy P M and P.T. Doyle 1993. Particle-size reduction by ruminants Effects of cell wall composition and structure. p 499-534. In H H. Jung, D.R. Buxton, R.D. Hatfield and J. Ralph (eds.) Forage cell wall structure and digestibility. ASA-CSSA SSSA Madison WI Kibon A., and W. Holmes. 1987. The effect of height of pasture and concentrate composition on dairy cows grazed on continuously stocked pastures J Agric. Sci. (Camb.) 109:293-301. King, V.L. S.K Denise D V Armstrong, M Torabi and F Wiersma. 1988. Effects of hot climate on the performance of first lactation Holstein cows grouped by coat color. J. Dairy Sci. 71:1093-1096. Kleiber, M. 1961. The fire of life : an introduction to animal energetics. John Wiley and Sons Inc. New York Kolver, E .S., and L.D Muller. 1998. Performance and nutrient intake of high producing Holstein cows consuming pasture or a total mixed ration. J. Dairy Sci 81 :1403-1411. Kristensen E.S. 1988. Influence of defoliation regime on herbage production and characteristics of intake by dairy cows as affected by grazing intensity. Grass and Forage Sci. 43:239-251. Krysl L.J. and B W Hess. 1993. Influence of supplementation on behavior of grazing cattle. J. Anim Sci. 71 :2546-2555. Laca, E.A. E D. Ungar N Seligman and M.W. Demment. 1992. Effects of sward heigh t and bulk density on bite dimensions of cattle grazing homogeneous swards Grass and Forage Sci 47 : 91-102

PAGE 317

298 Lascano, C.E. 1987. Canopy structure and composition in legume selectivity. p. 71-80. In J.E. Moore, K.H. Quesenberry, and R.A. Michaud (eds.) Forage-livestock research needs for the Caribbean Basin. CBAG. Univ. of Florida, Gainesville. Lean, I. 1987. Nutrition of dairy cattle. The Univ. of Sidney Post-Graduate Foundation in Veterinary Science, Sidney, Australia. Le Due, Y.L.P., J. Combellas, J. Hodgson, and R.D. Baker. 1979. Herbage intake and milk production by grazing dairy cows. II. The effect oflevel of winter feeding and daily herbage allowance. Grass and Forage Sci. 34:249-260. L'Huillier, P.J. 1987. Effect of dairy cattle stocking rate and degree of defoliation on herbage accumulation and quality in ryegrass white clover pasture. New Zealand J. Agric. Res. 30:149-157. Littell, R.C. 1989. Statistical analysis of experiments with repeated measurements. HortSci. 24:37-40. Littell, R.C., G.A. Milliken, W.W. Stroup, and R.D. Wolfinger. 1996. SAS system for mixed models. SAS Institute Inc., Cary, NC. Manyawa, G.J., and A.U.K. Madzudzo. 1995. Effect of energy and protein supplementation on milk yield and quality ofHolstein-Friesian cows grazing irrigated Italian ryegrass (Lolium multiflorum cv. Midmar) pastures during the dry season. Trop. Grassl. 29:241-247. Matches, A.G. 1992. Plant responses to grazing: A review. J. Prod. Agric. 5:1-7. Matches, A.G., F.A. Martz, D.A. Sleper, and M.T. Krysowaty. 1981. Selecting levels of herbage allowance to compare forages for animal performance. p. 331-339. Jn J.L. Wheeler and R.D. Moehrle (eds.) Forage evaluation: Concepts and techniques. CSIRO, Melbourne. McCartor, M.M., and F.M. Rouquette, Jr. 1977. Grazing pressures and animal performance from pearl millet. Agron. J. 69:983-987. Meijs, J.A.C. 1987. Concentrate supplementation of dairy cows. 2. Effect of concentrate composition on herbage intake and milk production. Grass and Forage Sci. 41 :229235.

PAGE 318

299 Merchen, N .R., and L.D Bourquin. 1994. Processes of digestion and factors influencing digestion of forage-based diets by ruminants. p. 564-612. In G.C. Fahey, M. Collins, D.R. Mertens, and L.E. Moser (eds.) Forage quality, evaluation, and utilization. ASA, CSSA, SSSA, Madison, WI Mertens D.R. 1994. Regulation of forage intake. p. 450-493. In G C. Fahey, M. Collins D.R. Mertens, and L.E Moser (eds ) Forage quality, evaluation, and utilization. ASA, CSSA SSSA, Madison WI. Mertens D.R. 1986. The dairy industry and its future. p. 264-271. In Proc. 1986 Am. Forage and Grassl. Conf., Athens, GA. Miller, D.P., and G.D. Schnitkey. 1994. Economic patterns and labor utilization. p. 27-38. In D.L. Zartman (ed.) Intensive grazing seasonal dairying: The Mahoning county dairy program 1987 1991. Dept. of Dairy Sci., Ohio Agric. Res. and Dev. Center. OARDC Bulletin# 1190. Minson, DJ. 1981. Forage quality: Assessing the plant animal complex. p. 23-27. In J.A. Smith and V.W. Hays (ed.) Proc. Int. Grassl. Cong. 14th, Lexington, KY. 15-24 June 1981. Westview Press Boulder, CO. Minson, D .J. 1982. Effects of chemical and physical composition of herbage eaten upon intake. p. 167-182. In J.B. Hacker (ed.) Nutritional limits to animal production from pastures. CSIRO and Commonwealth Agric. Bur. Farnham Royal, UK. Minson D.J. and J.R. Wilson. 1994 Prediction of intake as an element of forage quality. p. 533-563. In G.C. Fahey, M. Collins, D .R. Mertens, and L.E. Moser (eds.) Forage quality evaluation, and utilization. ASA, CSSA, SSSA, Madison WI. Moe, P.W. 1981. Energy metabolism of dairy cattle. J. Dairy Sci. 64:1120-1139. Moe, P.W., and H.F. Tyrell. 1973. The rationale of various energy systems for ruminants. J Anim. Sci. 37:183-189. Moe P.W., H.F. Tyrell, and W.P. Flatt 1970. Partial efficiency of energy use for maintenance, lactation, body gain and gestation in the dairy cow. p. 65-68 In A. Schurch and G Wenk (eds.) Energy metabolism in farm animals. E.A.A.P. Publication No. 13. Juris Druck and Verlag, Zurich. Moore J.E. 1980. Crop quality and utilization: Forage crops. p. 61-91. In C.S Hoveland (ed ) Crop quality, storage, and utilization. ASA CSSA, Madison, WI.

PAGE 319

300 Moore, J.E. 1983. Discussion: Forage quality: Assessing the plant animal complex. p. 2728. In J.A. Smith and V.W. Hays (ed.) Proc. Int. Grassl. Cong. 141 \ Lexington, KY. 15-24 June 1981. Westview Press, Boulder, CO. Moore, J.E. 1992. Matching protein and energy supplements to forage quality. p. 31-44. In B. Harris and B. Haskins (eds.) Proc. Ann. Florida Ruminant Nutrition Symposium, 3rd Univ. of Florida, Gainesville, FL. Moore, J.E. 1994. Forage quality indices. p. 967-998. In G.C. Fahey, M. Collins, D.R. Mertens, and L.E. Moser (eds.) Forage quality, evaluation, and utilization. ASA, CSSA, SSSA, Madison, WI. Moore, J.E. 1996. Practical approaches to estimating pasture intake. p. 193-202 In R.E. Joost and C.A. Roberts (ed.) Proceedings, Nutrient cycling in forage systems. 7-8 March 1996 The Center for Pasture Management. Univ of Missouri, Columbia, MO. Moore, J.E., J.G.P. Bowman and W.E. Kunkle. 1995. Effects of dry and liquid supplements on forage utilization by cattle. p. 81-95 In Proc., AFIA Liquid Feed Symposium, Irving, TX. 11-13 Sept. 1995. American Feed Ingredients Association, Arlington, VA. Moore, J.E., J.C. Burns, and D.S. Fisher. 1994. Predicting digestibility and intake of southern grass hays by routine laboratory analysis. J. Anim. Sci. 72 (Suppl. 1):115. Moore, J E. and G.O. Mott. 1974. Recovery of residual organic matter from in vitro digestion of forages. J. Dairy Sci. 57: 1258-1259. Moore, J.E., and L.E. Sollenberger. I 986. Canopy structure effects on ingestive behavior. p. 53-57. In Proc. Southern Pasture and Forage Crop Improvement Conference, 420(1, Athens, GA. 15-17 April 1986. National Tech. Info. Ser., Springfield. VA. Morley, F.W.H. 1981. Options in pasture research. Trop. Grassl. 15:71-84. Moss, R.J., and K.F. Lowe. 1993. Development of forage systems for dairying in subtropical Australia. p. 1991-1992. In M.J. Baker, J.R. Cush, and L.R. Humphreys (eds.). Proc. Int. Grassl. Congress, 17th, Palmerston North, New Zealand. 8-21 Feb. 1993. Keeling and Mundy, Ltd., Palmerston North, NZ. Mott, G.O., and J.E. Moore. 1969. Forage evaluation techniques in perspective. p LlLl0. In R.F. Barnes, D.C. Clanton, C.H. Gordon, T.J. Klopfenstein, and D.R. Waldo. (eds.) Proc. Nat. Conf. Forage Quality Eval. Util., Lincoln, NE. 3-4 Sept. 1969. Nebraska Center for Continuing Education, Lincoln.

PAGE 320

301 Mott, G.O., and J.E. Moore. 1985. Evaluating forage production. p. 422-429. In M.E. Heath, R.F. Barnes, and D.S. Metcalfe (eds.) Forages, the science of grassland agriculture. Iowa State Univ. Press, Ames, IA. Muller, L.D., E.S. Kolver, and L.A. Holden. 1995. Nutritional needs of high producing cows on pasture. p. 106-120. In Proc. Cornell Nutr. Conf. Feed Manuf., Rochester, NY. Cornell Univ., Ithaca, NY. Nelson, C.J., and L.E. Moser. 1994. Plant factors affecting forage quality. p. 115-154. In G.C. Fahey, M. Collins, D.R. Mertens, and L.E. Moser (eds.) Forage quality, evaluation, and utilization. ASA, CSSA, SSSA, Madison WI Nocek, J.E., and J.B. Russell. 1988. Protein and energy as an integrated system. Relationship of ruminal protein and carbohydrate availability to microbial systhesis and milk production. J. Dairy. Sci. 71 :2070-2107. NRC. 1984. Nutrient requirements of beef cattle. 6th edition. National Research Council, National Academy Press, Washington, DC. NRC. 1988. Nutrient requirements of dairy cattle. 6th edition. National Research Council, National Academy Press, Washington, DC. O'Brien, B., P. Dillon, J.J. Murphy, R.J. Mehra, T.P. Guinee, J.F. Connolly, A. Kelly, and P. Joyce. 1999. Effects of stocking density and concentrate supplementation of grazing dairy cows on milk production, composition, and processing characteristics J. Dairy Res. 66:165-176. 0rskov, E.R., and M. Ryle. 1990. Energy nutrition in ruminants. Elsevier Applied Science, New York, NY. Parker, W.J., L.D. Muller, S.L. Fales, and W.T Mcsweeny. 1993. A survey of dairy farms in Pennsylvania using minimal or intensive pasture grazing systems. The Professional Animal Scientist. 9:77-85. Parker, W.J., L.D. Muller, and D.R. Buckmaster. 1992. Management and economic implications of intensive grazing on dairy farms in the northeastern states. J. Dairy Sci. 75:2587-2597. Parsons, A.J., LR. Johnson, J.H.H. Williams. 1988. Leaf age structure and canopy photosynthesis in rotationally and continuously grazed swards. Grass and Forage Sci. 43:1-14.

PAGE 321

302 Parsons, A.J. and P.D. Penning 1988. The effect of the duration of regrowth on photosynthesis leaf death and average rate of growth in a rotationally grazed sward. Grass and Forage Sci. 43:15-27. Peterson R.G. H .L. Lucas and G O. Mott. 1965 Relationship between rate of stocking and per animal and per acre performance on pasture. Agron J 57:27-30 Pond K.R. J.C Burns D S Fisher and R.A. Quiroz. 1986. Appropriate markers and methodology for grazing studies. p 62-66. In Proc. Southern Pasture and Forage Crop Improvement Conf., 42nd Athens GA. 15-17 April 1986. National Tech. Info Ser. Springfield VA. Pond K.R. W.C Ellis J.H. Matis H.M. Ferreiro and J.D Sutton. 1988. Compartment models for estimating attributes of digesta flow in catt l e. Brit. J. Nutr 60 : 571-595 Popp J.D ., W.P. McCaughey and R.D.H. Cohen. 1998. Effect of grazing system stocking rate and season of use on herbage intake and behavior of stocker cattle grazing alfalfa-grass pastures. Anim. Feed Sci. and Tech 72:235-259. Poppi D.P. and S.R. McLennan. 1995 Protein and energy utiliza t ion by ruminants a t pasture. J. Anim. Sci. 73 : 278-290. Raymond. W .F. 1969 The nutritive value of forage crops Advances in Agronomy. 21: 1-108 Reeves, M ., W J Fulkerson R.C. Kellaway, and H. Dove. 1996. A comparison of three techniques to determine the herbage intake of dairy cows grazing kikuyu (Pennisetum clandestinum) pasture Aust. J. Exp. Agric. 36 : 23-30. Rochinotti D. 1998 Model components of forage-fed cattle systems: Energy expenditur e of grazing cattle and prediction of intake in dairy cows Ph.D. dissertation, Univ. of Florida Gainesville. SAS Institute Inc. 1982a. SAS user's guide: Basics. 1982 edition SAS Institute Inc ., Cary, NC. SAS Institute Inc. 1982b. SAS user's guide: Statistics. 1982 edition SAS Ins t itute Inc. Cary NC. SAS Institute Inc. 1987 SAS/ST A '[I'M guide for personal computers. Version 6 edition SAS Institute Inc. Cary NC.

PAGE 322

303 SAS Institute Inc 1992. SAS/STAT software: Changes and enhancements Release 6 07. SAS Tech. Rep. P-229. SAS Institute Inc ., Cary NC. SAS Institute Inc. 1996. SAS/ST AT software: Changes and enhancements for release 6.12 SAS Institute Inc Cary, NC. Sollenberger L.E ., and C G Chambliss. 1991 Regional and seasonal forage production limits. p 25-34 Florida Beef Cattle Short Course Proceedings. Univ. of Florida Gainesville F L. Sollenberger L.E., and J.E. Moore. 1997. Assessing forage allowance-animal performance relationships on grazed pasture. p 140. In Agron. Abstr. Amer. Soc Agron., Madison WI Sollenberger L.E J.E. Moore K H Quesenberry and P T Beede 1987a R e la ti onships between canopy botanical composition and diet se l ec t ion in aeschynomene limpograss pastures. Agron J. 79: 1049-1054. Sollenberger L.E. K.H Quesenberry, and J E. Moore 1987b Forage quality responses of an aeschynomene-limpograss association to grazing management. Agron. J. 79:8389. Stewart R.E 1953. Absorption of solar radiation by the hair of cattle. Agric. E ng 34:2352 38. Stobbs T.H. 1970. Automatic measurement of grazing time by dairy cows on tropical grass and legume pastures. Trop. Grassl. 4:237-244 Stobbs T.H. 1977 Short term effects of herbage allowance on milk production milk composition and grazing time of cows grazing nitrogen fertilized tropical grass pasture. Aust. J Exp. Agric. Anim Hush. 17:89 2 -898. Stockdale, C .R., A Callahan, and T.E. Trigg. 1987. Feeding high energy supplements to pasture-fed dairy cows Effects oflactation and level of supplement. Aust. J Agric Res 38:927-940. Stuedemann, J.A., and A.G. Matches. 1989. Measurement of animal responses in grazing research p 21-35. In G C. Marten (ed ) Grazing research: Design methodology and analysis Crop Sci. Soc Amer. Special Pub. No 16. Crop Sci Soc Amer., Inc. Madison WI. Tietz N. 1993. Graz i ng results get better with experience Haymaker Fal l 1993: 1-3.

PAGE 323

304 't Mannetje, L. 1978. Measuring quantity of grassland vegetation. p. 63-96. In L. 't Mannetje (ed.). Measurement of grassland vegetation and animal production. Commonwealth Agric. Bur. Bull. 52. Uden, P., P.E. Colucci, and P.J. Van Soest. 1980. Investigation of chromium, cerium and cobalt as markers in digesta rates of passage studies. J. Sci. Food Agric. 31 :625-631. Waldo, D.R. 1986. Effect of forage quality on intake and forage-concentrate interactions. J. Dairy Sci. 69:617-631. Walsberg, G.E., R.L. Tracy, and T.C.M. Hoffman. 1997. Do metabolic responses to solar radiation scale directly with intensity of irradiance? J. Exp. Biol. 200:2115-2121. Wildman, E.E., G.M. Jones, P.E. Wagner, R.L. Bowan, H.F. Trout, Jr., and T.N. Lesch. 1982. A dairy cow scoring system and its relationship to selected production characteristics. J. Dairy Sci. 65:495. Williams, C.H., D.J. David, and 0. Iismaa. 1962. The determination of chromic oxide in faeces samples by atomic absorption spectrophotometry. J. Agric. Sci. (Camb.) 59:381-385. Wilson, J.R., and D.J. Minson. 1980. Prospects for improving the digestibility and intake of tropical grasses. Trop. Grassl. 14:253-259. Young, B.A. and J .L. Corbett. 1972. Maintenance energy requirements of grazing sheep in relation to herbage availability. 2. Observations on grazing intake. Aust. J. Agric. Res. 23:77-85.

PAGE 324

BIOGRAPHICAL SKETCH Bisoondat Macoon (Mac) was born on 20 Jan. 1959 in Sparendaam, Guyana, 10 km from the capital city, Georgetown. He is the eldest of seven children of Agnes and the late Donald McCoon. His high school career was at Queen's College up to the General Certificate of Education "Ordinary Level" Examinations (1970 1975) then at the St. Rose's High School pursuing the Hinterland Development Program (1975 1977). Upon graduation from the Hinterland Development Program, he stayed on to work there first as a field assistant, then as the farm manager, then finally as the project officer responsible for all field operations. During that time he served a short stint as an agriculture science teacher from January to July 1979 in the regular school curriculum at St. Rose's High School to help alleviate a teacher shortage problem. At the end of 1979, he left to pursue further education. He received the Diploma of Agriculture from the Guyana School of Agriculture in 1981 and the Bachelor of Science degree in agriculture at the University of Guyana in 1984. From 1985 to 1988, he worked as a research technician for the Caribbean Agriculture Research and Development Institute (CARDI) on the CARDI/International Development Research Center Milk Productions Systems Project in Guyana. In March 1988, he joined the National Agricultural Research Institute (NARI) of 305

PAGE 325

306 Guyana as a research assistant but continued to work on same project as a result of collaborative effort between this institution and CARDI. In the fall of 1989 he cameto the University of Florida to pursue studies towards the Master of Science degree in Agronomy, benefitting from a fellowship under the auspices of the Food and Agriculture Organization of the United Nations He studied under the supervision of Dr. Lynn E Sollenberger specializing in forage crop production/management and graduated in spring of 1992. Upon completion of the Master s program Mac returned to Guyana and con t inued working with NARI. He was promoted to a research scientist position and was appointed program leader / forage agronomist of the national pasture and ruminant livestock research program. During his tenure with NARI he also served as chair of the editorial committee of the institution (1993 to departure) In addition to his responsibilities with pasture/livestock research, in July 1994 he was appoin t ed acting Head-of-Unit for NARI's Coastal Plains Field Research Unit where he served up to departure for doctoral studies. In spring of 1996 Mac was finally able to accept a teaching assistantship with the Agronomy Department at the University of Florida. He is currently a doctoral candidate under the supervision of Dr. Lynn E. Sollenberger and has conducted his dissertation research on grazing management studies evaluating pasture-based dairy production systems in collaboration with the Dairy and Poultry Sciences Department. Dr. Charles R. Staples of that department serves as co-chair on the supervisory committee for Mac's program of study.

PAGE 326

307 Mac has been a member of the Society of Professional Agriculturalists of Guyana since 1984 and the University of Florida Chapter of the Gamma Sigma Delta Honor Society of Agriculture since 1991. He has been married to Yonette (Crawford) Macoon since 1988 and they are blessed with two remarkable sons, eleven-year-old Ron and six year-old Russ.

PAGE 327

I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy E. Sollenberger, Chair Professor of Agronomy I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy Charles R. Staples, Cochair Professor of Dairy and Poultry Sciences I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality as a dissertation for the degree of Doctor of Philosophy ~,&;,~ Carrol G. Chambliss Associate Professor of Agronomy I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy r=f Animal Science {i,hn E. Moore

PAGE 328

I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality as a dissertation for the degree of Doctor of Philosophy Kenneth M. Portier Associate Professor of Statistics This dissertation was submitted to the Graduate Faculty of the College of Agriculture and to the Graduate School and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy /l ,1 1 /', December 1999 U~tS/-' Dean College of Agriculture Dean, Graduate School

PAGE 329

l {) I 7 86 ;G.9 11'] / -=t-1 UNIVE RSITY OF FLORIDA I\\ II \\1\11 \\\ \ 11111\\ \ 11111 \ 1111\1 \ 1 \ 1111 \ \\\ \ I l l \ \ Ill \ I\\\\\ 1 1 3 1262 08554 2453