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

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Title:
Forage and animal responses in pasture-based dairy production systems for lactating cows
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xix, 307 leaves : ill. ; 29 cm.
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Macoon, Bisoondat, 1959-
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Agronomy thesis, Ph. D   ( lcsh )
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Thesis:
Thesis (Ph. D.)--University of Florida, 1999.
Bibliography:
Includes bibliographical references (leaves 291-304).
Statement of Responsibility:
by Bisoondat Macoon.
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Printout.
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Vita.

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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