Citation
Analysis and modeling of cattle distribution in complex agro-ecosystems of south Florida

Material Information

Title:
Analysis and modeling of cattle distribution in complex agro-ecosystems of south Florida
Creator:
Pandey, Vibhuti ( Dissertant )
Kiker, Gregory ( Thesis advisor )
Shukla, Sanjay ( Thesis advisor )
Campbell, Kenneth L. ( Reviewer )
Annable, Michael ( Reviewer )
Clark, Mark W. ( Reviewer )
Place of Publication:
Gainesville, Fla.
Publisher:
University of Florida
Publication Date:
Copyright Date:
2007
Language:
English

Subjects

Subjects / Keywords:
Bodies of water ( jstor )
Cattle ( jstor )
Forage ( jstor )
Grazing ( jstor )
Modeling ( jstor )
Pastures ( jstor )
Seasons ( jstor )
Term weighting ( jstor )
Water tables ( jstor )
Wetlands ( jstor )
Agricultural and Biological Engineering thesis, Ph.D
Dissertations, Academic -- UF -- Agricultural and Biological Engineering
Miami metropolitan area ( local )
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )
theses ( marcgt )
Spatial Coverage:
United States -- Florida

Notes

Abstract:
It is perceived that cow-calf operations in south Florida can be a substantial source of phosphorus loading, to Lake Okeechobee. Spatial and temporal information of cattle location within a pasture can be instrumental in estimating the deposition location of cattle fecal matter. To address this issue, cattle position data were analyzed and a simplified distribution model was developed. Cattle position data were acquired through GPS collars and a cattle distribution model was developed and incorporated into a regionally tested hydrological/water quality model, ACRU2000. The GPS data were spatially and temporally analyzed to quantify the amount of time spent by cattle near shade and water locations. The analysis revealed the prominence of seasonal utilization of water troughs, ditches, and shade. Shade structures were utilized substantially during the warm seasons. Wetland utilization was similar across cool and warm periods but was variably distributed across times within periods. The analysis also revealed that there can be significant differences in an individual cow’s behavior and utilization of water features. The GPS analysis was instrumental in the identification of variables to be included in the cattle distribution model. This distribution model was added as an add-on module within the Java-based object-oriented framework of the ACRU2000 modeling system. The algorithms are composed of attractants of cattle (shade, water, and forage) and their weighting factors. The algorithms were developed using the techniques of Habitat Suitability Index (HSI) and criteria weighting was developed using the Analytical Hierarchy Process. The HSI model was integrated with the current hydrology, nutrient, and vegetation modules within ACRU2000. The HSI model was calibrated and verified on summer pastures of Buck Island Ranch, Lake Placid, FL. Model verification revealed that its performance was in good agreement with observed GPS data. Several Best Management Practice scenarios, designed to mimic fencing of selected pasture areas, revealed that the phosphorus release from senesced biomass may be a significant store amongst all other pools of phosphorus. The HSI model has enhanced the capability of ACRU2000 to represent the spatial variability and nutrient effects of cattle distribution within complex agro-ecosystems of south Florida. ( , )
Subject:
behavior, BMP, cattle, habitat, index, modeling, quality, suitability, water
General Note:
Title from title page of source document.
General Note:
Document formatted into pages; contains 151 pages.
General Note:
Includes vita.
Thesis:
Thesis (Ph.D.)--University of Florida, 2007.
Bibliography:
Includes bibliographical references.
General Note:
Text (Electronic thesis) in PDF format.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright Pandey, Vibhuti. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Embargo Date:
7/12/2007
Resource Identifier:
003874826 ( aleph )
660156457 ( OCLC )

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Full Text





ANALYSIS AND MODELING OF CATTLE DISTRIBUTION IN COMPLEX
AGRO-ECOSYSTEMS OF SOUTH FLORIDA





















By

VIBHUTI PANDEY


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

2007































Copyright 2006

by

Vibhuti Pandey


































To my parents and my loving wife









ACKNOWLEDGMENTS

I am greatly indebted to my supervisory committee chair (Dr. Gregory A. Kiker) for his

constant guidance, insight, encouragement, and most of all his enthusiastic and continuous

support and confidence in my research. His thorough and thoughtful coaching was unselfishly

tireless, and his enthusiasm has left me an everlasting impression. He always made himself

available and hence I was able to progress constantly in a sustainable manner. I would like to

acknowledge my thanks and appreciation to Dr. Chris J. Martinez for his help throughout the

development of my model. Without his guidance and support with programming, timely

completion of my research would have been impossible. His enthusiasm and helpful nature made

my research progress swiftly. I express my sincere appreciation to Dr. Kenneth L. Campbell for

his guidance during the first 3 years as my supervisory committee chair. He provided direction

that eventually helped me to identify the specific topic for my research. His advice on addressing

each of my technical problems and concerns has been invaluable as well. I am grateful to Dr.

Sanjay Shukla for his support and help during field work. Also, I would like to express thanks to

Drs. Michael Annable and Mark W. Clark who served on my committee and provided valuable

insights into my research.

I am greatly thankful to my lab-mates for their friendship and encouragement, and to the

staff of the Agricultural and Biological Engineering Department for their technical and moral

support. The department as a whole has been a wonderful working and learning environment.

Last but not the least, I would like to thank my family and friends for their relentless support and

advice throughout this endeavor.












TABLE OF CONTENTS



page

ACKNOWLEDGMENTS .............. ...............4.....


LIST OF TABLES ........._..... ...............8.._.._ ......


LIST OF FIGURES .............. ...............10....


AB S TRAC T ............._. .......... ..............._ 13...


CHAPTER



1 INTRODUCTION ................. ...............15.......... ......


Study Background .............. ......... ..... .. ..........1
Lake Okeechobee and Watershed Description ................. ........ ..... ...... ........ .......... .......1
Water Quality Best Management Practices (BMPs) for Lake Okeechobee Watershed.........1 8
Contribution to Information Required for Modeling and BMP Implementation .................19
Organization of This Dissertation............... ..............2

2 CATTLE BEHAVIOR DYNAMICS AND CURRENT MODELING APPROACHES ......22


Factors Influencing Cattle Distribution .............. ...............22....
Cognitive M echanisms .............. ...............23....
W ater Devel opment ............ ..... .._ ...............23...
Breed S el section ............ ..... .._ ...............24..
Seasonal Di stribution............... ..............2
Shade Structures .............. ...............25....
Social Behavior ..................... ...............27.
Cattle Location and Water Quality ................. ...............28.._._._ ....
Existing Modeling Approaches .............. ...............29....
Regression Model s ................... ...............29..
Habitat Suitability Index Models............... ...............30.
Mechanistic Models............... ...............35.
M etapopulation M odels.................. ... ........... .......3
Spatially Explicit-Individual Based Models .............. ...............38....
Numerical Fish Surrogate Model .............. ...............39....
Multi-Agent Systems ........._.___..... .___ ...............40.....
Cattle Tracking Techniques ........._._.. ....__... ...............41....
Summary ........._.___..... ._ __ ...............42.....

3 ANALYSIS OF GPS COLLAR DATA ............ ......__ ...............50.


Study Site: Buck Island Ranch .............. ...............50....












Summer Pastures .............. ...............5 1....
Winter Pastures ................. ...............52......__. .....

Hy drol ogic Data............... ...............52..
GP S D ata .............. ...............53....
Data Analy sis............... ...............54
Results and Discussion .............. ...............55....
Conclusion ............ ..... ._ ...............61...


4 DEVELOPMENT OF CATTLE MOVEMENT ALGORITHMS FOR ACRU2000 ............73


Habitat Suitability Index (HSI).................................. ........7
Model Design for Cattle Distribution in ACRU2000 ................. ............... ...._ ....73
Suitability Index for Cattle Distribution............... .. .............7
Preference Estimation Using Analytical Hierarchy Process .............. ....................7
Index for Heat Stress and Seasonal Distribution............... ..............7

Integration of HSI Model into ACRU2000 ........._ ..................... .... ........._ ...... 8
The Agricultural Catchments Research Unit (ACRU) Modeling System .....................81
Minimum Habitat Area for HSI Model in ACRU2000 ................. ........................86


5 MODEL RE SULT S ............_ ..... ..._. ...............9 1...


Testing Model Performance at Buck Island Ranch ........._.._.. ......._ ........_.._.......9
Calibration Results............... ...............92
Verification Results .............. ...............94....
Sensitivity Analysis .............. ........ ...............95
Hypothetical Scenario Model Testing .............. ...............96....
Summary ........._..... ...._... ...............100....

6 DISCUSSION AND CONCLUSION ........._._. ...._... ...............110...


GPS Collar Analysis .....__................. ...............110 .....
H SI M odel .............. ... ...............111.............

Management Implications ................. ...............113.............
Future Research Recommendation ....._.................. ...............113 .....
Herbivore Physiological Representation ................. ....._._ ....._._ .............1
Stream Routing Algorithm ..........._..._ ...............114..._.._ ......
Graphical User Interface ..........._...__........ ...............114....
Conclusion ..........._..._ ...............115.....__ ......


APPENDIX



A LIST OF NEW AND MODIFIED OBJECTS ..........._...__......_. ........... .........1


B HSI MODEL PROCESSES UNIFIED MODELING LANGUAGE (UML)
DIAGRAM S ................. ...............119......... ......


C SURVEY FOR DETERMINATION OF WEIGHTING FACTORS .............. .................122












D WEIGHTING FACTORS DETERMINED BY SURVEY ......____ ..... ... ._ ..............124


E RESULTS FROM SURVEY............... ...............126


F SENSITIVITY ANALYSIS RESULTS................ ...............13


LIST OF REFERENCES ............ ...... ..._. ...............137...


BIOGRAPHICAL SKETCH ........._.._.. ...._... ...............151....













LIST OF TABLES


Table page

2-1 Significant predictors of cattle behavior. .............. ...............48....

2-2 Coefficients in the seasonal grazing models. .............. ...............49....

2-3 Coefficients in the seasonal daytime resting models. ............. ...............49.....

3-1 Percent area of wetlands and ditches in summer and winter pastures. ........._.... .............69

3-2 Summary of climatological data during the study period. ................ ..................6

3-3 Summary of GPS collar data in the experimental pastures. ......____ ...... ....__..........70

3-4 Locations that are assumed to have presence of water. ..........._.....__ .............70

3-5 Mean percentage of daily time spent by cattle near water locations. ............. .................71

3-6 Mean percentage of daily time spent by cattle near water trough. ............. ...................71

3-7 Mean percentage of daily time spent by cattle in wetland. ....__ .............. ..... ..........71

3-8 Mean percentage of daily time spent by cattle in ditch. ............. .....................7

3-9 Mean daily distance traveled and mean daily MCP area by cattle .............. ..................72

5-1 Input parameters and their description used in the ACRU2000-HSI model. ..................108

5-2 Values of input parameters used in the ACRU2000-HSI model after calibration. ..........108

5-3 Input parameter values used in sensitivity analysis. .................... ...............0

5-4 Example of adjusted weighting factors used in sensitivity analysis. ............. ..... ........._.109

D-1 Summary of weightings of features as generated by the LDW program based on the
survey ........... ..... .._ ...............124..

D-2 Summary of weightings of three forage species as generated by the LDW program
based on the survey ........... ..... .._ ...............124.

F-1 Weighting factors used in sensitivity analysis. ......____ .... ... .__ .. ......__........3

F-2 Sensitivity of water, shade and forage weighting factors in warm season. .........._.........133

F-3 Sensitivity of the three vegetation species weighting factors in warm season. ...............134










F-4 Sensitivity of water, shade and forage weighting factors in cool season. ........................13 5

F-5 Sensitivity of the three vegetation species weighting factors in cool season. ...............136













LIST OF FIGURES


Figure page

1-1 Drainage Basins of Lake Okeechobee. ................ ...............21...............

1-2 Yearly average total phosphorus concentrations in the open-water (pelagic) region of
Lake Okeechobee............... ...............2

2-1 Average herbage yield of perennial grassesfrom year long access to water on
southern Arizona range. .............. ...............43....

2-2 The relationships between shrub habitat variables and suitability index values for
pronghom winter food quality.. ............ ...............44.....

2-3 The relationships between two variables of forage diversity and suitability index
values for pronghorn winter food quality.. ............ ...............44.....

2-4 The relationship between mean topographic diversity and suitability index values for
pronghom winter food quality. ............. ...............45.....

2-5 Graphical representation of the index ................. ...............45...............

2-6 Two performance suitability indicators expressed as functions of hydrologic
variables. ............. ...............46.....

2-7 A time series of values of a suitability indicator derived from time series of
hydrologic variable values. ............. ...............46.....

2-8 Creating a composite suitability indicator time series from multiple suitability
indicator time series. ............. ...............47.....

2-9 Three approaches to spatial ecology. ............. ...............47.....

2-10 Using GIS in metapopulation models. .............. ...............48....

3-1 Location of Buck Island Ranch and the Experimental Pastures. ..........._.. ......_.........64

3-2 Map displaying wetlands, ditches and water troughs in summer pastures. .......................64

3-3 Map displaying wetlands, ditches and water troughs in winter pastures. ..........................65

3-4 Example of rainfall and groundwater level data in summer pasture 3. ........._...._ .............65

3-5 Typical cattle movement in summer pasture 2 on June 11, 2001 ...........__... ................ 66

3-6 Average % time spent near water locations. .............. ...............67....











3-7 Average % time spent in shade structures. ............. ...............67.....

3-8 Typical MCP area in summer pasture 2 on June 31, 2001 ............_.. .. ...__ ...........68

4-1 Suitability index values of water features ................. ...............87........... ..

4-2 Suitability index values of shade area ................. ...............87........... ..

4-3 Suitability index values of forage consumption. ................ ............ ...................88

4-4 Goals hierarchy view in Logical Decisions for Windows@ software. ............. ................88

4-5 General structure of the ACRU (v 3.00) model ...._ ......_____ ...... .._ ........8

4-6 Configuration of multiple directional overland flows from source land segment to
adjacent land segments. ............. ...............89.....

4-7 Phosphorus cycle of the ACRU2000 model. ................ .....___.....___..........9

4-8 Nitrogen cycle of the ACRU2000 model ............... ...............90....___ ...

5-1 Land segment Discretization of summer pastures 4 and 5 for ACRU2000-HSI. ............102

5-2 Calibration results on SP4 in warm season ................. ...............103.............

5-3 Calibration results on SP4 in cool season. .............. ...............103....

5-4 Verification results on SP5 in warm season. ............. ...............104....

5-5 Verification results on SP5 in cool season ................. ...............104.............

5-6 Hypothetical scenario setup for ACRU2000-HSI model............... ...............105.

5-7 Total phosphorus results using ACRU2000-HSI model ................. .....___.............106

5-8 Total phosphorus results from various scenarios in ACRU2000-HSI model ..................1 06

5-9 Phosphorus budget of complete model domain using simulated results. ........................ 107

5-10 Phosphorus budget of top two model layers using simulated results. ............. ...... ......... 107

5-11 Total phosphorus retained within grazing cattle using simulated results. .......................108

B-1 PCalculateHabitatSuitabilityIndex UML diagram ................. .............................119

B-2 PForageConsumption UML diagram ................. ...............120...............

B-3 PDefecation UML diagram. .........____...... ..... ...............121....

C-1 Cattle preference of features in a pasture during summer. ............. ......................122










C-2 Cattle preference of features in a pasture during winter. ........._.__..... ..._._............122

C-3 Cattle preference of forage species in a pasture............... .................122

C-4 Example illustrating identification of the relative importance of one feature over the
other on the scale provided in the survey ......._..__ ........._._....... .........12

D-1 Range of weighting of features in warm and cool seasons ................. ......................124

D-2 Range of weighting of the three forage species. ....._._._ .... ... .__ ........_.........2

E-1 Simulation result on SP4 in warm season using weighting factors of researcher-1. ........126

E-2 Simulation result on SP4 in cool season using weighting factors of researcher-1. ..........126

E-4 Simulation result on SP4 in cool season using weighting factors of researcher-2. .........127

E-5 Simulation result on SP4 in warm season using weighting factors of ext. agent -1........128

E-6 Simulation result on SP4 in cool season using weighting factors of ext. agent -1. .........128

E-7 Simulation result on SP4 in warm season using weighting factors of ext. agent -2........129

E-8 Simulation result on SP4 in cool season using weighting factors of ext. agent -2. .........129

E-9 Simulation result on SP4 in warm season using weighting factors of rancher -1............130

E-10 Simulation result on SP4 in cool season using weighting factors of rancher -1..............130

E-11 Simulation result on SP4 in warm season using weighting factors of rancher -2............13 1

E-12 Simulation result on SP4 in cool season using weighting factors of rancher -2..............13 1









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

ANALYSIS AND MODELING OF CATTLE DISTRIBUTION IN COMPLEX
AGRO-ECOSYSTEMS OF SOUTH FLORIDA

By

Vibhuti Pandey

December 2006

Chair: Gregory A. Kiker
Cochair: Sanjay Shukla
Major Department: Agricultural and Biological Engineering

It is perceived that cow-calf operations in south Florida can be a substantial source of

phosphorus loading, to Lake Okeechobee. Spatial and temporal information of cattle location

within a pasture can be instrumental in estimating the deposition location of cattle fecal matter.

To address this issue, cattle position data were analyzed and a simplified distribution model was

developed. Cattle position data were acquired through GPS collars and a cattle distribution

model was developed and incorporated into a regionally tested hydrological/water quality model,

ACRU2000.

The GPS data were spatially and temporally analyzed to quantify the amount of time spent

by cattle near shade and water locations. The analysis revealed the prominence of seasonal

utilization of water troughs, ditches, and shade. Shade structures were utilized substantially

during the warm seasons. Wetland utilization was similar across cool and warm periods but was

variably distributed across times within periods. The analysis also revealed that there can be

significant differences in an individual cow' s behavior and utilization of water features.

The GPS analysis was instrumental in the identification of variables to be included in the

cattle distribution model. This distribution model was added as an add-on module within the









Java-based obj ect-oriented framework of the ACRU2000 modeling system. The algorithms are

composed of attractants of cattle (shade, water, and forage) and their weighting factors. The

algorithms were developed using the techniques of Habitat Suitability Index (HSI) and criteria

weighting was developed using the Analytical Hierarchy Process. The HSI model was integrated

with the current hydrology, nutrient, and vegetation modules within ACRU2000.

The HSI model was calibrated and verified on summer pastures of Buck Island Ranch,

Lake Placid, FL. Model verification revealed that its performance was in good agreement with

observed GPS data. Several Best Management Practice scenarios, designed to mimic fencing of

selected pasture areas, revealed that the phosphorus release from senesced biomass may be a

significant store amongst all other pools of phosphorus. The HSI model has enhanced the

capability of ACRU2000 to represent the spatial variability and nutrient effects of cattle

distribution within complex agro-ecosystems of south Florida.









CHAPTER 1
INTTRODUCTION

Study Background

The State of Florida has plentiful, diverse water resources that support a variety of

ecosystems, animals, food crops, industry, tourism, and recreation. However, rapid population

growth over the past 3 5 years is significantly affecting the quality of these systems. It is also

proj ected that Florida' s population will increase to 25.9 million in 2025 (US Census Bureau,

2005). Hence, Florida is facing a unique challenge of managing water quantity and quality with

the pressure of continuing population growth, accompanied with development and extensive

agricultural operations. The Florida Department of Environmental Protection (FDEP) is the

regulatory agency responsible for restoring and protecting the state's water quality. In its 2006

Integrated Water Quality Assessment Report, the FDEP documented increasing nonpoint source

pollution from urban stormwater and agricultural activities as a maj or environmental concern

(FDEP, 2006). Nonpoint source water pollution, sometimes called "diffuse" source pollution,

arises from a broad group of human activities for which pollutants have no obvious point of entry

into receiving watercourses. Because of its diffuse nature, nonpoint source pollution is much

more difficult to identify, quantify, and control than point source pollution. In south Florida,

especially in the Lake Okeechobee watershed, nonpoint source pollution from agricultural

operations is a matter of concern.

Lake Okeechobee and Watershed Description

Lake Okeechobee and its watershed are key components of south Florida's Kissimmee-

Okeechobee-Everglades ecosystem, which extends from the headwaters of the Kissimmee River

in the north, to Florida Bay in the south. Located in south central Florida, Lake Okeechobee

covers 1891 km2 (730 mi2) and functions as the central part of a large interconnected aquatic









ecosystem in south Florida. The lake is the second largest freshwater body located wholly within

the continental United States. Lake Okeechobee is a multipurpose reservoir providing drinking

water for urban areas, irrigation water for agricultural lands, recharge for aquifers, freshwater for

the Everglades, habitat for fish and waterfowl, flood control, navigation, and many recreational

opportunities (SFWMD, 1997). Under natural historic conditions, water flowed from Lake

Okeechobee to the Everglades. After events of heavy rainfall, water exited the lake' s littoral zone

by numerous small tributaries, and by a broad sheet-flow at the southeastern lake edge

(SFWMD, 1999). At that time, the lake bottom was composed primarily of sand that had low

phosphorus content. Conditions in and around Lake Okeechobee changed dramatically during

the last century, due to agricultural development in the watershed to the north of the lake, and

construction of the Central and South Florida (C&SF) Proj ect. Excess nutrient inputs from

agriculture and more efficient delivery of stormwater by the C&SF Proj ect have dramatically

increased in-lake total phosphorus concentrations.

The Okeechobee watershed is divided into six regions: Lower Kissimmee River (LKR) (S-

154, S-65D, and S-65E), Taylor Creek/Nubbin Slough (TCNS) (S-191), Fisheating Creek, Indian

Prairie/Harney Pond, the Lakeshore and the EAA (Figure 1-1). During the 20th century, much of

the land around Lake Okeechobee was rehabilitated to agricultural use. To the north, dairy farms

and beef cattle ranching became the maj or land uses. In the south, sugar cane and vegetable

farming increased rapidly. Associated with the land use changes were large increases in the rate

of nutrient inputs to the lake (SFWMD, 1999). The main sources of high nutrient loads in the

watershed are thought to be runoff from dairy barns and holding areas, direct stream access by

large numbers of dairy and beef cattle, and runoff from improved pastures.









The lake, a designated Class I water body (potable water supply), has been threatened

especially by high phosphorus levels, which have tripled since 1975 (Figure 1-2) causing large

algal blooms (SFWMD, 1999). The watershed has little relief, and the water table is near the soil

surface during the wet season. Before development this area was largely composed of wetlands

(Blatie 1980). During 1926 and 1928, flooding resulted in the loss of life and property, which

then resulted in the construction of a flood control levee (Herbert Hoover Dike) and a rim canal

around the lake to control flooding. Currently, all flows into and out of the lake are managed

through 140 miles of canals; control structures (gates, locks, and pumps); and levees, which were

completed in the late 1950s, as part of the Central and South Florida (C& SF) Flood Control

Proj ect.

The South Florida Water Management District (SFWMD), in conjunction with the United

States Army Corps of Engineers (USACE), regulates these structures and canals (SFWMD,

1997). This modified system has improved flood control and supplied irrigation water; however,

it negatively affected the water quality of Lake Okeechobee by expediting the delivery of

stormwater runoff to Lake Okeechobee.

Soils in the watershed (especially in northern regions) are mainly Spodosols which are,

sandy, low in clay content, low pH, low cation exchange capacity and low phosphorus retention

capacity. These soils (more than 90% sand) are characterized by high infiltration rates and poor

internal drainage due to low permeability of the Bh horizon. The Bh horizon contains large

deposits of Aluminum (Al) and Iron (Fe) along with organic matter and is known as the spodic

layer. When rainfall occurs, the soils can quickly become saturated. During much of the year, the

water table is located between the spodic horizon and the soil surface (Graetz and Nair, 1995).

Because of this restrictive layer, nutrient movement in Spodosols occurs through surface runoff









and also through subsurface flow (Campbell et al., 1995). Apart from landuse changes, soil and

hydrologic characteristics of the watershed have also facilitated in development of algal blooms

and other adverse impacts to water quality both in Lake Okeechobee and in downstream

receiving waters.

Consequently, in 1999, the FDEP initiated development of the Total Maximum Daily Load

(TMDL) of phosphorus for Lake Okeechobee. A TMDL is the maximum amount of a given

pollutant that a water body can absorb and still maintain its designated use. It was adopted by

rule in May 2001. The FDEP proposed a maximum annual load of 140 metric tons of phosphorus

to Lake Okeechobee to achieve an in-lake target phosphorus concentration of 40 ppb. The FDEP

is working in conjunction with other state agencies such as the Florida Department of

Agricultural and Consumer Services (FDACS), Water Management Districts (WMD), Soil and

Water Conservation Districts (SWCD), and U.S. Natural Resources Conservation Service

(NRCS) to support a healthy lake system, restore the designated uses of the lake, and allow the

lake to meet applicable water quality standards. These agencies are implementing a multifaceted

approach to reducing phosphorus loads by improving the management of phosphorus sources

within the Lake Okeechobee watershed through continued implementation of existing

regulations and Best Management Practices (BMPs) (FDEP, 2001).

Water Quality Best Management Practices (BMPs) for Lake Okeechobee Watershed

The state agencies responsible for water quality have recognized that mere implementation

of regulations will not be sufficient to achieve the targeted load reductions for the lake

(SFWMD, 1999). Other management strategies for this region are needed, including

development of non-enforceable guidelines and education of farmers and ranchers to adopt

BMPs to reduce the current pollution levels in surface waters. A step in this direction resulted in

development of the "Water Quality Best Management Practices for Cow-Calf Operations in









Florida" (FCA, 1999) by the Florida Cattlemen's Association (FCA). The BMPs included in the

manual include a variety of structural (e.g., fencing) and managerial (e.g., nutrient management)

BMPs. Although the BMPs developed represent the best efforts of the ranchers and state

agencies, limited information exists on the effectiveness of these BMPs. To address the

information gap, a study is currently underway by researchers at University of Florida, Institute

of Food and Agricultural Sciences (UF-IFAS) in conjunction with FDEP, FDACS, SFWMD, and

NRCS. The study is aimed at demonstrating and determining the efficacy of water quality BMPs

such as fencing and improved water management for reducing phosphorus loads to Lake

Okeechobee from cow-calf operations in the Okeechobee basin (UF-IFAS, 2002). Another

important factor governing the BMP implementation by the ranchers will be their economic

impact on ranch income. Unless a BMP is economically feasible for a rancher, its

implementation will be limited.

Contribution to Information Required for Modeling and BMP Implementation

Given the fact that cattle's presence in waters can be a source of direct loading of P, it is

important to quantify the time spent by them near/in waters so that an informative decision can

be made regarding water quality BMPs for ranches in the region of south Florida. It is also

essential that the current modeling systems should incorporate dynamics of localized grazing

pressure so that comprehensive representation of existing agro-ecosystems is accounted. Almost

all hydrological models lack representation of spatial distribution of cattle presence. A successful

model should not only represent hydrology and nutrients but also the dynamics of cattle

movement and behavior. If modeling systems are flexible and extensible they can be updated as

per the requirement of the system they are representing. One such modeling system is the

ACRU2000 which is available for expansion and incorporation of cattle distribution dynamics.

The specific objectives of this research are:









* Review cattle behavior studies, especially those associated with water quality impacts.

* Analyze cattle movement dynamics and their behavior using GPS Collar data.

* Construct a methodology to identify the spatial and temporal information of grazing cattle.

* Development of cattle distribution algorithms for the ACRU2000 modeling system.

* Test ACRU2000 on a site, which will best represent the condition for which the model is
being designed, i.e. agro-ecosystem of south Florida.

Organization of This Dissertation

Chapter 2 is a thorough review of two broad categories: cattle behavior studies and

existing modeling approaches that are used in defining ecological systems at various scales. In

Chapter 3, detailed analysis of GPS Collar data is presented. Results of various techniques have

also been presented that have been utilized to process GPS data. The following chapter (Chapter

4) includes model design, algorithm development and incorporation of cattle movement in the

ACRU2000 model. In Chapter 5 the cattle movement add-on module is tested and verified.

Finally Chapter 6 summarizes the effort of data analysis and modeling.



























































Lakewater Total Phosphorus
140

120-

-100 o



4 0 -



20


68 70 72 74 76 78 80 82 84 86 BB 90 92 94 96 98
Year


-f St. Lucie
*. anal






C-43 I, -
1- 10
PRIORITY
BASINS

L-18
North Newr
River
L-24
Mliamli Canal

DRAINAGE BASINS
OF LAKE OKEECHOBEE




Figure 1-1. Drainage Basins of Lake Okeechobee (SFWMD).


Figure 1-2. Yearly average total phosphorus concentrations in the open-water (pelagic) region of
Lake Okeechobee (SFWMD).









CHAPTER 2
CATTLE BEHAVIOR DYNAMICS AND CURRENT MODELING APPROACHES

High density animal operation is of interest as it can potentially be a cause of concern with

regards to its impact on the environment (Bottcher et al., 1995). Pastureland and dairies can

become an important source of diffuse or nonpoint source pollution if adequate practices are not

implemented or in cases when livestock are allowed to approach or enter surface waters. In

regions such as south Florida where cattle-ranching and dairy-farming are important agricultural

activities; there are concerns of increase in nutrient loadings from these agricultural lands.

Phosphorus loading from rangelands and its subsequent movement into the drainage waters

(Lake Okeechobee) is a maj or environmental concern in this watershed (Allen et al., 1982). The

primary source of phosphorus has been non-point source agricultural runoff, particularly from

beef cattle ranching and dairy farming, the two primary land uses in the Lake Okeechobee

watershed (Flaig and Reddy, 1995). Unlike dairy farms, beef cattle ranches are not yet treated as

sources of point source pollution due to lower animal stocking rates associated with cow/calf

production systems. Therefore, these ranches are not subject to any regulations from state and

federal agencies. A "voluntary" BMP implementation program exists for beef cattle ranches,

however, due to limited information on the effectiveness of these BMPs not many ranchers have

enrolled in the program.

Factors Influencing Cattle Distribution

Since cattle defecation is of maj or concern, it is evident that to develop a complete

understanding of the animal-plant-soil system in a ranch system, the spatial information of

grazing cattle will be crucial, which will aid in developing a comprehensive understanding of

ecological interactions. Various studies have examined the scope of improving pasture utilization

by increasing the distribution of cattle (Bailey et al., 1989a; Bailey et al., 1996; Ballard and










Krueger, 2005; Ganskoop, 2001; Marlow and Pogacnik, 1986; Owens et al., 1991, Schacht et al.,

1996; Smith et al., 1992, Sneft et al., 1985a, b).

Cognitive Mechanisms

In an invited synthesis paper Bailey et al. (1996) examined behavioral mechanisms that

produce large herbivore distribution patterns. It was reported that grazing distribution can be

attributed to biotic factors such as forage quality and abiotic factors such as slope. Abiotic factors

form cattle's conspicuous habit to graze "convenient areas" (Schacht et al., 1996). Selective

grazing of these convenient areas within pasture isolates area that do not get grazed or only

lightly grazed. This eventually causes reduction in the carrying capacity of grasslands and

efficiency of livestock enterprise (Anderson, 1967). Bailey et al. (1996) defined the foraging

process as an aggregate of two mechanisms: non-cognitive and cognitive. Non-cognitive

mechanisms do not require use of memory from large herbivores during foraging. Grazing

velocity and intake rate are examples of non-cognitive mechanisms that require little judgment

from the animal. Whereas, cognitive mechanism is a process of leaming and memory that have

shown to affect diet selection in selecting feeding sites. In earlier studies Bailey et al. (1989b, c)

demonstrated that large herbivores return to nutrient rich areas more frequently and generally

avoid nutrient poor areas. This is primarily because animals have an accurate spatial memory and

can associate food resource levels with the locations in which they were found.

Water Development

To ensure even pasture utilization, managers try to increase cattle' s uniformity of grazing

by changing these abiotic attributes of their pastures. Slope and distance to water have been

widely acknowledged as the two primary determinants of grazing patterns in large scale range

environments (Owens et al., 1991; Sneft et al., 1985b; Sneft et al., 1987; Schacht et al., 1996).

Areas that are steeper receive less use than those that are gentle (Mueggler, 1965), and locations









that are farther from water also receive less use than those that are near water (Valentine, 1990).

Development of water sources that are further than 1 km from existing water source usually

increases forage utilization and thereby increasing overall uniformity of grazing (Bailey, 2004).

Goebel (1956) increased the number of water developments from 9 to 52 over period of five

years and observed that this increase in water availability decreased concentrations of cattle in

overgrazed areas and increased use of areas which previously received little or no utilization.

During the growing season drainage channels and plant community near water locations gets

heavily grazed (Sneft et al., 1985b). Therefore, to increase distribution and also to lighten forage

over-utilization near water (Figure 2-1) some studies have controlled cattle's access to water

(Martin and Ward, 1970).

Water development has also been useful in protecting riparian areas thereby improving

stream water quality. Off stream water source has proven to decrease grazing pressure in the

riparian zone (Porath et al., 2002). Sheffield et al. (1997) reported that installation of off stream

water source reduced the average concentrations of total suspended solids, total nitrogen,

ammonium, sediment bound nitrogen, sediment bound phosphorous, total phosphorous and

stream bank erosion. In another study in Oregon, Miner et al. (1992) observed that cows reduced

their presence in the stream from 25.6 min/day to only 1.6 min/day (reduction of more than

90%),when off stream tank was made available.

Breed Selection

It has been reported that herbivores prefer gentle slopes near water (Mueggler, 1965).

Bailey et al. (2001) has suggested the use of breeds that originate from mountainous terrain

(Tarentaise) in rugged rangelands and use of breeds developed in gentler slopes (Hereford) in

rolling topographical rangelands. However, there can be some individuality associated with

regards to grazing on rugged terrain (Bailey et al., 2004). In a study conducted in the mountains










of Montana Bailey et al. (2004) compared the daily grazing patterns of cows that used steepest

slope and highest terrain to those that used gentler slopes and lower elevations. The authors

termed cows that spent more time grazing steeper slopes as "hill climbers" and those that used

gentler slope as "bottom dwellers". The study concluded that individual cows within a herd can

use different terrain.

Seasonal Distribution

A seasonal effect on cattle grazing behavior has also been reported in many studies

(Marlow and Pogacnik, 1986; Sneft et al., 1985b; Tanner et al., 1984). Typically during summer

(growing season), forage becomes mature and plentiful and there is more even grazing. Whereas,

during winter (dormant season) forage is not that palatable and hence there is more patchy

grazing. However, grazing distribution of weaning cows generally improves during late fall and

winter because of decreased water and nutrient requirements after weaning (Schacht et al., 1996).

A study conducted in northeastern Colorado used cluster analysis of forage-use to analyze the

consistent seasonal-grazing pattern and eventually construct a predictive model (Sneft et al.,

1985b). It was found that seasonal-grazing distribution was correlated with proximity to water

and site-quality indicators. Results of a 2-year behavior study in Montana also indicated seasonal

trend in cattle use of riparian and upland areas (Marlow and Pogacnik, 1986).

Shade Structures

In regions associated with high temperatures, another important factor in cattle distribution

and performance is availability of shade. At high temperatures, evaporative cooling is the

principal mechanism for heat dissipation in cattle (Blackshaw and Blackshaw, 1994). In order for

a cow to maintain a relatively constant body temperature with respect to its environment

homeostasiss), it must maintain thermal equilibrium via its developed heat-regulating

mechanisms. When the ambient temperature approaches or exceeds cattle's body temperature,









the cattle must increase their active cooling by evaporation of water from the respiratory tract or

from the skin by sweating (Lee, 1967). Failure to maintain homeostasis at high temperatures may

lead to reduced productivity or even death (Blackshaw and Blackshaw, 1994). Historically there

used to be a perception amongst producers that providing shade may reduce the time that cattle

spent grazing. However, recent studies have demonstrated that the amount of time cattle spent in

shade was related to environmental conditions and that shade seeking did not result in reduced

grazing time (Widowski, 2001). Also, by manipulating shade, cattle can be drawn to under-

utilized area of pasture (McIlvain and Shoop, 1971). Shade has also proven to bring financial

profits in ranching enterprise. In a 4 year study in Oklahoma it was quantified that shade

increased summerlong gain of yearling Hereford steers by a profitable 19 lb/head (McIlvain and

Shoop, 1971). The same study also concluded that "hot muggy days" (days when temperatures

were above 850 F and high humidity) reduced summerlong steer gains by 1 lb per day. In a

review paper on the effect of shade on production and cattle behavior Blackshaw and Blackshaw

(1994) reported that under high heat stress, Bos indicus breeds and their crosses have better heat

regulatory capacities than Bos taunts breeds. The authors attributed this difference due to

differences in metabolic rate, food and water consumption, sweating rate, coat characteristics and

color. The sweating of Bos indicus increases exponentially with rises in body temperatures;

whereas, in Bos taurus, sweating rates tended to plateau after an initial increase (Finch et al.,

1982) Therefore, Bos taurus must evaporate substantially more sweat than Bos indicus to

maintain normal body temperatures (Finch, 1986). Blackshaw and Blackshaw (1994) in their

review paper discussed some of the important physiological mechanisms that help cattle to cope

with heat stress:

* Evaporative Cooling
* Metabolic rate and tissue insulation










* Water consumption
* Cattle coat characteristics

Social Behavior

In mountainous terrain, cattle may form social groups (Roath and Krueger, 1982a).

Amongst these social groups cattle have been classified as leaders, followers and independents

with regards to movement during grazing (Sato, 1982). A dominance hierarchy exists in a herd

(Bennett et al., 1985; Bennett and Holmes, 1987; Broom and Leaver, 1978). Animals high in the

hierarchy have priority to feed, shelter, and water. Low-ranked animals maintain a certain

distance from dominant animals to avoid conflict. As subordinates get closer to dominant

animals, they may reduce their bite rate, stop feeding, relocate into areas of lower habitat quality

or wait their turn until the more dominant animals are satisfied and leave the area (Bennett et al.,

1985; Bennett and Holmes, 1987; Broom and Leaver, 1978). Therefore, management strategies

that involve social composition (e.g., herding, selective culling) can be used to relieve grazing

pressure on environmentally sensitive areas (Sowell et al., 1999).

Apart from the various factors mentioned above, there are plentiful other factors that may

be responsible in influencing cattle distribution dynamics. Schacht et al. (1996) have categorized

four techniques that can be employed for improving grazing distribution:

r Enticing the grazing animal to forage
Water placement
Salt and mineral placement
Supplemental feeding location
Rubs and oiler placement
Other methods (mowing, burning, shade etc.)
Pasture characteristics
Fencing
Pasture size
Pasture shape
r Grazing management strategies
Rotational Grazing
Stocking density










Flash grazing
Season of grazing
Livestock considerations
Class of livestock
Vegetation and terrain characteristics

Cattle Location and Water Quality

Considerable research pertaining to water quality impacts of grazing systems have been

well documented in the western states of USA (Belsky et al., 1999; Buckhouse and Gifford,

1976; Miner et al., 1992; Nader et al., 1998). Numerous studies have specifically targeted cattle

distribution patterns relative to water locations and riparian areas (Dickard et al., 1998; Gillen et

al., 1985; McIlvain and Shoop, 1971; Owens et al., 1991; Roath and Krueger, 1982b; Sneft et al.,

1985). Results from all the above studies have indicated water to be an influencing factor in

cattle distribution patterns.

In a cattle ranch system with stream there is concern of direct contamination within the

stream and significant impact on riparian areas. These impacts depend upon cattle behavior and

utilization of riparian vegetation (Marlow and Pogacnik, 1986). Cattle prefer to be closer to

water sources while grazing. This situation can lead to defecation, and eventually over

enrichment of the water bodies. High-density cattle activities near or on the stream banks can

result in rapid transport of manure to the streams (Bottcher et al., 1995). Apart from direct input

of nutrients into the stream, grazing near stream banks can also result in increased erosion of the

stream banks (Helfrich et al., 1998). Bowling and Jones (2003) listed four key potential impacts

grazing cattle can have on water quality:

Increased suspended sediment concentrations, due to the physical stirring up of the
bottom sediments when cattle are in the water, and due to increased sediment run off
from grazed foreshore areas.

Input of organic materials causing effects such as increased biological oxygen demand.










Increased nutrients by both direct deposition into the water or entrained in run off
entering the water body.

Increased fecal bacteria and potential pathogenic microorganisms, again through
defecation straight into the water, or in run off from nearby areas.

Cattle grazing and resting pattern will change with respect to water availability, climate,

presence of shade structures, and forage quantity and quality. Water seems to be the driving

force in attracting cattle towards in and around stream areas. Cattle wade into the shallow water

to graze on aquatic plants, to drink the water, and to wallow in it and remain cool on hot days

(Gary et al., 1983; Hagedorn et al., 1999). However, even when availability of water is not a

limiting factor still, cattle are known to spend significant time in grazing within the riparian area

due to availability of higher quality forage.

Existing Modeling Approaches

Model development is a crucial step in representing such a diverse ecosystem and it will

help define problems, organize our thoughts, develop an understanding of the data and

eventually be able to make predictions. There are various approaches for modeling population

response to environmental pattern. Following are some modeling methodologies that have been

widely utilized in the scientific community.

Regression Models

In some early model development effort pertaining to cattle's spatial distribution, Cook

(1966) used multiple regression equations to explain livestock spatial utilization patterns. The

same methodology was later used to predict spatial patterns of cattle behavior over an entire

landscape (Sneft et al., 1983, 1985a, 1985b). Data used to develop the regression model were

collected on the USDA-ARS Central Plains Experimental Range in northeastern Colorado during

1970-1973 (Sneft et al., 1983). Observations of cattle movement were made by following cattle

on foot for one 24-hour period during each month of the study period on two small paddocks, 1 1









ha and 22 ha. Over 60 independent variables were screened and seven were eventually

incorporated for analysis using stepwise multiple regression (Table 2-1).

In another observational study over a 2-year period (June 1980 through May 1982) at the

same site, researchers derived regression models of spatial patterns of grazing (Table 2-2) (Sneft

et al., 1985b) and resting (Table 2-3) (Sneft et al., 1985a).

It was concluded that even though mathematically the models are boundless (i.e. can be

applied to pastures of any size), it was noted that the models do not consider interactions among

variables. Hence, introduction of a complex herd structure might require more complicated

mathematical descriptions of spatial use. Sneft et al., (1983) also acknowledged that even though

the models are "fine-grained" in space, they are "coarse-grained" in time. This indicates that on

finer time scale, daily temperature variations, for example, may have an effect on the behavior of

cattle. These limitations have been overcome by using a different technique known as habitat

suitability index (HSI) modeling (Cook and Irwin, 1985; Schamberger et al., 1982).

Habitat Suitability Index Models

The U.S. Fish and Wildlife Services developed a methodology known as Habitat

Evaluation Procedures, a planning and evaluation technique that focuses on the habitat

requirements of fish and wildlife species (U.S. Fish and Wildlife Service, 1980). These

procedures were formulated according to standards for the development of Habitat Suitability

Index (HSI) Models (U. S. Fish and Wildlife Service, 1981). The HSI models are usually

presented in three basic formats: (1) graphic; (2) word; and (3) mathematical (Schamberger et al,

1982). The graphic format is a representation of the structure of the model and displays the

sequential aggregation of variables into an HSI. Following this, the model relationships are

discussed and the assumed relationships between variables, components, and HSI's are

documented. This discussion of model relationships provides a working version of the model and









is, in effect, a model described with words. Finally, the model relationships are described in

mathematical language, mimicking as closely and as simply as possible, the preceding word

descriptions.

HSI provides a probability that the habitat is suitable for the species, and hence a

probability that the species will occur where that habitat occurs. If the value of the index (Range

0 to 1) is high in a particular location, the chances of that species occurrence in that location are

high. For example, HSI of 0 would mean totally unsuitable habitat, whereas HSI value of 1

would mean optimum habitat. To determine habitat suitability, suitability indexes (SI) are

assigned to represent the degree in which the variable may contribute to species life requisites

(Hohler, 2004). The SI score is based upon empirical data, professional wisdom and at times,

inspired guesses (U.S. Fish and Wildlife Service, 1981).

Spatial location of herbivores has challenged many researchers who have tried to model

their distribution (Bailey et al., 1996; Coughenour, 1991; Pringle and Landsberg, 2004; Wade et

al., 2004). In an invited synthesis paper, Coughenour (1991) provided important insights into

models that integrate plant growth, ungulate movement, and foraging. A variety of modeling

approaches was discussed and HSI modeling was accredited of overcoming the limitations of

multiple regression models (application constraints). Bailey et al. (1996) developed a conceptual

model to demonstrate how cognitive foraging mechanisms can be integrated with abiotic factors

to predict grazing patterns of large herbivores. Abiotic factor multipliers were used in the

modeling systems which are similar to HSI models.

As an example of a typical HSI model, a step by step illustration of a HSI model

development is given by Allen et al. (1984) in a U.S. Department of Interior document. This

document is one in a series of publications that provides information on the habitat requirements









of selected fish and wildlife species. In this particular document, the HSI model was developed

for pronghorn (Antilocarpa amnericana) chiefly for application for the Great Basin and the Great

Plains region for winter weather. This simplistic model assumed the winter habitat characteristics

to be the most limiting conditions affecting pronghorn distribution. The model is based on the

assumptions that pronghorn survival and reproductive success are functions of winter food

availability. The model incorporates vegetation and topographic features that favor food

availability under mild snow conditions. After detailed review of literature describing the

relationship between habitat variables to the pronghorn's preference; the authors synthesized all

the information and identified six variables of interest:

Percent shrub crown closure (V1)
Average height of the shrub canopy (V2)
Number of species present (V3)
Percent herbaceous canopy cover (V4)
Amount of available habitat in winter wheat (Vs)
Slope of land (V6)

Each of these six variables has their respective suitability index relationships as shown in

the Figures 2-2 and 2-3 and synthesized in equation 2-1.


WFI = [SIVzx(SIV~xSIV~xSIV4 1/3] +SIVS (2-1)


Equation 2-1 accounts only for the forage factor towards the overall HSI where WFI is an

index representing the forage preference of the pronghorn' s diet. The geometric mean of the

three variable indexes (SIV2, SIV3 and SIV4) in the equation 2-1 is a compensatory function.

This function is used in multiplicative models so that partial compensation of the interacting

variables is accounted for (U. S. Fish and Wildlife Service, 1981). The three variable indexes are

assumed to have equal value, meaning that all three must be 1.0 (optimum) in order for this

function to be optimum. Also, a unit increase (e.g., increase an SI by 0.1) in the variable index is









assumed to have the greatest positive impact on the overall index (WFI). This relationship

(Equation 2-1) is combined with the suitability index of the sixth variable, slope of land (Figure

2-4), to calculate the combined food/cover index.


WFFI + SI F
FFFCI = 6 (2-2)



The HSI is equal to the WFCI as calculated in equation 2-2. Allen et al. (1984) extended

the application of the model for evaluation areas that may comprise several cover types. To

represent several cover types it was suggested to multiply the area of each cover type by its

respective WFI value, sum the products, and divide by the total area of cover types to determine

the area weighted WFI (equation 2-3) (Allen et al., 1984).




weighted FFI = (2-3)




where n is the number of cover types, WFli is the winter food index for individual non cropland

cover type, and Ai = area of cover type i.

A similar procedure was suggested to follow to determine the area weighted cover index

(CI) value (equation 2-4). Once both the weighted indexes are computed an overall HSI value is

determined by averaging the WFI and CI values.



C;I, A
weighted CI = '- (2-4)

i7=1









where Cli = cover index value for each cover type. A similar HSI model (equation 2-5) for elk

(Cervus elaphus nelsonii) has been documented by Thomas et al. (1988) for the Blue Mountain

winter ranges of Oregon and Washington. The authors made use of some published as well as

some unpublished data to derive a procedure for evaluating effectiveness of various habitat

variables.


HESRFC (HEs x HER x HEF x HEc)1/" (2-5)


where: HESRFC is the habitat-effectiveness index, allowing for the interaction of HEs, HER, HF,

and HEc, HEs is the habitat-effectiveness index derived from size and spacing of cover and

forage areas, HER is the habitat-effectiveness index derived from the density of the roads open to

vehicular traffic, HEF is the habitat-effectiveness index derived from the quantity and quality of

forage available to elk, HEc is the habitat-effectiveness index derived from cover quality, and

1/N is the Nth TOOt of the product taken to obtain the geometric mean. The mean reflects the

compensatory interaction of the N factors in the habitat-effectiveness model.

Similar to the pronghorn HSI model the geometric mean is also used in this model as a

compensatory function. The authors also incorporated graphical representation of the index

resulting from raising any product derived from (HES x HER x HEF x HEC) to the power of

1/N (1/4 in this case) (Figure 2-5).

In a more recent application, HSI technique has been utilized by the South Florida Water

Management District (SFWMD), for evaluating water management alternatives in the greater

Everglades ecological system, extending south of Lake Okeechobee in South Florida (Tarboton

et al., 2004). In their study, Tarboton et al. (2004) used conceptual ecological models to help

define water-dependent habitat suitability indices for select ecosystem indicator species and









landscape features. The first step in the process of defining habitat suitability functions was to

identify the indicator that would serve as a surrogate for the entire ecosystem. Six different

indicators were identified: three were landscape features and remaining three were fish, alligator

and wading birds. After identifying the indicator features and animals, the next step was to

determine the hydrologic variables, attributes, or characteristics that affect the selected indicator

feature and animals. Examples of hydrologic variables used are water depth, flow direction, and

hydroperiod. Once the specific hydrologic variables were selected for each feature or animal, the

next step was to identify the relationship between those variable (Figure 2-6) values and the

relative conditions of the indicator features or animals.

These functions were based on observed data and expert opinion. Once defined, these HSI

functions were combined with time series of hydrologic values to obtain an overall time series of

ecosystem habitat suitability values (Figure 2-7).

Eventually, based on time series values of multiple suitability functions, composite value

were obtained (Figure 2-8).

To obtain composite performance indicator values geometric means, weighted arithmetic

means, and maximum or minimum values were used. The methods selected for combining

different habitat suitability functions for the same ecosystem feature or species were determined

during the calibration procedure (Tarboton et al., 2004). The authors concluded that with this

approach they were able to link ecology to hydrology in a way that would make it easy for

anyone to understand, modify, test, and evaluate this linkage.

Mechanistic Models

Herbivore and plant dynamics have also been modeled utilizing classical predator-prey

relationship between two species in an ecosystem (Noy-Meir, 1975). In some cases researchers

have utilized an energy balance relationship to account for the balance between energy required









for herbivore body maintenance and the amount gathered by foraging (FAO, 1991; Hobbs and

Swift, 1985). Over time various simple as well as complex models have been developed which

along with other processes also attempt to describe animal responses to environmental inputs.

The SAVANNA ecosystem model (Coughenour, 1993) is a spatially explicit, process-oriented

modeling system developed to simulate ecosystems occupied by ungulate herbivores. The model

is composed of several submodels, which describe various processes and vary in complexity.

The herbivory submodel simulates forage intake by diet selection, forage abundance and forage

quality. An energy balance submodel simulates body weight of the mean animal of each species

based on differences between energy intake and energy spent. Smith (1988) described a detailed

mechanistic model in which they added a behavioral sub-model to simulate the ecology of an

arid zone sheep paddock in pastoral areas of south Australia. The spatial component was

included in the model by dividing the paddock into cells of 500*500 m2 and modeled on an

hourly timestep (movement of sheep while grazing is 500 m/hr). Movement of sheep was

determined by the state of its four physiological criteria: heat stress, thirst, hunger, and darkness.

Each of these criteria was defined in a hierarchy of trigger level conditions which determines the

dominant trigger and consequently determines where and at what speed animal movement will

take place.

Metapopulation Models

Levins (1969, 1970) defined metapopulation as "population of populations"; in which

distinct subpopulations (local populations) occupy spatially separated patches of habitat. In other

words metapopulation is a patchy distribution of population in which species exist in clusters that

are either isolated from one another or have limited exchange of individuals (Akgakaya et al.,

1999). It is a network of semi-isolated populations with some level of regular or intermittent









migration among them. In a review paper Hanski (1998) distinguished between three approaches

to large scale spatial ecology (Figure 2-9).

The approach of theoretical ecology assumes homogenous continuous or discrete (lattice)

space and the model does not incorporate any environmental heterogeneity. On the other hand,

landscape ecologists have developed models that are very descriptive of the complex real

environment. Hanski (1998) termed metapopulation models as a "compromise" where

landscapes are viewed as networks of idealized habitat patches in which species occur as discrete

local populations connected by migration. Metapopulation models are spatially structured so that

they incorporate information about habitat relationships and the characteristics of the landscape

in which the metapopulation exists (Akgakaya, 2001). RAMAS has been a popularly used model

which includes metapopulation dynamics integrated with GIS (Applied Mathematics, 2003)

(Figure 2-10).

Hanski (2004) pointed out that even though the idea of running simulation models using

metapopulation theory may seem tempting as it can be applied to any kind of population, it is

however prone to problems. Firstly, validating a complex simulation model will be virtually

impossible, and secondly, the simulation approach will yield specific results rather than more

general understanding. A good example of such a modeling approach has been exemplified in

Schtickzelle and Baguette (2004) where the researchers modeled the metapopulation dynamics

of the bog fritillary butterfly utilizing the abovementioned RAMAS/GIS. The model was

validated by comparing the predicted and observed distribution using the same empirical data

that were used to estimate model parameters. It is therefore important that modelers be careful in

the construction and parameter estimation of models using metapopulation theory. Hanski (2004)

has therefore, repeatedly emphasized that the classical metapopulation theory are most useful for









examining the dynamics of metapopulations living in highly fragmented landscapes. Such

landscapes are in which the suitable habitat for the focal species accounts for only a small

fraction of the total landscape area, and where the habitat occurs as discrete fragments.

Spatially Explicit-Individual Based Models

Spatially explicit population models are increasingly being used in modeling animal

populations and their movements (Dunning et al., 1995). These models can be simple as well as

complex. The extreme of simplicity in population models are the patch occupancy models that

are based on the number of occupied populations. On the other hand, the extreme of complexity

are the spatially explicit individual/agent based models, which describe spatial and habitat

information at the individual level. Logan (1994) has pointed out that complex systems need

complex solutions. The complexity of the processes involved in ecosystem, has compelled the

modelers to accommodate processes that vary across wide range of spatial and temporal scales

(Levin, 1992). Modelers of aquatic ecosystems have realized the constraint a limited spatial scale

simulation poses towards model accuracy and usefulness towards decision making. Individual-

based-model (IBM) is a relatively new approach in ecology. In an individual-based model, the

characteristics, behavior, growth, reproduction etc. of each individual is tracked through time.

This system is different than the commonly used modeling techniques where the characteristics

of the population were averaged together (Reynolds, 1999). These models provide ecologists

with an effective way to explore the mechanisms through which population and ecosystem

ecology arises from how individuals interact with each other and their environment. IBMs are

also known as entity or agent based models, and as individual/entity/agent-based simulations.

Similar to individual-based, agent-based models has also been utilized for simulating animals

with comprehensive and dynamic landscape structure (Topping et al., 2003). More recently

Ovaskainen and Hanski (2004) derived a stochastic patch occupancy (SPOM) model from an









IBM, where individuals obey the rules of correlated random walk. This unique and novel

modeling framework generates emigration and immigration events in a mechanistic manner and

avoids the need for particular assumptions about how the areas and connectivities of habitat

patches influence migration. It was concluded that in spite of being simplistic the SPOM

replicated the behavior of IBM remarkably well (Ovaskainen and Hanski, 2004).

Numerical Fish Surrogate Model

It is for this reason, Nestler et al. (2001) utilized a particle- tracking algorithm with

stimulus-response rules to develop a Numerical Fish Surrogate (NFS) system (Goodwin et al.,

2001), which creates virtual fish that are capable of making individual movement decisions

based on spatial physiochemical and biological information. The Numerical Fish Surrogate uses

a Eulerian-Lagrangian-agent method (Goodwin et al., 2006) for mechanistically decoding and

forecasting movement patterns of individual fish responding to abiotic stimuli. An ELAM model

is an individual-based model (IBM) coupling:

Eulerian framework to govern the physical, hydrodynamic, and water quality domains

Lagrangian framework to govern the sensory perception and movement traj ectories of
individual fish

Agent framework to govern the behavior decisions of individuals.

The modeling-philosophy behind ELAM is based upon two maj or theoretical approaches

that are coupled to represent the movement of fish (Nestler et al., 2005). Eulerian and Lagrangian

approaches are the two frameworks that have been integrated in ELAM. The former approach is

utilized by engineers to describe the physiochemical properties in hydraulics, while the latter

approach, used by biologists, is mostly centered on the stage development and movement

patterns of particles or individuals. The developers of ELAM hypothesize that by marrying these

two frameworks into a coupled Eulerian-Lagrangian (CEL) hybrid method, they can maintain the










integrity of individuals while concurrently simulate the physiochemical properties of the aquatic

ecosystem that affects fish' s movement. The hydrodynamic and water quality module of the CEL

hybrid model is CE-QUAL-W2 Version 3.0 (Cole and Wells, 2000), a 2-D laterally averaged

model developed at the U.S. Army Research and Development Center. The coupler in CEL

Hybrid model is based upon particle-tracking-algorithm that uses equations for computation of

forcing functions in the longitudinal and vertical directions.

The temporal scale of ELAM is exceptionally low, i.e. 2 sec time-step. Modelers argue that

to produce better fit of the fish' s movement in the vertical direction short time-steps are essential.

Given the fact that ELAM is an individual-based model, i.e., it tracks the behavior movement of

an "individual" fish at each time step, the mathematical computations become massively

demanding. It is for this reason that the model is currently run on U. S. Army Maj or Shared

Resource Center supercomputers. The computational infrastructure of the model (as of June

2004) handles simulations of 5,000 virtual fish in approximately 11 hr of run time (20,000 2-sec

time steps). More recently, Goodwin et al. (2005) have realized the involvement of substantial

run time associated with the mathematical computations of this model and have tried to increase

the computational efficiency by simulating more virtual fish in far less simulation times.

Multi-Agent Systems

Recently, several researchers have started to use multi-agent systems (MAS). MAS is

similar to agent-based modeling, but are more influenced by computer sciences and social

sciences (Bousquet and Page, 2004). MAS give more prominence to the decision-making process

of the agents and to the social organization in which these agents are embedded. Ferber (1999)

has defined a multi-agent system being composed of: environment, objects, agents, and relations

and operations. MAS has been effectively used in variety of cases, for example: modeling of

sheep's spatial memory (Dumont and Hill, 2001), prediction of duck population response to









anthropogenic cases (Mathevet et al., 2003) and predict the effects of alternative water

management scenarios in south Florida on the long-term populations of white-tailed deer and

Florida panther (Abbott et al., 1995).

Cattle Tracking Techniques

To develop comprehensive grazing management strategies to improve water quality in

watersheds consisting of beef cattle ranches, it is imperative to develop an understanding of

cattle's usage of water locations. This often involves observation of cattle movement in a pasture

setting. Earlier studies involved extensive field observations and in most cases observations were

limited to daylight only (Tanner et al., 1984). Research involving visual observations of the

cattle's position and its actions are prone to error as the observer can alter cattle behavior and

make visual errors. In such studies, observation periods are generally short due to its labor

intensity and concerns over observer fatigue. In subtropical regions such as south Florida, night

time observations can be critical because cattle exhibit bimodal grazing patterns (early morning

and evening) and with less adapted breeds of cattle spending a greater portion of the night

grazing as compared to day time (Bowers et al., 1995; Chase et al., 1999; Hammond and Olson,

1994). Global Positioning System (GPS) and Geographical Information System (GIS)

technology allow livestock grazing behavior and management to be evaluated with greater

spatial and temporal resolution (Ganskopp, 2001; Tumner et al., 2000; Ungar et al., 2005).

Animals can be tracked on a 24-hour basis using GPS receivers incorporated into collars.

Agouridis et al. (2004) tested GPS collars under static (open field, under trees and near fence)

and dynamic conditions to evaluate their accuracy for applications pertaining to animal tracking

in grazed watersheds. Their results indicated that the collars were accurate within 4 to 5 m,

deemed acceptable for most cattle operational areas. Collars can also record ambient temperature

and number of vertical and horizontal head movements. Head movements can be used to










determine grazing time and differentiate animal activity (resting or grazing) between location

fixes. Location and other programmed data are stored in the collar, and animals must be caught

and the collar removed to retrieve the data.

With more recent technical advancement, Cattle Traq LLC, an affiliate of American

Biomedical Group Inc. located in Oklahoma City, has developed software capable of monitoring

cattle and recording internal body temperature. Cattle Traq is an integrated system of microchips

located in ear tags, access control sensors and proprietary software (ABGI, 2005). It operates

with radio frequency waves sent from ear tags to software that decodes the signals and translates

them into usable information.

Summary

In their thorough review on grazing impacts on stream water quality in the southern region

of USA, Agouridis et al. (2005) credited the plentiful grazing studies of the western and mid-

western USA; but, also acknowledged that the differences between the arid west and the

southern humid region prohibit the universal transfer of research results. Models and concepts

developed elsewhere cannot be applied to the unique agro-ecosystems of the south-east (Platt

and Peet, 1998) such as south Florida. A limited number of grazing studies in the southern humid

regions (Tanner et al., 1984; Zuo, 2001) have provided valuable, yet incomplete information

with regard to the extent, if any, of water quality degradation by the grazing beef cattle in the

southeastern USA.

In recent times, with the advancement in computational power, researchers have exploited

new advanced computer-based technologies for the development of ecological simulation

systems. Primarily, the research in ecological model development has been greatly concentrated

in utilizing enhanced computer technology to incorporate the details of ecological phenomena.

The primary goal when building an ecological model should be to incorporate the knowledge and










understanding of a system's patterns and processes into a computerized tool that will simulate

the way in which the real system would behave under specific conditions. Simple models often

achieve this goal; they have simplistic assumptions, and can function with limited data.

However, they might neglect detailed aspects such as spatial heterogeneity and individual

variability. Alternately, complex models incorporate the details of ecological phenomenon but,

are often criticized because they are difficult to understand, parameterize, and hard to

communicate. Individual based models are good examples of complex modeling systems. These

models are useful classroom exercises to demonstrate effects at fine scale. Unfortunately, the

behavior rules at individual levels are poorly known and therefore modelers have to rely on

stochastic mechanisms. Another limitation of these models is the exorbitant computation demand

to represent large number of animals over large areas.



120

100






~ 0





100 200 300 400 500
Distance from water (yards)


Figure 2-1. Average Herbage Yield (lb/acre) of perennial grasses (1959-1966) from year long
access to water on southern Arizona range (Martin and Ward, 1970).










1.0 1.



S0.6 0.6

30.4 -1 t0.4



vn0.0 .I 0 6 12 18 24(in)
0 25 50 75 10) A 0 15.2 30.4 45.7 60.9(an)B






= t /Variable 3
30.4




I 2 3 4 5C


Figure 2-2. The relationships between shrub habitat variables and suitability index (SI) values
for pronghorn winter food quality. A) Percent shrub crown cover. B) Average height
of shrub canopy. C) Number of shrub species present per cover type (adapted from:
Allen et al., 1984).



1. II r X .


S0.6 0.6-
r Variable 4 variable 5
i 0.4 -1 C 0.4-

S0.2 0,2 -

vr0.0 vr0.0
U 25 50 75 1 0 A 0 25 50 75 100 B


Figure 2-3. The relationships between two variables of forage diversity and suitability index (SI)
values for pronghorn winter food quality. A). Percent herbaceous canopy cover B)
Percent of available habitat in winter wheat (adapted from: Allen et al., 1984).











* 1 9 1 1


A B C D.~.


1.0




0.6


0.4



0.2-


A) CI-2% slope; flat or

B) 3-8%8 slope; gently rolling
C) 9-25% slope; substantial
drainages, ridges, and/or
rims present
0) > 25% slope; mountainous


Figure 2-4. The relationship between mean topographic diversity and suitability index (SI)
values for pronghorn winter food quality (adapted from: Allen et al., 1984).


z
S0.8
TLI

ur.
S0.6


uJ .

e


.L 0.2


0.2 0.4 0.6 0.8 1.0
Value for (MEyx HER x HEF x HEC)


Figure 2-5. Graphical representation of the index (adapted from: Thomas et al., 1988).


















0 0



Hydrologic Variable Hydrologic Variable

Figure 2-6. Two performance suitability indicators expressed as functions of hydrologic
variables (adapted from: Tarboton et al., 2004).


Time, t


Figure 2-7. A time series of values of a suitability indicator derived from time series of
hydrologic variable values (adapted from: Tarboton et al., 2004).

























Time, t


Time, t


Tlkfe.l


Figure 2-8. Creating a composite suitability indicator time series from multiple suitability
indicator time series (adapted from: Tarboton et al., 2004).


T~heoretical
ecology


Metapopulation
ecology


Landscape
ecology


I
I
Ii '-(


...:.....i......... ..i.. ..L....~.....i....i
I--
I.....l.....i-~..1.....;.... i .


Figure 2-9. Three approaches to spatial ecology (adapted from: Hanski, 1998).











RAMAS/GIS
rmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmy
5 ~Patch
I HSI recognition PatchI


I Ia tutr


I I


SpatiI al
Population
Metapop.





Sensitivity Risk
analysis II analysis

111 1 11


Land-
GIS





Field studies


Demographic
data



Experiments


PVA
Reserve design
Wildlife management


Figure 2-10. Using GIS in metapopulation models (adapted from: Applied Mathematics, 2003).

Table 2-1. Significant predictors of cattle behavior (adapted from: Sneft et al., 1983).
Pasture Characteristic


Distance From


Cactus


Behavior Water Fence Corner Eley. Aspect Slope r
Freq.
Grazing + Travel S* S S S 0.50
Summer Resting S S S S 0.34
Winter Resting S S S 0.25
Bedding S S S S 0.20
Functional form 1 1 1 1 1
in models X X X X Cos(X +k) X X
*S denotes a pasture variable statistically significant in predicting the distribution of a given behavior at the 0.001
level.











Table 2-2. Coefficients in the seasonal grazing models (adapted from: Sneft et al., 1985b).
Independent variable
Proximity Oppo Agsm Eref Sihy Bogr rel.
Season Constant r
to water Freq. Freq. Freq. Freq. abund
Growing (Apr-Oct) 438.0 -.104 .316 .039 --4.30 .460
Dormant (Nov-Mar) 350.0 -.010 -.109 .014 .50 .269
Mathematical X1 x
express in model' X 2 3; 4q 5I X7
SSpecies symbols are defined in text
2 X1 = distance from stock tank (meters)
X2 to Xg = percent frequency to plant species
X6 = biomass of blue grama (Bogr) in community (g/m2)
X7 = biomass of all plant species in community, excluding pricklypear (g/m2)


Table 2-3. Coefficients in the seasonal daytime resting models (adapted from: Sneft et al.,
1985a).
Independent variable
Proximity Proximity to
Season Elevation Aspect Constant r
to water fence corners
Warm (Jun-Aug) 416.45 157.71 11.25 --1.89 .426
Cool (Sep-May) 408.80 106.60 5.34 k4 -1.51 .555
Mathematical 1 1 1
cos(X4 )C
express in model* X, X2 X3
* X, = distance from stock tank (meters)
X2 = distance from nearest fence corner (meters)
X3 = CICVation above 1646 m contour (meters)
k4 = 0.600 (cos(0.5236(month-12))
X4 = degrees deviation from due south









CHAPTER 3
ANALYSIS OF GPS COLLAR DATA

Prolonged hot summers in the region of south Florida can cause physiological heat stress

in cattle and drive them into shade and water-filled ditches and wetlands to cool down. The main

focus of this chapter is to quantify the amount of time spent by grazing cattle near or in water

locations (wetlands, ditches and water troughs) across seasons in a cow-calf production ranch in

south Florida. Partial data were used to conduct analysis on the impact of shade structures, total

distance traveled and total area utilization. This chapter is divided into five sections: description

of the study site, data collection, methodology utilized to quantify the time spent by cattle near/in

features of interest, results, and finally some conclusions from the analysis.

Study Site: Buck Island Ranch

The MacArthur Agro-ecology Research Center, Buck Island Ranch (BIR), Lake Placid,

Florida, USA (270 09'N, 81o 12'W) (Figure 3-1) is representative of the agro-ecosystem that

exists in the Okeechobee watershed. BIR (4, 168-ha) is a full-scale commercial cattle ranch

owned by the John D. and Catherine T. MacArthur Foundation and leased to Archbold

Biological Station and is located in the central portion of the Indian Prairie/Harney Pond Basin,

one of five maj or tributary basins of the Lake Okeechobee watershed. The ecology of this ranch

is composed of a mosaic of habitats that includes open grasslands, forests, and wetlands which

support a diverse and productive community of wildlife and plants (Arthington et al., 2006;

Swain et al., 2006). This ranch is representative of much of south Florida which was once a

native, subtropical, wet-prairie ecosystem. The ranch has been mostly drained and converted to

improved pasture; however, some patchy wetland areas still exist. For more than 10 years BIR

has been a platform of comprehensive interdisciplinary agro-ecological research (MAERC,

2005). The key goals of ongoing research efforts are to quantify the effects of various










management practices on surface water quality and protection of the natural biodiversity of

ranches while maintaining the economic viability of the ranching industry in central Florida.

The BIR is primarily a commercial ranch and therefore ranch operation management is

designed to support animal performance while optimizing the amount of beef production per unit

area of land. Cattle are rotated among the pastures to maximize the available forage for grazing

cattle. Cattle are stocked for longer periods in the improved pastures (typically during summer

season, May through October) and shorter periods in semi-native pastures (typically during

winter season, November through April). Two reasons necessitate this management strategy:

firstly, summer pastures are fertilized (NH4NO3 56 Kg N/ha) (Arthington et al., 2006) in spring

and, therefore, have better forage quantity and quality compared to winter pastures which have

never been fertilized (Swain et al., 2006). Secondly, winter pastures are less intensively drained

and as a result they are regularly flooded during the rainy season in summer. Rotation to winter

pastures provides time for the summer pastures to recuperate from active grazing.

This study was conducted over three years from 2001 to 2003 at BIR. Since the main

obj ective of this chapter is to quantify the time spent by cattle near water locations only the

physical description regarding ditches and wetlands is presented here. Detailed description of the

presence of pasture and wetland vegetation species has already been reported by Swain et al.

(2006); whereas, soil information has been reported by Capece et al. (2006).

Summer Pastures

Summer pastures (Figure 3-2) consist of eight (S1-S8), approximately 20 ha fields (range =

19.0 to 22.1 ha) with bahiagrass (Paspalum notatum) as the dominant forage species. These

pastures are located on soil types that for the area are considered relatively well drained. Pastures

S1 and S8 serve as control fields and were not stocked. The drainage ditch network in these

pastures is comprised of two orders of ditches: deep ditches (0.6 m deep) that run north-south









and receive flow from feeder ditches (0.3 m deep) that run east-west approximately every 30 m.

In the stocked pastures, the average total length of the ditch network is 6175 m (range = 5793.5

to 6864.8 m) and the average area of wetlands is 0.90 ha (range = 0.20 to 1.57 ha). Water troughs

were located at the north end of all stocked pastures (Figure 3-2).

Winter Pastures

Winter pastures (Figure 3-3) also consist of eight fields (W1-W8), that are slightly larger,

averaging 32.2 ha (range = 30.3 to 34. 1 ha). These fields consist of mixture of forage species but

were predominantly bahiagrass, and located on soil types that are considered poorly drained for

the area. All fields, except W4 and W7 which served as controls, were stocked during the period

of this study. Similar to summer pastures, winter pastures have a ditch network; however, W8

consists of an additional order (0.9 m deep) of ditch. In the stocked winter pastures, the average

total length of the ditch network is 4437 m (range = 6618.2 to 253 5.6 m) and the average area of

wetlands is 3.28 ha (range = 1.58 to 5.66 ha). Runoff from summer and winter pastures drains in

a collection ditch and is then conveyed into the Harney Pond Canal which discharges directly

into Lake Okeechobee. A summary of individual pastures, ditches and wetlands is provided in

Table 3-1.

Hydrologic Data

As part of the ongoing water quality study (Capece et al., 2006), on-site climatological data

and groundwater elevation data are collected for both summer and winter fields. All

experimental pastures are bermed so that surface water runoff from each pasture exits through a

single trapezoidal flume. This study utilized climatological information (Table 3-2) in

conjunction with groundwater level data (Figure 3-4) to estimate antecedent soil moisture

conditions and consequently determine the presence and level of water in ditches and wetlands.











The following criteria were utilized to determine the presence of water:

* A water table depth of 0.3 0.6 m was deemed to inundate wetlands, shallow and deep
ditches

* A water table depth of 0.6 0.9 m was deemed to inundate wetlands, and deep ditches

* A water table depth of 0.9 1.2 m was deemed to inundate wetlands

* A water table depth of below 1.2 m was deemed to inundate no features

GPS Data

Cattle position data was monitored continuously using GPS collars (GPS_2200, Lotek

Wireless Inc., Newmarket, Ontario, Canada). These collars are relatively lightweight (950 gm)

and primarily designed for use on smaller animals such as cattle, deer, wolves and bears. The

manufacturer reports that with differential correction deployed, accuracies of position reading

consist of errors that are less than 5 m. For the purpose of this study, data were recorded every 15

min during a 5-day period in spring (March), summer (June), fall (late August), and winter

(November or December) of each year. These periods were selected to be representative of

environmental extremes or expected seasonal differences in forage quality and to fit in the

standard animal handling routine of the ranch. Data collected included: collar identification,

latitude, longitude, temperature and time. A summary of the quantity of GPS Data is provided in

Table 3-3.

Figure 3-5 shows typical GPS collar data on summer pasture 2 collected on June 11, 2001.

The GPS point data have been joined by a line to illustrate the cattle's sequential movement.

Figure 3-5 represents a small portion of the large data set that was collected over the entire study

period (27,924 Total GPS Location Fixes).









Data Analysis

To analyze the data collected from the GPS collars ArcView@ (ESRI, Redlands, CA)

package was utilized. The first step in the analysis was to ascertain that the movement pattern

was not random. To accomplish that, a Nearest Neighbor analysis was performed. Developed by

Clark and Evans (1954) for work in the field of botany, Nearest Neighbor method computes the

ratio (R) of distance between nearest points and distances that would be expected on the basis of

chance. A freely available software that is an add-on extension to ArcView@ Animal Movement

extension (Hooge and Eichenlaub, 1997) was utilized to perform this statistical technique.

For cattle location analysis, all the fix data (latitude, longitude format) was converted to

UTM Cartesian coordinates (NAD 83, Zone 17N) for analysis with other features. The buffer

distance that was utilized for the features were: wetland = 5 m, ditch = 2 m, water trough = 20 m

and shade = 5 m. The extensive network of the ditches is a unique feature in these pastures as

they occupy a considerable area of the pastures. The buffer distance was assumed to be 2m on

each side of the line coverage to represent the narrow nature (2 m width) of this shallow ditch

system. A more flexible (5 m) buffer was utilized for the wetlands to capture the presence of

cattle in the transitional (ecotone) areas of the wetlands which can be wet or dry depending upon

moisture conditions. Cattle do not spend much time in actually drinking water (Wagon, 1963).

Therefore, to capture their presence near the trough the buffer for water trough was set to be at

20 m. Shade structures (5m by Sm) were present at the north end of all stocked summer pastures.

Apart from the shade structures and few patchy trees in SP5, there was complete absence of

natural shade. Winter pastures did not contain any shade structures as most of them had natural

shade from trees. The buffer used for shade structures was 5 m.

The data points that existed within the buffer zone were compared with total data points for

a day (typically 96). Consequently the data were converted into a percentage of time for a given










day. In addition, temporal dynamics with regards to the utilization of water features were

identified by categorizing hours of the day into 4 time zones: (a) Early Morning (12:00 am to

6:00 am) (b) Late Morning (6:00 am to 12:00 pm) (c) Afternoon (12:00pm to 6:00 pm) and (d)

Night (6:00 pm to 12:00 pm). Animal Movement extension was further utilized to compute total

distance traveled by collared cattle and a minimum convex polygon (MCP) home range.

Statistical analysis for comparison of mean percentage of time was performed using JMP

Statistical Software (SAS Institute, Inc., 2005). Tukey-Kramer' s Honest Significant Difference

(Tukey's HSD) test was performed. An alpha level of 0.05 was accepted as a nominal level of

significance and results were considered statistically significant when a P < 0.05 was obtained.

Results and Discussion

The test of nearest neighbor analysis for complete spatial randomness was performed for

all data. Values close to R = 1.0 indicate that the observed average distance is the same as the

mean random distance, suggesting that the spread of data is random. However, R values < 1.0

imply that the observed distance is smaller than the mean random distance, suggesting that data

is clustered. The average R value during summer was 0.51 (range = 0.80 to 0.13) and the average

winter R value was 0.47 (range = 0.74 to 0. 11), suggesting that the data was non-random and the

GPS fixes displayed more clustering during winter than summer months.

Climatological information (Table 3-2) and groundwater level data (Figure 3-4) were

utilized to make hydrologic judgment regarding the presence and level of water in ditches and

wetlands. This information was especially useful when making judgment regarding presence of

water in shallow or deep ditches. Table 3-4 summarizes the estimated presence and location of

water during different time periods of the study.









Since temperatures in summer and fall are often similar (Table 3-2) these two seasons were

grouped into one category of warm period. Accordingly, spring and winter was combined into a

cool period category. Average percentage of daily time spent by cattle near/in all possible water

locations (water trough, wetland and ditch) was relatively low (<15% of 24-hr period) compared

to the remainder of the pasture area, but was higher (P<0.01) during the warm than the cool

period (11.45 & 0.39%[(mean a s.e.; n = 215] vs. 6.09 & 0.69% [mean a s.e.; n = 160],

respectively). Statistical analysis was performed to compare means of percent utilization of

individual water features in different Seasons and also all water features within the same Season.

For example, wetland use was statistically the same and lower in all seasons except warm 2003

as indicated by the lowercase "b". In warm 2001 the use of all the three different water features

was not statistically different as indicated by the uppercase "A".

Wetland and ditches had similar, higher cattle presence compared to troughs (4.4110.35

and 5.2910.38 % vs. 1.9710. 18%, respectively) across periods (Table 3-5 and Figure 3-6). This

was not unexpected because wetlands and ditches buffer areas (approx. 20% of average pasture

area) was much larger than the buffer area of water troughs which were essentially a single point.

Utilization of the water sources differed within periods. This difference was mainly due to a

lower than average utilization of water sources in warm 2001. Unlike what was found in the

other years, utilization in warm 2001 was similar to what was found in cool 2001-02. Across

periods and years, cattle utilization of the different water features remained fairly consistent with

the exception of the ditch feature which showed higher use in warm periods and lower use in

cool periods for all years except 2001. This may have been related to the drier conditions that

occurred during the summer sampling period that year (Table 3-4). During the summer sampling

period in 2001, all ditch classes and wetland areas were dry. This observation supports the









hypothesis that cattle utilize water in ditch features for cooling in addition to possibly for

drinking or feed sources.

Water trough use was consistently higher in the warm periods than cool periods (Table 3-

5). Since troughs could only be used for drinking water, this observation supports what has been

observed in other studies (Goodwin and Miner, 1996; Kelly et al., 1955; Miner et al., 1992;

Sheffield et al., 1997) that cattle will preferentially use alternate and clean sources of water for

drinking. In contrast, the use of water-filled wetlands was fairly consistent regardless of periods

and did not differ, with the exception of an almost doubling of average utilization of wetlands in

warm 2003 (8.25% + 2. 11). The high wetland utilization during warm 2003 can be explained by

a single cow' s strong affinity towards wetland. During the warm 2003 period, data were

collected only from five collared cows during the summer season (no data were collected in fall,

Table 3-3). Amongst the five cattle, one displayed very high affinity towards wetland and

ditches. Average percent of time spent by this specific cow in the wetlands was 24.95%, which is

substantially higher than any other collared cow in any period. This individual cow entered the

wetland every day (all 5 observed days) during 8am to 9am in the morning and remained in the

wetland until 5pm to 6pm. Even if environmental factors are similar, differences in individual

cattle behavior have been previously reported as well (Bailey et al., 2004). If the data from this

individual cow are excluded, the average time spent in wetlands for the period of warm 2003

becomes 4.08%, which is similar to percent utilization in other periods.

There were two periods (spring 2002 and winter 2002) when cattle were stocked in

summer pastures instead of winter (Table 3-3). This occurred because of prescribed burning of

the winter pastures during spring 2002 and accidental burning during winter 2002. The rotation

of cattle due to fire events did allow the determination of whether cattle proximity to water









location was influenced by differences in summer vs. winter pastures (size, average depth of

water, forage differences, etc.) or driven by temperature. Spring 2002 and winter 2002

experienced 5.46% and 5.09% utilization of all water features respectively. This result was

consistent with cool season utilization of water features by cattle and demonstrated that water

usage in pastures was independent of pasture composition and forage quality.

The amount of time cattle spent near each water feature during a 24-h period was

investigated to identify any temporal dynamics associated with the use of these features. Water

troughs were generally not utilized during early mornings and night time regardless of periods

(Table 3-6). As expected, water trough usage was highest during afternoon times of all periods

with the exception of warm 2001. In warm 2001 cattle utilized the trough more during late

mornings than the afternoon. Warm 2001 had the highest maximum daily temperatures of the

whole trial (37.50C, table 2), and it has been acknowledged that increased water consumption is a

major response to thermal stress (Johnson and Yeck., 1964; McDowell, 1972). Drinking water

may have a direct comforting effect by cooling the reticulum as well as by reducing the thermal

load (Beede and Collier, 1986). Hence, it is possible that in periods of hot conditions such as

Warm 2001 the cattle utilized the trough earlier to mitigate their thermal stress. Data from late

morning as well as afternoon of remaining periods reveals that there was always higher presence

of cattle at the water troughs during warm periods as compared to cool periods. This observation

is in agreement with a previous study in which it was observed that in hot climates most water is

consumed by cattle during two 4-h periods: 7 a.m. to 11 a.m. and 4 p.m. to 8 p.m., which were

also the times when cattle grazed (Ittner et al., 1951). In early studies various researchers had

established that water intake of cattle is a function of forage consumption and ambient

temperature (Leitch and Thompson, 1944; Ritzman and Benedict, 1924; Winchester and Morris,










1956). Hence, during warm periods cattle utilized water troughs more during their two grazing

bouts.

Unlike cattle' s utilization of water troughs, the presence of cattle in wetlands appeared to

be similar across cool and warm periods but was variably distributed across times within periods

(Table 3-7). Wetland utilization was consistently lowest (0.015+ 0.04, P<0.05) in the early

morning hours and highest (1.5910. 18, P<0.05) in the afternoon hours regardless of period. Late

morning and night presence in wetlands was similar and intermediate to the other two times of

day, although there is a suggestion that period of year influenced the time of the day the cattle

started utilizing wetlands. Cattle presence was not recorded during late mornings in the two of

the warm periods; whereas, the data showed consistent utilization of wetlands during the same

time in the cold periods. The extraordinary use of wetlands during warm 2003 has been

explained in the previous section by the exorbitant use of wetland by one cow. As late morning

is a time when grazing activity normally occurs in Florida (Bowers et al., 1995; Chase et al.,

1999; Hammond and Olson, 1994), this data suggests that cattle were using wetlands for grazing

during the cool period but not during the warm period. Additionally, presence of cattle in

wetlands during warm period afternoon hours, when grazing does not normally occur (Bowers et

al., 1995; Chase et al., 1999; Hammond and Olson, 1994), also suggests that wetlands were used

for cooling and not grazing during the summer period. Since wetlands are expected to be the

deepest water containing feature in the landscape, it is reasonable to expect they would be used

for cooling. In contrast, since it is unlikely that cattle would not need to cool themselves during

the cool period, presence during the afternoon period in the cool season probably represents a

continuation of the morning grazing bout into the afternoon period due to lower forage

availability due to slower forage growth.









Unlike wetland presence, cattle' s presence in the ditches during all times of the day

exhibited a fairly consistent pattern of being higher during the warm periods and lower in the

cool periods (Table 3-8). The exception to this pattern was early and late mornings of the 2001

warm period, when cattle presence was similar during warm and cool periods. Cattle can utilize

the ditches for water as well as for higher quality of forage along the periphery of the ditches.

Generally lower presence of cattle in ditches during the cool period may reflect differences in

growth patterns of the forage species found in the ditches. Bahiagrass and bermudagrass were

the dominate forage species in the ditch areas and as warm season grasses, their growth rate

would be lower in the cool periods of the year. Lower growth rate and hence less forage

availability of these grasses in the cool season would explain both, lower cattle presence in the

ditches and higher cattle presence in the wetland areas, which contained more native forage

species. Unlike wetlands, though, there was no consistent pattern for time of day within warm or

cool periods. This suggests that cattle presence may not have been related to forage availability

or the need to regulate body temperature, and may simply reflect an artifact of pasture design

that necessitated a lot of ditches.

Partial data were used to analyze the utilization of shade structures in summer pastures.

The results are presented in a box plot format in Figure 3-7. Error bars represent standard

deviations. Summer 2001 was the driest season, wetlands and ditches are assumed to have no

water presence and hence highest use of shade during this season is expected. However, results

indicate that cattle did not use shade in summer 2002 and nominally in fall 2002. It is noteworthy

that this analysis was conducted using only partial data. Only two collared cows result was used

for shade analysis for summer 2002. The error bars in Figure 3-7 illustrate the high variability in

the use of shade. As mentioned before, it is possible that these two cows are not representative










of the herd behavior. Relatively low use during Fall 2002 could be attributed to high rainfall

during the five monitored days in this season.

Using the animal movement extension, two home range analyses were performed on the

entire data set. The first one was total distance traveled and the second one was Minimum

Convex Polygon (MCP). Total distance traveled is the sum of the length of polylines generated

by joining GPS location fixes. MCP is the smallest (convex) polygon which contains all points

which the cattle has visited. It should be kept in mind that the MCP will also contain a lot of

empty space that the animal never visited. Figure 3-8 shows a typical MCP area in SP2 during

the summer season of 2001. Both these analyses can be used in conjunction to get an

understanding of the area covered and effort made by grazing cattle. Seasonal means of these

two analyses is presented in Table 3-9. A seasonal pattern is evident in distance traveled by

cattle. Cattle traveled more during cool seasons and less during warm seasons. In terms of MCP

area covered by grazing cattle, both cool seasons were higher than warm seasons; however, Cool

2002-03 was the only season that was statistically higher than remaining seasons. Since forage

growth is slower in the cool season, it is likely that cattle have to travel greater distances to look

for palatable forage to meet their intake requirements and in doing so, they browse a greater

pasture area as well.

Conclusion

Beef cattle can utilize the water sources in south Florida to graze, to drink water, and to

keep cool. During these activities, urination and defecation can occur which can result in direct

contamination of watered locations. If BMPs are needed to minimize the impact of beef cattle

production on water bodies in south Florida, a better understanding of beef cattle utilization of

natural (wetland) and artificial (ditches and water trough) water sources is necessary. To quantify

the amount of time spent by grazing cattle near or in water locations GPS collars were used. The









GPS collars were successful in identifying, quantifying and eventually deriving pertinent

information regarding cattle utilization of water sources. Climatological information was used in

conjunction with observed groundwater level data to make hydrologic judgment regarding the

presence and level of water in ditches and wetlands.

The data illustrated that there was higher presence of cattle near water locations during

warm periods than in cool periods (11.45 f 0.39% vs. 6.09 f 0.69%). On a daily basis, cattle

utilization of all water sources (as determined by % time present) was relatively low (<15% in a

24-hr period). Cattle seemed to utilize water troughs in a fairly consistent manner, going to water

troughs earlier (late morning) and staying in the area longer during warm periods, compared to

cool periods when they went later (afternoon) in the day and for shorter periods of time. The

presence of cattle in the wetlands was generally well distributed across all periods as well as all

times (approx. 4% in a 24-hr period). Unlike water trough utilization, cattle utilized wetlands

considerably in the cool periods as well. This suggests that wetlands in Florida are used for

different purposes at different times of the year. During the cool periods, cattle were present in

wetlands when grazing would be expected to occur (late morning), indicating the need for feed

was the driving factor. In contrast, during the warm periods, cattle were present when grazing

was not an expected occurrence (afternoon), suggesting that cooling was the reason the cattle

were in the wetlands. Unlike wetlands, presence of cattle in ditches was generally higher in the

warm periods than the cool periods; though there was no consistent pattern for time of day within

warm or cool periods. This suggests that cattle presence in ditch areas may not have been

related to forage availability or the need to regulate body temperature, and simply reflect an

artifact of pasture design.









Another important factor this study identified was that there can be substantial variability

in individual cow behavior. This was recognized by an exceptionally high presence of cattle in

wetlands during the 2003 warm period, which was due to one individual's affinity towards

wetland. It is perceived that during this period this cow utilized wetland not only to drink water

but to cool itself by staying in water for extended hours. It is suggested that future studies deploy

multiple GPS collars on cattle to account for variability in the population distributions. Shade,

total distance traveled and MCP area also indicate seasonal utilization and browsing patterns in

grazmng.

The result findings may be useful from a ranch management perspective. Knowledge

regarding cattle' s preference of water location will be useful in developing a comprehensive

understanding of the pasture utilization. Information from this study is not comprehensive

enough to design appropriate management strategies to achieve targeted P load reductions.

Nevertheless, this study does provide useful information regarding cattle utilization of water

features in sub-tropical-humid pastoral environments of south Florida. From BMP

implementation perspective, information from this study can be utilized in conjunction with

other studies to suggest pertinent structural or managerial BMPs for this region. However, the

installation or use of one structural or management BMP will rarely be sufficient to solve the P

loading problem. Combinations of BMPs (BMP System) that control the same pollutant are

generally more effective than individual BMPs (Gilliam et al., 1997). In order for the BMPs to

be successful in the unique settings of subtropical agro-ecosystems of south Florida, they should

be strategically tailored to be site specific, effective, and cost efficient.




























Figure 3-1. Location of Buck Island Ranch and the experimental pastures.


SUMMER PASTURES


Figure 3-2. Map displaying wetlands, ditches and water troughs in summer pastures.



































Figure 3-3. Map displaying wetlands, ditches and water troughs in winter pastures.


GROUNDWATER ELEVATION
SUMMER PASTURE 3
MRain (cm) Ground Elevation -Ground Water Level








15 12






Figr 3-4 Exml ofrifl n rudae ee aain ume ate3


WINTER PASTURES


0 260 620 1.040 Melrs
I 1 1 1 t I r rI


Dit













--O


-I I / END





















O Early Morning (12am 6am)
O Late Morning (6am 12am)
O Afternoon (12am -6pm)

ANight (6pm 12pm)
Water Trough
Wetland
-Ditch
Fence

o 0 50 100 2 10 Meters
SI I I I I I I I I





Figure 3-5. Typical cattle movement in summer pasture 2 on June 11, 2001



















on Time Spent








Seasons

Figure 3-6. Average % time spent near water locations.


SHADE UTILIZATION


30


5 -






Figure 3-7.


SUMMER 2001 FALL 2001 SPRING 2002 SLIMMER 2002 FALL 2002 WINTER 2002 SLIMMEP 2003
Periods

Average % time spent in shade structures.










-0O


N


GPS Location on 31 June 2001
V~hter Trough
SMCP Area

SWetland
SFence

S0 50 100 2( 0 Meters
SI I I l I I I


Figure 3-8. Typical MCP area in summer pasture 2 on June 31, 2001










Table 3-1. Percent area (ha) of wetlands and ditches in summer and winter pastures.


Ditch
Length
(m)
4511.65
5878.47
6382.36
5598.73
6864.82
6202.32
5893.76
3463.35


2535.59
2843.65
4118.76
5243.47
4848.63
5656.41
6217.83
6618.18


Wetland
Area
(ha)
4.52
1.57
1.20
1.33
0.20
0.48
0.67
2.95


5.66
2.46
3.80
0.90
3.35
1.58
1.86
2.85


% Area
of
Wetland
20.51
8.26
5.88
6.49
0.95
2.46
3.49
14.53


17.03
7.86
11.30
2.64
10.37
4.93
6.15
9.42


% Area of
Buffered
Wetland **

11.47
8.47
8.83
1.53
3.49
5.15




21.28
10.38
14.48

13.59
6.92

13.41


% Area of
Buffered
Ditch **

12.37
12.50
10.93
13.11
12.73
12.27




3.05
3.63
4.90

6.00
7.05

8.75


Summer
Pastures

Sl*
S2
S3
S4
S5
S6
S7
S8*
Winter
Pastures
W1
W2
W3
W4*
W5
W6
W7*
W8


Area
(ha)
22.04
19.01
20.42
20.49
20.95
19.49
19.22
20.3


33.23
31.3
33.64
34.12
32.31
32.08
30.24
30.27


Amimal
Units

0
20
35
15
35
15
20
0


15
20
35
0
35
15
0
20


* Control Pastures (Not Stocked)
** Assumes a 5-m buffer around wetlands and a 2-m buffer around ditches


Table 3-2. Summary of climatological data during the study period.


Rainfall
Max During
Temp Study Peniod
(cm)
37.50 1.80
37.00 0.66
33.50 0.20
33.50 0.03
36.50 5.46
35.50 8.35


Av
Start Date End Date
Temp


Min
Temp

15.50
17.00
9.00
3.00
12.50
19.00
3.30
16.99
22.03


Season


Summer 2001
Fall 2001
Winter 2001
Spring_2002
Summer 2002
Fall 2002
Winter 2002
Spring_2003
Summer 2003


06/11/2001
08/27/2001
12/03/2001
03/04/2002
06/10/2002
08/26/2002
11/25/2002
03/03/2003
06/09/2003


06/15/2001
08/31/2001
12/07/2001
03/08/2002
06/14/2002
08/30/2002
11/29/2002
03/07/2003
06/13/2003


25.36
26.26
20.25
15.49
25.08
24.56
15.78
23.25
26.58


26.51
31.44
32.85


0.25










Table 3-3. Summary of GPS collar data in the experimental pastures. The number before the
parenthesis is the number of collars used within a pasture and number within
parenthesis is the average daily fixes during 5 day collection period in each season.
Summer Fall Spring Summer Fall Winter Summer
Summer Pastures 2001 2001 2002 2002 2002 2002 2003


Sl*
S2
S3
S4
S5
S6
S7
S8*

Winter Pastures
W1
W2
W3
W4*
W5
W6
W7*


4 (92)
4 (91)
4 (91)
2 (94)
3 (91)
3 (92)


Winter
2001
3 (95)
2 (96)
2 (96)

3 (95)
2 (73)


2 (87)
1 (95) 3 (85)
4 (91)
2 (94) 3 (95)
1 (95) 1 (96)
3 (84)


1 (95) 1 (96)
2 (95) 1 (96)
1 (93) 1 (89)
1 (81)
1 (96)
1 (96) 2 (84)


2 (96)
1 (96)
1 (94)

1 (94)


1 (96)
1 (95)
1 (96)


Spring
2002




2 (95)


1 (96) 1 (95)


Table 3-4. Locations that are assumed to have presence of water (water trough always contained
water) .


Water
Presence

W,DD
W,DD
W
W
W,D
W,DD
W,DD
W,D


Season

Summer
Fall
Winter
Spring
Summer


Start Date End Date
06/11/2001 6/15/2001


08/27/2001
12/03/2001
03/04/2002
06/10/2002


8/31/2001
12/7/2001
3/8/2002
6/14/2002


08/26/2002 8/30/2002


Winter
Spring
Summer
(W = Wetland, D


11/25/2002
03/03/2003
06/09/2003


11/29/2002
3/7/2003
6/13/2003


Both Shallow and Deep ditch, DD = Deep Ditches only)













SEASON* WATER TROUGH WETLAND DITCH j Season Mean
Warm 2001 2.7910.34; n = 130 a A 4.1210.98; n = 35 b A 3.8310.76; n= 35 b A 7.9010.68 c
Cool 2001-02 0.6410.15; n = 130 b C 3.4910.32; n = 130 b A 2.4210.34; n= 65 bB 5.5110.42 c
Warm 2002 3.6610.70; n = 60 aB 3.1310.42; n = 60 b B 9.9311.04; n = 35 aA 13.1611.16 b
Cool 2002-03 0.2410.09; n = 30 b B 4.0410.69; n= 30 b A 4.3310.55; n= 30 b A 8.6110.90 c
Warm 2003 2.6710.86; n = 25 a, b B 8.2512.11; n = 25 aA 9.4411.09; n = 25 a A 20.5912.19 a
Feature Mean 1.9710.18 B 4.4110.35 A 5.2910.38 A


Table 3-5. Mean percentage of daily time spent by cattle near water locations (mean & std error;
n = days).


* Warm = Summer + Fall; Cool = Winter + Spring
a b c: means within columns sharing a common letter are not significantly different (P>0.05)
A B C: means within rows sharing a common letter are not significantly different (P>0.05)


Table 3-6. Mean percentage of daily time spent by cattle near water trough (mean std Error).
Water Trough
Season Early Momning Late Momning Aftemnoon Night
(12am 6am) (6am 12pm) (12pm 6pm) (6pm 12pm)
Warm 2001 0.0010.00 b C 1.8110.25 aA 0.9510.15 b, c B 0.0110.01 aC
Cool 2001-02 0.1010.03 aA 0. 1110.04 b A 0.3210.11 cA 0. 11 0.04 aA
Warm 2002 0.0010.00 a, b B 1.4810.34 aA 2.1710.44 a A 0.00 0.00 a B
Cool 2002-03 0.0010.00 a, b A 0.1010.05 b A 0. 1310.08 c A 0.00 0.00 aA

Time Mean 0.0310.01 B 0.9710.11 A 0.9310.11 A 0.0410.01 B
a b c: means within columns sharing a common letter are not significantly different (P>0.05)
A B C: means within rows sharing a common letter are not significantly different (P>0.05)


Table 3-7. Mean percentage of daily time spent by cattle in wetland (mean a std Error).
Wetland
Season Early Momning Late Momning Aftemnoon Night
(12am 6am) (6am 12pm) (12pm 6pm) (6pm 12pm)
Warm 2001 0.0310.03 a B 0.0010.00 b B 1.6510.67 b A 0.63 f 0.17 a A, B
Cool 2001-02 0.1510.06 a B 1.0810.16 a A 1.0710. 14 b A 0.73 f 0.11 a A
Warm 2002 0.2410.13 a B 0.0010.00 b B 1.3610.26 b A 0.3210.09 a B
Cool 2002-03 0.0010.00 a B 1.3010.33 a A 1.4710.38 b A 0.8710.41 a A, B

Time Mean 0.1510.04 C 0.8110.11 B 1.5910. 18 A 0.6110.07 B
a b c: means within columns sharing a common letter are not significantly different (P>0.05)
A B C: means within rows sharing a common letter are not significantly different (P>0.05)










Table 3-8. Mean percentage of daily time spent by cattle in ditch (mean a std Error).
Ditch


Early Morning
Season
(12am 6am)


0. 1710.09 b C
0.2510.07 b B
2.2310.41 aA
0.5610.18 bB

1.0110.13 B


Late Morning Afternoon Night
(6am 12pm) (12pm 6pm) (6pm 12pm)
0.3310.17 c B, C 1.7610.58 a, b A 1.5510.30 a, b, c A, B
0.6710.13 c A, B 0.9310.14 bA 0.5610.18 c A, B
2.1810.35 a,bA 2.8610.38 aA 2.6510.44 aA
1.2810.27 b, c A, B 1.6110.31 a, b A 0.8710.20 b, c A, B

1.2110.12 A, B 1.6910.15 A 1.3710.13 A, B


Warm 2001
Cool 2001-02
Warm 2002
Cool 2002-03

Time Mean


a b c: means within columns sharing a common letter are not significantly different (P>0.05)
A B C: means within rows sharing a common letter are not significantly different (P>0.05)


Table 3-9. Mean daily distance traveled and mean daily MCP area by cattle (mean a std Error).
Distance Traveled MCP Area
Season
(meters) (acres)
Warm 2001 3179.17157.64 b 13.6910.22 b


Cool 2001-02 3994.68192.33 a


14.1510.49 b

13.8510.35 b

17.671.04a

13.3612.58 b


Warm 2002


3193.04182.16 b


Cool 2002-03 4331.051237.77 a


Warm 2003


2980.49198.58 b


a b c: means within columns sharing a common letter are not significantly different (P>0.05)









CHAPTER 4
DEVELOPMENT OF CATTLE MOVEMENT ALGORITHMS FOR ACRU2000

Habitat Suitability Index (HSI)

Modelers develop and use HSI models for land-use management plans because they are

simple to use and the outputs are generally in form of GIS-based maps, which are easy to

understand. These models are also preferred because they may be applied in an efficient manner

and are relatively inexpensive to operate (Schamberger and O'Neil, 1986). The first step in

developing HSI is to identify habitat variables. The second step is to develop suitability index

functions for each individual habitat variable. The final step is to combine these functions into an

equation for the HSI. In HSI modeling, animals get distributed in proportion to the habitat

suitability. More detail about HSI modeling methodology and some applications have been

discussed in Chapter 2.

Model Design for Cattle Distribution in ACRU2000

Limited information is available regarding cattle's preference in grazing systems of south

eastern USA. It has been elucidated in Chapter 2 that plentiful grazing studies have been

conducted in the western and mid-western USA; however, differences between the arid west and

the southern humid region prohibit the universal transfer of research results. The land is very flat

and the climate is warm and humid in the south Florida for most of the year. This is in contrast to

western regions where land is hilly and the temperatures are dry and extreme. The use of water

features is existent (Chapter 3); however, their utilization may be for different reasons.

Controlled as well as uncontrolled ranges in south Florida consist of abundant wetlands.

Therefore, accessibility to water is not a limiting condition, which may be the case in the west.

Hence, models developed elsewhere cannot be applied to the unique agro-ecosystems of the









south-east. Therefore, functions for individual habitat variable in a HSI model must be defined to

represent the distribution of cattle in ecosystems of south Florida.

Suitability Index for Cattle Distribution

The first step in the process of defining habitat suitability functions is to identify the

variables that would affect the distribution of cattle in a paddock system. Shade and water

features are the obvious attractants that dictate the distribution of cattle; hence they have been

included as variables for HSI computation. Water features such as wetlands and ponds may be

attractive for different reasons in different seasons. Depending on the presence or absence of

water, cattle may display a difference in the utilization of a wetland or pond. A dry wetland may

not be an attractive feature for hot or thirsty cattle; however, it may be luring for hungry cattle

that may graze in it for better quality of forage. Hence a dynamic suitability index is required for

features that may become devoid of standing water during dry periods and thereby changing their

attractiveness for grazing cows. The current hydrologic module in ACRU2000 simulates the

depth of water table for each land segment. The methodology used in Chapter 3, which

determines the presence of standing water in wetlands, is used here as well. Water table depth of

less than 0.6 m from the ground surface is deemed to have standing water in the wetlands and

ponds. Hence, the HSI for water is optimum (1.0) when the water table depth is less than 0.6 m

(2 ft). On the other hand, water table depth of more than 1.21 m (4 ft) from the ground surface is

deemed not to inundate wetlands and ponds. Therefore, HSI for water is minimum (0.0) when

the water table depth is more than 1.21 m. Thus, a dry day (i.e. when water table falls below 1.21

m) will have a different suitability index for a land segment with wetland than a wet day; thereby

making it very dynamic. The suitability index values for a water feature with respect water table

depth in between 0.6 m and 1.21 m is a linear relationship and is are illustrated in Figure 4-1 and

as:









HSI, (WT, ) = for WTDEP, > 1.21 (4-la)

HSI, (WT, ) = 1.67 -1.63 x WTDEP, for 0.60 < WTDEP, <1.21 (4-1b)

HSI, (WT, ) =1 for WTDEP, < 0.60 (4-1c)

where WTDEPr is the water table depth on day t; and HSIt(WTt) is the HSI of water feature on

day t.

Under extended warm humid conditions of southeastern USA when the ambient

temperature approaches or exceeds cattle's body temperature, the cattle will seek shade to cool

themselves. In a study conducted during summer in Louisiana, McDaniel and Roark (1956)

found that shade, either artificial or natural, increased the gains of cows and their calves. The

area of the shade will depend on the size of the herd. In an experimental study Clarke (1993)

tested the effects of shade on behavior, rectal temperature, and live weight gain. It was found that

2.5 m2 Shade/cow reduced rectal temperatures in both, zebu-cross steers and in Hereford steers.

Buffington et al. (1983) recommended at least 4.2 m2 Shade/cow but also agreed with Bond et al.

(1958) that 5.6 m2 Shade/cow was desirable. Alternately, Hahn (1985) only suggested 1.8-2.5 m2

shade/cow was required. For southeastern climatic conditions, a shaded area of 50 m2 was

considered representative of the agro-ecosystems of south Florida. Some isolated trees may also

attract a few cattle; however, a shade area of less than 30 m2 will be less attractive, and may

cause crowding (Buffington et al., 1983). Hence, the suitability index values linearly increase

from 30 m2 to 50 m2 (Figure 4-2) and as:

HSI, (SH) = 0 for SA < 0.0 (4-2a)

HSI, (SH) = SA x 0.02 for 0.0 < SA < 50.0 (4-2b)

HSI, (SH) =1 for SA > 50.0 (4-2c)









where SA is the shade area (m2); and HSIt(SH) is the HSI of shade on day t. Shade area is an

input from the user, and will remain constant throughout the simulation.

Herbivores eat to satisfy their need and desire for nutrients, the most prominent being

energy and protein (NRC, 1996; 2001). The mechanism via which herbivores satisfy their energy

and protein requirement is through consumption of available forage. The current version of

ACRU2000 is set up to simulate three functional forage groups: bahiagrass, floralta, and

panicum. These vegetation species represent functional groups that correspond to vegetation

found in uplands, transition zones and wetlands, respectively (Yang, 2006). The forage is also an

important factor that dictates herbivores movement and distribution. The HSI of forage

consumption (Figure 4-3) is based upon data published by Rayburn (1986), who summarized a

group of experiments and developed a more general relationship of relative intake (a proportion

of maximum or potential intake) to herbage mass. The forage suitability index is represented in

the model as:

HSI, (F ) = 0 for Wa,2,, < 150 (4-3 a)

HSI, (F~, ,)= Wa,~,, x 0.00066 +0. 1 for 150 < Wa,2,, < 1350 (4-3b)

HSI, (F,~,) = 1 for Wa,~,, > 13 50.0 (4-3 c)

where Wa,i~t is the aboveground biomass of species i on day t (Kg/ha); and HSIt(Ft) is the HSI of

forage on day t.

Preference Estimation Using Analytical Hierarchy Process

After development of a suite of suitability indices that are deemed influential in

herbivore's spatial location preference, the next step is to determine the relative importance of

parameters with one another. In case of limited literature availability a good strategy is to utilize

the technique of decision analysis to quantify the preferences of one variable over the other.










Analytical Hierarchy Process (AHP) is a mathematical tool within the field of multi-

criteria decision analysis that allows consideration of both qualitative and quantitative aspects of

decisions. AHP is especially suitable for complex decisions which involve the comparison of

decision elements which are difficult to quantify (Saaty, 1980). It is based on the assumption that

when faced with a complex decision the natural human reaction is to cluster the decision

elements according to their common characteristics. The AHP methodology involves building a

hierarchy (Ranking) of decision elements and then making comparisons between each possible

pair in each cluster (as a matrix). These pair-wise comparisons provide a weighting for each

element within a cluster (or level of the hierarchy) and also a consistency ratio (useful for

checking the consistency of the user-defined weights). This process requires the user to make

direct comparisons of the relative importance of the alternatives on the measure. In case of

herbivore distribution model there are three alternatives (Shade, Water, and Forage) that need to

be compared and evaluated. Within forage there are three types of forages: bahiagrass, panicum,

and floralta. Also, since the distribution is dominated by two seasons, two sets of preferences

need to be developed for all the alternatives. Logical Decisions@ for Windows (LDW) is a

decision analysis tool that helps define alternatives and variables (Logical Decisions, 2005).

Within LDW, AHP technique is available. To use this technique the user needs to pair-wise

compare two variables as part of the assessment process. The user enters the weight ratios for

each possible pair of variables in a matrix. This ratio describes the ratio of importance of a

variable as compared to the other. Within LDW there is also an option of printing the preference

assessment in a questionnaire format. This lets the user obtain a hard copy of the preference

assessment questions) being posed by LDW. The questionnaire asks the user to identify the

importance of one feature with respect to the other (e.g., Forage vs. Shade) on a scale of 1-9.









This is a useful feature of LDW where the questionnaire can be distributed to the participants in

the study who may not be readily available for direct questioning. For this research we utilized

this feature and distributed the questionnaire to many researchers, ranch managers, and extension

agents (Appendix B). This allowed us to acquire and incorporate a broad spectrum of expertise

and opinion into the herbivore distribution model. Once this information is entered into LDW

(Figure 4-4), it uses the AHP computation process to compute set of weights for the variables

(Appendix C). Model simulation results using weighting factors from the survey are shown in

Appendix D.

The model for herbivore distribution is given in equation 4-4:

HSI = [ (Shade WF Made IIIHSI) + (WaterWF WaterHSI) + {Forage WF ForageHSI) ] (4-4)

where ShadeWF and ShadeHSI are the weighting factor and habitat suitability index for shade,

WaterWF and WaterHSI are the weighting factor and habitat suitability index for water, and

ForageWF and ForageHSI are the weighting factor and habitat suitability index for forage. Since

ACRU2000 is capable of simulating multiple vegetation species, the forage factor in equation 4-

1 can be further expanded to include desired number of vegetation species (Equation 4-5).

(Veg, WF Veg, HSI) +(Veg,WF" eg,HSI)+.... .(Veg,,WF eg,,HSI) (4-5)

where VegWF and VegHSI are the weighting factor and habitat suitability index for the specific

forage species.

Index for Heat Stress and Seasonal Distribution

Summer heat stress has long been recognized as a factor that reduces both, the productivity

and reproductive efficiency of cattle in the Southeastern regions of the USA (Jordan, 2003). In

grazing systems cattle are exposed to varying amounts of solar radiation. This radiant energy can

come directly from the sun or indirectly from the immediate surroundings. During summer, this









radiant load may exceed metabolic heat production of cattle significantly. To cope with the hot

environment, cattle will strategize their behavior and physiology to relieve the total heat burden.

Cattle will seek shade, increase water intake and orient themselves away from direct sunlight.

These behavioral changes increase the tissue conductance to facilitate heat transfer from the

body core to the skin and eventually away from the skin by convection and radiation. There will

be increased sweating to increase evaporative loss as well as increased respiratory volume

(heavy breathing) (Blackshaw and Blackshaw, 1994). Cattle will reduce feed intake as an

immediate response to heat stress (Blackshaw and Blackshaw, 1994) and expenditure of energy

to maintain homeothermy (NRC, 1981). Heat stress is caused by environmental factors such as

air temperature, radiation, humidity, and wind velocity (Gwazdauskas, 1985). Over the years

researchers have created indexes that relate specific environmental characteristics to the

physiological variables of heart rate, respiratory rate and volume, sweating rate, and body

temperature (Blackshaw and Blackshaw, 1994). The two environmental parameters that have

been popularly used have been dry-bulb temperature and humidity. In a research conducted

during the summers of 1975-78 at the University of Florida' s Dairy Research Unit near Hague,

FL, Buffington et al. (1981) established that dry-bulb temperature and dewpoint temperature was

directly related to rectal temperature and respiration rate and inversely related to milk

production. They established that Black Globe-Humidity Index (BGHI) is a comfort index that is

based on the combined effects of dry-bulb temperature, humidity, net radiation, and air

movement, as can be seen in equation 4-6:

BGHI = tbg + 0.36.t, + 41.5 (4-6)

where: tbg = black globe temperature (oC) and tdp = dew point temperature (oC) calculated from

wet bulb temperature. When the BGHI is 75 or higher, milk yield and feed intake are seriously










depressed (Buffington et al., 1981). The black globe temperature, (tbg), iS measured by the black

globe thermometer, which usually consists of a 150 mm (6 inch) black globe with a thermometer

located at the centre. Since black globe temperature measurements are not available for BIR,

another index can be used which is based on relative humidity. Thom (1958) suggested a

temperature and relative humidity index (THI) to evaluate a cow's heat stress as:

THI = td (0.55(1 RH /100)(td 58) } (4-7)

where td = dry bulb temperature (oF) and RH = relative humidity (%).The heat stress is defined

as occurring whenever the THI exceeds 72 (Armstrong, 1994, De Dios and Hahn, 1993; Hahn,

1982; Hahn and Mader, 1997; Igono et al., 1991). However, most of the research has been

conducted in midwestern states on dairy cows with reduction in milk production due to heat

stress as the main concern. The cattle in south Florida are mostly beef cattle. More specifically,

the cattle on BIR are Brahman-crossbred (Arthington et al., 2006). Numerous studies have

documented that Brahman cattle have better heat regulatory capacities than other breeds

(Blackshaw and Blackshaw, 1994). This physiological advantage has been attributed to higher

respiratory rate (Finch et al., 1982; Kibler and Brody, 1952), lower metabolic rate (Kibler and

Brody, 1954; Vercoe, 1970; Worstell and Brody, 1953), less water consumption at higher

temperatures (Winchester and Morris, 1956), and thinner-brighter hides (Allen et al., 1970;

Finch, 1985; Finch et al., 1984; Hutchinson and Brown, 1969; Yeates; 1954). Keeping the better

heat resistance of Brahman cows in mind, the critical level of THI was set to 75. On a given day

if the index reaches 75, the weighting factor of shade and water increases by 20% and 10%,

respectively. Consequently, the forage weighting factor decreases by 30%. This change in the

weighting factors of shade, water, and forage account for the change in behavior the cattle will

exhibit during days when they experience thermal stress.









Analysis of cattle movement data in Chapter 2 clearly indicates that cattle display a

difference in their behavior in the two seasons of Florida. To represent the difference in behavior

in the two seasons in Florida, two entire sets of weighting factors have been developed with the

abovementioned variables, one for warm season and the other for cool. The warm season is

defined as March to October and the remaining four months are defined as the cool season.

Integration of HSI Model into ACRU2000

It has been demonstrated that HSI is a relatively swift way to utilize information either

from literature or even from opinions of experts to develop a model. Therefore, a similar

approach has been used to distribute the cattle in a modeling system, ACRU2000.

The Agricultural Catchments Research Unit (ACRU) Modeling System

The ACRU agrohydrological modeling system was originally developed in the Department

of Agricultural Engineering (now the School of Bioresources Engineering and Environmental

Hydrology) at the University of Natal by Schulze (1995). The developers of ACRU model

describe it as a multi-purpose and multi-level integrated physical conceptual model that can

simulate streamflow, total evaporation, and land cover/management and abstraction impacts on

water resources at a daily time step (Figure 4-5). The ACRU program code was developed in the

FORTRAN 77 programming language. As the model got developed and modified by researchers

around the world, the existing programming language (FORTRAN) posed problems with regards

to its compatibility to accommodate these newer versions.

It is for this reason the model was restructured entirely with an obj ect oriented

programming language: Java and was named ACRU2000 (Kiker et al., 2006). The advent of

ACRU2000 made the model more compliant with spatial hydrological aspects and addition of

newer modules became unproblematic (Campbell et al., 2001; Kiker and Clark, 2001; Kiker et

al., 2006; Martinez, 2006; Yang, 2006). ACRU2000 can operate either as a lumped small









catchment model with relatively homogeneous soil and land cover attributes, or as a distributed

cell-type model where complex catchments are separated into sub-catchments or land segments

(Figure 4-6).

The nutrient module within ACRU2000 modeling system was incorporated by Campbell et

al. (2001) (ACRU-NP), which borrowed the concepts used in GLEAMS (Knisel and Davis,

1999; Leonard et al., 1987). The nutrient module added capability in ACRU2000 to 1) simulate

nitrogen (N) and phosphorus (P) losses in surface runoff, sediment transport, and leaching, 2)

simulate N and P cycling in the soil-water-plant-animal system, and 3) simulate N and P mass

balances in the watershed system. However, ACRU-NP module was incapable of simulating

multidirectional lateral nutrient transport between multiple land segments through either surface

or subsurface water movement. The lateral nutrient transport component in ACRU-NP was

mainly designed for transporting nutrients dissolved in runoff and adsorbed in sediments through

single outflow from one land segment. Consequently, Yang (2006) restructured ACRU-NP and

added new components to enable multi-directional spatial transport of N and P through surface

runoff and lateral groundwater flow. In addition, a new conservative solute transport component

was also added to rectify the process of nutrient extraction and adsorption in which the ratio for

partitioning the nutrients between the water and soil phases was a function of clay content. Yang

(2006) compared the performance of the new conservative solute transport algorithm from

PMPATH (Chiang and Kinzelbach, 2005), which is an advective transport model using

groundwater pore velocities from MODFLOW (McDonald and Harbaugh, 1998) using a

hypothetical scenario. The comparison revealed that both models produced qualitatively similar

results. In addition, the ability of the modified nutrient model to predict non-point source nutrient

pollution, at BIR was evaluated. It was concluded that the model performed reasonably well.









Yang (2006) also added significant framework to the ACRU2000 modeling system by

adding a new vegetation component to enable multi-directional spatial simulation of

hydrological, chemical, and biological processes simultaneously in a daily time step. The

vegetation model is a simple model that avoids overwhelming data requirements, but is still

capable of capturing the vegetation dynamics. The model is based on the land segment system

developed by Yang (2006), where each land segment is initialized with one or multiple species,

which compete for light, water and nutrients. For each time step, plant growth is driven by

climate variables including solar radiation and temperature. The basic processes in this model are

light interception, conversion of light into dry matter production and allocation of dry matter

between aboveground and belowground dry matter. The impacts from the changes in hydrology

and nutrient concentrations are expressed in growth limiting factors. Yang (2006) accounts for

two types of pressure on the vegetation: lack or excess of water and nutrient. These stress factors

are combined to define a growth reduction factor that is used in the model to reflect the adverse

growth conditions causing the reduction of the potential dry matter production.

In the model the potential growth rate (AWot~i~t [kg/m2/day]) for each species i on day t is

calculated through a linear function of the absorbed light and a mean radiation-use efficiency

parameter as shown in Equation (4-8).

AWpo,l,, = RUE, xlabs,l,t xF,,(Tt) (4-8)

where RUEi is the average radiation use efficiency of species i [kg/MJ(PAR)] and is a summary

variable for all processes dealing with photosynthesis and respiration. Iabs,i,t and Fi~t(Tt) are the

light interception and temperature factor for assimilation by species i on day t, respectively. The

plant growth rate may be limited by N or P deficiency, water shortage or water logging during

different parts of the growing season:










AWred,i,t = AWpot,i,t x RFi,t (4-9)

ANi,t = AWpot,i,t x RFi,t x CNi,t (4-10)

Al,t =Apot,i,t x RFi~t x CPi,t (-1

where AWred,i~t is the reduced dry matter production rate of species i [kg/m2/day]; ANi~t and APi~t

are the N and P uptake rates corresponding to AWred,i~t [kg/ha], respectively; CNi~t and CPi~t are

the biomass N and P percent, respectively; RFi~t is a growth reduction factor of species i, which

integrates the limiting factors from water, N and P. RFi~t, is a unit less, species-specific growth

reduction factor with a value ranging from 0 to 1, which is obtained by taking the minimum

value of water stress, water logging, and N and P stress factors as shown in the following

equation:

RFi,t = min(Fw,,i,t, FWL,i,t ,FN,i,t, FP,i,t) (4-12)

where Fws,i,t is the water stress factor for species i; FwL,i,t is the water logging factor for species i;

FN,i,t is the N stress factor; and FP,i,t is the P stress factor. Plant senescence is assumed to start

when the daily sum of leaf' areas (LAlsum = C LAi,t, [m2 leaf/m2 grOund]) of all species on one


land segment exceeds the critical leaf area (LAler [m2 leaf/m2 grOund]), an input to the model.

The daily total senesced biomass (Ws,its [kg/ha]) and the corresponding N (Ns,i~t [kg/ha]) and P

and (Ps,i,t [kg/ha]) removed through the senesced biomass for each species on that land segment

are calculated as:

LAI, LAI,
Ws,i,t = Fracbs su c ) /SLAi (4-13)
LAICT


Ns,i,t = Ni,t x Ws,i,t / Wred,i,t (4-14)

Ps,i,t = Pi,t x Ws,i,t / Wred,i,t (4-15)









where Fracbs is the fraction of biomass above the critical level to senesce per day, which is

assumed to be a constant value in the model for all species. The senesced biomass and biomass

N and P decrease the amount of live biomass and its corresponding N and P pools:

Wred,i,t = red,i,t Ws,i,t= (4-16)


Ni~t = Ni~t Ns,i~t (4-17)

Pi,t = Pi,t Ps,i,t (4-18)

Currently the model simulates three perennial species including bahiagrass (Paspahtna

notatunt Fhigge~), floralta (Hentarthria altissinta), and panicum (panicunt rigiduhlnt) which are

believed to be the dominating forage species in south Florida. One of the outputs from this model

is aboveground biomass on a daily basis. This output is critical in the development of the cattle

distribution model.

Recent ACRU2000 model developers (Martinez, 2006; Yang, 2006) have enabled and

tested the model to be capable of simulating both hydrology and nutrient dynamics in field-scale

catchments. Pandey (2006) applied the distributed ACRU2000 modeling system to predict

hydrology and non-point source nutrient pollution, on a commercial beef cattle ranch (Pelaez

Ranch) in the Lake Okeechobee region. Pandey (2006) applied the model on the entire ranch by

finely discretizing the modeling domain into various sizes of 134 land segments. Thus,

ACRU2000 can be confidently used as a basis for coupling with an animal distribution

simulation model to form a more complete ecohydrological modeling system.

Once the HSI' s are computed for every land segment in the model's domain, they are

summed, and normalized (so that they sum to 1.0). The cattle in the population are then

distributed across their range in proportion to the distribution of the normalized HSI' s among

land segments. The redistribution occurs on a daily basis. After redistribution, the cattle consume









existing forage proportional to their population presence on land segments. It is assumed that

each cow will eat 16 Kg of forage per day. This amount is based on the typical cattle weight (640

Kg) (ASAE Standards, 2000) and forage consumption (2.5% of Body Weight) (NRC, 1996). The

total forage amount that gets consumed by grazing cattle is removed from the vegetation model

on a daily basis. The removal is based on the preference weighting assigned to individual forage

species. For example, if Vegl has a higher weighting factor than Veg2 and Veg3, Vegl will get

consumed more. This consumption will be in proportion to the weighting factors. According to

suitability index of forage consumption (Figure 4-3), the grazing herbivores will not "see" any

biomass that is less than 150 kg/ha. Once the forage biomass reaches that low level in a specific

land segment, the forage suitability becomes zero and thereby lowering the HSI for that land

segment. Lower HSI in turn allows less herbivores to be assigned and this allows forage to

recover in that specific land segment.

Cattle also defecate proportional to their population presence on land segments. It is

assumed that each cow will defecate 8.5 Kg/day (ASAE Standards, 2000). The cattle waste gets

applied to the top (litter) layer of the model. The waste is characterized into various nutrients

pools (Organic -P, Labile-P, Organic-N, Ammonium-N, Active-N, Organic Matter) (Figures 4-7,

4-8) based on the rates set by the nutrient model of ACRU2000.

Minimum Habitat Area for HSI Model in ACRU2000

Application of habitat suitability criteria requires that some specific spatial parameters be

defined. Minimum habitat area is defined as the minimum area of contiguous habitat that can

support a cattle population on a long term basis. In case of modeling cattle distribution within

ACRU2000-HSI, it is imperative the users maintain a minimum land segment of 0. 1 ha in order

for cattle distribution module to be able to perform reasonably. Maintaining the right spatial












scale is important in order for the suitability indexes to be realistic over temporal and spatial


variation.


Suitability Index of Water Table




08-


~06-


m04-


02-



152 121 06 03
Water Table Depth (m)



Figure 4-1. Suitability index values of water features.



I Suitability Index of Shade Area


08


006


~04


02


S0 50
I Shaded Area (m2)


Figure 4-2. Suitability index values of shade area.













Suitability Index of Forage


08-


i~06-





02-



0 150 1350 1650
Standing Aboveground Biomass (Kg/ha)



Figure 4-3. Suitability index values of forage consumption.


Figure 4-4. Goals hierarchy view in Logical Decisions for Windows@ software (Logical
Decisions, 2005).

















LI i


Figure 4-5. General structure of the ACRU (v 3.00) model (Schulze, 1995).


Neighbo~r 1


Neighbor


Neighbor 2


Figure 4-6. Configuration of multiple directional overland flows from source land segment to
adj acent land segments (adapted from: Yang, 2006).

































Groundwater


Denitrification Volatilization


Runoff

Percolation


Figure 4-7. Phosphorus cycle of the ACRU2000 model (adapted from: Knisel et al., 1993).


Runol. and
Percolation


Runo~fand
Percolatiocn


Figure 4-8. Nitrogen cycle of the ACRU2000 model (adapted from: Knisel et al., 1993).









CHAPTER 5
MODEL RESULTS

Testing Model Performance at Buck Island Ranch

A cattle distribution model (ACRU2000-HSI) was developed for the region of south

Florida in Chapter 4. The algorithms were developed using the procedure of Habitat Suitability

Index and criteria weightings were developed by processing expert opinion using the technique

of Analytical Hierarchy Process. The GPS data analysis in Chapter 3 was helpful in providing

insights into cattle's behavior in warm humid regions. However, the GPS data were not utilized

to create algorithms for the HSI model. The algorithms are composed of "attractants" of cattle

(shade, water, and forage) and their weighting factors. The attractants were determined based on

the features that exist in the landscape of this region. Weighting factors were determined using

surveys from experts and were then calibrated to obtain better results. Depending on the presence

or absence of water, cattle may display a difference in the utilization of a wetland or pond.

Therefore, the depth of water table, which is an output from the hydrologic model, was utilized

to determine presence or absence of water in wetlands. South Florida' s long hot and humid

summer can cause heat stress in grazing cattle. Cattle's change in grazing and resting pattern as a

result of heat stress in hot-humid environments of the southeast is also incorporated into the

model. To incorporate the difference in behavior in the two seasons of Florida, two sets of

weighting factors were developed.

After model development, the next crucial step is to verify the performance of the model.

The model should be verified and tested at a site that is representative of the region for which the

model is developed. It is also preferable to test the model using observed data. Since the GPS

data from BIR (described in Chapter 3) were available, they were utilized to verify the

performance of the HSI model. Table 3-3 is a summary of the sampling of the GPS data. During










any season the number of cows collared for the GPS study was not consistent. Since the GPS

collar data were available only from a few selected cows from the whole herd, for comparative

purposes, it is assumed that the collared cows are representative of the total cattle population in

the model domain. As there can be significant variability in an individual cow's behavior

(discussed in the conclusion section of Chapter 3), it was essential to select a pasture that had the

largest availability of GPS data. Consequently, SP4 and SP5 were selected for HSI model

application (Figure 5-1). The rationale for selecting these two pastures during the

abovementioned times is because of plentiful GPS data availability (Table 3-3) that qualifies the

data to be representative of the whole herd. For each collared cow, number of recorded "hits" in

a land segment were divided by the total number of hits and then multiplied by herd size to

convert the hits into number of cows.

The description of input parameters are given in Table 5-1 and their values that were used

for model calibration and verification are given in Table 5-2. The values in Table 5-2 are the

result of calibration of the HSI model input parameters that were obtained from the LDW

software using AHP technique. Since the water, shade, and forage parameters change during the

two seasons, it was essential to calibrate and verify the model during both warm and cool

seasons. Summer 2001 (June 11-15) and spring 2002 (March 4-8) were the two seasons selected

for testing of the HSI model. SP4 was selected for calibration and SP5 for verification.

Calibration Results

Figure 5-2 is the result of calibration on summer pasture 4 during warm season of 2001.

The box plot (Figure 5-2) shows the range of the observed GPS data in all land segments. Within

the box the dark dotted line and the light solid line represents the mean and median number of

cattle in each land segment, respectively. The light dotted line (running across all land segments)

illustrates the assumption of equal distribution of cattle (1.25 cattle per land segment) by the









ACRU2000 version. Land segments consisting of water trough, wetland, and shade are

abbreviated WT, WET, and S, respectively. The calibration results of the ACRU2000-HSI during

the warm season captures the overall dynamics of individual land segments. Under prediction of

the number of cattle in land segment 8 can be attributed to the lower forage biomass, especially

panicum (less than 150 kg/ha) which has the highest weighting factor amongst the three forage

species. There is a slight over prediction in land segment 3 due to higher biomass availability.

The variability in the GPS data is also noteworthy.

Figure 5-3 is the result of calibration on summer pasture 4 during cool season of 2002. The

calibration result for the cool season also captures the overall dynamics of land segments, with

exception to LS1 and LS2. A water trough and two shade structures are present in LS1. The

model is unable to represent the presence of two shade structures and it is possible that due to

more availability of shading area, more cattle are present. However, the simulation result is still

within the lower range of observed data. Cattle's presence is exceptionally high in LS2. This

high presence has been observed in the north section of all the summer pastures. Closer

examination of GPS data and personal communication with the ranch manager of BIR has

revealed that in this area cattle would stand or lie down, ruminate, for approximately 2 to 3

hours. Various studies indicate that cattle graze mostly in early morning and evening, and rest

mostly in the middle of the day (Bagshaw, 2001; Hafez & Bouissou, 1975; Martin, 1978; Sneva,

1970). A similar observation was made in an experimental study conducted to establish beef

cattle defecation frequency and distribution on hill country in New Zealand (Bagshaw, 2002). It

was observed that often cattle would rest between 11 am and midday. They would rest and

ruminate during this time on flat areas either at the top or middle of the Hield. In a Hield where

there was a large flat area at the top of the Hield next to a trough, cattle were observed to spend









the maj ority of their resting time in this area. Similar pasture setting exists at BIR where the

cattle display an affinity to rest in the northern section of all summer pastures. This high

presence is not recorded in the summer season because cattle spend most of the afternoon resting

and avoiding direct solar radiation under a shade.

The ACRU2000-HSI slightly over predicts the number of cattle in the southern land

segments. This is mainly due to the normalization of cattle population across all land segments.

The impact of under prediction in LS1 and LS2 is translated into slight over prediction in LS9-

12.

Verification Results

Figure 5-4 displays the result of verification of ACRU2000-HSI on summer pasture 5

during warm season of 2001. The stocking rate on this pasture was higher than SP4 (3 5 Cows).

There is also more variability in the observed GPS data in this pasture as compared to SP4. There

is slight over prediction in LS2 due to high initial biomass. Nevertheless, in all the land

segments, the model's performance is always within the range of observed GPS data. The

dynamics of water trough, shade, and wetland seems to be well represented by the model.

Figure 5-5 displays the result of verification on summer pasture 5 during cool season of

2002. Similar to warm season, the model is able to capture the dynamics of water trough, shade

and wetlands in the cool season as well. The phenomena where the cattle display an affinity to

rest in the northern section of the pasture is once again evident. The number of cattle recorded in

LS2 is higher than any other land segment (Figure 5-5). Additionally, there is slight over

prediction in the number of cattle in the land segments from mid field to the southern end of the

pasture (S6-12). The model results for most of these land segments (S6-9) are within the range of

observed data and overall the number of cattle prediction is better than the original model.









Sensitivity Analysis

In a typical modeling system, the model results are more sensitive to certain inputs

compared to others. This information is of essential use for future model users who may need to

calibrate the model for application on a different site. Therefore, it is important to perform a

sensitivity analysis to establish priorities in collecting and determining model parameters. An

analysis was performed to determine the sensitivity of model simulated cattle distribution to the

weighting factors.

The sensitivity analysis was performed using the six-year simulation (January 1, 1998

through December 31, 2003) on the experimental pasture at BIR. Since the model was already

parameterized for the SP5 (Figure 5-1), it was applied on the same pasture for this analysis.

Model sensitivity was determined for + 25, 50, 75, and 100% of the base input value (Table 5-3).

Summer 2001 (June) and spring 2002 (March) were the two seasons selected from the simulation

period. The sensitivity analyses were focused on the population of cattle in all land segments

during the two seasons. It is important to bear into mind that the ACRU2000-HSI model is

different from a typical process based modeling system where change in an input parameter will

result in an expected change in output. The ACRU2000-HSI model is dependent on the

hydrologic model for determination of water presence, nutrient model for rate of growth of

vegetation, and vegetation model for total biomass. In addition, as per model design, the three

weighting factors (water, shade and forage) must sum to 1.0. For example if warm season

weighting factor of water (WWFAC) is increased by 25% the other two corresponding variables

(i.e. warm season weighting factors of shade and forage, WSFAC, WOVRLFAC) must be

adjusted so that the sum of the three weighting factors equals 1 (Table 5-3). For this analysis the

adjustment of the two variables was carried out so as to maintain the ratio amongst the adjusted

variables. In Table 5-3, the sensitivity analysis is performed on WWFAC and WSFAC and









WOVRLFAC have been adjusted accordingly. Complete list of weighting factors values used in

sensitivity analysis and the corresponding adjustment in the weighting factors of other two

variables is given in Table F-1 of Appendix F.

Results of sensitivity analysis are given in Tables F-2 to F-5 of Appendix F. There is

considerable change in cattle population with change in shade and water weighting factors in the

warm period (Table F-2); especially in the land segments that consist of those features (LS 1 and

LS2). Even though water or shade availability do not exist in land segments apart from LS1 and

LS2, there is still change in cattle population in other land segments (LS3-12) due to change in

shade and water weighting factors. As explained before, this is due to corresponding change in

forage weighting factor which has to be adjusted so that all the three weighting factors sum to 1.

There is also considerable variation in presence of cattle in LS1 and LS2 with change in

weighting factor of forage in both, warm season as well as cool season (Table F-2 and F-4). This

drastic change in cattle population is due to the proportional change in shade and water

weighting factors (shade in LS1 and water in LS2). Since the base value of forage weighting

factor is higher in cool season (Table F-4), there is higher variability in cattle population in the

cool season as compared to warm season (Table F-2). A similar trend is observed with variability

in cattle population due to variation in weighting factor of individual forage species, more

variation in cool season (Table F-3) as compared to warm season (Table F-5).

Hypothetical Scenario Model Testing

Sufficient GPS data are not currently available from the region of south Florida to

quantitatively test the ACRU2000-HSI model towards BMP implications. Adequate data are

however, rarely available to make management decisions. This is the reason modeling is an

important tool which allows managers to envision future implications based on current decisions.









"In a decision-making context, the ultimate test of a model is not how accurate or truthful it is,

but only whether one is likely to make a better decision with it than without it" (Starfield, 1997).

Therefore, a hypothetical test was designed to evaluate the algorithms of the ACRU2000-

HSI model, coupled with the vegetation, hydrologic, and nutrient models. This scenario testing

determined whether the ACRU2000-HSI model can be utilized to determine the feasibility of a

BMP with phosphorus loading as an obj ective function. One of the obvious water quality-BMP

in a cow-calf operation is to exclude the cows from streams. By restricting cow's access to a

stream, direct deposition of the cattle feces in flowing water can be prevented. In its current state,

the ACRU2000 does not consist of a stream routing algorithm for water and P. Therefore,

fencing the cows away from streams or ditches cannot be defined in the model. However, a

similar scenario was designed to mimic restrictive access of cattle.

First of all, a basic set of simulation were made with the ACRU2000-HSI model to observe

change in P loading due to presence and absence of cows (Figure 5-7). The results from these

simulations came out to be counter intuitive. The P load in absence of cows was greater than in

presence of cows. These results warranted further investigation and more detailed scenario

simulations. Hence a new data obj ect called DCattleExclusion (Appendix A) was created to

accomplish the exclusion of cattle from user-specified land segments. This functionality allows

the user to specify the land segment from which the cattle are to be excluded. During simulation

the cattle are distributed only on land segments in which cattle exclusion option is turned off.

Primarily, it was important to see a difference in the P load prediction, if any, from the two

versions of the ACRU2000 model: one with the HSI algorithms and the other using equal

distribution of animal manure. Following the above stated basic run it was also crucial to see

whether the HSI additions within ACRU2000 modeling system have enhanced its capabilities to









make relevant management decisions. Three scenarios were tested on summer pasture 5 (Figure

5-1) at BIR using a 6 year (1998-2003) simulation time period. In the first scenario (Figure 5-6a)

cattle were excluded from the land segments that were close to the flume (LS7 LS12); in the

second scenario cattle were excluded from the land segments that were away from the flume

(LS1 LS6); and finally in the third scenario all cattle were stocked on the land segment that

adj oined the flume (LS 11) and they were excluded from all other land segments.

The three scenarios were designed to fence the cattle in various locations on the pasture to

observe any changes in the nutrient loading. In total, five set of simulations were made using the

ACRU2000 and the ACRU2000-HSI model and compared with observed P loading data (Figure

5-8). There is considerable difference in the nutrient predictions in the two versions of

ACRU2000. The ACRU2000-HSI' s prediction of TP is less than ACRU2000 and closer to

observed data (Figure 5-8). The ACRU2000 version assumed the animal manure to be

distributed equally amongst all land segments. The HSI version deposits manure on land

segments based on the number of animals assigned to specific land segments. However, in both

versions the total quantity of manure remains same; therefore, some difference was anticipated

yet the magnitude of difference required further investigation.

It should be noted that even though there was provision to include animal manure in case

of stocking in ACRU2000, there was no accountability of forage consumption by grazing cattle.

In the vegetation model, plant senescence is assumed to start when the daily sum of leaf areas of

all species on one land segment exceeds the critical leaf area. Each vegetation species senesces

biomass, N, and P in proportion to its leaf area. The daily total senesced biomass (Ws,i~t [kg/ha])

and the corresponding N (Ns,i~t [kg/ha]) and P and (Ps,i~t [kg/ha]) removed through the senesced

biomass are given in Equation 4-13 to 4-15 of Chapter 4. Since there was no consumption of the









vegetation by grazing cattle in ACRU2000 there was a high amount of nutrients being released

from senesced biomass. This seems to have been corrected by the ACRU2000-HSI model where

the cattle consume the vegetation as per their nutritional requirement.

Figures 5-7 shows that when the cattle are closer to the flume there is reduction in nutrient

load. This reduction can be explained by the change in quantity of senesced biomass that is

closer to the flume. When there are more cattle present near the flume they consume more forage

and hence reduction in senesced biomass. On the other hand, when cattle are away from the

flume there is increase in nutrient load due to increase in above ground biomass that senesces

and release nutrients. In the third scenario, when all the cattle are stocked on LS11 (land segment

that is adj acent to flume) there is a slight increase in nutrient loading. This increase can be

attributed to the exorbitant stocking rate (35 cows on 1.6 ha).

A budget of the ACRU2000-HSI modeling system was prepared to quantify various

"pools" of P using 6 years of simulation (Figure 5-9). It is evident that P from senesced residue

(two order magnitude higher than P from defecation) is the largest component. When the

ACRU2000-HSI model is turned on, the grazing cattle consume forage and reduce the amount of

senesced residue (Equation 4-16) which consequently reduces P load. Within the model, only the

top two layers (plant residue layer and soil surface layer) interact with surface runoff; therefore a

P budget with the top two layers of ACRU2000-HSI as a control volume was also computed

(Figure 5-10).

Apart from the P budget within the model domain it was also important to test the retention

of P within the cattle over time (Figure 5-1 1). With the exception of initial increase, the P

retained by cow remains within the bounds of 20-25 g. The P retained values correspond well to

the values published in literature (NRC, 1996). The initial jump in P retention can be explained










by the utilization of nutrient uptake algorithms in the vegetation model (Yang, 2006). The N and

P uptake algorithms in the vegetation model were adopted from GLEAMS (Knisel and Davis,

1999) with a slight modification to account for the nutrient uptake by multiple plant species in

one land segment. In the GLEAMS model the nutrient uptake is based on demand and supply of

nutrients. The P demand for species i at time t, DEMPi~t [kg/ha], is determined by the difference

between the dry matter P on two successive days as:

DEMPi~, = TDMPi,t TDMPi,t-1 (5-1)

Uptake of labile P, UPLPi~t [kg/ha], is estimated for each layer where transpiration, occurs using

UPLPi,,tt = CPLABWs,d,j x Ti~j~t (5-2)

where, CPLABWs,d~t, is the concentration of labile P. The total uptake of P is the sum over all

species i and all layers j where transpiration occurs. The P taken up is converted into the plant

biomass P:


Pu,Dt = IfUPL~Pi,;J (5-3)


where Pu,~t is the plant biomass P [kg/ha]. The amount of initial biomass will dictate the role of P

uptake during the initial phase of simulation. It is perceived that in some land segments there can

be high initial biomass of any of the three vegetation species (Bahiagrass, Floralta, and

Panicum). This will cause an increase in the supply of nutrients to support the growth of the

vegetation. Thus, during the initial stages, consumption of P enriched biomass is resulting in

higher P retention within the cow' s body. Over time, as the model equilibrates the high retention

"levels-off' to a more sustainable level.

Summary

The algorithms in the HSI model are composed of "attractants" of cattle (shade, water

trough, and wetland) and their weighting factors. The HSI methodology represents the dynamics




Full Text

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1 ANALYSIS AND MODELING OF CATTLE DISTRIBUTION IN COMPLEX AGRO-ECOSYSTEMS OF SOUTH FLORIDA By VIBHUTI PANDEY A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

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2 Copyright 2006 by Vibhuti Pandey

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3 To my parents and my loving wife

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4 ACKNOWLEDGMENTS I am greatly indebted to my supervisory co mmittee chair (Dr. Gregory A. Kiker) for his constant guidance, insight, encouragement, a nd most of all his enthusiastic and continuous support and confidence in my research. His t horough and thoughtful coaching was unselfishly tireless, and his enthus iasm has left me an everlasting impression. He always made himself available and hence I was able to progress consta ntly in a sustainable manner. I would like to acknowledge my thanks and appreciation to Dr. Chris J. Martinez for his help throughout the development of my model. Without his guidance and support with programming, timely completion of my research would have been impo ssible. His enthusiasm and helpful nature made my research progress swiftly. I e xpress my sincere appreciation to Dr. Kenneth L. Campbell for his guidance during the first 3 y ears as my supervisory committee chair. He provided direction that eventually helped me to identify the specif ic topic for my research. His advice on addressing each of my technical problems and concerns has b een invaluable as well. I am grateful to Dr. Sanjay Shukla for his support and help during field work. Also, I would like to express thanks to Drs. Michael Annable and Mark W. Clark who served on my committee and provided valuable insights into my research. I am greatly thankful to my lab-mates for th eir friendship and encouragement, and to the staff of the Agricultural and Biological Engine ering Department for their technical and moral support. The department as a whole has been a wonderful working and learning environment. Last but not the least, I would like to thank my family and frie nds for their relentless support and advice throughout this endeavor.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........8 LIST OF FIGURES................................................................................................................ .......10 ABSTRACT....................................................................................................................... ............13 CHAPTER 1 INTRODUCTION................................................................................................................. .15 Study Background............................................................................................................... ...15 Lake Okeechobee and Watershed Description.......................................................................15 Water Quality Best Management Practices (BMPs) for Lake Okeechobee Watershed.........18 Contribution to Information Required for Modeling and BMP Implementation...................19 Organization of This Dissertation...........................................................................................20 2 CATTLE BEHAVIOR DYNAMICS AND CU RRENT MODELING APPROACHES......22 Factors Influencing Cattle Distribution..................................................................................22 Cognitive Mechanisms....................................................................................................23 Water Development.........................................................................................................23 Breed Selection................................................................................................................ 24 Seasonal Distribution.......................................................................................................25 Shade Structures..............................................................................................................2 5 Social Behavior...............................................................................................................2 7 Cattle Location and Water Quality.........................................................................................28 Existing Modeling Approaches..............................................................................................29 Regression Models..........................................................................................................29 Habitat Suitability Index Models.....................................................................................30 Mechanistic Models.........................................................................................................35 Metapopulation Models...................................................................................................36 Spatially Explicit-Indi vidual Based Models............................................................38 Numerical Fish Surrogate Model.............................................................................39 Multi-Agent Systems.......................................................................................................40 Cattle Tracking Techniques....................................................................................................4 1 Summary........................................................................................................................ .........42 3 ANALYSIS OF GPS COLLAR DATA.................................................................................50 Study Site: Buck Island Ranch...............................................................................................50

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6 Summer Pastures.............................................................................................................51 Winter Pastures................................................................................................................ 52 Hydrologic Data................................................................................................................ ......52 GPS Data....................................................................................................................... .........53 Data Analysis.................................................................................................................. ........54 Results and Discussion......................................................................................................... ..55 Conclusion..................................................................................................................... .........61 4 DEVELOPMENT OF CATTLE MOVEME NT ALGORITHMS FOR ACRU2000............73 Habitat Suitability Index (HSI)...............................................................................................7 3 Model Design for Cattle Distribution in ACRU2000.............................................................73 Suitability Index for Cattle Distribution..........................................................................74 Preference Estimation Using Anal ytical Hierarchy Process...........................................76 Index for Heat Stress and Seasonal Distribution.............................................................78 Integration of HSI Model into ACRU2000............................................................................81 The Agricultural Catchments Research Unit (ACRU) Modeling System.......................81 Minimum Habitat Area for HSI Model in ACRU2000...................................................86 5 MODEL RESULTS................................................................................................................ 91 Testing Model Performance at Buck Island Ranch................................................................91 Calibration Results............................................................................................................ ......92 Verification Results........................................................................................................... .....94 Sensitivity Analysis........................................................................................................... .....95 Hypothetical Scenario Model Testing....................................................................................96 Summary........................................................................................................................ .......100 6 DISCUSSION AND CONCLUSION..................................................................................110 GPS Collar Analysis............................................................................................................ .110 HSI Model...................................................................................................................... ......111 Management Implications....................................................................................................113 Future Research Recommendation.......................................................................................113 Herbivore Physiological Representation.......................................................................114 Stream Routing Algorithm............................................................................................114 Graphical User Interface................................................................................................114 Conclusion..................................................................................................................... .......115 APPENDIX A LIST OF NEW AND MODIFIED OBJECTS......................................................................116 B HSI MODEL PROCESSES UNIFIED MODELING LANGUAGE (UML) DIAGRAMS....................................................................................................................... ..119 C SURVEY FOR DETERMINATION OF WEIGHTING FACTORS..................................122

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7 D WEIGHTING FACTORS D ETERMINED BY SURVEY..................................................124 E RESULTS FROM SURVEY................................................................................................126 F SENSITIVITY ANALYSIS RESULTS...............................................................................132 LIST OF REFERENCES............................................................................................................. 137 BIOGRAPHICAL SKETCH.......................................................................................................151

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8 LIST OF TABLES Table page 2-1 Significant predictors of cattle behavior............................................................................48 2-2 Coefficients in the s easonal grazing models......................................................................49 2-3 Coefficients in the seasonal daytime resting models.........................................................49 3-1 Percent area of wetlands and ditche s in summer and winter pastures...............................69 3-2 Summary of climatological data during the study period..................................................69 3-3 Summary of GPS collar data in the experiment al pastures................................................70 3-4 Locations that are assumed to have presence of water......................................................70 3-5 Mean percentage of daily time spent by cattle near water locations.................................71 3-6 Mean percentage of daily time spent by cattle near water trough.....................................71 3-7 Mean percentage of daily time spent by cattle in wetland.................................................71 3-8 Mean percentage of daily time spent by cattle in ditch.....................................................72 3-9 Mean daily distance traveled and mean daily MCP area by cattle....................................72 5-1 Input parameters and their descrip tion used in the ACRU2000-HSI model...................108 5-2 Values of input parameters used in the ACRU2000-HSI model after calibration...........108 5-3 Input parameter values used in sensitivity analysis.........................................................109 5-4 Example of adjusted weighting f actors used in sensitivity analysis................................109 D-1 Summary of weightings of features as generated by the LDW program based on the survey......................................................................................................................... ......124 D-2 Summary of weightings of three forage species as generated by the LDW program based on the survey..........................................................................................................12 4 F-1 Weighting factors used in sensitivity analysis.................................................................132 F-2 Sensitivity of water, shade and forage weighting factors in warm season......................133 F-3 Sensitivity of the three vegetation speci es weighting factors in warm season................134

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9 F-4 Sensitivity of water, shade and fora ge weighting factor s in cool season.........................135 F-5 Sensitivity of the three vegetation speci es weighting factors in cool season..................136

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10 LIST OF FIGURES Figure page 1-1 Drainage Basins of Lake Okeechobee...............................................................................21 1-2 Yearly average total phos phorus concentrations in the open-water (pelagic) region of Lake Okeechobee...............................................................................................................2 1 2-1 Average herbage yield of perennial grassesfrom year long access to water on southern Arizona range......................................................................................................43 2-2 The relationships between shrub habitat variables and suitability index values for pronghorn winter food quality...........................................................................................44 2-3 The relationships between two variables of forage diversity and suitability index values for pronghorn winter food quality..........................................................................44 2-4 The relationship between mean topographic diversity and suitability index values for pronghorn winter food quality...........................................................................................45 2-5 Graphical representation of the index................................................................................45 2-6 Two performance suitability indicato rs expressed as functions of hydrologic variables...................................................................................................................... .......46 2-7 A time series of values of a suitabil ity indicator derived from time series of hydrologic variable values.................................................................................................46 2-8 Creating a composite suitability indicato r time series from multiple suitability indicator time series.......................................................................................................... .47 2-9 Three approaches to spatial ecology..................................................................................47 2-10 Using GIS in metapopulation models................................................................................48 3-1 Location of Buck Island Ranch and the Experimental Pastures........................................64 3-2 Map displaying wetlands, ditches and water troughs in summer pastures........................64 3-3 Map displaying wetlands, ditches and water troughs in wi nter pastures...........................65 3-4 Example of rainfall and groundwater level data in summer pasture 3..............................65 3-5 Typical cattle movement in summer pasture 2 on June 11, 2001......................................66 3-6 Average % time spent near water locations.......................................................................67

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11 3-7 Average % time spent in shade structures.........................................................................67 3-8 Typical MCP area in summer pasture 2 on June 31, 2001................................................68 4-1 Suitability index valu es of water features..........................................................................87 4-2 Suitability index values of shade area................................................................................87 4-3 Suitability index values of forage consumption.................................................................88 4-4 Goals hierarchy view in Logical Decisions for Windows software...............................88 4-5 General structure of the ACRU (v 3.00) model.................................................................89 4-6 Configuration of multiple directional overland flows from source land segment to adjacent land segments......................................................................................................89 4-7 Phosphorus cycle of the ACRU2000 model......................................................................90 4-8 Nitrogen cycle of the ACRU2000 model...........................................................................90 5-1 Land segment Discretization of summ er pastures 4 and 5 for ACRU2000-HSI.............102 5-2 Calibration results on SP4 in warm season......................................................................103 5-3 Calibration results on SP4 in cool season........................................................................103 5-4 Verification results on SP5 in warm season....................................................................104 5-5 Verification results on SP5 in cool season.......................................................................104 5-6 Hypothetical scenario setup for ACRU2000-HSI model.................................................105 5-7 Total phosphorus results using ACRU2000-HSI model..................................................106 5-8 Total phosphorus results from vari ous scenarios in ACRU2000-HSI model..................106 5-9 Phosphorus budget of complete mode l domain using simulated results.........................107 5-10 Phosphorus budget of top two model layers using simulated results..............................107 5-11 Total phosphorus retained within gr azing cattle using simulated results........................108 B-1 PCalculateHabitatSuitabilityIndex UML diagram...........................................................119 B-2 PForageConsumption UML diagram...............................................................................120 B-3 PDefecation UML diagram..............................................................................................121 C-1 Cattle preference of features in a pasture during summer...............................................122

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12 C-2 Cattle preference of features in a pasture during winter..................................................122 C-3 Cattle preference of forage species in a pasture...............................................................122 C-4 Example illustrating identification of the relative importance of one feature over the other on the scale provi ded in the survey.........................................................................123 D-1 Range of weighting of featur es in warm and cool seasons..............................................124 D-2 Range of weighting of the three forage species...............................................................125 E-1 Simulation result on SP4 in warm season us ing weighting factors of researcher-1........126 E-2 Simulation result on SP4 in cool season us ing weighting factors of researcher-1..........126 E-4 Simulation result on SP4 in cool season us ing weighting factors of researcher-2..........127 E-5 Simulation result on SP4 in warm season us ing weighting factors of ext. agent -1........128 E-6 Simulation result on SP4 in cool season us ing weighting factors of ext. agent -1..........128 E-7 Simulation result on SP4 in warm season us ing weighting factors of ext. agent -2........129 E-8 Simulation result on SP4 in cool season us ing weighting factors of ext. agent -2..........129 E-9 Simulation result on SP4 in warm season using weighting factors of rancher -1............130 E-10 Simulation result on SP4 in cool season using weighting factors of rancher -1..............130 E-11 Simulation result on SP4 in warm season using weighting factors of rancher -2............131 E-12 Simulation result on SP4 in cool season using weighting factors of rancher -2..............131

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13 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ANALYSIS AND MODELING OF CATTLE DISTRIBUTION IN COMPLEX AGRO-ECOSYSTEMS OF SOUTH FLORIDA By Vibhuti Pandey December 2006 Chair: Gregory A. Kiker Cochair: Sanjay Shukla Major Department: Agricultur al and Biological Engineering It is perceived that cow-calf operations in s outh Florida can be a s ubstantial source of phosphorus loading, to Lake Okeechobee. Spatial and temporal information of cattle location within a pasture can be instrumental in estima ting the deposition location of cattle fecal matter. To address this issue, cattle position data were analyzed and a simplified distribution model was developed. Cattle position data were acquired through GPS collars and a cattle distribution model was developed and incorporated into a re gionally tested hydrological/water quality model, ACRU2000. The GPS data were spatially and temporally analyzed to quantify the amount of time spent by cattle near shade and water locations. The an alysis revealed the prominence of seasonal utilization of water troughs, ditc hes, and shade. Shade structur es were utilized substantially during the warm seasons. Wetland utilization was similar across cool and warm periods but was variably distributed across times within periods. The analysis also revealed that there can be significant differences in an i ndividual cowÂ’s behavior and ut ilization of water features. The GPS analysis was instrumental in the iden tification of variables to be included in the cattle distribution model. This distribution m odel was added as an add-on module within the

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14 Java-based object-oriented framework of th e ACRU2000 modeling system. The algorithms are composed of attractants of cattle (shade, water, and forage) and their weighting factors. The algorithms were developed using the techniques of Habitat Suitability Index (HSI) and criteria weighting was developed using th e Analytical Hierarchy Process. The HSI model was integrated with the current hydrology, nutrient, and vegetation modul es within ACRU2000. The HSI model was calibrated and verified on summer pastures of Buck Island Ranch, Lake Placid, FL. Model verification revealed th at its performance was in good agreement with observed GPS data. Several Best Management Pract ice scenarios, designed to mimic fencing of selected pasture areas, revealed that the phos phorus release from senesced biomass may be a significant store amongst all ot her pools of phosphorus. The HS I model has enhanced the capability of ACRU2000 to represent the spatia l variability and nutrient effects of cattle distribution within complex agro -ecosystems of south Florida.

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15 CHAPTER 1 INTRODUCTION Study Background The State of Florida has plentiful, divers e water resources that support a variety of ecosystems, animals, food crops, industry, tour ism, and recreation. However, rapid population growth over the past 35 years is si gnificantly affecting the quality of these systems. It is also projected that FloridaÂ’s populat ion will increase to 25.9 milli on in 2025 (US Census Bureau, 2005). Hence, Florida is facing a unique challenge of managing wate r quantity and quality with the pressure of continuing population growth, accompanied w ith development and extensive agricultural operations. The Florida Department of Environmental Protection (FDEP) is the regulatory agency responsible fo r restoring and protecting the st ateÂ’s water quality. In its 2006 Integrated Water Quality Assessment Report, th e FDEP documented increasing nonpoint source pollution from urban stormwater and agricultural activities as a major environmental concern (FDEP, 2006). Nonpoint source water pollution, so metimes called "diffuse" source pollution, arises from a broad group of hu man activities for which pollutants have no obvious point of entry into receiving watercourses. Because of its diffuse nature, nonpoint source pollution is much more difficult to identify, quantify, and control than point source pollution. In south Florida, especially in the Lake Okeechobee watershed, nonpoint source pollution from agricultural operations is a matter of concern. Lake Okeechobee and Watershed Description Lake Okeechobee and its watershed are key components of south Florida's KissimmeeOkeechobee-Everglades ecosystem, which extends from the headwaters of the Kissimmee River in the north, to Florida Bay in the south. Located in south cen tral Florida, Lake Okeechobee covers 1891 km2 (730 mi2) and functions as the central part of a large interconnected aquatic

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16 ecosystem in south Florida. The lake is the seco nd largest freshwater body located wholly within the continental United States. Lake Okeechobee is a multipurpose reservoir providing drinking water for urban areas, irrigation water for agricultural lands, rechar ge for aquifers, freshwater for the Everglades, habitat for fish and waterfowl, flood control, navigati on, and many recreational opportunities (SFWMD, 1997). Under natural historic conditions, water flowed from Lake Okeechobee to the Everglades. After events of hea vy rainfall, water exited th e lakeÂ’s littoral zone by numerous small tributaries, and by a broa d sheet-flow at the southeastern lake edge (SFWMD, 1999). At that time, the lake bottom was composed primarily of sand that had low phosphorus content. Conditions in and around La ke Okeechobee changed dramatically during the last century, due to agricultural development in the watershed to the north of the lake, and construction of the Central and South Florida (C&SF) Project. Excess nutrient inputs from agriculture and more efficient delivery of stormwater by the C& SF Project have dramatically increased in-lake total p hosphorus concentrations. The Okeechobee watershed is divided into si x regions: Lower Kissimmee River (LKR) (S154, S-65D, and S-65E), Taylor Creek/Nubbin Slough (TCNS) (S191), Fisheating Creek, Indian Prairie/Harney Pond, the Lakeshore a nd the EAA (Figure 1-1). During the 20th century, much of the land around Lake Okeechobee was rehabilitated to agricultural use. To the north, dairy farms and beef cattle ranching became the major land uses. In the south, sugar cane and vegetable farming increased rapidly. Associated with the la nd use changes were large increases in the rate of nutrient inputs to the lake (SFWMD, 1999). The main sources of high nutrient loads in the watershed are thought to be runo ff from dairy barns and holding areas, direct stream access by large numbers of dairy and beef cattle, and runoff from improved pastures.

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17 The lake, a designated Class I water body (pot able water supply), ha s been threatened especially by high phosphorus levels, which have tripled since 1975 (Figure 1-2) causing large algal blooms (SFWMD, 1999). The watershed has little relief, and the water table is near the soil surface during the wet season. Before development this area was largely composed of wetlands (Blatie 1980). During 1926 and 1928, flooding resulte d in the loss of life and property, which then resulted in the construction of a flood cont rol levee (Herbert Hoover Dike) and a rim canal around the lake to control flooding. Currently, all flows into and out of the lake are managed through 140 miles of canals; control structures (gates, locks, and pumps); and levees, which were completed in the late 1950s, as part of the Central and South Florid a (C&SF) Flood Control Project. The South Florida Water Management District (SFWMD), in conjunction with the United States Army Corps of Engineers (USACE), re gulates these structures and canals (SFWMD, 1997). This modified system has improved flood c ontrol and supplied irri gation water; however, it negatively affected the water quality of Lake Okeechobee by expediting the delivery of stormwater runoff to Lake Okeechobee. Soils in the watershed (especially in north ern regions) are mainly Spodosols which are, sandy, low in clay content, low pH, low cation exchange capacity and low phosphorus retention capacity. These soils (more than 90% sand) are characterized by high inf iltration rates and poor internal drainage due to low permeability of the Bh horizon. The Bh horizon contains large deposits of Aluminum (Al) and Iron (Fe) along with organic matter and is known as the spodic layer. When rainfall occurs, the soils can quickly become saturated. During much of the year, the water table is located between the spodic horiz on and the soil surface (Graetz and Nair, 1995). Because of this restrictive layer, nutrient m ovement in Spodosols occurs through surface runoff

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18 and also through subsurface flow (Campbell et al ., 1995). Apart from landuse changes, soil and hydrologic characteristics of the wa tershed have also f acilitated in development of algal blooms and other adverse impacts to water qualit y both in Lake Okeechobee and in downstream receiving waters. Consequently, in 1999, the FDEP initiated deve lopment of the Total Maximum Daily Load (TMDL) of phosphorus for Lake Okeechobee. A TMDL is the maximum amount of a given pollutant that a water body can absorb and still maintain its designated use. It was adopted by rule in May 2001. The FDEP proposed a maximum annual load of 140 metric tons of phosphorus to Lake Okeechobee to achieve an in-lake ta rget phosphorus concentrat ion of 40 ppb. The FDEP is working in conjunction with other state ag encies such as the Florida Department of Agricultural and Consumer Services (FDACS), Water Management Districts (WMD), Soil and Water Conservation Districts (SWCD), and U.S. Natural Resources Conservation Service (NRCS) to support a healthy lake system, restore the designated uses of the lake, and allow the lake to meet applicable water quality standards. These agencies are implementing a multifaceted approach to reducing phosphorus loads by impr oving the management of phosphorus sources within the Lake Okeechobee watershed thr ough continued implementation of existing regulations and Best Management Practices (BMPs) (FDEP, 2001). Water Quality Best Management Practices (BMPs) for Lake Okeechobee Watershed The state agencies responsible for water qualit y have recognized that mere implementation of regulations will not be sufficient to achieve the targeted load reductions for the lake (SFWMD, 1999). Other management strategi es for this region are needed, including development of non-enforceable guidelines and e ducation of farmers and ranchers to adopt BMPs to reduce the current pollution levels in surf ace waters. A step in this direction resulted in development of the “Water Quality Best Manage ment Practices for Cow-Calf Operations in

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19 Florida” (FCA, 1999) by the Florida Cattlemen’s Association (FCA). The BMPs included in the manual include a variety of structural (e.g., fenc ing) and managerial (e.g., nutrient management) BMPs. Although the BMPs developed represent th e best efforts of the ranchers and state agencies, limited information exists on the effectiveness of these BMPs. To address the information gap, a study is currently underway by rese archers at University of Florida, Institute of Food and Agricultural Sciences (UF-IFAS) in conjunction with FDEP FDACS, SFWMD, and NRCS. The study is aimed at demonstrating and determining the efficacy of water quality BMPs such as fencing and improved water management for reducing phosphorus loads to Lake Okeechobee from cow-calf operations in the Okeechobee basin (UF-IFAS, 2002). Another important factor governing the BMP implementa tion by the ranchers wi ll be their economic impact on ranch income. Unless a BMP is ec onomically feasible for a rancher, its implementation will be limited. Contribution to Information Required fo r Modeling and BMP Implementation Given the fact that cattle’s pres ence in waters can be a source of direct loading of P, it is important to quantify the time spent by them near/in waters so that an informative decision can be made regarding water quality BMPs for ranche s in the region of south Florida. It is also essential that the current m odeling systems should incorporat e dynamics of localized grazing pressure so that comprehensive representation of existing agro-ecosystems is accounted. Almost all hydrological models lack representation of spa tial distribution of cattle presence. A successful model should not only represent hydrology and nutrients but also the dynamics of cattle movement and behavior. If modeling systems are fl exible and extensible they can be updated as per the requirement of the system they are representing. One such modeling system is the ACRU2000 which is available for expansion and in corporation of cattle distribution dynamics. The specific objectives of this research are:

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20 Review cattle behavior studies, especially those associated with water quality impacts. Analyze cattle movement dynamics and their behavior using GPS Collar data. Construct a methodology to identif y the spatial and temporal in formation of grazing cattle. Development of cattle distribution algor ithms for the ACRU2000 modeling system. Test ACRU2000 on a site, which will best represent the condition for which the model is being designed, i.e. agro-ecosystem of south Florida. Organization of This Dissertation Chapter 2 is a thorough review of two broa d categories: cattle behavior studies and existing modeling approaches that are used in de fining ecological systems at various scales. In Chapter 3, detailed analysis of GPS Collar data is presented. Results of various techniques have also been presented that have been utilized to process GPS data. The following chapter (Chapter 4) includes model design, algorithm development and incorporation of cattle movement in the ACRU2000 model. In Chapter 5 the cattle moveme nt add-on module is tested and verified. Finally Chapter 6 summarizes the effo rt of data analysis and modeling.

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21 Figure 1-1. Drainage Basins of Lake Okeechobee (SFWMD). Figure 1-2. Yearly average total phosphorus concentrations in th e open-water (pelagic) region of Lake Okeechobee (SFWMD).

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22 CHAPTER 2 CATTLE BEHAVIOR DYNAMIC S AND CURRENT MOD ELING APPROACHES High density animal operation is of interest as it can potentially be a cause of concern with regards to its impact on the environment (Bottc her et al., 1995). Pastureland and dairies can become an important source of diffuse or nonpoint source pollution if adequate practices are not implemented or in cases when livestock are a llowed to approach or enter surface waters. In regions such as south Florida where cattle-ranch ing and dairy-farming ar e important agricultural activities; there are concerns of increase in nutrient loadings from these agricultural lands. Phosphorus loading from rangeland s and its subsequent movement into the drainage waters (Lake Okeechobee) is a major e nvironmental concern in this wa tershed (Allen et al., 1982). The primary source of phosphorus has been non-point s ource agricultural runoff, particularly from beef cattle ranching and dairy farming, the tw o primary land uses in the Lake Okeechobee watershed (Flaig and Reddy, 1995). Unlike dairy farms, beef cattle ranches are not yet treated as sources of point source pollution due to lower an imal stocking rates associated with cow/calf production systems. Therefore, these ranches are not subject to any regulations from state and federal agencies. A “voluntary” BMP implementa tion program exists for beef cattle ranches, however, due to limited information on the effectiveness of these BMPs not many ranchers have enrolled in the program. Factors Influencing Cattle Distribution Since cattle defecation is of major concern, it is eviden t that to develop a complete understanding of the animal-plant-soil system in a ranch system, the spatial information of grazing cattle will be crucial, which will aid in developing a comprehensive understanding of ecological interactions. Various st udies have examined the scope of improving pasture utilization by increasing the distribution of cattle (Baile y et al., 1989a; Bailey et al., 1996; Ballard and

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23 Krueger, 2005; Ganskoop, 2001; Marlow and Pogacn ik, 1986; Owens et al., 1991, Schacht et al., 1996; Smith et al., 1992, Sneft et al., 1985a, b). Cognitive Mechanisms In an invited synthesis paper Bailey et al. (1996) examined behavioral mechanisms that produce large herbivore distributi on patterns. It was reported that grazing distribution can be attributed to biotic factors such as forage quality and abiotic fact ors such as slope. Abiotic factors form cattle’s conspicuous habit to graze “conve nient areas” (Schacht et al., 1996). Selective grazing of these convenient areas within pasture isolates area that do no t get grazed or only lightly grazed. This eventually causes reducti on in the carrying capacity of grasslands and efficiency of livestock enterprise (Anderson, 1967). Bailey et al. (1996) defined the foraging process as an aggregate of two mechanis ms: non-cognitive and cognitive. Non-cognitive mechanisms do not require use of memory from large herbivores during foraging. Grazing velocity and intake rate are examples of non-c ognitive mechanisms that require little judgment from the animal. Whereas, cognitive mechanism is a process of learning and memory that have shown to affect diet sele ction in selecting feeding sites. In earlier studies Baile y et al. (1989b, c) demonstrated that large herbivores return to nut rient rich areas more frequently and generally avoid nutrient poor areas. This is primarily beca use animals have an accur ate spatial memory and can associate food resource levels with the locations in which they were found. Water Development To ensure even pasture utilization, managers try to increase cattle’s uniformity of grazing by changing these abiotic attributes of their pa stures. Slope and distance to water have been widely acknowledged as the two primary determinan ts of grazing patterns in large scale range environments (Owens et al., 1991; Sneft et al., 1985b; Sneft et al., 1987; Schacht et al., 1996). Areas that are steeper receive less use than thos e that are gentle (Mueggler, 1965), and locations

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24 that are farther from water also receive less use than those that are near water (Valentine, 1990). Development of water sources that are further than 1 km from existing water source usually increases forage utilization and thereby increa sing overall uniformity of grazing (Bailey, 2004). Goebel (1956) increased the number of water de velopments from 9 to 52 over period of five years and observed that this increase in water av ailability decreased concentrations of cattle in overgrazed areas and increased us e of areas which previously r eceived little or no utilization. During the growing season drainage channels and plant community near water locations gets heavily grazed (Sneft et al., 1985b). Therefore, to increase distribu tion and also to lighten forage over-utilization near water (Figure 2-1) some studi es have controlled ca ttleÂ’s access to water (Martin and Ward, 1970). Water development has also been useful in protecting riparian areas thereby improving stream water quality. Off stream water source ha s proven to decrease grazing pressure in the riparian zone (Porath et al., 2002). Sheffield et al. (1997) reported that installation of off stream water source reduced the averag e concentrations of total susp ended solids, total nitrogen, ammonium, sediment bound nitrogen, sedi ment bound phosphorous, total phosphorous and stream bank erosion. In another study in Oregon, Miner et al. (1992) obser ved that cows reduced their presence in the st ream from 25.6 min/day to only 1.6 min/day (reduction of more than 90%),when off stream tank was made available. Breed Selection It has been reported that herbivores pref er gentle slopes near water (Mueggler, 1965). Bailey et al. (2001) has suggested the use of breeds that orig inate from mountainous terrain (Tarentaise) in rugged rangelands and use of breeds developed in gentler slopes (Hereford) in rolling topographical rangelands. However, there can be some individuality associated with regards to grazing on rugged terrain (Bailey et al., 2004). In a study condu cted in the mountains

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25 of Montana Bailey et al. (2004) compared the da ily grazing patterns of co ws that used steepest slope and highest terrain to t hose that used gentler slopes a nd lower elevations. The authors termed cows that spent more time grazing steeper slopes as “hill climbers” and those that used gentler slope as “bottom dwellers ”. The study concluded that indi vidual cows within a herd can use different terrain. Seasonal Distribution A seasonal effect on cattle grazing behavior has also been reported in many studies (Marlow and Pogacnik, 1986; Sneft et al., 1985b ; Tanner et al., 1984). Typically during summer (growing season), forage becomes mature and plen tiful and there is more even grazing. Whereas, during winter (dormant season) forage is not that palatable and hence there is more patchy grazing. However, grazing distribution of weani ng cows generally improves during late fall and winter because of decreased wa ter and nutrient requirements afte r weaning (Schach t et al., 1996). A study conducted in northeastern Colorado used cl uster analysis of fora ge-use to analyze the consistent seasonal-grazing pattern and eventual ly construct a predictive model (Sneft et al., 1985b). It was found that seasonal-grazing distribu tion was correlated with proximity to water and site-quality indicators. Results of a 2-year be havior study in Montana also indicated seasonal trend in cattle use of riparian and up land areas (Marlow and Pogacnik, 1986). Shade Structures In regions associated with high temperatures, an other important factor in cattle distribution and performance is availability of shade. At high temperatures, eva porative cooling is the principal mechanism for heat dissipation in catt le (Blackshaw and Blackshaw, 1994). In order for a cow to maintain a relatively constant body temperature with respect to its environment (homeostasis), it must maintain thermal equilibrium via its developed heat-regulating mechanisms. When the ambient temperature appr oaches or exceeds cattle’s body temperature,

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26 the cattle must increase th eir active cooling by evaporation of wa ter from the respiratory tract or from the skin by sweating (Lee, 1967 ). Failure to maintain homeostasis at high temperatures may lead to reduced productivity or even death (Blackshaw and Bl ackshaw, 1994). Historically there used to be a perception amongst producers that providing shade may reduce the time that cattle spent grazing. However, recent studies have demons trated that the amount of time cattle spent in shade was related to environmental conditions an d that shade seeking di d not result in reduced grazing time (Widowski, 2001). Also, by manipula ting shade, cattle can be drawn to underutilized area of pasture (McIlvain and Shoop, 1971) Shade has also proven to bring financial profits in ranching enterprise. In a 4 year study in Oklahoma it was quantified that shade increased summerlong gain of yearling Hereford steers by a profitable 19 lb/head (McIlvain and Shoop, 1971). The same study also concluded that “hot muggy days” (days when temperatures were above 850 F and high humidity) reduced summerlong steer gains by 1 lb per day. In a review paper on the effect of shade on producti on and cattle behavior Blackshaw and Blackshaw (1994) reported that und er high heat stress, Bos indicus breeds and their cro sses have better heat regulatory capacities than Bos taurus breeds. The authors attributed this difference due to differences in metabolic rate, food and water cons umption, sweating rate, co at characteristics and color. The sweating of Bos indicus increases exponentially with rises in body temperatures; whereas, in Bos taurus, sweating rates tended to plateau after an initial increase (Finch et al., 1982) Therefore, Bos taurus must evaporate substantially more sweat than Bos indicus to maintain normal body temperatures (Finch, 1986). Blackshaw and Blackshaw (1994) in their review paper discussed some of the important physiological mechan isms that help cattle to cope with heat stress: Evaporative Cooling Metabolic rate and tissue insulation

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27 Water consumption Cattle coat characteristics Social Behavior In mountainous terrain, cattle may form so cial groups (Roath and Krueger, 1982a). Amongst these social groups cattle have been cla ssified as leaders, followers and independents with regards to movement during grazing (Sato, 1982). A dominance hierarchy exists in a herd (Bennett et al., 1985; Bennett and Holmes, 1987; Br oom and Leaver, 1978). Animals high in the hierarchy have priority to feed, shelter, and water. Low-ranked animals maintain a certain distance from dominant animals to avoid conf lict. As subordinates get closer to dominant animals, they may reduce their bi te rate, stop feeding, relocate into areas of lower habitat quality or wait their turn until the more dominant animals are satisfied a nd leave the area (Bennett et al., 1985; Bennett and Holmes, 1987; Broom and Leaver 1978). Therefore, management strategies that involve social composition (e .g., herding, selective culling) can be used to relieve grazing pressure on environmentally sensi tive areas (Sowell et al., 1999). Apart from the various factors mentioned above there are plentiful ot her factors that may be responsible in influencing ca ttle distribution dynamics. Schacht et al. (1996) have categorized four techniques that can be employe d for improving graz ing distribution: Enticing the grazing animal to forage Water placement Salt and mineral placement Supplemental feeding location Rubs and oiler placement Other methods (mowing, burning, shade etc.) Pasture characteristics Fencing Pasture size Pasture shape Grazing management strategies Rotational Grazing Stocking density

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28 Flash grazing Season of grazing Livestock considerations Class of livestock Vegetation and terrain characteristics Cattle Location and Water Quality Considerable research pertai ning to water quality impacts of grazing systems have been well documented in the western states of US A (Belsky et al., 1999; Buckhouse and Gifford, 1976; Miner et al., 1992; Nader et al ., 1998). Numerous studies have specifically ta rgeted cattle distribution patterns relative to wa ter locations and riparian areas (Dickard et al., 1998; Gillen et al., 1985; McIlvain and Shoop, 1971; Owens et al., 1991; Roath and Krueger, 1982b; Sneft et al., 1985). Results from all the above studies have indi cated water to be an influencing factor in cattle distribution patterns. In a cattle ranch system with stream there is concern of di rect contamination within the stream and significant impact on riparian areas. These impacts depend upon cattle behavior and utilization of riparian vegetati on (Marlow and Pogacnik, 1986). Catt le prefer to be closer to water sources while grazing. This situation can lead to defecation, and eventually over enrichment of the water bodies. Hi gh-density cattle ac tivities near or on the stream banks can result in rapid transport of manure to the stream s (Bottcher et al., 1995). Apart from direct input of nutrients into the stream, grazing near stream ba nks can also result in increased erosion of the stream banks (Helfrich et al., 1998 ). Bowling and Jones (2003) lis ted four key potential impacts grazing cattle can have on water quality: Increased suspended sediment concentrati ons, due to the physical stirring up of the bottom sediments when cattle are in the wate r, and due to increased sediment run off from grazed foreshore areas. Input of organic materials causing effects su ch as increased biological oxygen demand.

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29 Increased nutrients by both direct depositi on into the water or entrained in run off entering the water body. Increased fecal bacteria and potential pathogenic mi croorganisms, again through defecation straight into the water, or in run off from nearby areas. Cattle grazing and resting pattern will change with respect to water availability, climate, presence of shade structures, and forage quant ity and quality. Water seems to be the driving force in attracting cattle towards in and around st ream areas. Cattle wade into the shallow water to graze on aquatic plants, to dr ink the water, and to wallow in it and remain cool on hot days (Gary et al., 1983; Hagedorn et al ., 1999). However, even when availability of water is not a limiting factor still, cattl e are known to spend significant time in grazing within th e riparian area due to availability of higher quality forage. Existing Modeling Approaches Model development is a crucial step in representing such a diverse ecosystem and it will help define problems, organize our thoughts, develop an understanding of the data and eventually be able to make predictions. Ther e are various approaches for modeling population response to environmental patt ern. Following are some modeling methodologies that have been widely utilized in the scientific community. Regression Models In some early model development effort pert aining to cattleÂ’s spatial distribution, Cook (1966) used multiple regression equations to expl ain livestock spatial utilization patterns. The same methodology was later used to predict spatia l patterns of cattle behavior over an entire landscape (Sneft et al., 1983, 1985a, 1985b). Data us ed to develop the regression model were collected on the USDA-ARS Central Plains Experi mental Range in northeastern Colorado during 1970-1973 (Sneft et al., 1983). Observations of cat tle movement were made by following cattle on foot for one 24-hour period during each month of the study period on two small paddocks, 11

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30 ha and 22 ha. Over 60 independent variables were screened and seven were eventually incorporated for analysis using stepwise multiple regression (Table 2-1). In another observational study over a 2-year period (June 1980 through May 1982) at the same site, researchers derived regression models of spatial patterns of graz ing (Table 2-2) (Sneft et al., 1985b) and resting (Table 2-3) (Sneft et al., 1985a). It was concluded that even though mathemati cally the models are boundless (i.e. can be applied to pastures of any size), it was noted th at the models do not cons ider interactions among variables. Hence, introduction of a complex herd structure mi ght require more complicated mathematical descriptions of spatial use. Snef t et al., (1983) also acknow ledged that even though the models are “fine-grained” in space, they are “coarse-grained” in time This indicates that on finer time scale, daily temperature variations, for example, may have an effect on the behavior of cattle. These limitations have been overcome by using a different technique known as habitat suitability index (HSI) m odeling (Cook and Irwin, 1985; Schamberger et al., 1982). Habitat Suitability Index Models The U.S. Fish and Wildlife Services developed a methodology known as Habitat Evaluation Procedures, a pla nning and evaluation t echnique that focuses on the habitat requirements of fish and wildlife species (U .S. Fish and Wildlife Service, 1980). These procedures were formulated according to standa rds for the development of Habitat Suitability Index (HSI) Models (U.S. Fish and Wildlife Service, 1981). The HSI models are usually presented in three basic formats: (1) graphic; (2 ) word; and (3) mathematical (Schamberger et al, 1982). The graphic format is a representation of the structure of the model and displays the sequential aggregation of variables into an HS I. Following this, the model relationships are discussed and the assumed relationships betw een variables, components, and HSI's are documented. This discussion of model relationships provides a working version of the model and

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31 is, in effect, a model described with words. Fi nally, the model relationships are described in mathematical language, mimicking as closely and as simply as possible, the preceding word descriptions. HSI provides a probability that the habitat is suitable for the species, and hence a probability that the species will o ccur where that habitat occurs. If the value of the index (Range 0 to 1) is high in a particular location, the chan ces of that species occurrence in that location are high. For example, HSI of 0 woul d mean totally unsuitable hab itat, whereas HSI value of 1 would mean optimum habitat. To determine habi tat suitability, suitabi lity indexes (SI) are assigned to represent the degree in which the vari able may contribute to species life requisites (Hohler, 2004). The SI score is based upon empirical data, professional wisdom and at times, inspired guesses (U.S. Fish and Wildlife Service, 1981). Spatial location of herbivores has challenged many researchers who have tried to model their distribution (Bailey et al., 1996; Coughenour, 1991; Pringle and Landsberg, 2004; Wade et al., 2004). In an invited synthesis paper, Coughe nour (1991) provided impor tant insights into models that integrate plant growth, ungulate movement, and foraging. A variety of modeling approaches was discussed and HSI modeling wa s accredited of overcom ing the limitations of multiple regression models (application constraints) Bailey et al. (1996) developed a conceptual model to demonstrate how cognitive foraging mechan isms can be integrated with abiotic factors to predict grazing patterns of large herbivores. Abiotic factor multipliers were used in the modeling systems which are similar to HSI models. As an example of a typical HSI model, a step by step illustration of a HSI model development is given by Allen et al. (1984) in a U.S. Department of Interior document. This document is one in a series of publications that provides information on the habitat requirements

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32 of selected fish and wildlife species. In this particular document, the HSI model was developed for pronghorn ( Antilocarpa americana ) chiefly for application for the Great Basin and the Great Plains region for winter weather. This simplistic model assumed the winter habitat characteristics to be the most limiting conditions affecting pr onghorn distribution. The model is based on the assumptions that pronghorn survival and repr oductive success are func tions of winter food availability. The model incorporates vegeta tion and topographic feat ures that favor food availability under mild snow conditions. After de tailed review of literature describing the relationship between habitat vari ables to the pronghornÂ’s preferen ce; the authors synthesized all the information and identified six variables of interest: Percent shrub crown closure (V1) Average height of the shrub canopy (V2) Number of species present (V3) Percent herbaceous canopy cover (V4) Amount of available hab itat in winter wheat (V5) Slope of land (V6) Each of these six variables has their respectiv e suitability index relationships as shown in the Figures 2-2 and 2-3 and synthesized in equation 2-1. 5 3 / 1 4 3 2 1] ) ( [ SIV SIV SIV SIV SIV WFI (2-1) Equation 2-1 accounts only for the forage factor towards the overall HSI where WFI is an index representing the forage preference of th e pronghornÂ’s diet. The geometric mean of the three variable indexes (SIV2, SIV3 and SIV4) in the equation 2-1 is a compensatory function. This function is used in multiplicative models so that partial compensation of the interacting variables is accounted for (U.S. Fi sh and Wildlife Service, 1981). The three variable indexes are assumed to have equal value, meaning that all three must be 1.0 (optimum) in order for this function to be optimum. Also, a unit increase (e.g., increase an SI by 0.1) in the variable index is

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33 assumed to have the greatest positive impact on the overall index (WFI). This relationship (Equation 2-1) is combined with the suitability in dex of the sixth variable, slope of land (Figure 2-4), to calculate the co mbined food/cover index. 26SIV WFI WFCI (2-2) The HSI is equal to the WFCI as calculated in equation 2-2. Allen et al. (1984) extended the application of the model for evaluation areas that may comprise several cover types. To represent several cover types it was suggested to multiply the area of ea ch cover type by its respective WFI value, sum the products, and divide by the total area of cover types to determine the area weighted WFI (equation 2-3) (Allen et al., 1984). n i i n i i iA A WFI WFI weighted1 1 (2-3) where n is the number of cover types, WFIi is the winter food index for individual non cropland cover type, and Ai = area of cover type i. A similar procedure was suggested to follow to determine the area weighted cover index (CI) value (equation 2-4). Once both the weighted indexes are computed an overall HSI value is determined by averaging the WFI and CI values. n i i n i i iA A CI CI weighted1 1 (2-4)

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34 where CIi = cover index value for each cover type. A similar HSI model (equation 2-5) for elk (Cervus elaphus nelsonii) has been documented by Thomas et al. (1988) for the Blue Mountain winter ranges of Oregon and Washington. The aut hors made use of some published as well as some unpublished data to derive a procedure for evaluating eff ectiveness of various habitat variables. N C F R S SRFCHE HE HE HE HE/ 1) ( (2-5) where: HESRFC is the habitat-effectiveness index, allowing for the interaction of HES, HER, HEF, and HEC, HES is the habitat-effectiveness index deri ved from size and spacing of cover and forage areas, HER is the habitat-effectiveness index derive d from the density of the roads open to vehicular traffic, HEF is the habitat-effectiveness index de rived from the quantity and quality of forage available to elk, HEC is the habitat-effectiveness i ndex derived from cover quality, and 1/N is the Nth root of the product taken to obtain the geometric mean. The mean reflects the compensatory interaction of the N factors in the habitat-effectiveness model. Similar to the pronghorn HSI model the geometric mean is also used in this model as a compensatory function. The authors also incorporated gra phical representation of the index resulting from raising any product derived from (HES HER HEF HEC) to the power of 1/N (1/4 in this case) (Figure 2-5). In a more recent application, HSI technique ha s been utilized by the South Florida Water Management District (SFWMD), for evaluating wa ter management alternatives in the greater Everglades ecological system, extending south of Lake Okeechobee in South Florida (Tarboton et al., 2004). In their study, Tar boton et al. (2004) used conceptu al ecological models to help define water-dependent habitat suitability indices for select ecosystem indicator species and

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35 landscape features. The first step in the process of defining habitat suitability functions was to identify the indicator that would serve as a su rrogate for the entire ecosystem. Six different indicators were identified: three were landscape features and remaining th ree were fish, alligator and wading birds. After identifying the indicator features and animals, the next step was to determine the hydrologic vari ables, attributes, or characteristics that affect the se lected indicator feature and animals. Examples of hydrologic variables used are water depth, flow direction, and hydroperiod. Once the specific hydrologic variables were selected for each feature or animal, the next step was to identify the re lationship between those variable (Figure 2-6) values and the relative conditions of the indi cator features or animals. These functions were based on observed data and expert opinion. Once defined, these HSI functions were combined with time series of hydr ologic values to obtain an overall time series of ecosystem habitat suitability values (Figure 2-7). Eventually, based on time series values of multiple suitability functions, composite value were obtained (Figure 2-8). To obtain composite performance indicator va lues geometric means, weighted arithmetic means, and maximum or minimum values were used. The methods selected for combining different habitat suitability functions for the same ecosystem feature or sp ecies were determined during the calibration procedure (T arboton et al., 2004). The author s concluded that with this approach they were able to link ecology to hydrology in a way that would make it easy for anyone to understand, modify, test and evaluate this linkage. Mechanistic Models Herbivore and plant dynamics have also been modeled utilizing cl assical predator-prey relationship between two species in an ecosyst em (Noy-Meir, 1975). In some cases researchers have utilized an energy balance relationship to account for the balance between energy required

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36 for herbivore body maintenance and the amount gathered by foraging (FAO, 1991; Hobbs and Swift, 1985). Over time various simple as well as complex models have been developed which along with other processes also attempt to descri be animal responses to environmental inputs. The SAVANNA ecosystem model (Coughenour, 1993) is a spatially explicit, process-oriented modeling system developed to simulate ecosyst ems occupied by ungulate herbivores. The model is composed of several submodels, which descri be various processes and vary in complexity. The herbivory submodel simulates forage intake by diet selection, forage abundance and forage quality. An energy balance submodel simulates body weight of the mean animal of each species based on differences between energy intake and energy spent. Smith (1988) described a detailed mechanistic model in which they added a behavior al sub-model to simulate the ecology of an arid zone sheep paddock in past oral areas of south Australi a. The spatial component was included in the model by dividi ng the paddock into cells of 500*500 m2 and modeled on an hourly timestep (movement of sheep while gr azing is 500 m/hr). Movement of sheep was determined by the state of its f our physiological criteria: heat st ress, thirst, hunger, and darkness. Each of these criteria was define d in a hierarchy of trigger leve l conditions which determines the dominant trigger and consequently determines where and at what speed animal movement will take place. Metapopulation Models Levins (1969, 1970) defined metapopulation as “population of populations”; in which distinct subpopulations (local popul ations) occupy spatially separate d patches of habitat. In other words metapopulation is a patchy distribution of populat ion in which species ex ist in clusters that are either isolated from one a nother or have limited exchange of individuals (Akakaya et al., 1999). It is a network of semi-i solated populations with some le vel of regular or intermittent

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37 migration among them. In a review paper Hanski (1998) distinguished between three approaches to large scale spatial ecology (Figure 2-9). The approach of theoretical ecology assumes homogenous continuous or discrete (lattice) space and the model does not incorporate any en vironmental heterogeneity. On the other hand, landscape ecologists have developed models that are very descriptive of the complex real environment. Hanski (1998) termed metapopulation models as a “compromise” where landscapes are viewed as networks of idealized habitat patches in which species occur as discrete local populations connected by migr ation. Metapopulation models are spatially structured so that they incorporate information about habitat relationships and the characteristics of the landscape in which the metapopulation exists (Akakaya, 2001). RAMAS has been a popularly used model which includes metapopulation dynamics integr ated with GIS (Applied Mathematics, 2003) (Figure 2-10). Hanski (2004) pointed out that even though the idea of running simulation models using metapopulation theory may seem tempting as it can be applied to any kind of population, it is however prone to problems. Firs tly, validating a complex simula tion model will be virtually impossible, and secondly, the simulation approach will yield specific results rather than more general understanding. A good example of such a modeling approach has been exemplified in Schtickzelle and Baguette (2004) where the researchers modele d the metapopulation dynamics of the bog fritillary butterfly utilizing the abovementioned RAMAS/GIS. The model was validated by comparing the predicted and observe d distribution using the same empirical data that were used to estimate model parameters. It is therefore important that modelers be careful in the construction and parameter es timation of models using metapopul ation theory. Hanski (2004) has therefore, repeatedly emphasi zed that the classica l metapopulation theory are most useful for

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38 examining the dynamics of metapopulations li ving in highly fragmented landscapes. Such landscapes are in which the suitable habitat for the focal species accounts for only a small fraction of the total landscape area, and where the habitat occurs as discrete fragments. Spatially Explicit-Individual Based Models Spatially explicit popula tion models are increasingly being used in modeling animal populations and their movements (D unning et al., 1995). These models can be simple as well as complex. The extreme of simplicity in population models are the patch occupancy models that are based on the number of occupied populations. On the other hand, the extreme of complexity are the spatially explicit indivi dual/agent based models, which describe spatial and habitat information at the individual level. Logan (1994) has pointed out that complex systems need complex solutions. The complexity of the proce sses involved in ecosystem, has compelled the modelers to accommodate processes that vary acro ss wide range of spatial and temporal scales (Levin, 1992). Modelers of aquatic ecosystems have realized the constraint a limited spatial scale simulation poses towards model accuracy and us efulness towards decision making. Individualbased-model (IBM) is a relatively new approach in ecology. In an individual-based model, the characteristics, behavior, growth, reproduction et c. of each individual is tracked through time. This system is different than the commonly us ed modeling techniques wh ere the characteristics of the population were averaged together (Rey nolds, 1999). These models provide ecologists with an effective way to e xplore the mechanisms through which population and ecosystem ecology arises from how individu als interact with each other a nd their environment. IBMs are also known as entity or agent based models, a nd as individual/entity/agent-based simulations. Similar to individual-based, agent-based models has also been utilized for simulating animals with comprehensive and dynamic landscape struct ure (Topping et al., 200 3). More recently Ovaskainen and Hanski (2004) derived a stocha stic patch occupancy (SPOM) model from an

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39 IBM, where individuals obey th e rules of correlated random wa lk. This unique and novel modeling framework generates emigration and immi gration events in a mechanistic manner and avoids the need for particular assumptions a bout how the areas and c onnectivities of habitat patches influence migration. It was concluded that in spite of being simplistic the SPOM replicated the behavior of IBM remark ably well (Ovaskainen and Hanski, 2004). Numerical Fish Surrogate Model It is for this reason, Nestler et al. (2001) utilized a particletracking algorithm with stimulus-response rules to develop a Numerical Fish Surrogate (NFS) system (Goodwin et al., 2001), which creates virtual fish that are capable of making individual movement decisions based on spatial physiochemical and biological in formation. The Numerical Fish Surrogate uses a Eulerian-Lagrangian-agent method (Goodwin et al., 2006) for mechanistically decoding and forecasting movement patterns of individual fish responding to abiotic stimuli. An ELAM model is an individual-based model (IBM) coupling: Eulerian framework to govern the physical, hydrodynamic, and water quality domains Lagrangian framework to govern the sensory perception and movement trajectories of individual fish Agent framework to govern the beha vior decisions of individuals. The modeling-philosophy behind ELAM is base d upon two major theoretical approaches that are coupled to represent the movement of fi sh (Nestler et al., 2005). Eulerian and Lagrangian approaches are the two frameworks that have been integrated in ELAM. The former approach is utilized by engineers to describe the physiochemi cal properties in hydrau lics, while the latter approach, used by biologists, is mostly cente red on the stage development and movement patterns of particles or indivi duals. The developers of ELAM hypot hesize that by marrying these two frameworks into a coupled Eulerian-Lagrang ian (CEL) hybrid method, they can maintain the

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40 integrity of individuals while c oncurrently simulate the physiochem ical properties of the aquatic ecosystem that affects fish’s movement. The hydrodynamic and water quality module of the CEL hybrid model is CE-QUAL-W2 Version 3.0 (Cole and Wells, 2000), a 2-D laterally averaged model developed at the U.S. Army Research and Development Center The coupler in CEL Hybrid model is based upon particle-tracking-algo rithm that uses equations for computation of forcing functions in the longit udinal and vertical directions. The temporal scale of ELAM is exceptionally low, i.e. 2 sec time-step. Modelers argue that to produce better fit of the fish’s movement in the vertical directi on short time-steps are essential. Given the fact that ELAM is an individual-based model, i.e., it tracks the behavior movement of an “individual” fish at each time step, the mathematical computations become massively demanding. It is for this reason that the model is currently ru n on U.S. Army Major Shared Resource Center supercomputers. The computationa l infrastructure of the model (as of June 2004) handles simulations of 5,000 virtual fish in approximately 11 hr of run time (20,000 2-sec time steps). More recently, Goodwin et al. (2005) have realized the involvement of substantial run time associated with the mathematical computat ions of this model and have tried to increase the computational efficiency by simulating more virtual fish in far less simulation times. Multi-Agent Systems Recently, several researchers have started to use multi-agent systems (MAS). MAS is similar to agent-based modeling, but are more influenced by computer sciences and social sciences (Bousquet and Page, 2004). MAS give mo re prominence to the decision-making process of the agents and to the social organization in which these ag ents are embedded. Ferber (1999) has defined a multi-agent system being composed of: environment, objects, agents, and relations and operations. MAS has been eff ectively used in variety of cas es, for example: modeling of sheep’s spatial memory (Dumont and Hill, 2001 ), prediction of duck population response to

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41 anthropogenic cases (Mathevet et al., 2003) a nd predict the effects of alternative water management scenarios in south Florida on the long-term populations of white-tailed deer and Florida panther (A bbott et al., 1995). Cattle Tracking Techniques To develop comprehensive grazing management strategies to impr ove water quality in watersheds consisting of beef cattle ranches, it is imperative to develop an understanding of cattleÂ’s usage of water locations. This often invol ves observation of cattle movement in a pasture setting. Earlier studies involved ex tensive field observations and in most cases observations were limited to daylight only (Tanner et al., 1984). Research involving visual observations of the cattleÂ’s position and its actions ar e prone to error as the observe r can alter cattle behavior and make visual errors. In such studies, observati on periods are generally short due to its labor intensity and concerns over observer fatigue. In s ubtropical regions such as south Florida, night time observations can be critical because cattle exhibit bimodal grazing patterns (early morning and evening) and with less adapted breeds of cattle spending a greater portion of the night grazing as compared to day time (Bowers et al., 1995; Chase et al., 1999; Hammond and Olson, 1994). Global Positioning System (GPS) and Geographical Information System (GIS) technology allow livestock grazi ng behavior and management to be evaluated with greater spatial and temporal resolution (Ganskopp, 2001; Turner et al., 2000; Ungar et al., 2005). Animals can be tracked on a 24-hour basis usi ng GPS receivers incorporated into collars. Agouridis et al. (2004) tested GPS collars under static (open fi eld, under trees and near fence) and dynamic conditions to evaluate their accuracy for applications pertaining to animal tracking in grazed watersheds. Their results indicated that the collars were accurate within 4 to 5 m, deemed acceptable for most cattle operational areas Collars can also record ambient temperature and number of vertical and hor izontal head movements. Head movements can be used to

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42 determine grazing time and differentiate animal activity (resting or graz ing) between location fixes. Location and other programmed data are stor ed in the collar, and animals must be caught and the collar removed to retrieve the data. With more recent technical advancement, Cattle Traq LLC, an affiliate of American Biomedical Group Inc. located in Oklahoma City, has developed software capable of monitoring cattle and recording internal body te mperature. Cattle Traq is an integrated system of microchips located in ear tags, access control sensors and proprietary software (ABGI, 2005). It operates with radio frequency waves sent fr om ear tags to software that d ecodes the signals and translates them into usable information. Summary In their thorough review on grazing impacts on str eam water quality in the southern region of USA, Agouridis et al. (2005) credited the plentiful graz ing studies of the western and midwestern USA; but, also acknowle dged that the differences be tween the arid west and the southern humid region prohibit the universal transf er of research results. Models and concepts developed elsewhere cannot be applied to the un ique agro-ecosystems of the south-east (Platt and Peet, 1998) such as south Florida. A limited number of grazing studies in the southern humid regions (Tanner et al., 1984; Z uo, 2001) have provided valuable, yet incomplete information with regard to the extent, if any, of water qual ity degradation by the graz ing beef cattle in the southeastern USA. In recent times, with the advancement in co mputational power, researchers have exploited new advanced computer-based technologies for the development of ecological simulation systems. Primarily, the research in ecological m odel development has been greatly concentrated in utilizing enhanced computer technology to incorporate the de tails of ecological phenomena. The primary goal when building an ecological mode l should be to incorpor ate the knowledge and

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43 understanding of a systemÂ’s patterns and processes into a computerized t ool that will simulate the way in which the real system would behave under specific conditions Simple models often achieve this goal; they have simplistic assu mptions, and can function with limited data. However, they might neglect detailed aspects such as spatial heterogeneity and individual variability. Alternately, complex models incorporate the details of ecological phenomenon but, are often criticized because they are difficu lt to understand, parameterize, and hard to communicate. Individual based m odels are good examples of complex modeling systems. These models are useful classroom exercises to demons trate effects at fine sc ale. Unfortunately, the behavior rules at individual le vels are poorly known and therefore modelers have to rely on stochastic mechanisms. Another limitation of thes e models is the exorbitant computation demand to represent large number of animals over large areas. 0 20 40 60 80 100 120 100200300400500Distance from water (yards)Herbage Yield (lb/acre ) Figure 2-1. Average Herbage Yiel d (lb/acre) of perennial gra sses (1959-1966) from year long access to water on southern Arizon a range (Martin and Ward, 1970).

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44 A B C Figure 2-2. The relationships between shrub hab itat variables and suitability index (SI) values for pronghorn winter food quality. A) Percent shrub crown cover. B) Average height of shrub canopy. C) Number of shrub species present per cover type (adapted from: Allen et al., 1984). A B Figure 2-3. The relationships between two variables of forage diversity and suitability index (SI) values for pronghorn winter food quality. A). Percent herbaceous canopy cover B) Percent of available habitat in winter wheat (adapted from: Allen et al., 1984).

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45 Figure 2-4. The relationship between mean t opographic diversity and suitability index (SI) values for pronghorn winter food quality (adapted from: Allen et al., 1984). Figure 2-5. Graphical represen tation of the index (adapted from: Thomas et al., 1988).

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46 Figure 2-6. Two performance suitability indi cators expressed as functions of hydrologic variables (adapted from : Tarboton et al., 2004). Figure 2-7. A time series of values of a suita bility indicator derived from time series of hydrologic variable values (adapt ed from: Tarboton et al., 2004).

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47 Figure 2-8. Creating a composite suitability indi cator time series from multiple suitability indicator time series (adapted from: Tarboton et al., 2004). Figure 2-9. Three approaches to spat ial ecology (adapted from: Hanski, 1998).

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48 Figure 2-10. Using GIS in metapopulation models (adapted from: Applied Mathematics, 2003). Table 2-1. Significant predicto rs of cattle behavior (adapt ed from: Sneft et al., 1983). Pasture Characteristic Distance From Behavior Water Fence Corner Elev. Aspect Cactus Freq. Slope r2 Grazing + Travel S* S S S 0.50 Summer Resting S S S S 0.34 Winter Resting S S S 0.25 Bedding S S S S 0.20 Functional form in models X 1 X 1 X 1 X 1 ) ( k X Cos X X 1 *S denotes a pasture variable statistically significant in predicting the distribution of a given behavior at the 0.001 level.

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49 Table 2-2. Coefficients in th e seasonal grazing models (ada pted from: Sneft et al., 1985b). Independent variable1 Season Proximity to water Oppo Freq. Agsm Freq. Eref Freq. Sihy Freq. Bogr rel. abund Constant r Growing (Apr-Oct) 438.0 -.104 .316 .039 4.30 .460 Dormant (Nov-Mar) 350.0 -.010 -.109 .014 .50 .269 Mathematical express in model2 11X 2X 3X 4X 5X 7 5 100 X X X c 1 Species symbols are defined in text 2 X1 = distance from stock tank (meters) X2 to X5 = percent frequency to plant species X6 = biomass of blue grama (Bogr) in community (g/m2) X7 = biomass of all plant species in community, excluding pricklypear (g/m2) Table 2-3. Coefficients in the seasonal daytim e resting models (adapted from: Sneft et al., 1985a). Independent variable Season Proximity to water Proximity to fence corners Elevation Aspect Constant r Warm (Jun-Aug) 416.45 157.71 11.25 -1.89 .426 Cool (Sep-May) 408.80 106.60 5.34 k4 -1.51 .555 Mathematical express in model* 11X 21X 31X ) cos(4X c X1 = distance from stock tank (meters) X2 = distance from nearest fence corner (meters) X3 = elevation above 1646 m contour (meters) k4 = 0.600 (cos(0.5236(month-12)) X4 = degrees deviation from due south

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50 CHAPTER 3 ANALYSIS OF GPS COLLAR DATA Prolonged hot summers in the region of sout h Florida can cause physiological heat stress in cattle and drive them into sh ade and water-filled d itches and wetlands to cool down. The main focus of this chapter is to qua ntify the amount of time spent by grazing cattle near or in water locations (wetlands, ditches and water troughs) across seasons in a cow-calf production ranch in south Florida. Partial data were used to conduct analysis on the im pact of shade st ructures, total distance traveled and total area ut ilization. This chapter is divided into five sections: description of the study site, data collection, methodology utilized to quantify the time spent by cattle near/in features of interest, results, and fina lly some conclusions from the analysis. Study Site: Buck Island Ranch The MacArthur Agro-ecology Research Center Buck Island Ranch (BIR), Lake Placid, Florida, USA (27o 09Â’N, 81o 12Â’W) (Figure 3-1) is representa tive of the agro-ecosystem that exists in the Okeechobee waters hed. BIR (4,168-ha) is a full-scal e commercial cattle ranch owned by the John D. and Catherine T. M acArthur Foundation and leased to Archbold Biological Station and is located in the central portion of the Indian Pr airie/Harney Pond Basin, one of five major tributary basins of the Lake Okeechobee watershed. The ecology of this ranch is composed of a mosaic of habitats that in cludes open grasslands, fore sts, and wetlands which support a diverse and productive community of wildlif e and plants (Art hington et al., 2006; Swain et al., 2006). This ranch is representative of much of south Florida which was once a native, subtropical, wet-prairie ecosystem. The ra nch has been mostly drained and converted to improved pasture; however, some patchy wetland ar eas still exist. For more than 10 years BIR has been a platform of comprehensive interd isciplinary agro-ecological research (MAERC, 2005). The key goals of ongoing research efforts are to quantify the effects of various

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51 management practices on surface water quality and protection of the na tural biodiversity of ranches while maintaining the ec onomic viability of the ranching industry in central Florida. The BIR is primarily a commercial ranch a nd therefore ranch operation management is designed to support animal performance while op timizing the amount of beef production per unit area of land. Cattle are rotated among the pastures to maximize the available forage for grazing cattle. Cattle are stocked for longer periods in the improved pa stures (typically during summer season, May through October) and shorter periods in semi-native pastures (typically during winter season, November through April). Two re asons necessitate this management strategy: firstly, summer pastures are fertilized (NH4NO3 – 56 Kg N/ha) (Arthington et al., 2006) in spring and, therefore, have better forage quantity and qu ality compared to winter pastures which have never been fertilized (Swain et al., 2006). Secondl y, winter pastures are less intensively drained and as a result they are regularly flooded during the rainy season in summer. Rotation to winter pastures provides time for the summer pa stures to recuperate from active grazing. This study was conducted over three years from 2001 to 2003 at BIR. Since the main objective of this chapter is to quantify the time spent by cattle near water locations only the physical description regarding ditches and wetlands is presented here. Detailed description of the presence of pasture and wetland vegetation species has already b een reported by Swain et al. (2006); whereas, soil information has been reported by Capece et al. (2006). Summer Pastures Summer pastures (Figure 3-2) c onsist of eight (S1-S8), approximately 20 ha fields (range = 19.0 to 22.1 ha) with bahiagrass ( Paspalum notatum ) as the dominant forage species. These pastures are located on soil types that for the area are considered relatively well drained. Pastures S1 and S8 serve as control fields and were not stocked. The drainage ditch network in these pastures is comprised of two orders of ditche s: deep ditches (0.6 m deep) that run north-south

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52 and receive flow from feeder di tches (0.3 m deep) that run eastwest approximately every 30 m. In the stocked pastures, the average total le ngth of the ditch network is 6175 m (range = 5793.5 to 6864.8 m) and the average area of wetlands is 0.90 ha (range = 0.20 to 1.57 ha). Water troughs were located at the north end of all stocked pastures (Figure 3-2). Winter Pastures Winter pastures (Figure 3-3) al so consist of eight fields (W1-W8), that are slightly larger, averaging 32.2 ha (range = 30.3 to 34.1 ha). These fi elds consist of mixture of forage species but were predominantly bahiagrass, and located on so il types that are consid ered poorly drained for the area. All fields, except W4 and W7 which se rved as controls, were stocked during the period of this study. Similar to summer pastures, wint er pastures have a ditc h network; however, W8 consists of an additional order (0.9 m deep) of di tch. In the stocked winter pastures, the average total length of the d itch network is 4437 m (range = 6618.2 to 2535.6 m) and the average area of wetlands is 3.28 ha (range = 1.58 to 5.66 ha). Runoff from summer and winter pastures drains in a collection ditch and is then conveyed into th e Harney Pond Canal which discharges directly into Lake Okeechobee. A summary of individual pastures, ditches and wetlands is provided in Table 3-1. Hydrologic Data As part of the ongoing water qu ality study (Capece et al., 2006) on-site climatological data and groundwater elevation data are collected for both summer and winter fields. All experimental pastures are bermed so that su rface water runoff from each pasture exits through a single trapezoidal flume. This study utilized climatological information (Table 3-2) in conjunction with groundwater level data (Figure 3-4) to estimate antecedent soil moisture conditions and consequently determine the presen ce and level of water in ditches and wetlands.

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53 The following criteria were utilized to determine the presence of water: A water table depth of 0.3 – 0.6 m was deemed to inundate wetlands, shallow and deep ditches A water table depth of 0.6 – 0.9 m was deemed to inundate wetlands, and deep ditches A water table depth of 0.9 – 1.2 m was deemed to inundate wetlands A water table depth of below 1.2 m wa s deemed to inundate no features GPS Data Cattle position data was monitored con tinuously using GPS collars (GPS_2200, Lotek Wireless Inc., Newmarket, Ontario, Canada). Thes e collars are relatively lightweight (950 gm) and primarily designed for use on smaller animals such as cattle, deer, wolves and bears. The manufacturer reports that with differential co rrection deployed, accuracies of position reading consist of errors that are less th an 5 m. For the purpose of this study, data were recorded every 15 min during a 5-day period in spring (March), summer (June), fall (late August), and winter (November or December) of each ye ar. These periods were select ed to be representative of environmental extremes or expected seasonal differences in forage quality and to fit in the standard animal handling routine of the ranch. Data collected includ ed: collar identification, latitude, longitude, temperature a nd time. A summary of the quantit y of GPS Data is provided in Table 3-3. Figure 3-5 shows typical GPS collar data on summer pasture 2 collected on June 11, 2001. The GPS point data have been joined by a line to illustrate the cattle’s sequential movement. Figure 3-5 represents a small portion of the large data set that was colle cted over the entire study period (27,924 Total GPS Location Fixes).

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54 Data Analysis To analyze the data collected from the G PS collars ArcView (ESRI, Redlands, CA) package was utilized. The first step in the analys is was to ascertain that the movement pattern was not random. To accomplish that, a Nearest Neighbor analysis was performed. Developed by Clark and Evans (1954) for work in the field of botany, Nearest Neighbor method computes the ratio (R) of distance between near est points and distances that would be expected on the basis of chance. A freely available software that is an add-on extension to ArcView Animal Movement extension (Hooge and Eichenlaub, 1997) was utili zed to perform this statistical technique. For cattle location analysis, all the fix data (latitude, longitude format) was converted to UTM Cartesian coordinates (NAD 83, Zone 17N) fo r analysis with other features. The buffer distance that was utilized for the features were : wetland = 5 m, ditch = 2 m, water trough = 20 m and shade = 5 m. The extensive ne twork of the ditches is a unique feature in these pastures as they occupy a considerable area of the pastures. The buffer distance was assumed to be 2m on each side of the line coverage to represent the narrow nature (2 m width) of this shallow ditch system. A more flexible (5 m) buffer was utili zed for the wetlands to capture the presence of cattle in the transitional (ecotone ) areas of the wetlands which can be wet or dry depending upon moisture conditions. Cattle do not spend much time in actually drinking water (Wagon, 1963). Therefore, to capture their pres ence near the trough the buffer fo r water trough was set to be at 20 m. Shade structures (5m by 5m) were present at the north end of all stocked summer pastures. Apart from the shade structures and few patchy trees in SP5, there was complete absence of natural shade. Winter pastures di d not contain any shade structures as most of them had natural shade from trees. The buffer used for shade structures was 5 m. The data points that existed within the buffer z one were compared with total data points for a day (typically 96). Consequently the data were converted into a percentage of time for a given

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55 day. In addition, temporal dynamics with regards to the utilization of water features were identified by categorizing hours of the day into 4 time zones: (a) Early Morning (12:00 am to 6:00 am) (b) Late Morning (6:00 am to 12:00 pm ) (c) Afternoon (12:00pm to 6:00 pm) and (d) Night (6:00 pm to 12:00 pm). Animal Movement ex tension was further utilized to compute total distance traveled by collare d cattle and a minimum convex polygon (MCP) home range. Statistical analysis for comparison of mean percentage of time was performed using JMP Statistical Software (SAS Inst itute, Inc., 2005). Tukey-KramerÂ’s Honest Significant Difference (TukeyÂ’s HSD) test was performed. An alpha le vel of 0.05 was accepted as a nominal level of significance and results were cons idered statistically significa nt when a P < 0.05 was obtained. Results and Discussion The test of nearest neighbor analysis for complete spatia l randomness was performed for all data. Values close to R = 1.0 indicate that the observed averag e distance is the same as the mean random distance, suggesting that the spread of data is random. However, R values < 1.0 imply that the observed distance is smaller than the mean random distance, suggesting that data is clustered. The average R value during summ er was 0.51 (range = 0.80 to 0.13) and the average winter R value was 0.47 (range = 0.74 to 0.11), s uggesting that the data was non-random and the GPS fixes displayed more clustering during winter than summer months. Climatological information (Table 3-2) and groundwater level data (Figure 3-4) were utilized to make hydrologic judgment regarding the presence and level of water in ditches and wetlands. This information was especially useful when making judgment regarding presence of water in shallow or deep ditches. Table 3-4 summarizes the estimated presence and location of water during different time periods of the study.

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56 Since temperatures in summer and fall are often similar (Table 3-2) these two seasons were grouped into one category of warm period. Accordingly, spring and winter was combined into a cool period category. Average percentage of dail y time spent by cattle near/in all possible water locations (water trough, wetland and ditch) was relatively low (<15% of 24-hr period) compared to the remainder of the pasture area, but was higher (P<0.01) during the warm than the cool period (11.45 0.39%[(mean s.e.; n = 215] vs. 6.09 0.69% [mean s.e.; n = 160], respectively). Statistical analysis was performe d to compare means of percent utilization of individual water features in diffe rent Seasons and also all water features within the same Season. For example, wetland use was stat istically the same and lower in all seasons except warm 2003 as indicated by the lowercase “b”. In warm 2001 th e use of all the three di fferent water features was not statistically different as indicated by the uppercase “A”. Wetland and ditches had simila r, higher cattle presence co mpared to troughs (4.410.35 and 5.290.38 % vs. 1.970.18%, respectively) across periods (Table 3-5 and Figure 3-6). This was not unexpected because wetlands and ditche s buffer areas (approx. 20% of average pasture area) was much larger than the buffer area of wa ter troughs which were es sentially a single point. Utilization of the water sources differed within periods. This difference was mainly due to a lower than average utilization of water sour ces in warm 2001. Unlike what was found in the other years, utilization in warm 2001 was similar to what was found in cool 2001-02. Across periods and years, cattle utilizati on of the different water features remained fairly consistent with the exception of the ditch feature which showed higher use in warm periods and lower use in cool periods for all years except 2001. This may have been related to th e drier conditions that occurred during the summer sampling period that y ear (Table 3-4). During the summer sampling period in 2001, all ditch classe s and wetland areas were dry. This observation supports the

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57 hypothesis that cattle utilize wate r in ditch features for coolin g in addition to possibly for drinking or feed sources. Water trough use was consistently higher in th e warm periods than cool periods (Table 35). Since troughs could only be us ed for drinking water, this obs ervation supports what has been observed in other studies (Goodwin and Miner, 1996; Kelly et al., 1955 ; Miner et al., 1992; Sheffield et al., 1997) that cattle will preferentially use altern ate and clean sources of water for drinking. In contrast, the use of water-filled wetlands was fair ly consistent regardless of periods and did not differ, with the exception of an almo st doubling of average utilization of wetlands in warm 2003 (8.25% 2.11). The high wetland utili zation during warm 2003 can be explained by a single cowÂ’s strong affinity towards wetland. During the warm 2003 period, data were collected only from five collare d cows during the summer season ( no data were collected in fall, Table 3-3). Amongst the five cattle, one displaye d very high affinity towards wetland and ditches. Average percent of time spent by this specific cow in the wetlands was 24.95%, which is substantially higher than any ot her collared cow in any period. This individual cow entered the wetland every day (all 5 observed days) during 8am to 9am in the morning and remained in the wetland until 5pm to 6pm. Even if environmental factors are similar, di fferences in individual cattle behavior have been previ ously reported as well (Bailey et al., 2004). If the data from this individual cow are excluded, the average time spent in wetlands for the period of warm 2003 becomes 4.08%, which is similar to pe rcent utilization in other periods. There were two periods (spr ing 2002 and winter 2002) when cattle were stocked in summer pastures instead of winter (Table 3-3). Th is occurred because of prescribed burning of the winter pastures during spring 2002 and acci dental burning during winter 2002. The rotation of cattle due to fire events did allow the determination of whether cattle proximity to water

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58 location was influenced by differences in summer vs. winter pastures (s ize, average depth of water, forage differences, etc.) or driven by temperature. Spring 2002 and winter 2002 experienced 5.46% and 5.09% uti lization of all water features respectively. This result was consistent with cool season util ization of water feat ures by cattle and demonstrated that water usage in pastures was independent of pasture composition and forage quality. The amount of time cattle spent near each water feature during a 24-h period was investigated to identify any temporal dynamics associated with the use of these features. Water troughs were generally not utiliz ed during early mornings and ni ght time regardless of periods (Table 3-6). As expected, water trough usage wa s highest during afternoon times of all periods with the exception of warm 2001. In warm 2001 cattle utilized the trough more during late mornings than the af ternoon. Warm 2001 had the highest maximum daily temperatures of the whole trial (37.5C, table 2), and it has been ack nowledged that increased water consumption is a major response to thermal stress (Johnson a nd Yeck., 1964; McDowell, 1972). Drinking water may have a direct comforting eff ect by cooling the reticulum as well as by reducing the thermal load (Beede and Collier, 1986). Hence, it is possibl e that in periods of hot conditions such as Warm 2001 the cattle utilized the tr ough earlier to mitigate their th ermal stress. Data from late morning as well as afternoon of re maining periods reveals that ther e was always higher presence of cattle at the water troughs dur ing warm periods as compared to cool periods. This observation is in agreement with a previous study in which it was observed that in hot climates most water is consumed by cattle during two 4-h periods: 7 a.m. to 11 a.m. and 4 p.m. to 8 p.m., which were also the times when cattle grazed (Ittner et al., 1951). In early studies various researchers had established that water intake of cattle is a function of forage consumption and ambient temperature (Leitch and Thompson, 1944; Ritzman and Benedict, 1924; Winchester and Morris,

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59 1956). Hence, during warm periods cattle utilized water troughs more during their two grazing bouts. Unlike cattleÂ’s utilizatio n of water troughs, the presence of cattle in wetlands appeared to be similar across cool and warm periods but was variably distributed across times within periods (Table 3-7). Wetland utilization was consistently lowest ( 0.015 0.04, P<0.05) in the early morning hours and highest (1.590.18, P<0.05) in th e afternoon hours regardle ss of period. Late morning and night presence in wetlands was simila r and intermediate to the other two times of day, although there is a suggestion that period of year influenced the time of the day the cattle started utilizing wetlands. Cattle presence was not recorded during late mornings in the two of the warm periods; whereas, the data showed cons istent utilization of wetlands during the same time in the cold periods. The extraordinar y use of wetlands during warm 2003 has been explained in the previous secti on by the exorbitant use of wetland by one cow. As late morning is a time when grazing activity normally occurs in Florida (Bowers et al., 1995; Chase et al., 1999; Hammond and Olson, 1994), this data suggests that cattle were usi ng wetlands for grazing during the cool period but not during the warm period. Additionally, presence of cattle in wetlands during warm period afte rnoon hours, when grazing does not normally occur (Bowers et al., 1995; Chase et al., 1999; Ha mmond and Olson, 1994), also suggest s that wetlands were used for cooling and not grazing during the summer pe riod. Since wetlands ar e expected to be the deepest water containing feature in the landscape, it is reasonable to exp ect they would be used for cooling. In contrast, since it is unlikely that cattle would not need to cool themselves during the cool period, presence during the afternoon peri od in the cool season probably represents a continuation of the morning grazing bout into the afternoon period due to lower forage availability due to sl ower forage growth.

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60 Unlike wetland presence, cattleÂ’s presence in the ditches during all times of the day exhibited a fairly consistent pattern of being higher during the warm periods and lower in the cool periods (Table 3-8). The exception to this pattern was early and la te mornings of the 2001 warm period, when cattle presence was similar du ring warm and cool periods. Cattle can utilize the ditches for water as well as for higher quality of forage along the periphery of the ditches. Generally lower presence of cattl e in ditches during the cool pe riod may reflect differences in growth patterns of the forage species found in the ditches. Bahiagrass and bermudagrass were the dominate forage species in the ditch areas an d as warm season grasses, their growth rate would be lower in the cool pe riods of the year. Lower grow th rate and hence less forage availability of these grasses in the cool seas on would explain both, lower cattle presence in the ditches and higher cattle presence in the wetl and areas, which containe d more native forage species. Unlike wetlands, though, there was no consiste nt pattern for time of day within warm or cool periods. This suggests that cattle presence may not have been related to forage availability or the need to regulate body temperature, and ma y simply reflect an artifact of pasture design that necessitated a lot of ditches. Partial data were used to analyze the utiliz ation of shade structures in summer pastures. The results are presented in a box plot format in Figure 3-7. Error bars represent standard deviations. Summer 2001 was the driest season, wetlands and ditches are assumed to have no water presence and hence highest use of shade dur ing this season is expected. However, results indicate that cattle did not use shade in summ er 2002 and nominally in fall 2002. It is noteworthy that this analysis was conducte d using only partial data. Only tw o collared cows result was used for shade analysis for summer 2002. The error bars in Figure 3-7 illustrate the high variability in the use of shade. As mentioned before, it is possible that these two co ws are not representative

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61 of the herd behavior. Relatively low use duri ng Fall 2002 could be attributed to high rainfall during the five monitored days in this season. Using the animal movement extension, two ho me range analyses were performed on the entire data set. The first one was total di stance traveled and the second one was Minimum Convex Polygon (MCP). Total distance traveled is the sum of the length of polylines generated by joining GPS location fixes. MCP is the sma llest (convex) polygon which contains all points which the cattle has visited. It should be kept in mind that the MCP will also contain a lot of empty space that the animal never visited. Figu re 3-8 shows a typical MCP area in SP2 during the summer season of 2001. Both these analyses can be used in conjunction to get an understanding of the area covere d and effort made by grazing cat tle. Seasonal means of these two analyses is presented in Table 3-9. A season al pattern is evident in distance traveled by cattle. Cattle traveled more during cool seasons and less during warm s easons. In terms of MCP area covered by grazing cattle, both cool seasons were higher than warm seasons; however, Cool 2002-03 was the only season that was statistically higher than remaining seasons. Since forage growth is slower in the cool seas on, it is likely that ca ttle have to travel gr eater distances to look for palatable forage to meet their intake requi rements and in doing so, they browse a greater pasture area as well. Conclusion Beef cattle can utilize the water sources in so uth Florida to graze, to drink water, and to keep cool. During these activities, urination and de fecation can occur which can result in direct contamination of watered locations If BMPs are needed to mini mize the impact of beef cattle production on water bodies in south Florida, a bette r understanding of beef cattle utilization of natural (wetland) and artificial (ditches and water trough) water s ources is necessary. To quantify the amount of time spent by grazing cattle near or in water locations GPS collars were used. The

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62 GPS collars were successful in identifying, quantifying and eventually deriving pertinent information regarding cattle utili zation of water sources. Climatolog ical information was used in conjunction with observed groundwat er level data to make hydrol ogic judgment regarding the presence and level of wate r in ditches and wetlands. The data illustrated that there was higher presence of cattle near water locations during warm periods than in cool periods (11.45 0.39% vs. 6.09 0.69%). On a daily basis, cattle utilization of all water sources (as determined by % time present) was relatively low (<15% in a 24-hr period). Cattle seemed to utilize water trou ghs in a fairly consistent manner, going to water troughs earlier (late morning) and staying in th e area longer during warm periods, compared to cool periods when they went later (afternoon) in the day and for shorter periods of time. The presence of cattle in the wetlands was generally well distributed across all periods as well as all times (approx. 4% in a 24-hr period). Unlike wa ter trough utilization, cattle utilized wetlands considerably in the cool periods as well. This suggests that wetlands in Florida are used for different purposes at different times of the year. During the cool periods, cattle were present in wetlands when grazing would be e xpected to occur (late morning) indicating the need for feed was the driving factor. In c ontrast, during the warm periods, cattle were present when grazing was not an expected occurrence (afternoon), sugg esting that cooling wa s the reason the cattle were in the wetlands. Unlike wetlands, presence of cattle in ditches was generally higher in the warm periods than the cool peri ods; though there was no consistent pattern for time of day within warm or cool periods. This suggests that ca ttle presence in ditch areas may not have been related to forage availa bility or the need to regulate body te mperature, and simply reflect an artifact of pasture design.

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63 Another important factor this study identified wa s that there can be substantial variability in individual cow behavior. This was recognized by an exceptionally high presence of cattle in wetlands during the 2003 warm period, which was due to one individualÂ’s affinity towards wetland. It is perceived that during this period th is cow utilized wetland not only to drink water but to cool itself by staying in water for extended hours. It is s uggested that future studies deploy multiple GPS collars on cattle to account for vari ability in the population distributions. Shade, total distance traveled and MCP area also indica te seasonal utilization and browsing patterns in grazing. The result findings may be useful from a ranch management perspective. Knowledge regarding cattleÂ’s preference of water location wi ll be useful in developing a comprehensive understanding of the pasture utilization. Inform ation from this study is not comprehensive enough to design appropriate management strategi es to achieve targeted P load reductions. Nevertheless, this study does provi de useful information regardi ng cattle utilization of water features in sub-tropical-humid pastoral environments of south Florida. From BMP implementation perspective, information from th is study can be utilized in conjunction with other studies to suggest pertinent structural or managerial BMPs for this region. However, the installation or use of one structur al or management BMP will rarely be sufficient to solve the P loading problem. Combinations of BMPs (BMP System) that control the same pollutant are generally more effective than individual BMPs (Gilliam et al., 1997). In order for the BMPs to be successful in the unique settings of subtropica l agro-ecosystems of south Florida, they should be strategically tailored to be site sp ecific, effective, and cost efficient.

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64 Figure 3-1. Location of Buck Island Ra nch and the experimental pastures. Figure 3-2. Map displaying wetlands, ditche s and water troughs in summer pastures.

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65 Figure 3-3. Map displaying wetlands, ditche s and water troughs in winter pastures. GROUNDWATER ELEVATIONSUMMER PASTURE 35 10 151 /1 /2002 2/1/2002 3 / 1/2002 4/1/20 0 2 5/1 /2 0 0 2 6 /1 /2 0 02 7/1 /2 0 0 2 8 /1 /2 0 02 9 / 1/2002 10/ 1 /2 0 0 2 1 1/ 1 / 2002 1 2/ 1 /2 0 0 2GW Elevation (m)0 2 4 6 8 10 12Rainfall (cm) Rain (cm) Ground Elevation Ground Water Level Figure 3-4. Example of rainfall and gr oundwater level data in summer pasture 3.

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66 Figure 3-5. Typical cattle movement in summer pasture 2 on June 11, 2001

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67 Figure 3-6. Average % time spent near water locations. Figure 3-7. Average % time spent in shade structures.

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68 Figure 3-8. Typical MCP area in summer pasture 2 on June 31, 2001

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69 Table 3-1. Percent area (ha) of wetlands and ditches in summer and winter pastures. Summer Pastures Area (ha) Animal Units Ditch Length (m) Wetland Area (ha) % Area of Wetland % Area of Buffered Wetland ** % Area of Buffered Ditch ** S1* 22.04 0 4511.65 4.52 20.51 S2 19.01 20 5878.47 1.57 8.26 11.47 12.37 S3 20.42 35 6382.36 1.20 5.88 8.47 12.50 S4 20.49 15 5598.73 1.33 6.49 8.83 10.93 S5 20.95 35 6864.82 0.20 0.95 1.53 13.11 S6 19.49 15 6202.32 0.48 2.46 3.49 12.73 S7 19.22 20 5893.76 0.67 3.49 5.15 12.27 S8* 20.3 0 3463.35 2.95 14.53 Winter Pastures W1 33.23 15 2535.59 5.66 17.03 21.28 3.05 W2 31.3 20 2843.65 2.46 7.86 10.38 3.63 W3 33.64 35 4118.76 3.80 11.30 14.48 4.90 W4* 34.12 0 5243.47 0.90 2.64 W5 32.31 35 4848.63 3.35 10.37 13.59 6.00 W6 32.08 15 5656.41 1.58 4.93 6.92 7.05 W7* 30.24 0 6217.83 1.86 6.15 W8 30.27 20 6618.18 2.85 9.42 13.41 8.75 Control Pastures (Not Stocked) ** Assumes a 5-m buffer around wetlands and a 2-m buffer around ditches Table 3-2. Summary of climatologi cal data during the study period. Season Start Date End Date Av Temp Min Temp Max Temp Rainfall During Study Period (cm) Summer_2001 06/11/2001 06/15/200125.36 15.50 37.50 1.80 Fall_2001 08/27/2001 08/31/200126.26 17.00 37.00 0.66 Winter_2001 12/03/2001 12/07/200120.25 9.00 33.50 0.20 Spring_2002 03/04/2002 03/08/200215.49 3.00 33.50 0.03 Summer_2002 06/10/2002 06/14/200225.08 12.50 36.50 5.46 Fall_2002 08/26/2002 08/30/200224.56 19.00 35.50 8.35 Winter_2002 11/25/2002 11/29/200215.78 3.30 26.51 Spring_2003 03/03/2003 03/07/200323.25 16.99 31.44 Summer_2003 06/09/2003 06/13/200326.58 22.03 32.85 0.25

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70 Table 3-3. Summary of GPS colla r data in the experimental pa stures. The number before the parenthesis is the number of collars used within a pasture and number within parenthesis is the average daily fixes dur ing 5 day collection pe riod in each season. Summer Pastures Summer 2001 Fall 2001 Spring 2002 Summer 2002 Fall 2002 Winter 2002 Summer 2003 S1* S2 4 (92) 2 (87) 1 (95) 1 (96) S3 4 (91) 1 (95) 3 (85) 2 (95) 1 (96) 2 (96) S4 4 (91) 4 (91) 1 (93) 1 (89) 1 (96) S5 2 (94) 2 (94) 3 (95) 1 (81) 1 (96) 1 (94) S6 3 (91) 1 (95) 1 (96) 1 (96) 1 (95) S7 3 (92) 3 (84) 1 (96) 2 (84) 1 (96) 1 (94) S8* Winter Pastures Winter 2001 Spring 2002 W1 3 (95) W2 2 (96) W3 2 (96) W4* W5 3 (95) 2 (95) W6 2 (73) W7* W8 1 (96) 1 (95) Table 3-4. Locations that are assumed to have presence of water (water trough always contained water). Season Start Date End Date Water Presence Summer 06/11/2001 6/15/2001 Fall 08/27/2001 8/31/2001 W,DD Winter 12/03/2001 12/7/2001 W,DD Spring 03/04/2002 3/8/2002 W Summer 06/10/2002 6/14/2002 W Fall 08/26/2002 8/30/2002 W,D Winter 11/25/2002 11/29/2002W,DD Spring 03/03/2003 3/7/2003 W,DD Summer 06/09/2003 6/13/2003 W,D (W = Wetland, D = Both Shallow and D eep ditch, DD = D eep Ditches only)

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71 Table 3-5. Mean percentage of daily time spent by cattle near water locations (mean std error; n = days). SEASON* WATER TROUGH WETLAND DITCH Season Mean Warm 2001 2.790.34; n = 130 a A 4.120.98; n = 35 b A 3.830.76; n = 35 b A 7.900.68 c Cool 2001-02 0.640.15; n = 130 b C 3.490.32; n = 130 b A 2.420.34; n = 65 b B 5.510.42 c Warm 2002 3.660.70; n = 60 a B 3.130.42; n = 60 b B 9.931.04; n = 35 a A 13.161.16 b Cool 2002-03 0.240.09; n = 30 b B 4.040.69; n = 30 b A 4.330.55; n = 30 b A 8.610.90 c Warm 2003 2.670.86; n = 25 a, b B 8.252.11; n = 25 a A 9.441.09; n = 25 a A 20.592.19 a Feature Mean 1.970.18 B 4.410.35 A 5.290.38 A Warm = Summer + Fall; Cool = Winter + Spring a b c: means within columns sharing a common letter are not significantly different (P>0.05) A B C: means within rows sharing a common letter are not significantly different (P>0.05) Table 3-6. Mean percentage of daily time spen t by cattle near water tr ough (mean std Error). Water Trough Season Early Morning (12am – 6am) Late Morning (6am – 12pm) Afternoon (12pm – 6pm) Night (6pm – 12pm) Warm 2001 0.000.00 b C 1.810.25 a A 0.950.15 b, c B 0.010.01 a C Cool 2001-02 0.100.03 a A 0.110.04 b A 0.320.11 c A 0.110.04 a A Warm 2002 0.000.00 a, b B 1.480.34 a A 2.170.44 a A 0.000.00 a B Cool 2002-03 0.000.00 a, b A 0.100.05 b A 0.130.08 c A 0.000.00 a A Warm 2003 0.000.00 a, b B 0.880.32 a, b A, B 2.000.61 a, b A 0.000.00 a B Time Mean 0.030.01 B 0.970.11 A 0.930.11 A 0.040.01 B a b c: means within columns sharing a common letter are not significantly different (P>0.05) A B C: means within rows sharing a common letter are not significantly different (P>0.05) Table 3-7. Mean percentage of daily time spent by cattle in wetland (mean std Error). Wetland Season Early Morning (12am – 6am) Late Morning (6am – 12pm) Afternoon (12pm – 6pm) Night (6pm – 12pm) Warm 2001 0.030.03 a B 0.000.00 b B 1.650.67 b A 0.63 0.17 a A, B Cool 2001-02 0.150.06 a B 1.080.16 a A 1.070.14 b A 0.73 0.11 a A Warm 2002 0.240.13 a B 0.000.00 b B 1.360.26 b A 0.320.09 a B Cool 2002-03 0.000.00 a B 1.300.33 a A 1.470.38 b A 0.870.41 a A, B Warm 2003 0.330.15 a B 1.940.80 b A, B 4.931.38 a A 0.330.21 a B Time Mean 0.150.04 C 0.810.11 B 1.590.18 A 0.610.07 B a b c: means within columns sharing a common letter are not significantly different (P>0.05) A B C: means within rows sharing a common letter are not significantly different (P>0.05)

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72 Table 3-8. Mean percentage of daily time spent by cattle in ditch (mean std Error). Ditch Season Early Morning (12am – 6am) Late Morning (6am – 12pm) Afternoon (12pm – 6pm) Night (6pm – 12pm) Warm 2001 0.170.09 b C 0.330.17 c B, C 1.760.58 a, b A 1.550.30 a, b, c A, B Cool 2001-02 0.250.07 b B 0.670.13 c A, B 0.930.14 b A 0.560.18 c A, B Warm 2002 2.230.41 a A 2.180.35 a, b A 2.860.38 a A 2.650.44 a A Cool 2002-03 0.560.18 b B 1.280.27 b, c A, B 1.610.31 a, b A 0.870.20 b, c A, B Warm 2003 2.950.47 a A 2.440.43 a A 2.020.31 a, b A 2.020.30 a, b A Time Mean 1.010.13 B 1.210.12 A, B 1.690.15 A 1.370.13 A, B a b c: means within columns sharing a common letter are not significantly different (P>0.05) A B C: means within rows sharing a common letter are not significantly different (P>0.05) Table 3-9. Mean daily distance traveled and mean daily MCP ar ea by cattle (mean std Error). Season Distance Traveled (meters) MCP Area (acres) Warm 2001 3179.1757.64 b 13.690.22 b Cool 2001-02 3994.6892.33 a 14.150.49 b Warm 2002 3193.0482.16 b 13.850.35 b Cool 2002-03 4331.05237.77 a 17.671.04 a Warm 2003 2980.4998.58 b 13.362.58 b a b c: means within columns sharing a common letter are not significantly different (P>0.05)

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73 CHAPTER 4 DEVELOPMENT OF CATTLE MOVEMENT ALGORITHMS FOR ACRU2000 Habitat Suitability Index (HSI) Modelers develop and use HSI models for la nd-use management plans because they are simple to use and the outputs are generally in form of GIS-based maps, which are easy to understand. These models are also preferred because they may be applied in an efficient manner and are relatively inexpensive to operate (Schamberger and OÂ’ Neil, 1986). The first step in developing HSI is to identify habitat variables. The second step is to develop suitability index functions for each individual habitat variable. The fi nal step is to combine these functions into an equation for the HSI. In HSI modeling, animals get distributed in proportion to the habitat suitability. More detail about HSI modeling methodology and some applications have been discussed in Chapter 2. Model Design for Cattle Distribution in ACRU2000 Limited information is available regarding catt leÂ’s preference in gr azing systems of south eastern USA. It has been elucidated in Chapte r 2 that plentiful grazing studies have been conducted in the western and mid-western USA; how ever, differences between the arid west and the southern humid region prohibit the universal transfer of research results. The land is very flat and the climate is warm and humid in the south Florid a for most of the year. Th is is in contrast to western regions where land is hilly and the temper atures are dry and extreme. The use of water features is existent (Chapter 3); however, their utilization ma y be for different reasons. Controlled as well as uncontro lled ranges in south Florida co nsist of abundant wetlands. Therefore, accessibility to water is not a lim iting condition, which may be the case in the west. Hence, models developed elsewhere cannot be applied to the unique ag ro-ecosystems of the

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74 south-east. Therefore, functions fo r individual habitat variable in a HSI model must be defined to represent the distribution of cattle in ecosystems of south Florida. Suitability Index for Cattle Distribution The first step in the process of defining hab itat suitability functions is to identify the variables that would affect th e distribution of cattle in a paddock system. Shade and water features are the obvious attractants that dictate the distribution of cattle; hence they have been included as variables for HSI co mputation. Water features such as wetlands and ponds may be attractive for different reasons in different seas ons. Depending on the presence or absence of water, cattle may display a difference in the utilization of a wetland or pond. A dry wetland may not be an attractive feature fo r hot or thirsty cattle; however it may be luring for hungry cattle that may graze in it for better quality of forage Hence a dynamic suitability index is required for features that may become devoid of standing wa ter during dry periods an d thereby changing their attractiveness for grazing cows. The current hydrologic module in ACRU2000 simulates the depth of water table for each land segmen t. The methodology used in Chapter 3, which determines the presence of standing water in wetlands, is used here as well. Water table depth of less than 0.6 m from the ground su rface is deemed to have sta nding water in the wetlands and ponds. Hence, the HSI for water is optimum (1.0) when the water table depth is less than 0.6 m (2 ft). On the other hand, water table depth of more than 1.21 m (4 ft) from the ground surface is deemed not to inundate wetlands and ponds. Theref ore, HSI for water is minimum (0.0) when the water table depth is more th an 1.21 m. Thus, a dry day (i.e. when water table falls below 1.21 m) will have a different suitability index for a land segment with wetland than a wet day; thereby making it very dynamic. The suitability index values for a water feature with respect water table depth in between 0.6 m and 1.21 m is a linear relationship and is are illustrated in Figure 4-1 and as:

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75 0 ) ( t tWT HSI for 21 1 tWTDEP (4-1a) t t tWTDEP WT HSI 63 1 67 1 ) ( for 21 1 60 0 tWTDEP (4-1b) 1 ) ( t tWT HSI for 60 0 tWTDEP (4-1c) where WTDEPt is the water table de pth on day t; and HSIt(WTt) is the HSI of water feature on day t. Under extended warm humid conditions of southeastern USA when the ambient temperature approaches or exceeds cattleÂ’s body te mperature, the cattle will seek shade to cool themselves. In a study conducted during summer in Louisiana, McDaniel and Roark (1956) found that shade, either artificial or natural, increased the gain s of cows and their calves. The area of the shade will depend on the size of the herd. In an experimental study Clarke (1993) tested the effects of shade on behavior, rectal te mperature, and live weight gain. It was found that 2.5 m2 shade/cow reduced rectal temperatures in bot h, zebu-cross steers and in Hereford steers. Buffington et al. (1983) r ecommended at least 4.2 m2 shade/cow but also agreed with Bond et al. (1958) that 5.6 m2 shade/cow was desirable. Alternat ely, Hahn (1985) only suggested 1.8-2.5 m2 shade/cow was required. For southeastern cl imatic conditions, a shaded area of 50 m2 was considered representative of the agro-ecosystems of south Florida. Some isolated trees may also attract a few cattle; however, a shade area of less than 30 m2 will be less attractive, and may cause crowding (Buffington et al., 1983). Hence, the suitability index values linearly increase from 30 m2 to 50 m2 (Figure 4-2) and as: 0 ) ( SH HSIt for 0 0 SA (4-2a) 02 0 ) ( SA SH HSIt for 0 50 0 0 SA (4-2b) 1 ) ( SH HSIt for 0 50 SA (4-2c)

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76 where SA is the shade area (m2); and HSIt(SH) is the HSI of shade on day t. Shade area is an input from the user, and will remain constant throughout the simulation. Herbivores eat to satisfy their need and de sire for nutrients, the most prominent being energy and protein (NRC, 1996; 2001). The mechanism via which herbivores satisfy their energy and protein requirement is through consumption of available forage. The current version of ACRU2000 is set up to simulate three functiona l forage groups: bahi agrass, floralta, and panicum. These vegetation speci es represent functi onal groups that correspond to vegetation found in uplands, transition zones and wetlands, respectively (Yang, 2006). The forage is also an important factor that dictates herbivores movement and distribution. The HSI of forage consumption (Figure 4-3) is based upon data published by Rayburn (1986), who summarized a group of experiments and developed a more genera l relationship of relati ve intake (a proportion of maximum or potential intake) to herbage mass. The forage suit ability index is represented in the model as: 0 ) (.t i tF HSI for 150, t i aW (4-3a) 1 0 00066 0 ) (, , t i a t i tW F HSI for 1350 150, t i aW (4-3b) 1 ) (,t i tF HSI for 0 1350, ,t i aW (4-3c) where Wa,i,t is the aboveground biomass of speci es i on day t (Kg/ha); and HSIt(Ft) is the HSI of forage on day t. Preference Estimation Using Analytical Hierarchy Process After development of a suite of suitability indices that are deemed influential in herbivoreÂ’s spatial location preference, the next step is to determine the relative importance of parameters with one another. In case of limited literature availability a good stra tegy is to utilize the technique of decision analys is to quantify the pr eferences of one vari able over the other.

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77 Analytical Hierarchy Process (AHP) is a ma thematical tool within the field of multicriteria decision analysis that a llows consideration of both qualita tive and quantitative aspects of decisions. AHP is especially suitable for comp lex decisions which involve the comparison of decision elements which are difficult to quantify (S aaty, 1980). It is based on the assumption that when faced with a complex decision the natura l human reaction is to cluster the decision elements according to their common characteristics. The AHP methodology involves building a hierarchy (Ranking) of decision elements and then making comparisons between each possible pair in each cluster (as a matrix). These pair -wise comparisons provide a weighting for each element within a cluster (or level of the hierar chy) and also a consis tency ratio (useful for checking the consistency of the user-defined wei ghts). This process requi res the user to make direct comparisons of the rela tive importance of the alternativ es on the measure. In case of herbivore distribution model there are three alternatives (Shade, Wa ter, and Forage) that need to be compared and evaluated. Within forage there are three types of forage s: bahiagrass, panicum, and floralta. Also, since the di stribution is dominated by two s easons, two sets of preferences need to be developed for all the alternativ es. Logical Decisions for Windows (LDW) is a decision analysis tool that helps define alternatives a nd variables (Logical Decisions, 2005). Within LDW, AHP technique is available. To us e this technique the us er needs to pair-wise compare two variables as part of the assessment process. The user enters the weight ratios for each possible pair of variables in a matrix. This ratio describes the ratio of importance of a variable as compared to the other. Within LDW there is also an option of printing the preference assessment in a questionnaire format. This lets the user obtain a hard copy of the preference assessment question(s) being posed by LDW. The questionnaire asks the user to identify the importance of one feature with respect to the ot her (e.g., Forage vs. Shade) on a scale of 1-9.

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78 This is a useful feature of LDW where the questio nnaire can be distributed to the participants in the study who may not be readily av ailable for direct questioning. Fo r this research we utilized this feature and distributed the questionnaire to many researchers, ranch managers, and extension agents (Appendix B). This allowed us to acquire and incorporate a broad spectrum of expertise and opinion into the herbivore di stribution model. Once this info rmation is entered into LDW (Figure 4-4), it uses the AHP co mputation process to compute se t of weights for the variables (Appendix C). Model simulation results using wei ghting factors from the survey are shown in Appendix D. The model for herbivore distribut ion is given in equation 4-4: )] { ) ( ) [( ForageHSI ForageWF WaterHSI WaterWF ShadeHSI ShadeWF HSI (4-4) where ShadeWF and ShadeHSI are the weighting f actor and habitat suitability index for shade, WaterWF and WaterHSI are the weighting factor and habitat suitability index for water, and ForageWF and ForageHSI are the weighting factor and habitat suitability index for forage. Since ACRU2000 is capable of simulating multiple vegetati on species, the forage factor in equation 41 can be further expanded to include desire d number of vegetation species (Equation 4-5). )} .....( ) ( ) (2 2 1 1HSI Veg WF Veg HSI Veg WF Veg HSI Veg WF Vegn n (4-5) where VegWF and VegHSI are the weighting factor and habitat suitability index for the specific forage species. Index for Heat Stress and Seasonal Distribution Summer heat stress has long been recognized as a factor that reduces both, the productivity and reproductive efficiency of cattle in the Sout heastern regions of the USA (Jordan, 2003). In grazing systems cattle are exposed to varying amount s of solar radiation. This radiant energy can come directly from the sun or indirectly fr om the immediate surroundings. During summer, this

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79 radiant load may exceed metabolic heat producti on of cattle significantly. To cope with the hot environment, cattle will strategi ze their behavior and physiology to relieve the total heat burden. Cattle will seek shade, increase water intake and orient themselves away from direct sunlight. These behavioral changes increase the tissue con ductance to facilitate heat transfer from the body core to the skin and eventually away from the skin by convection and radiation. There will be increased sweating to increa se evaporative loss as well as increased respiratory volume (heavy breathing) (Blackshaw and Blackshaw, 1994) Cattle will reduce feed intake as an immediate response to heat st ress (Blackshaw and Blackshaw, 1994) and expenditure of energy to maintain homeothermy (NRC, 1981). Heat stress is caused by environmental factors such as air temperature, radiation, hu midity, and wind velocity (Gwazdauskas, 1985). Over the years researchers have created indexe s that relate specific envir onmental characteristics to the physiological variables of hear t rate, respiratory rate and volume, sweating rate, and body temperature (Blackshaw and Bl ackshaw, 1994). The two environmental parameters that have been popularly used have been dry-bulb temp erature and humidity. In a research conducted during the summers of 1975-78 at the University of FloridaÂ’s Da iry Research Unit near Hague, FL, Buffington et al. (1981) esta blished that dry-bulb temperatur e and dewpoint temperature was directly related to rectal te mperature and respiration rate and inversely related to milk production. They established that Black Globe-Humidity Index (BGH I) is a comfort index that is based on the combined effects of dry-bulb te mperature, humidity, net radiation, and air movement, as can be seen in equation 4-6: 5 41 36 0 dp bgt t BGHI (4-6) where: tbg = black globe temperature (C) and tdp = dew point temperatur e (C) calculated from wet bulb temperature. When the BGHI is 75 or hi gher, milk yield and feed intake are seriously

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80 depressed (Buffington et al., 1981). The black globe temperature, (tbg), is measured by the black globe thermometer, which usually consists of a 150 mm (6 inch) black globe with a thermometer located at the centre. Since black globe temper ature measurements are not available for BIR, another index can be used which is based on relative humidity. Thom (1958) suggested a temperature and relative humidity index (T HI) to evaluate a cowÂ’s heat stress as: )} 58 )( 100 / 1 ( 55 0 { d dt RH t THI (4-7) where td = dry bulb temperature (F) and RH = relati ve humidity (%).The heat stress is defined as occurring whenever the THI exceeds 72 (Armstrong, 1994, De Dios and Hahn, 1993; Hahn, 1982; Hahn and Mader, 1997; Igono et al., 1991). However, most of the research has been conducted in midwestern states on dairy cows with reduction in milk production due to heat stress as the main concern. The cattle in south Fl orida are mostly beef cattle. More specifically, the cattle on BIR are Brahman-crossbred (Arthi ngton et al., 2006). Nu merous studies have documented that Brahman cattle have better he at regulatory capacities than other breeds (Blackshaw and Blackshaw, 1994). This physiological advantage ha s been attributed to higher respiratory rate (Finch et al., 1982; Kibler a nd Brody, 1952), lower metabolic rate (Kibler and Brody, 1954; Vercoe, 1970; Worstell and Br ody, 1953), less water consumption at higher temperatures (Winchester and Morris, 1956), an d thinner-brighter hide s (Allen et al., 1970; Finch, 1985; Finch et al., 1984; Hutchinson and Brown, 1969; Yeates; 1954) Keeping the better heat resistance of Brahman cows in mind, the cri tical level of THI was set to 75. On a given day if the index reaches 75, the weighting factor of shade and water increases by 20% and 10%, respectively. Consequently, the forage weighti ng factor decreases by 30%. This change in the weighting factors of shade, water, and forage acco unt for the change in behavior the cattle will exhibit during days when they experience thermal stress.

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81 Analysis of cattle movement data in Chapte r 2 clearly indicates that cattle display a difference in their behavior in th e two seasons of Florida. To repr esent the difference in behavior in the two seasons in Florida, two entire sets of weighting factors have been developed with the abovementioned variables, one for warm season and the other for cool. The warm season is defined as March to October and the remaining four months are defined as the cool season. Integration of HSI Model into ACRU2000 It has been demonstrated that HSI is a rela tively swift way to utilize information either from literature or even from opinions of experts to develop a model. Therefore, a similar approach has been used to distribute the cattle in a modeling system, ACRU2000. The Agricultural Catchments Resea rch Unit (ACRU) Modeling System The ACRU agrohydrological modeling system wa s originally developed in the Department of Agricultural Engineering (now the School of Bioresources Engineer ing and Environmental Hydrology) at the University of Natal by Sc hulze (1995). The devel opers of ACRU model describe it as a multi-purpose and multi-level in tegrated physical conceptual model that can simulate streamflow, total evaporation, and la nd cover/management and abstraction impacts on water resources at a daily time step (Figure 4-5). The ACRU program code was developed in the FORTRAN 77 programming language. As the model got developed and modi fied by researchers around the world, the existing programming langua ge (FORTRAN) posed problems with regards to its compatibility to accommodate these newer versions. It is for this reason the model was restructured entirely with an object oriented programming language: Java and was named AC RU2000 (Kiker et al., 2006). The advent of ACRU2000 made the model more compliant with spatial hydrological aspects and addition of newer modules became unproblematic (Campbell et al., 2001; Kiker and Clark, 2001; Kiker et al., 2006; Martinez, 2006; Yang, 2006). ACRU2000 can operate either as a lumped small

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82 catchment model with relatively homogeneous soil and land cover a ttributes, or as a distributed cell-type model where complex catchments are se parated into sub-catchments or land segments (Figure 4-6). The nutrient module within ACRU2000 modeling system was incorporated by Campbell et al. (2001) (ACRU-NP), which borrowed the con cepts used in GLEAMS (Knisel and Davis, 1999; Leonard et al., 1987). The nutrient module added capability in ACRU2000 to 1) simulate nitrogen (N) and phosphorus (P) loss es in surface runoff, sediment transport, and leaching, 2) simulate N and P cycling in the soil-water-plant-animal system, and 3) simulate N and P mass balances in the watershed system. However, ACRU-NP module was incapable of simulating multidirectional lateral nutrient transport between mu ltiple land segments through either surface or subsurface water movement. The lateral nutrient transport com ponent in ACRU-NP was mainly designed for transporting nutrients dissolv ed in runoff and adsorbed in sediments through single outflow from one land se gment. Consequently, Yang ( 2006) restructured ACRU-NP and added new components to enable multi-directiona l spatial transport of N and P through surface runoff and lateral groundwater flow. In addition, a new conservative solu te transport component was also added to rectify the pr ocess of nutrient extrac tion and adsorption in which the ratio for partitioning the nutrients between the water and so il phases was a function of clay content. Yang (2006) compared the performance of the new c onservative solute tran sport algorithm from PMPATH (Chiang and Kinzelbach, 2005), which is an advective tr ansport model using groundwater pore velocities from MODFLO W (McDonald and Harbaugh, 1998) using a hypothetical scenario. The comparison revealed that both models produced qualitatively similar results. In addition, the ability of the modified nutrient model to predic t non-point source nutrient pollution, at BIR was evaluated. It was concluded that the mo del performed reasonably well.

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83 Yang (2006) also added significant framew ork to the ACRU2000 modeling system by adding a new vegetation component to enab le multi-directional spatial simulation of hydrological, chemical, and biological processe s simultaneously in a daily time step. The vegetation model is a simple model that avoids overwhelming data requirements, but is still capable of capturing the vegeta tion dynamics. The model is based on the land segment system developed by Yang (2006), where each land segment is initialized with one or multiple species, which compete for light, water and nutrients. For each time step, plant growth is driven by climate variables including solar radiation and temperature. The ba sic processes in this model are light interception, conversion of light into dry matter production and al location of dry matter between aboveground and belowground dry matter. The impacts from the changes in hydrology and nutrient concentrations are expressed in gr owth limiting factors. Yang (2006) accounts for two types of pressure on the vegetation: lack or excess of water and nutrient. These stress factors are combined to define a growth reduction factor that is used in the model to reflect the adverse growth conditions causing the reducti on of the potential dry matter production. In the model the potential growth rate ( Wpot,i,t [kg/m2/day]) for each species i on day t is calculated through a linear function of the absorbed light a nd a mean radiation-use efficiency parameter as shown in Equation (4-8). ) (, , , t t i t i abs i t i potT F I RUE W (4-8) where RUEi is the average radiation use efficiency of species i [kg/MJ(PAR)] and is a summary variable for all processes dealing w ith photosynthesis and respiration. Iabs,i,t and Fi,t(Tt) are the light interception and temperature factor for assimilation by species i on day t, respectively. The plant growth rate may be limited by N or P defi ciency, water shortage or water logging during different parts of the growing season:

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84 t i t i pot t i redRF W W (4-9) t i t i t i pot t iCN RF W N (4-10) t i t i t i pot t iCP RF W P (4-11) where Wred,i,t is the reduced dry matter produc tion rate of sp ecies i [kg/m2/day]; Ni,t and Pi,t are the N and P uptake rates corresponding to Wred,i,t [kg/ha], respectively; CNi,t and CPi,t are the biomass N and P percents, respectively; RFi,t is a growth reduction factor of species i, which integrates the limiting factors from water, N and P. RFi,t, is a unit less, species-specific growth reduction factor with a value ranging from 0 to 1, which is obtained by taking the minimum value of water stress, water logging, and N a nd P stress factors as shown in the following equation: ) F F F F min( RFt i P t i N t i WL t i WS t i (4-12) where FWS,i,t is the water stress f actor for species i; FWL,i,t is the water logging factor for species i; FN,i,t is the N stress factor; and FP,i,t is the P stress factor. Plant senescence is assumed to start when the daily sum of leaf areas ( i t i sumLAI LAI[m2 leaf/m2 ground]) of all species on one land segment exceeds the critical leaf area (LAIcr [m2 leaf/m2 ground]), an input to the model. The daily total senesced biomass (Ws,i,t [kg/ha]) and the corresponding N (Ns,i,t [kg/ha]) and P and (Ps,i,t [kg/ha]) removed through the senesced biom ass for each species on that land segment are calculated as: i cr cr sum bs t i sSLA / ) LAI LAI LAI ( Frac W (4-13) t i red t i s t i t i sW / W N N (4-14) t i red t i s t i t i sW / W P P (4-15)

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85 where Fracbs is the fraction of biomass above the cri tical level to senesce per day, which is assumed to be a constant value in the model fo r all species. The senesced biomass and biomass N and P decrease the amount of live biom ass and its corresponding N and P pools: t i s t i red t i redW W W (4-16) t i s t i t iN N N (4-17) t i s t i t iP P P (4-18) Currently the model simulates three pere nnial species including bahiagrass (Paspalum notatum Flgge), floralta (Hemarthria altissima), and panicum (panicum rigidulum) which are believed to be the dominating forage species in s outh Florida. One of the outputs from this model is aboveground biomass on a daily basis. This out put is critical in the de velopment of the cattle distribution model. Recent ACRU2000 model developers (Martin ez, 2006; Yang, 2006) have enabled and tested the model to be capable of simulating both hydrology and nutrient d ynamics in field-scale catchments. Pandey (2006) applied the distri buted ACRU2000 modeling system to predict hydrology and non-point source nutrient pollution, on a commercial beef cattle ranch (Pelaez Ranch) in the Lake Okeechobee region. Pandey (2006) applied the model on the entire ranch by finely discretizing the modeling domain into various sizes of 134 land segments. Thus, ACRU2000 can be confidently used as a basis for coupling with an animal distribution simulation model to form a more comp lete ecohydrological modeling system. Once the HSIÂ’s are computed for every land segment in the modelÂ’s domain, they are summed, and normalized (so that they sum to 1.0). The cattle in the population are then distributed across their range in proportion to the distributi on of the normalized HSIÂ’s among land segments. The redistribution occurs on a daily basis. After redistribu tion, the cattle consume

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86 existing forage proportional to their population presen ce on land segments. It is assumed that each cow will eat 16 Kg of forage per day. This amount is based on the ty pical cattle weight (640 Kg) (ASAE Standards, 2000) and forage cons umption (2.5% of Body Weight) (NRC, 1996). The total forage amount that gets consumed by graz ing cattle is removed fr om the vegetation model on a daily basis. The removal is based on the pref erence weighting assigned to individual forage species. For example, if Veg1 has a higher weig hting factor than Veg2 and Veg3, Veg1 will get consumed more. This consumption will be in pr oportion to the weighting factors. According to suitability index of forage consumption (Figure 4-3), the grazing herbivores will not “see” any biomass that is less than 150 kg/ha. Once the fora ge biomass reaches that low level in a specific land segment, the forage suitability becomes zero and thereby lowering the HSI for that land segment. Lower HSI in turn allows less herbivor es to be assigned and this allows forage to recover in that specific land segment. Cattle also defecate proportional to their population presence on land segments. It is assumed that each cow will defecate 8.5 Kg/day (ASAE Standards, 2000). The cattle waste gets applied to the top (litter) layer of the model. The waste is characterized into various nutrients pools (Organic -P, Labile-P, Organic-N, Ammonium -N, Active-N, Organic Matter) (Figures 4-7, 4-8) based on the rates set by th e nutrient model of ACRU2000. Minimum Habitat Area for HSI Model in ACRU2000 Application of habitat suitability criteria requires that some specific spatial parameters be defined. Minimum habitat area is defined as th e minimum area of contiguous habitat that can support a cattle population on a long term basis. In case of modeling catt le distribution within ACRU2000-HSI, it is imperative th e users maintain a minimum land segment of 0.1 ha in order for cattle distribution module to be able to pe rform reasonably. Maintaining the right spatial

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87 scale is important in order for the suitability i ndexes to be realistic over temporal and spatial variation. Suitability Index of Water Table0 0.2 0.4 0.6 0.8 1 1.521.210.60.3Water Table Depth (m)Suitability Values Figure 4-1. Suitability inde x values of water features. Suitability Index of Shade Area0 0.2 0.4 0.6 0.8 1 05060Shaded Area (m2)Index Values Figure 4-2. Suitability i ndex values of shade area.

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88 Suitability Index of Forage 0 0.2 0.4 0.6 0.8 1 015013501650Standing Aboveground Biomass (Kg/ha)Suitability Values Figure 4-3. Suitability index va lues of forage consumption. Figure 4-4. Goals hierarchy view in Logical Decisions fo r Windows software (Logical Decisions, 2005).

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89 Figure 4-5. General structure of the ACRU (v 3.00) model (Schulze, 1995). Figure 4-6. Configuration of multiple directional overland flows from source land segment to adjacent land segments (adapted from: Yang, 2006).

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90 Figure 4-7. Phosphorus cycle of the ACRU2000 model (adapted from: Knisel et al., 1993). Figure 4-8. Nitrogen cycle of the ACRU2000 m odel (adapted from: Knisel et al., 1993).

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91 CHAPTER 5 MODEL RESULTS Testing Model Performance at Buck Island Ranch A cattle distribution model (ACRU2000-HSI) was developed for the region of south Florida in Chapter 4. The algorithms were developed using the procedure of Habitat Suitability Index and criteria weightings were developed by processing expert opinio n using the technique of Analytical Hierarchy Process. The GPS data analysis in Chapter 3 was helpful in providing insights into cattle’s beha vior in warm humid regions. However, the GPS data were not utilized to create algorithms for the HSI model. The algo rithms are composed of “attractants” of cattle (shade, water, and forage) and their weighting f actors. The attractants were determined based on the features that exist in the la ndscape of this region. Weighti ng factors were determined using surveys from experts and were then calibrated to obtain better results. Depending on the presence or absence of water, cattle may display a diffe rence in the utilizati on of a wetland or pond. Therefore, the depth of water table, which is an output from the hydrologic model, was utilized to determine presence or absence of water in wetlands. South Florid a’s long hot and humid summer can cause heat stress in grazing cattle. Cattl e’s change in grazing a nd resting pattern as a result of heat stress in hot-humid environments of the southeast is also incorporated into the model. To incorporate the difference in behavior in the two seasons of Florida, two sets of weighting factors were developed. After model development, the next crucial step is to verify the performance of the model. The model should be verified and tested at a site that is representative of the region for which the model is developed. It is also preferable to test the model using observed data. Since the GPS data from BIR (described in Chapter 3) were available, they were utilized to verify the performance of the HSI model. Table 3-3 is a summary of the sampling of the GPS data. During

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92 any season the number of cows collared for th e GPS study was not consistent. Since the GPS collar data were available only from a few select ed cows from the whole herd, for comparative purposes, it is assumed that the collared cows ar e representative of the total cattle population in the model domain. As there can be significant variability in an indi vidual cow’s behavior (discussed in the conclusion section of Chapter 3), it was essential to select a pasture that had the largest availability of GPS data. Consequent ly, SP4 and SP5 were selected for HSI model application (Figure 5-1). Th e rationale for selecting these two pastures during the abovementioned times is because of plentiful GPS data availability (Table 3-3) that qualifies the data to be representative of the whole herd. For each collared cow, number of recorded “hits” in a land segment were divided by the total number of hits and then multiplied by herd size to convert the hits into number of cows. The description of input paramete rs are given in Table 5-1 and their values that were used for model calibration and verifica tion are given in Table 5-2. Th e values in Table 5-2 are the result of calibration of the HSI model input parameters that were obtained from the LDW software using AHP technique. Since the water, sh ade, and forage parameters change during the two seasons, it was essential to calibrate and verify the model during both warm and cool seasons. Summer 2001 (June 11-15) and spring 2002 (Mar ch 4-8) were the two seasons selected for testing of the HSI model. SP4 was select ed for calibration and SP5 for verification. Calibration Results Figure 5-2 is the result of calibration on summer pasture 4 during warm season of 2001. The box plot (Figure 5-2) shows the range of the observed GPS data in all land segments. Within the box the dark dotted line and the light solid li ne represents the mean and median number of cattle in each land segment, respectively. The li ght dotted line (running across all land segments) illustrates the assumption of equal distribution of cattle (1.25 cattle per land segment) by the

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93 ACRU2000 version. Land segments consisting of water trough, wetland, and shade are abbreviated WT, WET, and S, respectively. The calibration re sults of the ACRU2000-HSI during the warm season captures the overall dynamics of individual land segments. Under prediction of the number of cattle in land segment 8 can be attributed to the lower forage biomass, especially panicum (less than 150 kg/ha) which has the high est weighting factor amongst the three forage species. There is a slight over prediction in la nd segment 3 due to higher biomass availability. The variability in the GPS data is also noteworthy. Figure 5-3 is the result of ca libration on summer pasture 4 du ring cool season of 2002. The calibration result for the cool s eason also captures the overall dyna mics of land segments, with exception to LS1 and LS2. A water trough and two shade structures are present in LS1. The model is unable to represent the presence of two shade structures and it is possible that due to more availability of shading area, more cattle ar e present. However, the simulation result is still within the lower range of observed data. CattleÂ’s presence is exceptionally high in LS2. This high presence has been observed in the north section of all the su mmer pastures. Closer examination of GPS data and personal communi cation with the ranch manager of BIR has revealed that in this area cattle would stand or lie down, ruminate, for approximately 2 to 3 hours. Various studies indicate that cattle graze mostly in early morning and evening, and rest mostly in the middle of the day (Bagshaw, 2001; Hafez & Bouissou, 1975; Martin, 1978; Sneva, 1970). A similar observation was made in an ex perimental study conducted to establish beef cattle defecation frequency and distribution on h ill country in New Zealand (Bagshaw, 2002). It was observed that often cattle would rest betw een 11 am and midday. They would rest and ruminate during this time on flat areas either at the top or middle of the field. In a field where there was a large flat area at th e top of the field next to a tro ugh, cattle were observed to spend

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94 the majority of their resting time in this area. Similar pasture setting exists at BIR where the cattle display an affinity to rest in the nor thern section of all su mmer pastures. This high presence is not recorded in the summer season b ecause cattle spend most of the afternoon resting and avoiding direct solar radiation under a shade. The ACRU2000-HSI slightly ove r predicts the number of cattle in the southern land segments. This is mainly due to the normalization of cattle population across all land segments. The impact of under prediction in LS1 and LS2 is tr anslated into slight over prediction in LS912. Verification Results Figure 5-4 displays the resu lt of verification of ACRU2000-HSI on summer pasture 5 during warm season of 2001. The stocking rate on this pasture was higher than SP4 (35 Cows). There is also more variability in the observed G PS data in this pasture as compared to SP4. There is slight over prediction in LS2 due to high in itial biomass. Nevertheless, in all the land segments, the modelÂ’s performance is always within the range of observed GPS data. The dynamics of water trough, shade, and wetland seems to be well represented by the model. Figure 5-5 displays the result of verification on summer past ure 5 during cool season of 2002. Similar to warm season, the model is able to capture the dynamics of water trough, shade and wetlands in the cool season as well. The pheno mena where the cattle display an affinity to rest in the northern section of the pasture is once again evident. The number of cattle recorded in LS2 is higher than any other land segment (Fi gure 5-5). Additionally, th ere is slight over prediction in the number of cattle in the land segmen ts from mid field to the southern end of the pasture (S6-12). The model results for most of these land segments (S6-9) are within the range of observed data and overall the number of cattle prediction is better than the original model.

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95 Sensitivity Analysis In a typical modeling system, the model resu lts are more sensitive to certain inputs compared to others. This information is of esse ntial use for future model users who may need to calibrate the model for application on a different site. Therefore, it is important to perform a sensitivity analysis to establish priorities in collecting and determining model parameters. An analysis was performed to determine the sensitiv ity of model simulated cattle distribution to the weighting factors. The sensitivity analysis was performed usi ng the six-year simulation (January 1, 1998 through December 31, 2003) on the experimental pastur e at BIR. Since the model was already parameterized for the SP5 (Figure 5-1), it was applied on the same past ure for this analysis. Model sensitivity was determined for 25, 50, 75, and 100% of the base i nput value (Table 5-3). Summer 2001 (June) and spring 2002 (March) were the two seasons selected from the simulation period. The sensitivity analyses were focused on the population of cattle in all land segments during the two seasons. It is important to be ar into mind that the ACRU2000-HSI model is different from a typical process based modeling system where cha nge in an input parameter will result in an expected change in output. The ACRU2000-HSI model is dependent on the hydrologic model for determination of water pres ence, nutrient model for rate of growth of vegetation, and vegetation model for total biomass. In additio n, as per model design, the three weighting factors (water, shade and forage) must sum to 1.0. For example if warm season weighting factor of water (WWF AC) is increased by 25% the ot her two corresponding variables (i.e. warm season weighting factor s of shade and forage, WSFAC, WOVRLFAC) must be adjusted so that the sum of the three weighting fa ctors equals 1 (Table 5-3). For this analysis the adjustment of the two variables was carried out so as to maintain the ratio amongst the adjusted variables. In Table 5-3, th e sensitivity analysis is pe rformed on WWFAC and WSFAC and

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96 WOVRLFAC have been adjusted accordingly. Comple te list of weighting factors values used in sensitivity analysis and the corresponding adjust ment in the weighting factors of other two variables is given in Ta ble F-1 of Appendix F. Results of sensitivity analysis are given in Tables F-2 to F-5 of Appendix F. There is considerable change in cattle population with cha nge in shade and water weighting factors in the warm period (Table F-2); especially in the land se gments that consist of those features (LS1 and LS2). Even though water or shade availability do not exist in land segments apart from LS1 and LS2, there is still change in cattle population in other land segments (LS3-12) due to change in shade and water weighting factors. As explained before, this is due to corresponding change in forage weighting factor which has to be adjusted so that all the three weighting factors sum to 1. There is also considerable variation in presen ce of cattle in LS1 and LS2 with change in weighting factor of forage in both, warm season as well as cool season (Table F-2 and F-4). This drastic change in cattle populat ion is due to the proportional change in shade and water weighting factors (shade in LS1 and water in LS2). Since the ba se value of forage weighting factor is higher in cool season (Table F-4), ther e is higher variability in cattle population in the cool season as compared to warm season (Table F2). A similar trend is observed with variability in cattle population due to variation in weigh ting factor of individua l forage species, more variation in cool season (T able F-3) as compared to warm season (Table F-5). Hypothetical Scenario Model Testing Sufficient GPS data are not currently availa ble from the region of south Florida to quantitatively test the ACRU2000-HSI model towa rds BMP implications. Adequate data are however, rarely available to make management decisions. This is the reason modeling is an important tool which allows mana gers to envision future implications based on current decisions.

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97 "In a decision-making context, the ultimate test of a model is not how accurate or truthful it is, but only whether one is likely to make a better decision with it than with out it" (Starfield, 1997). Therefore, a hypothetical test was designed to evaluate the algorithms of the ACRU2000HSI model, coupled with the vegetation, hydrolog ic, and nutrient models. This scenario testing determined whether the ACRU2000-HS I model can be utilized to de termine the feas ibility of a BMP with phosphorus loading as an objective f unction. One of the obvious water quality-BMP in a cow-calf operation is to exclude the cows from streams. By restricting cowÂ’s access to a stream, direct deposition of the cattle feces in flowi ng water can be prevented. In its current state, the ACRU2000 does not consist of a stream r outing algorithm for water and P. Therefore, fencing the cows away from streams or ditches cannot be defined in the model. However, a similar scenario was designed to mimic restrictive access of cattle. First of all, a basic set of simulation were made with the ACRU2000-HSI model to observe change in P loading due to presence and absence of cows (Figure 5-7). The results from these simulations came out to be counter intuitive. The P load in absence of co ws was greater than in presence of cows. These results warranted furt her investigation and more detailed scenario simulations. Hence a new data object called DC attleExclusion (Appendix A) was created to accomplish the exclusion of cattle from user-specified land segments. This functionality allows the user to specify the land segment from which the cattle are to be excluded. During simulation the cattle are distributed only on la nd segments in which cattle exclusion option is turned off. Primarily, it was important to see a difference in the P load prediction, if any, from the two versions of the ACRU2000 model: one with th e HSI algorithms and the other using equal distribution of animal manure. Fo llowing the above stated basic run it was also crucial to see whether the HSI additions within ACRU2000 modeling system have enhanced its capabilities to

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98 make relevant management decisions. Three s cenarios were tested on summer pasture 5 (Figure 5-1) at BIR using a 6 year (19982003) simulation time period. In th e first scenario (Figure 5-6a) cattle were excluded from the land segments that were close to the flume (LS7 – LS12); in the second scenario cattle were excluded from the land segments that were away from the flume (LS1 – LS6); and finally in the third scenario all cattle were stocked on the land segment that adjoined the flume (LS11) and they were excluded from all other land segments. The three scenarios were designed to fence the cattle in various locations on the pasture to observe any changes in the nutrient loading. In total, five set of simulations were made using the ACRU2000 and the ACRU2000-HSI model and compared with observed P loading data (Figure 5-8). There is considerable difference in the nutrient predictions in the two versions of ACRU2000. The ACRU2000-HSI’s prediction of TP is less than ACRU2000 and closer to observed data (Figure 5-8). The ACRU2000 ve rsion assumed the animal manure to be distributed equally amongst all land segments The HSI version deposits manure on land segments based on the number of animals assigne d to specific land segments. However, in both versions the total quantity of manure remains same; therefore, some difference was anticipated yet the magnitude of difference required further investigation. It should be noted that even though there was provision to include animal manure in case of stocking in ACRU2000, there was no accountabili ty of forage consumption by grazing cattle. In the vegetation model, plant senescence is assume d to start when the daily sum of leaf areas of all species on one land segment exceeds the critical leaf area. Each vege tation species senesces biomass, N, and P in proportion to its leaf area. The daily tota l senesced biomass (Ws,i,t [kg/ha]) and the corresponding N (Ns,i,t [kg/ha]) and P and (Ps,i,t [kg/ha]) removed through the senesced biomass are given in Equation 4-13 to 4-15 of Chap ter 4. Since there was no consumption of the

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99 vegetation by grazing cattle in ACRU2000 there wa s a high amount of nutr ients being released from senesced biomass. This seems to have been corrected by the ACRU2000-HSI model where the cattle consume the vegetation as per their nutritional requirement. Figures 5-7 shows that when the cattle are closer to the flume there is reduction in nutrient load. This reduction can be expl ained by the change in quantity of senesced biomass that is closer to the flume. When there are more cattle present near the flume they consume more forage and hence reduction in senesced biomass. On th e other hand, when cattle are away from the flume there is increase in nutri ent load due to increase in a bove ground biomass that senesces and release nutrients. In the third scenario, when all the cattle are stoc ked on LS11 (land segment that is adjacent to flume) there is a slight in crease in nutrient loading. This increase can be attributed to the exorbitant st ocking rate (35 cows on 1.6 ha). A budget of the ACRU2000-HSI modeling syst em was prepared to quantify various “pools” of P using 6 years of simulation (Figure 59). It is evident that P from senesced residue (two order magnitude higher than P from def ecation) is the largest component. When the ACRU2000-HSI model is turned on, the grazing cat tle consume forage and reduce the amount of senesced residue (Equation 4-16) which consequen tly reduces P load. Within the model, only the top two layers (plant residue layer and soil surf ace layer) interact with su rface runoff; therefore a P budget with the top two layers of ACRU2000-HS I as a control volume was also computed (Figure 5-10). Apart from the P budget within the model domain it was also important to test the retention of P within the cattle over time (Figure 5-11). With the exception of initial increase, the P retained by cow remains within the bounds of 20-25 g. The P reta ined values correspond well to the values published in literature (NRC, 1996). The initial jump in P rete ntion can be explained

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100 by the utilization of nutrient uptake algorithms in the vegetation model (Yang, 2006). The N and P uptake algorithms in the vegetation model we re adopted from GLEAMS (Knisel and Davis, 1999) with a slight modification to account for the nutrient uptake by multiple plant species in one land segment. In the GLEAMS model the nutr ient uptake is based on demand and supply of nutrients. The P demand for sp ecies i at time t, DEMPi,t [kg/ha], is determined by the difference between the dry matter P on two successive days as: 1 t i t i t iTDMP TDMP DEMP (5-1) Uptake of labile P, UPLPi,t [kg/ha], is estimated for each layer where transpiration, occurs using t j i j d s t j iT CPLABW UPLP (5-2) where, CPLABWs,d,t, is the concentration of labile P. The total uptake of P is the sum over all species i and all layers j where transpiration occurs. The P take n up is converted into the plant biomass P: ij t j i t upUPLP P (5-3) where Pup,t is the plant biomass P [kg/ha]. The amount of initial biomass will dictate the role of P uptake during the initial pha se of simulation. It is perceived th at in some land segments there can be high initial biomass of any of the three vegetation species (Bahiagrass, Floralta, and Panicum). This will cause an increase in the supp ly of nutrients to support the growth of the vegetation. Thus, during the initial stages, consum ption of P enriched biomass is resulting in higher P retention within the cow’ s body. Over time, as the mode l equilibrates the high retention “levels-off” to a more sustainable level. Summary The algorithms in the HSI model are composed of “attractants” of cattle (shade, water trough, and wetland) and their we ighting factors. The HSI met hodology represents the dynamics

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101 of cattle distribution within the land segment system of ACRU2000 fairly well. The HSI model is not able to represent a resting/ruminating activ ity by cattle in a prefer red area of the pasture during the cool season. Consequently, the result of cattle population in that specific land segment is under predicted. Sensitivity analyses revealed a crucial difference between the HSI model and a typical process based modeling sy stem. Since the weighting factors must sum to 1.0, increase in the value of one parameter must correspond to comparable d ecrease in the value of other parameters. Hence, it is possible that the change in cattle distribution might not be only due to change in an individual parameter value but also due to change in other “adjusted” variables. Scenarios were tested to see any nutrient load changes associ ated with different spatial locations of cattle. Testing of cattle exclusion from certain land segments provided vital insights regarding the components that dictate nutrient loading in agro-ecosystems. The scenarios revealed that the nutrient loadi ng at summer pasture at BIR is mostly dictated by the N and P in senesced residue. Grazing cattle are a means of reducing the senesced biomass (as they consume forage) and consequently nutrient loadings. A P budget quantifies and supports this theory.

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102 Figure 5-1. Land segment Discretization of summer pastures 4 and 5 for ACRU2000-HSI.

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103 Figure 5-2. Calibration results on SP4 in warm season. Figure 5-3. Calibration resu lts on SP4 in cool season. WET WET WT, S WET WT = Water Trough S = Shade WET = WetlandACRU2000H.S.I. ACRU2K Mean of GPS WT, S WET WET WET WT = Water Trough S = Shade WET = WetlandACRU2000H.S.I. ACRU2K Mean of GPS

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104 Figure 5-4. Verification resu lts on SP5 in warm season. Figure 5-5. Verification resu lts on SP5 in cool season. WT, S WET WT = Water Trough S = Shade WET = WetlandACRU2000H.S.I. ACRU2K Mean of GPS WT, S WET WT = Water Trough S = Shade WET = WetlandACRU2000H.S.I. ACRU2K Mean of GPS

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105 (A) (B) (C) Figure 5-6. Hypothetical scen ario setup for ACRU2000-HSI mode l. (A) Away from flume (B) Close to Flume (C) All on LS11.

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106 Total Phosphorus Results Using ACRU2000-HSI Model0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.001/1/1998 4/1/1998 7/1/1998 10/1/1998 1/1/1999 4/1/1999 7/1/1999 10/1/1999 1/1/2000 4/1/2000 7/1/2000 10/1/2000 1/1/2001 4/1/2001 7/1/2001 10/1/2001 1/1/2002 4/1/2002 7/1/2002 10/1/2002 1/1/2003 4/1/2003 7/1/2003 10/1/2003TimeTotal Phosphorus (Kg/ha) Observed HSI Model HSI Model No Cows Figure 5-7. Total phosphorus re sults using ACRU2000-HSI model. Comparison of Total Phosphorus Results in a BMP Scenario0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.001/1/1998 4/1/1998 7/1/1998 10/1/1998 1/1/1999 4/1/1999 7/1/1999 10/1/1999 1/1/2000 4/1/2000 7/1/2000 10/1/2000 1/1/2001 4/1/2001 7/1/2001 10/1/2001 1/1/2002 4/1/2002 7/1/2002 10/1/2002 1/1/2003 4/1/2003 7/1/2003 10/1/2003TimeTotal Phosphorus (Kg/ha) Observed ACRU2000 ACRU2000/HSI Away Flume Close Flume All LS11 Figure 5-8. Total phosphorus results from various scenar ios in ACRU2000-HSI model.

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107 Complete Soil Profile Rain 2.43 kg/ha Defecation 3.95 kg/ha Senesced Residue 257.00 kg/ha Groundwater 1.26 kg/ha Runoff 10.54 kg/ha Uptake 503.64 kg/ha Figure 5-9. Phosphorus budget of complete model domain using simulated results. Plant Residue Layer Soil Surface Layer Rain 2.43 kg/ha Defecation 3.95 kg/ha Senesced Residue 257.00 kg/ha Groundwater 0.0 kg/ha Leaching 9.76 kg/ha Upward Flux 1.26 kg/ha Runoff 10.54 kg/ha Uptake 503.64 kg/ha Figure 5-10. Phosphorus budget of top two model layers using simulated results.

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108 Total Phosphorus Retained per Cow15 20 25 301/1/1998 4/1/1998 7/1/1998 10/1/1998 1/1/1999 4/1/1999 7/1/1999 10/1/1999 1/1/2000 4/1/2000 7/1/2000 10/1/2000 1/1/2001 4/1/2001 7/1/2001 10/1/2001 1/1/2002 4/1/2002 7/1/2002 10/1/2002 1/1/2003 4/1/2003 7/1/2003 10/1/2003TimeP Retained in Cattle (g) TP Retain Figure 5-11. Total phosphorus retained with in grazing cattle using simulated results. Table 5-1. Input parameters and their de scription used in the ACRU2000-HSI model. Parameter Description WWFAC Warm season water weighting factor WSAFC Warm season shade weighting factor WOVRLFAC Warm season forage weighting factor CWFAC Cool season water weighting factor WSAFC Cool season shade weighting factor COVRLFAC Cool season forage weighting factor V1FAC Weighting factor for vegetation 1 (Bahiagrass) V2FAC Weighting factor for vegetation 2 (Floralta) V3FAC Weighting factor for vegetation 3 (Panicum) Table 5-2. Values of input parameters used in the ACRU2000-HSI mo del after calibration. Forage 0.420 Shade 0.160 Summer Season Water 0.420 Forage 0.640 Shade 0.060 Winter Season Water 0.300 Bahiagrass 0.280 Floralta 0.280 Forage Panicum 0.440

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109 Table 5-3. Input parameter values used in sensitivity analysis. Parameter -100% -50% -25% Base +0.25%+50% +100% WWFAC 0.00 0.21 0.32 0.42 0.53 0.64 0.85 WSAFC 0.00 0.07 0.10 0.14 0.17 0.21 0.28 WOVRLFAC 0.00 0.21 0.32 0.42 0.53 0.64 0.85 CWFAC 0.00 0.15 0.22 0.30 0.38 0.45 0.60 WSAFC 0.00 0.03 0.04 0.06 0.07 0.09 0.12 COVRLFAC 0.00 0.31 0.47 0.63 0.79 0.95 1.00 V1FAC 0.00 0.14 0.21 0.28 0.35 0.42 0.57 V2FAC 0.00 0.14 0.21 0.28 0.35 0.42 0.57 V3FAC 0.00 0.21 0.32 0.43 0.53 0.64 0.86 Table 5-4 Example of adjusted weigh ting factors used in sensitivity analysis. -100% -0.50%-0.25%Base+0.25%+50% +100% WWFAC 0.000 0.2150.3220.4290.5360.644 0.858 WSAFAC 0.330 0.1890.1620.1430.1150.088 0.035 WOVRLFAC 0.670 0.6030.5210.4290.3490.268 0.107

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110 CHAPTER 6 DISCUSSION AND CONCLUSION Lake Okeechobee, the second largest fres hwater body located wholly within the continental United States is located at th e center of the Kissimmee-Okeechobee-Everglades aquatic ecosystem in south Florida. Non-point agricultural runoff from dairies and cow-calf operations in the northern watershed of the lake is considered to be the primary source of excess phosphorus (P) loading discharged into the la ke. To protect the water quality of Lake Okeechobee and reach environmental restoration goals, a variety of BMPs pertaining to cow-calf operations can be implemented in the Lake Okee chobee watershed. Significant uncertainty exists in the perceived and actual efficiency of these proposed BMPs. Decades of work in hydrologic monitoring a nd modeling has been carried out in the regions of South Florida. However, an integrated research approach is required that can help explain how agricultural activities contribute to, and can be modified to ameliorate P loading problems. All the components in grazing lands of complex agro-ecosystems are still largely unknown. If the BMPs are to minimize the impact of beef cattle productio n on water bodies in South Florida, a better understanding of beef cattle utilization of natural (wetland) and artificial (ditches and water trough) wa ter sources is necessary. GPS Collar Analysis To quantify the amount of time spent by grazing cattle near or in water locations, spatially and temporally explicit position data from GPS collars in an agro-eco system were gathered. Analysis of GPS locations identified, quantifie d and eventually derived important information regarding cattle utilizatio n of water sources, shade, MCP area and distance traveled (Chapter 3). This analysis provided vital in sights regarding cattleÂ’s preferen ces and their behavior. It was evident that cattleÂ’s behavior and utilization of biotic as well as abiotic features was seasonal.

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111 Some features were utilized more in the cool season whereas some more in the warm. Also, based on presence or absence of water, the cattle di splayed different interest in features such as wetlands. Even though the GPS collar analysis was not explicitly used for parameterizing the HSI model, it proved vital in the identification of the attributes that dictate the distribution of cattle. HSI Model To comprehensively represent the agro-ecosystem as well as to evaluate the effectiveness of the BMPs, an eco-hydrological model that incor porates simple algorithms pertaining to spatial distribution of cattle was developed. There is limite d data available to establish and calibrate an animal distribution model for any region of south east. Therefore, a simple model that avoids overwhelming data requirements, but is still capable of capturing the animal dynamics in a logical manner was developed as a practical first approach. Wh en sufficient data become available in the future, the model can be furt her improved and calibrate d. A cattle distribution model was designed to integrate as a module with the current hydr ology, nutrient and vegetation modules within ACRU2000 for south Florida fl atwoods watersheds. The module was developed within the Java-based, object-oriented fram ework of the existing ACRU2000 model (Campbell et al., 2001; Kiker and Clark, 2001). The algorithms are composed of “attractants” of cattle (shade, water, and forage) and their weighting f actors. The attractants were determined based on the features that exist in the la ndscape of this region. Weighti ng factors were determined using surveys from experts and were then calibrated to obtain better results. The previous version of ACRU2000 did not ac count for consumption of forages by grazing cattle, if they are present. Integration with the vegetation model now accounts for vegetation to be consumed by grazing cattle. Cattle consum e the three vegetation functional groups based on the analytical hierarchy process that is elucidated in Chapter 4. The HSI model now accounts for

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112 deposition of cattle manure on land segments base d upon the number of cattle present on a daily basis. Since the number of cattle that are present on each land segment is now based upon the HSI system, the cattle population on each land segm ent might change daily. Therefore, instead of a uniform deposition of animal manure within the modeling domain, a spatially dynamic deposition occurs that varies temporally as well. The HSI model has now allowed a more realistic and comprehensive representation of the agro-ecosystems of south Florida. A hypothetical test was designed to determine whether the HSI model can be utilized as a tool to determine the feasibility of a water qua lity-BMP with P loading as an objective function. Modeling scenarios were designed to mimic rest rictive access of cattle. The results from these scenarios were often counter intuitive. The results showed that when the cattle are closer to the flume there is reduction in P load. When more cattle are present near the fume, they consume more forage and thus reduce the pool of senesced biomass (near the flume) which consequently reduces the P loading. Since he rbaceous vegetation is abundant at BIR (even after reduction of aboveground biomass through grazing) the P release from senesced biomass supersedes all other sources of nutrient. A P budget was computed to quantify this phenomenon. The agro-ecosystem at BIR is unique in the sense that the presence of cattle seems to have few measurable effect on P in runoff during six years of model simulation. Similar effects were noted by Capece et al. (2006) in their stocking rate expe riment carried out at the experi mental pastures of BIR. Capece et al. (2006) noted that variati ons in stocking rate had no signi ficant effect on nutrients. They hypothesized that P loads were proba bly related to historic use of P fertilizer and not due to cattle stocking rates. The authors suggested that decreasing the movement of accumulated soil phosphorus into surface runoff would be more effective approach than focusing on cattle

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113 management for reducing P loads in surface r unoff from cattle pastures. The simulated BMP scenarios of ACRU2000-HSI tend to agree with the Capece et al. (2006) observations. Management Implications From a BMP implementation perspective, it is important to identify the primary source of P in the system before considering any management strategy. In the case of BIR, it was assumed that cattle were depositing a subs tantial amount of P that eventual ly discharged in the form of surface runoff and sub-surface lateral flow from th e edge of the field. However, simulating the effects of fencing in selected pa sture areas on BIR revealed that the senesced biomass is a more significant store of P than animal defecation. In this specific case at BIR, any BMP designed to reduce P from cattle manure would probably ha ve little impact. Removal or cropping of aboveground vegetation may be an effective alte rnative practice to reduce P loading. Further experimental studies would be needed to ve rify the effectiveness of this practice. There are a few general management implicatio ns that can also be drawn from the GPS collar analysis and ACRU2000-HSI model application at BIR. Cattle are likely to be attracted to ditches and wetlands, which can be source of water and forage. The length of time cattle spend in a particular area influences the amount of feces deposited in that area. Even though the ACRU2000-HSI model application at BIR suggest that cattle may not be a significant contributor towards P loading, deposition of fecal matter directly into a stream can potentially increase the P loading. Manipulation of the availabi lity of the attractive features (shade, salt lick) near the stream can also be carried out to reduce the time spent in/near these areas. Future Research Recommendation Following are some recommendations for resear chers who may be interested in further developing ACRU2000:

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114 Herbivore Physiological Representation Apart from the factors used in the HSI model (i .e. water features, shade, forage), there are other aspects that also determine the behavior of grazing cattle. It has been documented that herbivores graze to satisfy their nutritiona l requirements (NRC, 1996; 2001). Some basic physiological aspects need to be incorporated into the model in order for it to be comprehensive. Factors such as energy and protei n requirement, and herbage intake need to be included into the HSI model. Stream Routing Algorithm The current ACRU2000 model does not have a stream routing algorithm. An algorithm that routes water and its constitu ents through land segments and attenuates to a stream system is highly recommended. Attenuation of flow and nutrient dynamics w ithin the stream should be incorporated as well. A mechanism should be deve loped that will allow user to define a stream network within their modeling domain. This mechanism should also account for direct deposition of animal manure within a stream system. Graphical User Interface A graphical user interf ace would be a very helpful feature for the current ACRU2000. This feature would be especially helpful when the mo del would be required to be set up for a large watershed with multiple land segments. Entering values of various required parameters and initial boundary conditions for each individual la nd segment file is very cumbersome. A GISbased user interface can be utilized to auto generate inputs such as area, centroid, length of land segment sides, adjoining land segments etc. Th e GIS-based user interf ace can be utilized to define and parameterize stream networ k within the model domain as well.

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115 Conclusion Extensive animal operations along with rapi dly increasing urban development in South Florida have stressed the fragile ecosystem that ex ists in the region. One of the stress is due to non-point source pollution of P from the agricultu ral industry located north of Lake Okeechobee. The main focus of this dissertation was to analyze the temporal and spatial location of cattle and to incorporate basic algorithms of cattle distri bution in a regionally tested hydrological/water quality model. The GPS analysis quantified the average percentage of daily time spent by cattle near/in water locations (water trough, wetland and ditch) and sh ade during the warm and cool season. The ACRU2000-HSI model algorithms were developed using the t echnique of habitat suitability index and the weighti ngs of variables were obtained from expert opinion using the analytical hierarchy process. The ACRU2000-HS I model development now accounts for forage consumption by grazing cattle, which was previously not represented by ACRU2000. To represent a complex ecological system with in a modeling domain requires synthesis of current scientific understanding, field observations, and expert judgment. Integrated modeling approaches can help to achieve this goal. The addition of the new HSI model as a module into ACRU2000 has made it more robust and comprehens ive for application in agro-ecosystems of south Florida. Application of the ACRU2000-HSI model on Buck Island Ranch has proven useful in its representation of cattle distri bution and enhanced nutrient load prediction.

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116 APPENDIX A LIST OF NEW AND MODIFIED OBJECTS Following are process and data objects that were added or modified to the ACRU2000 modeling system. New Process Objects PCalculateHabitatSuitabilityIndex PDefecation PForageConsumption New Data Objects DCattleExclusion DCoolShadeAreaWeightingFactor DCoolWaterWeightingFactor DDefecation DHabitatSuitabilityIndex DLandSegmentCattle DOverallForageWeightingFactor DShadeArea DTemperatureHumidityIndex DTotalNumberOfCattle DVegetationWeightingFactor DWarmShadeAreaWeightingFactor DWarmWaterWeightingFactor DWaterTroughPresence DWetlandPresence Description of New Process Objects PCalculateHabitatSuitabilityIndex This process can be described in 5 steps. The firs t step checks for the time of the year i.e. warm season or cool season. In the s econd step, the process loops through all land segments and based on the presence of features (water, shade, and fo rage), a HSI is computed for individual land segments. The third step normalizes the HSI th roughout the model domain. The fourth step is distribution of animals on all la nd segments according to the normalized HSI. The fifth and final step is computation of THI and redistribution of animals based on the threshold value of THI.

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117 PDefecation This process determines the amount of solid manure deposited on each land segment based on number of cattle present on land segment. PForageConsuption This process determines the amount of fora ge consumed from each land segment based on number of cattle present on the land segment. Description of Ne w Data Objects DCattleExclusion This DInteger data object contains a value of 0/1 (Include/Exclude). This value determines whether cattle are to be included or excluded from land segment. (Unit less) DCoolShadeAreaWeightingFactor This DDouble data object contains the weighting factor of shade area for the cool season. (Unit less) DCoolWaterWeightingFactor This DDouble data object contains the weighting factor of water feature for the cool season. (Unit less) DDefecation This DDouble data object contains the amount of animal manure on in dividual land segments based on the number of animals present on land segments. (kg/ha/day) DHabitatSuitabilityIndex This DDouble data object contains the Habitat Suita bility Index of each land segment. (Unit less) DLandSegmentCattle This DDouble data object contains the total num ber of animals in individual land segments. (Unit less) DOverallForageWeightingFactor This DDouble data object contains the we ighting factor of forage. (Unit less)

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118 DShadeArea This DDouble data object contains the value of the area of shade in a land segment. (m2) DTempratureHumidityIndex This DDouble data object contains the te mperature humidity index. (Unit less) DTotalNumberofCattle This DDouble data object contains the total numbe r of animals in the entire modeling domain. (Unit less) DVegetationWeightingFactor This DDouble data object contains the weighting factor of indivi dual forage species. (Unit less) DWarmShadeAreaWeightingFactor This DDouble data object contains the weighting factor of shade area for the warm season. (Unit less) DWarmWaterWeightingFactor This DDouble data object contains the weighting factor of water features for the warm season. (Unit less) DWaterTroughPresence This DInteger data object cont ains a 0/1 (absent/present) va lue indicating the presence or absence of a water trough in a land segment. (Unit less) DWetlandPresence This DInteger data object cont ains a 0/1 (absent/present) va lue indicating the presence or absence of a wetland in a land segment. (Unit less)

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119 APPENDIX B HSI MODEL PROCESSES UNIFIED M ODELING LANGUAGE (UML) DIAGRAMS Figure B-1. PCalculateHabitatSuitabilityIndex UML diagram.

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120 Figure B-2. PForageConsumption UML diagram.

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121 Figure B-3. PDefecation UML diagram.

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122 APPENDIX C SURVEY FOR DETERMINATION OF WEIGHTING FACTORS Figure C-1. Cattle preference of features in a pasture during summer. Figure C-2. Cattle preference of f eatures in a pasture during winter. Figure C-3. Cattle preference of forage species in a pasture. This survey asks you to identify the importance of one feature with respect to the other (e.g., Forage vs. Shade). Each comparison assumes al l other features to be constant. On a scale of 1-9, you may circle the number that you believe best represents the preference of cattle. For example:

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123 If you believe for a cattle during a typical summer day in Florida remaining in the shade is more important than spending time eating fo rage, you would circle a number on the right portion of the scale (Figure A-3). If you believe that the shade is “equally” impor tant as forage you will circle 1 (triangle) If you believe that the shade is “somewhat” important than forage you will circle 4 (square) If you believe that the shade is “extremely ” important than forage you will circle 9 (diamond) Figure C-4. Example illustrating identification of the relative importance of one feature over the other on the scale provided in the survey.

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124 APPENDIX D WEIGHTING FACTORS DETE RMINED BY SURVEY Table D-1. Summary of weightings of features as gene rated by the LDW program based on the survey. Summer Season Winter Season Participant Forage Shade Water Forage Shade Water Researcher-1 0.523 0.284 0.193 0.474 0.053 0.474 Researcher-2 0.429 0.143 0.429 0.633 0.063 0.304 Extension Agent-1 0.330 0.330 0.330 0.637 0.105 0.258 Extension Agent-2 0.311 0.493 0.196 0.691 0.149 0.160 Rancher-1 0.500 0.064 0.437 0.367 0.051 0.582 Rancher-2 0.484 0.092 0.423 0.472 0.084 0.444 Table D-2. Summary of weightings of three forage speci es as generated by the LDW program based on the survey. Forage Participant Bahiagrass FloraltaPanicumResearcher -1 0.519 0.177 0.304 Researcher -2 0.086 0.297 0.618 Extension Agent-1 0.687 0.244 0.069 Extension Agent-2 0.649 0.279 0.072 Rancher-1 0.637 0.258 0.105 Rancher-2 0.353 0.586 0.061 Figure D-1. Range of weighting of features in warm and cool seasons.

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125 Figure D-2. Range of weighti ng of the three forage species.

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126 APPENDIX E RESULTS FROM SURVEY Figure E-1. Simulation result on SP4 in warm s eason using weighting factors of researcher-1. Figure E-2. Simulation result on SP4 in cool s eason using weighting factors of researcher-1. WT = Water Trough S = Shade WET = Wetland ACRU2K/H.S.I. ACRU2K Mean of GPS WT, S WET WT = Water Trough S = Shade WET = Wetland ACRU2K/H.S.I. ACRU2K Mean of GPS WT, S WET

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127 Figure E-3. Simulation result on SP4 in warm s eason using weighting factors of researcher-2. Figure E-4. Simulation result on SP4 in cool s eason using weighting factors of researcher-2. WT = Water Trough S = Shade WET = Wetland ACRU2K/H.S.I. ACRU2K Mean of GPS WT, S WET WT = Water Trough S = Shade WET = Wetland ACRU2K/H.S.I. ACRU2K Mean of GPS WT, S WET

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128 Figure E-5. Simulation result on SP4 in warm s eason using weighting factors of extension agent -1. Figure E-6. Simulation result on SP4 in cool seas on using weighting factors of extension agent 1. WT = Water Trough S = Shade WET = Wetland ACRU2K/H.S.I. ACRU2K Mean of GPS WT, S WET WT = Water Trough S = Shade WET = Wetland ACRU2K/H.S.I. ACRU2K Mean of GPS WT, S WET

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129 Figure E-7. Simulation result on SP4 in warm s eason using weighting factors of extension agent -2. Figure E-8. Simulation result on SP4 in cool seas on using weighting factors of extension agent 2. WT = Water Trough S = Shade WET = Wetland ACRU2K/H.S.I. ACRU2K Mean of GPS WT, S WET WT = Water Trough S = Shade WET = Wetland ACRU2K/H.S.I. ACRU2K Mean of GPS WT, S WET

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130 Figure E-9. Simulation result on SP4 in warm season using weighting factors of rancher -1. Figure E-10. Simulation result on SP4 in cool season using weighting factors of rancher -1. WT = Water Trough S = Shade WET = Wetland ACRU2K/H.S.I. ACRU2K Mean of GPS WT, S WET WT = Water Trough S = Shade WET = Wetland ACRU2K/H.S.I. ACRU2K Mean of GPS WT, S WET

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131 Figure E-11. Simulation result on SP4 in warm season using weighting factors of rancher -2. Figure E-12. Simulation result on SP4 in cool season using weighting factors of rancher -2. WT = Water Trough S = Shade WET = Wetland ACRU2K/H.S.I. ACRU2K Mean of GPS WT, S WET WT = Water Trough S = Shade WET = Wetland ACRU2K/H.S.I. ACRU2K Mean of GPS WT, S WET

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132 APPENDIX F SENSITIVITY ANALYSIS RESULTS Table F-1. Weighting factors used in sensitivity analysis. -100% -0.50%-0.25%BASE+0.25%+50% +100% WWFAC 0.000 0.2150.3220.4290.5360.644 0.858 WSAFAC 0.330 0.1890.1620.1430.1150.088 0.035 WOVRLFAC 0.670 0.6030.5210.4290.3490.268 0.107 WSAFC 0.000 0.0720.1070.1430.1790.215 0.286 WWFAC 0.500 0.4640.4460.4290.4100.392 0.357 WOVRLFAC 0.500 0.4640.4460.4290.4100.392 0.357 WOVRLFAC 0.000 0.2150.3220.4290.5360.644 0.858 WWFAC 0.751 0.5900.5090.4290.4100.392 0.357 WSAFC 0.248 0.1950.1690.1430.4100.392 0.357 CWFAC 0.000 0.1520.2280.3040.3800.456 0.608 CSAFAC 0.010 0.0800.0720.0630.0620.054 0.035 COVRLFAC 0.990 0.8000.7200.6330.5600.490 0.357 CSAFC 0.000 0.0320.0470.0630.0790.095 0.126 CWFAC 0.324 0.3140.3090.3040.2990.294 0.284 COVRLFAC 0.675 0.6540.6430.6330.6220.611 0.590 COVRLFAC 0.000 0.3170.4750.6330.7910.950 1.266 CWFAC 0.833 0.5690.4370.3040.1740.041 0.000 CSAFC 0.167 0.1140.0880.0630.0350.009 0.000 V1FAC 0.000 0.1430.2140.2850.3560.428 0.570 V2FAC 0.397 0.3400.3120.2850.2640.228 0.171 V3FAC 0.602 0.5160.4730.4300.3800.344 0.259 V2FAC 0.000 0.1430.2140.2850.3560.428 0.570 V1FAC 0.397 0.3400.3120.2850.2640.228 0.171 V3FAC 0.602 0.5160.4730.4300.3800.344 0.259 V3FAC 0.000 0.2150.3230.4300.5380.645 0.860 V1FAC 0.500 0.3920.3380.2850.2310.177 0.070 V2FAC 0.500 0.3920.3380.2850.2310.177 0.070 Variable above the dotted line was varied for sensitivity analysis and the variables below the dotted line were adjusted so the sum of all three variables amounted to 1.

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133 Table F-2. Sensitivity of water, shade and fora ge weighting factors in warm season (reported as percentage difference of base simulation on each land segment of SP5). WWFAC -100% -0.50%-0.25%+0.25%+50% +100% LS1 -10.71 -7.14-10.71 -17.86-25.00 -53.57 LS2 -10.77 -47.69-32.31 13.8549.23 189.23 LS3 17.86 25.0021.43 10.710.00 -35.71 LS4 -13.33 -6.67-6.67 -20.00-26.67 -53.33 LS5 10.53 21.0515.79 5.26-5.26 -39.47 LS6 -13.33 -6.67-6.67 -20.00-26.67 -53.33 LS7 3.57 10.717.14 -3.57-10.71 -42.86 LS8 -15.38 -7.69-7.69 -23.08-30.77 -53.85 LS9 12.50 21.8818.75 6.25-3.13 -37.50 LS10 3.57 10.717.14 -3.57-10.71 -42.86 LS11 10.53 21.0515.79 5.26-5.26 -39.47 LS12 -10.53 -5.26-10.53 -15.79-26.32 -52.63WSAFAC -100% -0.50%-0.25%+0.25%+50% +100% LS1 -53.57 -28.57-14.29 14.2932.14 64.29 LS2 6.15 3.081.54 -1.54-3.08 -6.15 LS3 0.00 0.00-3.57 -3.57-7.14 -10.71 LS4 6.67 0.000.00 0.00-6.67 -6.67 LS5 5.26 2.630.00 -2.63-2.63 -5.26 LS6 6.67 0.000.00 0.00-6.67 -6.67 LS7 3.57 0.000.00 0.00-3.57 -7.14 LS8 0.00 0.000.00 -7.69-7.69 -7.69 LS9 3.12 0.000.00 -3.13-3.13 -9.38 LS10 3.57 0.000.00 -3.57-3.57 -7.14 LS11 5.26 2.632.63 0.000.00 -5.26 LS12 5.26 0.000.00 0.000.00 -5.26 WOVRLFAC -100% -0.50%-0.25%+0.25%+50% +100% LS1 207.14 46.4317.86 67.8650.00 21.43 LS2 304.62 70.7727.69 -18.46-26.15 -36.92 LS3 -100.00 -25.00-10.71 -7.140.00 3.57 LS4 -100.00 -26.67-13.33 0.000.00 6.67 LS5 -100.00 -23.68-7.89 -2.632.63 7.89 LS6 -100.00 -26.67-13.33 0.000.00 6.67 LS7 -100.00 -21.43-10.71 -3.573.57 7.14 LS8 -100.00 -23.08-15.38 -7.690.00 7.69 LS9 -100.00 -25.00-12.50 -6.250.00 3.12 LS10 -100.00 -21.43-7.14 -3.573.57 7.14 LS11 -100.00 -23.68-7.89 -2.632.63 7.89 LS12 -100.00 -26.32-10.53 0.000.00 5.26

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134 Table F-3. Sensitivity of the three vegetation sp ecies weighting factors in warm season (reported as percentage difference of base si mulation on each land segment of SP5). V1FAC -100% -0.50%-0.25%+0.25%+50% +100% LS1 -28.57 -10.71-7.14 3.5710.71 17.86 LS2 9.23 4.621.54 -1.54-4.62 -7.69 LS3 42.86 17.867.14 -10.71-17.86 -28.57 LS4 -73.33 -33.33-13.33 13.3326.67 46.67 LS5 23.68 10.535.26 -2.63-5.26 -13.16 LS6 -73.33 -33.33-13.33 13.3326.67 46.67 LS7 0.00 0.000.00 0.000.00 0.00 LS8 -100.00 -46.15-23.08 15.3830.77 61.54 LS9 46.88 18.759.38 -9.38-18.75 -31.25 LS10 0.00 0.000.00 0.000.00 0.00 LS11 23.68 10.535.26 -2.63-7.89 -13.16 LS12 -42.11 -21.05-10.53 5.2610.53 21.05V2FAC -100% -0.50%-0.25%+0.25%+50% +100% LS1 3.57 3.570.00 0.00-3.57 -3.57 LS2 -13.85 -7.69-3.08 4.627.69 18.46 LS3 10.71 7.143.57 -7.14-10.71 -17.86 LS4 6.67 0.000.00 0.00-6.67 -6.67 LS5 5.26 2.632.63 0.00-2.63 -5.26 LS6 6.67 0.000.00 0.00-6.67 -6.67 LS7 -3.57 -3.570.00 0.000.00 3.57 LS8 23.08 7.690.00 -7.69-15.38 -30.77 LS9 -3.13 -3.13-3.13 -3.13-3.13 -3.13 LS10 -3.57 -3.570.00 0.000.00 3.57 LS11 5.26 2.632.63 0.00-2.63 -5.26 LS12 -15.79 -10.53-5.26 5.2610.53 21.05 V3FAC -100% -0.50%-0.25%+0.25%+50% +100% LS1 21.43 10.713.57 -3.57-10.71 -25.00 LS2 7.69 3.081.54 -1.54-4.62 -10.77 LS3 -57.14 -28.57-17.86 14.2932.14 71.43 LS4 53.33 26.6713.33 -20.00-33.33 -73.33 LS5 -23.68 -13.16-5.26 7.8915.79 34.21 LS6 53.33 26.6713.33 -20.00-33.33 -73.33 LS7 3.57 0.000.00 0.00-3.57 -7.14 LS8 53.85 23.0815.38 -23.08-38.46 -76.92 LS9 -43.75 -25.00-12.50 9.3821.88 53.13 LS10 3.57 3.570.00 0.000.00 -3.57 LS11 -23.68 -13.16-5.26 7.8915.79 34.21 LS12 52.63 26.3210.53 -15.79-36.84 -73.68

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135 Table F-4. Sensitivity of water, shade and fora ge weighting factors in cool season (reported as percentage difference of base simulation on each land segment of SP5). CWFAC -100% -0.50%-0.25%+0.25%+50% +100% LS1 -15.79 5.265.26 0.000.00 -15.79 LS2 -45.24 -21.43-9.52 30.9554.76 119.05 LS3 6.45 0.000.00 -6.45-9.68 -19.35 LS4 -5.26 -10.53-10.53 -15.79-15.79 -26.32 LS5 6.82 0.000.00 -6.82-9.09 -18.18 LS6 -5.26 -10.53-10.53 -15.79-15.79 -26.32 LS7 25.93 18.5214.81 11.117.41 -3.70 LS8 14.29 7.147.14 0.00-7.14 -14.29 LS9 5.56 0.00-2.78 -8.33-11.11 -19.44 LS10 25.93 18.5214.81 11.117.41 -3.70 LS11 2.27 2.270.00 -6.82-9.09 -18.18 LS12 4.55 0.000.00 -9.09-9.09 -18.18 CSAFAC -100% -0.50%-0.25%+0.25%+50% +100% LS1 -21.05 -10.53-5.26 10.5315.79 31.58 LS2 2.38 2.382.38 0.000.00 0.00 LS3 0.00 0.000.00 0.00-3.23 -3.23 LS4 0.00 0.000.00 0.000.00 -5.26 LS5 0.00 0.000.00 -2.27-2.27 -2.27 LS6 0.00 0.000.00 0.000.00 -5.26 LS7 0.00 0.000.00 0.000.00 -3.70 LS8 7.14 7.147.14 0.000.00 0.00 LS9 0.00 0.000.00 -2.78-2.78 -2.78 LS10 3.70 3.703.70 0.000.00 0.00 LS11 0.00 0.000.00 -2.27-2.27 -2.27 LS12 0.00 0.000.00 0.000.00 0.00 COVRLFAC -100% -0.50%-0.25%+0.25%+50% +100% LS1 205.26 36.8415.79 -5.26-10.53 -10.53 LS2 592.86 107.1440.48 -23.81-42.86 -45.24 LS3 -100.00 -19.35-9.68 3.239.68 12.90 LS4 -100.00 -21.05-10.53 5.2610.53 10.53 LS5 -100.00 -20.45-6.82 4.55-9.09 -9.09 LS6 -100.00 -21.05-5.26 5.2610.53 10.53 LS7 -100.00 -14.813.70 3.707.41 7.41 LS8 -100.00 -21.43-7.14 7.1414.29 14.29 LS9 -100.00 -22.22-8.33 5.5611.11 11.11 LS10 -100.00 -11.11-3.70 3.7011.11 11.11 LS11 -100.00 -20.45-6.82 4.559.09 11.36 LS12 -100.00 -13.64-4.55 4.559.09 9.09

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136 Table F-5. Sensitivity of the three vegetation sp ecies weighting factors in cool season (reported as percentage difference of base si mulation on each land segment of SP5). V1FAC -100% -0.50%-0.25%+0.25%+50% +100% LS1 -10.53 -15.79-5.26 -5.26-10.53 47.37 LS2 107.14 7.1440.48 -23.81-42.86 21.43 LS3 22.58 61.29-9.68 3.239.68 -51.61 LS4 -15.79 -15.79-10.53 5.2610.53 42.11 LS5 -38.64 -31.82-6.82 4.55-9.09 -38.64 LS6 -10.53 -15.79-5.26 5.2610.53 42.11 LS7 -14.81 -14.813.70 3.707.41 37.04 LS8 -100.00 -35.71-7.14 7.1414.29 64.29 LS9 2.78 13.89-8.33 5.5611.11 -44.44 LS10 -11.11 -11.11-3.70 3.7011.11 37.04 LS11 -20.45 20.45-6.82 4.559.09 -47.73 LS12 0.00 -9.09-4.55 4.559.09 36.36V2FAC -100% -0.50%-0.25%+0.25%+50% +100% LS1 47.37 10.530.00 5.2610.53 15.79 LS2 19.05 4.760.00 4.7611.90 28.57 LS3 25.81 16.136.45 -3.230.00 0.00 LS4 15.79 0.00-5.26 0.000.00 -5.26 LS5 -31.82 -25.00-18.18 -4.55-6.82 -13.64 LS6 15.79 0.00-5.26 0.000.00 0.00 LS7 -3.70 0.007.41 0.007.41 3.70 LS8 57.14 14.290.00 0.000.00 0.00 LS9 -30.56 2.788.33 -2.78-2.78 -8.33 LS10 -3.70 0.00-3.70 3.703.70 3.70 LS11 -31.82 -4.556.82 -2.27-6.82 -13.64 LS12 0.00 4.550.00 0.000.00 -4.55 V3FAC -100% -0.50%-0.25%+0.25%+50% +100% LS1 89.47 36.8421.05 -10.530.00 36.84 LS2 42.86 16.679.52 -2.3821.43 135.71 LS3 -77.42 -41.94-22.58 35.4835.48 3.23 LS4 68.42 26.3210.53 -15.79-10.53 -15.79 LS5 -50.00 -15.91-6.82 -22.73-29.55 -43.18 LS6 68.42 26.3215.79 -15.79-10.53 -15.79 LS7 25.93 25.9314.81 -14.813.70 -11.11 LS8 114.29 50.0021.43 -14.29-14.29 -28.57 LS9 -77.78 -44.44-22.22 47.222.78 -16.67 LS10 25.93 29.6314.81 -11.113.70 -7.41 LS11 -63.64 -36.36-18.18 0.00-18.18 -36.36 LS12 54.55 22.7313.64 -9.0913.64 -4.55

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151 BIOGRAPHICAL SKETCH Vibhuti Pandey was born in the state of Utta r Pradesh, India, on 26th January 1977. He graduated from Allahabad Agricultural Institute India, in 1999 with a bachelorÂ’s degree in agricultural engineering. He move d to Utah State University to pursue his graduate studies, in 2000. He graduated with a Master of Science degree in irrigation engi neering in 2002. To continue his higher education he moved to Florida. That same year he joined the Ph.D. program in the University of FloridaÂ’s Department of Ag ricultural and Biological Engineering, where he specialized in ecological modeling and water resources engineering.