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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 |