Developing a Management-focused, Spatial Decision Support Tool for Climate Forecasting in Beef Cattle Agro-ecosystems

Sudarshan Jagannathan1, Greg Kiker1, Patrick Bohlen2, Hilary Swain2 1Univ. of Florida, Dept. of Biological and Agricultural Engineering, 2MacArthur Agro-ecology Research Center

Introduction

Grazing lands in the southeastern US are complex agro-ecosystems that are considered to be potential sources of agricultural pollution. Complex agro-ecosystems require an inter-disciplinary approach that can examine the interrelations among ecological factors in order to promote ecologically and economically sustainable practices. A generic environmental modeling system, Questions and Decisions (QnD) (Kiker et al., 2006) has been created for this purpose. With the use of simple rules and relationships, a version of QnD has been created (QnD BIR Ver_0.0). The model will be implemented and tested using four complete beef cow production cycles (years) data collected by the researchers at the MacArthur Agro-Ecology Research Center (MAERC) at Buck Island Ranch, a 10,500 acre cattle ranch managed at full-scale commercial levels for research purposes (Swain et al., 2006; Arthington et al., 2006; Capece et al., 2006).

There are not many existing models that are capable of simulating the multi-scale variety of ranch processes: habitat utilization, impacts on ecosystems, cattle body condition, cattle rotation and overall ranch profitability. It is therefore important to have a modeling system that can be easily developed and custom-designed to be used by the stakeholders. The primary goal when building an agro-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 scenarios.

This research effort uses stakeholder-derived information, literature review and on-going modeling/monitoring data to construct a management-focused model/game to analyze the potential utility of various climate predictions on month-to-month management decisions, ecosystem function and water resources.

Specific research questions include the following:

• What decisions are currently made that effect runoff, water quality on beef cattle ranches?
• What would be the impact of using climate forecasts for these management decisions with respect to water resources factors such as runoff and nutrient loading?

The primary limits of productivity in grasslands are the availability of water, nutrients and light.  There have been many excellent long-term studies of how the major ecological drivers in grassland systems, such as fire and grazing, influence productivity by altering the relative availability of these different resources.  There have been few studies in subtropical grasslands to examine basic controls on productivity and nutrient cycling, inter-annual variability in productivity, and the interactive effects of fire and nutrients on productivity, community structure and soil processes.  This experiment examines these interactions in a subtropical wet prairie at MAERC.  We will examine the effect of fire frequency, season of fire and nutrient addition on annual productivity, and species composition of dominant vegetation and over time will also examine the interactive effects of these treatments on belowground nutrient cycling processes and microbial functioning.  Season of fire is particularly applicable to these subtropical prairies because the management burns used by cattle ranches occur in the winter; whereas the "natural" fires in these systems occurred primarily in summer.  These systems are likely limited by the availability of N and we expect N additions to increase productivity and alter species composition of the dominant vegetation.  Phosphorus may also be limiting in these nutrient poor ecosystems and P addition is an additional component of this experiment.

QnD Model Development

• QnD can be constructed by using any combination of detailed technical data or estimated interactions of the ecological/ management/ social/ economic forces influencing an ecosystem. development is iterative and can be initiated quickly through conversations with users or stakeholders.
• Alterations and/or more detailed processes can be added throughout the model development process.
• QnD can be used in a rigorous modeling role to mimic system elements obtained from scientific data or it can be used to create a “cartoon” style depiction of the system to promote greater learning and discussion from decision participants.

Fig 1. The QnD model has two primary parts, the simulation engine (for calculations) and the game
view (for presentation of information).

QnD:BIR Version 0.0

• Develop and expand QnD:BIR to spatially cover the entire BIR ranch.
• Expansion of the simulation engine within QnD:BIR will include these WAM results to create simplified runoff and P loading relationships to be integrated with the management choices of stocking rate and pasture location.
• Review the herd performance data (Standardized Performance Analysis) and ranch financial statements to create a more detailed financial simulation of BIR activities
• Once a complete spatial simulation has been created and tested for BIR, we will be integrating climate forecast scenarios from the AgClimate web site for simulations of ranch management, water resources and Pload best management practices.

Fig 2. The QnD:BIR version 0.0 model for Buck Island Ranch.

QnD:BIR Simulation Engine Overview.

• Spatial units represent specific pastures and have distinct spatial features (location, connection and permanence).
• Habitats exist as spatially inexplicit areas within spatial units (wetlands, non-native range, native range, etc..).
• Cattle herds are represented as local components existing within various habitats within a spatial unit
• The external drivers include production costs, calf prices, precipitation, and nutrient movement. Cattle herding BMP’s, grass type also have to be taken into consideration and are given to the model as a part of the spatial input.
• The management decisions include: stocking rate/distribution;  cull/increase herd size/demographics;  fertilize/lime pastures; import supplemental feed;  pump water from canal; harvest/sell sod.

Fig 3. Overview of components for various pasture/habitat spatial areas.

• QnD allows the creation of custom scenarios with sets of drivers which are either stochastically generated or read from time series files.
• Various Driver objects (climate and social influences) can be designed into various Scenario objects.
• Elements of the global scenarios, regional scenarios or climate change scenarios can be incorporated into QnD drivers.

References

Kiker, G.A., Rivers-Moore, N.A., Kiker, M.K. and Linkov, I. (2006). QnD: A scenario-based gaming system for modeling environmental processes and management decisions. (Chapter in Morel, B. Linkov, I., (Eds) “Environmental Security and Environmental Management: The Role of Risk Assessment.” Springer, Netherlands. Pp:151-185.

Swain, H., Bohlen, P.J., Campbell, K.L., Lollis, L.O., Steinman, A.D., 2006. Integrated Ecological and Economic Analysis of Ranch Management Systems. Rangeland Ecology Mgmt. 60(1): 1-11.

Arthington, J.D., Roka, F.M., Mullahey, J. J., Coleman, S.W., Lollis, L.O., Muchovej, R.M., 2006. Integrating ranch forage production, cattle performance and economics in ranch management systems. Rangeland Ecology and Management. 60(1): 12-18.

Capece, C.J., Campbell, K.L., Bohlen, P.J., Graetz, D.A., Portier, K.M., 2006. Soil Phosphorus, Cattle Stocking Rates and Water Quality in Subtropical Pastures in Florida. Rangeland Ecology and Management 60(1): 19-30.