Master of Agribusiness / Department of Agricultural Economics / Hikaru H. Peterson / Profitability is a primary economic motivator for a farm to remain in business. As conventional crop farming endures rising fertilizer and chemical costs, small farmers that raise grains must look towards innovative cropping practices that are economically affordable or depart the business. As small farmers evaluate other cropping prospects, organic cropping systems and the availability of organic price premiums should be considered as an alternative in meeting farm profitability goals. This study compared the economic return per acre of converting to an organic cropping system from a conventional system against the conventional crop enterprise of the same crop mix. A simulation model was created using assumed organic yield data, actual organic prices, historical conventional yield data and historical conventional prices to determine the economic return. An initial simulation was run, ignoring the three-year transitional period that farms must undergo with no synthetic inputs to become certified organic, to determine if organic cropping systems using organic price premiums on the 600-acre farm would be competitive with conventional production. The simulation showed that organic production is economically competitive with conventional production. Previous studies and personal interviews indicated that the three-year transitional period could easily cause the farm economic loss, since conventional inputs cannot be used and organic premiums cannot be obtained for crops sold. Therefore, three different conversion schemes were simulated to find which one would cause the farm the least economic damage: converting the entire farm to organic production at once, converting 20% of the farm's tillable acres to organic production annually and converting 10% of the farm's tillable acres to organic production annually. All three of the proposed transition schedules revealed economic loss to the farm at some point during their transition periods. The only scheme that showed no average loss was the existing conventional system. However, after complete transition, the three transition schemes showed higher profitability than the conventional cropping system. The downside was that this took a minimum of 13 years to accomplish. The only scheme that did not cause the farm's cumulative present value to drop into negative numbers was the 10% per year transition rate.

The number of very small farms (<10 acres) is increasing and beginning farmers (in practice for <10 years) are more likely to run them. Very small farms are typically complex systems in which the farmer manages both production of a diverse array of crops and marketing of crops directly to consumers and their failure rate in early years is high. This work seeks to increase the likelihood of success for beginning farmers by understanding these complex systems better. We collected qualitative and quantitative data from interviews with three successful beginning farm operations in Southwest Virginia covering practical and philosophical aspects of farm production, sales and management. We mapped social, environmental and economic aspects of farming systems and studied how farmers use resources (Community Capitals) and management to enhance their system's success, developing a broader definition of success that encompasses what farmers gain from farming beyond profitability. Using these maps, we created a system dynamics model of a small farm system in STELLA including unique components such as customer attraction and retention. Through model development, we learned that these successful farmers began their operations with experience and financial resources, and employed their skills, resourcefulness and cultural and social capital to charge prices for their products that could sustain their operations financially. Using our model, current and aspiring farmers, service providers, and small farm advocates will be able to simulate real or hypothetical farm systems to better understand what establishing a successful small farm might require and how to confront potential challenges. / Master of Science

Mathematical modeling in animal agriculture can be applied at various levels including at the tissue, organ, animal, farm, regional and global levels. The purposes of this research were i) to evaluate models used to estimate volatile fatty acid (VFA) and methane (CH4) production and assess their impact on regional enteric CH4 inventory, and ii) to develop a process-based, whole-farm model to estimate net farm GHG emissions. In the first study, four VFA stoichiometric models were evaluated for their prediction accuracy of rumen VFA and enteric CH4 production. Comparison of measured and model predicted values demonstrated that predictive capacity of the VFA models varied with respect to the type of VFA in rumen fluid which impacted estimated enteric CH4 production. Moving to a larger scale assessment, we examined the enteric CH4 inventory from Manitoba beef cattle (from 1990 to 2008) using two mechanistic rumen models that incorporate VFA stoichiometric models: COWPOLL and MOLLY, and two empirical models: Intergovernmental Panel on Climate Change (IPCC) Tier 2 and a nonlinear equation (Ellis). The estimated absolute enteric CH4 production varied among models (7 to 63%) indicating that estimates of GHG inventory depend on model selection. This is an important consideration if the values are to be used for management and/or policy-related decisions. Development of models at the individual farm component level (animal, soil, crop) does not accurately reflect net GHG emissions generated from the whole production system. We developed a process-based, whole-farm model (Integrated Components Model, ICM), using the existing farm component models COWPOLL, manure-DNDC and some aspects of IPCC to integrate farm components and their associated GHG emissions. Estimates of total farm GHG emissions and their relative contribution using the ICM were comparable to estimates using two other whole-farm models (Integrated Farm System Model and Holos model). Variation was observed among models both in estimating whole-farm GHG emissions and the relative contribution of the different sources in the production system. Overall, whole-farm models are required to explore management options that will mitigate GHG emissions and promote best management practices. However, for full assessment of the production system, other benefits of the system (e.g., carbon sequestration, ecosystem services), which are not part of current whole-farm models, must be considered.

Mathematical modeling in animal agriculture can be applied at various levels including at the tissue, organ, animal, farm, regional and global levels. The purposes of this research were i) to evaluate models used to estimate volatile fatty acid (VFA) and methane (CH4) production and assess their impact on regional enteric CH4 inventory, and ii) to develop a process-based, whole-farm model to estimate net farm GHG emissions. In the first study, four VFA stoichiometric models were evaluated for their prediction accuracy of rumen VFA and enteric CH4 production. Comparison of measured and model predicted values demonstrated that predictive capacity of the VFA models varied with respect to the type of VFA in rumen fluid which impacted estimated enteric CH4 production. Moving to a larger scale assessment, we examined the enteric CH4 inventory from Manitoba beef cattle (from 1990 to 2008) using two mechanistic rumen models that incorporate VFA stoichiometric models: COWPOLL and MOLLY, and two empirical models: Intergovernmental Panel on Climate Change (IPCC) Tier 2 and a nonlinear equation (Ellis). The estimated absolute enteric CH4 production varied among models (7 to 63%) indicating that estimates of GHG inventory depend on model selection. This is an important consideration if the values are to be used for management and/or policy-related decisions. Development of models at the individual farm component level (animal, soil, crop) does not accurately reflect net GHG emissions generated from the whole production system. We developed a process-based, whole-farm model (Integrated Components Model, ICM), using the existing farm component models COWPOLL, manure-DNDC and some aspects of IPCC to integrate farm components and their associated GHG emissions. Estimates of total farm GHG emissions and their relative contribution using the ICM were comparable to estimates using two other whole-farm models (Integrated Farm System Model and Holos model). Variation was observed among models both in estimating whole-farm GHG emissions and the relative contribution of the different sources in the production system. Overall, whole-farm models are required to explore management options that will mitigate GHG emissions and promote best management practices. However, for full assessment of the production system, other benefits of the system (e.g., carbon sequestration, ecosystem services), which are not part of current whole-farm models, must be considered.

A decision support system for preventive integrated pest management (IPM) and nonpoint source (NPS) pollution control was designed, implemented and evaluated. The objective of the system was to generate plans at the farm level to satisfy economic and production goals while limiting risks of insect pest outbreaks, nitrate and pesticide leaching and runoff, and soil erosion. The system is composed of a constraint satisfaction planner (CROPS-LT), a modified version of CROPS (Stone, 1995), a farm-level resource management system (FLAME), an NPS module, which includes a weather generator, CLIGEN (Nicks et al. 1995), and an NPS distributed-parameter model, ANSWERS (Bouraoui, 1994), databases, a database engine and utility programs. The performance of the system was analyzed and performance enhancing features were added to increase the planner's ability to find near-optimal plans within a limited planning time. Using heuristics to sort potential crop rotations based on profit generally improved the planner's performance, as did removal of fields that were not suitable for growing target crops. Not surprisingly, the planner was best able to find plans for crops that can be grown in a variety of rotational systems. Throughout, the ability to apply environmental constraints selectively to individual fields greatly improved the planner's ability to find acceptable plans. Preventive IPM (PIPM) heuristics to control corn rootworms CRW (<I>Diabrotica virgifera virgifera</I> and <I>D. barberi</I>) were added to the planner. The model was represented and solved as a constraint satisfaction problem. Results indicated that plans obtained using PIPM heuristics had less risk of CRW damage, reduced chemical control costs, higher profit and reduced soil erosion as compared to a control plan. Linking the planner to the NPS model in a feedback control loop improved the planner's ability to reduce soil losses while preserving economic and production goals. / Ph. D.

This thesis is composed of two essays that investigate whole farm planning and crop marketing in western Kentucky. In the first essay, contracting decisions between food corn producers and a mill are analyzed to observe factors affecting the bushel amount farmers contract. Unbalanced panel data containing seven years’ worth of pricing and contract information are used with a fixed-effects model to generate parameter estimates and quantify their effect on bushels contracted. It was found that contract attributes, market condition, and relationship-specific assets had a significant effect on producers’ food corn contracting decisions. The second essay utilizes mixed-integer programming to optimize resource allocation and marketing strategy for a hypothetical farm. Post-optimal analysis is performed to determine non-binding capacities for drying and storage equipment. The model is re-run with these non-binding capacities to observe changes in net returns as well as planting, harvesting, and marketing strategies. New equipment and associated costs are identified, and the change in net returns from the base case is used as net cash flow in a net present value investment analysis. Results of the investment analysis indicate increasing drying and storage capacity is a wise investment given the scenario modeled.