This thesis examines the use of two specific types of precision agriculture technologies: cost maps and lightbar. Cost maps visually depict spatial differences in production costs. The visual depictions of these costs are represented using ArcGIS in an attempt to aide farmers in further decision making. Results will show that cost maps have great possibilities in their addition to the set of tools that farmers use in decision making. This thesis will expand the understanding of lightbar from a partial budget study to a whole farm model incorporating competition across different enterprises for labor and capital. The results from the study of cost maps indicate that inaccuracy of machinery movement, whether in the application stage or the harvesting stage is very costly. As a result, the suggestion of lightbar as a guidance aide to improve farm profitability is recommended under the conditions analyzed and shows a net farm return increase in just over 6%.

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.