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Dynamic Programming Methods for Characterizing In-Season Farm Management Decisions

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  • Gallagher, Nicholas James

Abstract

The agriculture industry is unique in the high level of influence from randomness in the environment due to the many ways in which an agricultural production system interacts with the environment. Static models fail to accurately characterize the sequential decision process of the farmer. Capturing the temporal dynamics of farm management decisions necessitates a sequential decision model. The demands of conceptualizing and computing sequential decisionmaking models are especially salient in agriculture, where substantial influence from countless exogenous random variables has made useful formulations of many farm-level decisions intractable. I begin with an assessment of decision making in agriculture, focusing on the potential of sequential decision models, and provide a brief history of their applications in farmlevel decision making. I then present a generalized model of in-season crop production that ties all field level decisions to the temporal constraint inherent to a single production season. I discuss the considerations necessary to effectively model farm-level decision making with a dynamic model, and additional considerations for possible extensions of the model. I then present three applications of the model. The first application is to a simplified crop decision problem where a farmer is deciding the allocation of acres between corn and soybeans. The second application is to the prevented planting policy of Revenue Protection crop insurance. The third application is to a short-horizon alfalfa harvest optimization problem.

Suggested Citation

  • Gallagher, Nicholas James, 2024. "Dynamic Programming Methods for Characterizing In-Season Farm Management Decisions," Dissertations and Theses 344827, Ekiti State University, Ado-Ekiti, Department of Agricultural Economics and Extension Services.
    • Handle: RePEc:ags:uekndt:344827
    DOI: 10.22004/ag.econ.344827
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