http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Toward a holistic approach to the cropping mix decision
The cropping mix decision is one of the most important made each year by a farmer. Many factors are generally considered in the decision process, including the farmer's goals and objectives, expected returns and riskiness of returns for each crop, resource availability, government program provisions, and agronomic considerations. The objective of this study was to develop, document, and provide preliminary validation for a model that simultaneously considers these and other pertinent factors when identifying a farmer's preferred crop mix. The purposes in developing this model were (a) to provide an analytical tool that could be used to aid farmers as they ponder their cropping decision, and (b) to develop a model that could be used in predicting the effect of changes in government policy on farmers' cropping decisions. A mixed integer-quadratic programming (MIQP) model was identified as the approach that could best account for the factors important in the cropping decision. A matrix generator (DYNLMAG) was developed to facilitate creation of MIQP models unique to each farm analyzed. Solutions to the MIQP model were obtained by dividing the problem into integer and quadratic programming subproblems based on Benders' Decomposition. The MINOS and MIPZI algorithms were employed to iteratively solve the problem. Two actual farming operations in Texas were selected as test cases for analysis. The first is a rice and feed grain farm located in the Middle Gulf Coast region; the second is a cotton and feed grain farm in the Coastal Bend region. The models generated for these test cases were judged valid representations of their respective farms. Although both farms were projected to follow historical crop rotations if the 1981 Farm Bill continued in effect, implementations of the 1985 farm bill resulted in a significant shift in the crop mix for the Middle Gulf Coast farm. This shift was attributed to the lower loan rate for rice. The large size of the quadratic programming model, combined with the Benders' Decomposition process, resulted in a time-consuming solution process. Analysis of the results suggested smaller models may identify the same crop mix when farms are already at or near long-run equilibrium.
Ph. D. in Agricultural Economics Texas A & M University 1986
Includes bibliographical references (leaves 220-228).