Research

Dynamic Characterization of On-farm Decision Making

Agricultural management is sequential and random necessitating agricultural models to be dynamic and stochastic. I have built a generalized finite-horizon stochastic dynamic model of in-season farm management to capture temporal risk stemming from capital and labor allocation constraints in conjunction with the influence of random weather outcomes. I have applied the model to a simple crop selection problem of allocating acres on an Illinois farm to corn and soybeans and compare the model to two linear programming formulations, one static, and one with delineated periods to add temporal constraints. Results show an improved understanding of the farmer’s decision process and how a variety of temporal constraints experienced by the farmer lead to a compounded perception of risk. I am working on extending the model to analyze prevented plant insurance provided by RMA. 

Regional Environmental and Agriculture Programming (REAP) Model

Developed and maintained by  USDA ERS, REAP is a partial equillibrium model of the US agricultural sector. The goal of the model is to connect policy and market shocks to changes in production and management distributions of crops and livestock, and to estimate subsequent environmental effects. Currently we are building REAP version 7, which will expand on the functionality of the previous model and increase its adaptability to new lines of research and policy analysis. 

Composite Indicators and Farmer Information Uptake

Increased farm and market level data availablity along with the uptake of farm managment software is providing a unique opportunity to improve the understanding of interactions between agriculture and the environment. Key to this effort is the development of useful composite indicators, both economic and environmental. This reseach is focused on identifying the need for composite indicators in modern farm management to address externalities of farming and showing that combining several composite indicators can provide a comprehensive understanding of the economic and environmental consequences of farm management decisions. For my Master's thesis, I provide an example of using a combination of composite indicators to test the hypothesis that adding winter cover crops to a common cropping system in Wisconsin can reduce environmental externalities without increased risk of lost profits.