Agricultural finance & farm and ranch management

Data from the Federal Crop Insurance Corporation- Risk Management Agency will be continue to be examined to more accurately estimate the effects of insurance upon farm level financial performance and survival. Past efforts with respect to analyzing the efficiency of various aspects of the crop insurance program and other risk management tools will be continued. These efforts will involve statistical and analytical procedures consistent with economic (Borch) and actuarial theory (Daykin et. al).Research with respect to the potential for weather and other derivative products to mitigate farm, agribusiness, local, regional, and national level risk will be continued. The large scale weather data base previously developed under this project will be maintained and utilized under this objective. Requested data extracts from the data base will continue to be processed and made available to graduate students as well as MSU and external researchers and governmental agencies investigating weather related issues such as potential localized or regional climate change. Static and dynamic behavioral models will be used to examine potential producer responses to proposed changes in governmental policy including efforts to governmental insurance costs and to reduce fraud, waste, and abuse costs in the crop insurance system. Statistical procedures will be applied to empirical data sets to test the validity of the models' predictions. The potential effect of policies upon the agricultural firm's economic and financial situation will be estimated. Where applicable, estimates of the societal costs associated with a particular policy will be generated using standard welfare analysis procedures.Under an agreement with the National Agricultural Statistical Service (NASS), longitudinal and cross sectional farm level census response data will be used in an attempt to identify operational and financial characteristics of surviving agricultural firms. Statistical and data mining procedures, as appropriate will be applied during the course of the analysis. The project will study important aspects of futures markets and futures trading relevant to agricultural producer decision-making and agricultural risk management, including but not limited to: the use of intertemporal price spreads to inform farm and ranch manager decisions with respect to storage decisions, the intraday microstructure of agricultural futures markets and its effects on the execution of futures trades for farmers and other market participants, and the barriers to price discovery in agricultural markets where futures trading is not established and forward markets are limited and opaque.Both mathematical programming and time-series econometric methodologies may be employed to analyze these problems. The results are applicable to farm decision making and farm finance; since futures market price discovery affects production and marketing decisions across time, it is inherently tied to farm financial decision-making and the use of credit.Research with respect to benchmarking procedures and the development of useful comparative production and financial efficiency metrics will be continued. Research upon the statistical properties and efficacy of Quantile Data Envelopment Analysis (QDEA) will be conducted. Current farm level research efforts with researchers and producers from other states will be continued. Cooperative efforts with agricultural lending agencies with respect to the potential usefulness of efficiency and other performance metrics in assessing the financial performance and credit worthiness of agricultural borrowers will be continued.