Farm financial persistence and characteristic analysis

Abstract

Farmers and agricultural lenders often seek the ability to identify positive or negative characteristics to improve farm operations. Determining these characteristics has been the goal of many research studies. More often than not, a unique set of uncontrollable events was credited for contributing the majority of one farm’s success relative to their peers. The goal of this study was to evaluate the assumption that farmers can control their financial persistence defined as remaining in their current financial category, based upon a farm’s debt to asset ratio (D/A), and net farm income per acre (NFI acre⁻¹). Financial categories give agricultural producers a concrete answer to the question of one farm’s ability to maintain their financial persistence during market downturns and poor growing conditions and include Favorable, Marginal Income, Marginal Solvency, and Vulnerable. Farmers across the United States are subject to many uncontrollable variables (temperature, precipitation, market volatility, land value fluctuations, interest rates) leaving them vulnerable to agricultural market downturns, such as the one that began in 2014. Seasonal cash inflows and outflows of farms and their profitability create a difficult situation for farmers and agricultural lenders alike to predict the future. Identifying and estimating the likelihood of financial persistence has become an area of interest for farmers, their advisors, and their financial lenders. Currently, agricultural lenders rely on loan assessment techniques, such as net present values and loss-based methods. These techniques fail to account for the unique and often long-term investment nature of farming. If an additional method for identifying at-risk farms or at least understanding the likelihood of persistence in farms could be found, it would provide an insight into the riskiness of lending to a farm and provide agricultural lenders with an additional analysis tool. The dynamic nature of farm financials and the ever-changing variables of farming limit traditional statistical methods. Considering the difficulty associated with predicting farm default rates due to the complexity of the question, a secondary approach is possible. This study utilized an approach in determining farm financial persistence by estimating the Markov Chain probabilities of four financial categories ranging from Favorable, solvent with positive income to Vulnerable, an insolvent and negative income financial position. Kansas Farm Management Association (KFMA) data from 1993 to 2014 were used to estimate the probability of transitioning between financial categories. This thesis combines transition probabilities of Kanas farms and a multinomial logit model (MNL) to identify farm characteristics of significance. The matrix of probabilities generated, when interpreted, provide information about Kansas farms and their probability of financial persistence, and the MNL model allows for insights into favorable or un-favorable farm characteristics. Farms were found to transition easily between financial categories that had the same debt to asset ratio (D/A), but different net farm income per acre (NFI acre⁻¹, positive or negative) indicating that farm income is more easily changed than farm D/A ratios. Farms in the Favorable category (D/A < 0.4, + NFI acre⁻¹) had the largest probability of financial persistence at 0.83, whereas Vulnerable farms (D/A > 0.4, - NFI acre⁻¹) were most likely to transition to the Marginal Solvency category (D/A > 0.4, + NFI acre⁻¹) with a probability of transitioning of 0.55 versus the probability of remaining in the Vulnerable category of 0.33. It was also found that crop mixture and age were not statistically significant in the MNL model, but gross profit margin and a farm’s percentage of owned land out of total crop acres were statistically significant in explaining why farms were in each category.