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The average U.S. farm size has risen dramatically over the last three decades. Motives for this trend are the subject of a large body of literature. This study incorporates farm size risk and return analysis into this research stream. In this paper, cross-sectional and temporal relations between farm size and returns are examined and characterized.

Relying on farm level panel data from Kansas Farm Management Association (KFMA) for 140 farms from 1996 to 2018, this article examines the relationship between farm size and returns and investigates whether farm size is related to risk. Two measures of farm returns are used: excess return on equity and risk-adjusted return on equity. Value of farm production and total farm acres are used as measures of farm size.

Findings suggest a significant and positive relationship between farm size and excess return on equity as well as farm size and risk-adjusted return on equity. However, this return premium associated with farm size is not associated with additional risk. Stated differently, farm size can be viewed as a farm characteristic that is associated with higher return without additional risk.

These findings provide further support for ongoing farm consolidation.

The results suggest the trend towards consolidation in production agriculture is likely to continue. Larger farms bear less risk.

A farm household consumption model based on the life‐cycle permanent income hypothesis (LPIH) has been specified and the Euler equations derived in this analysis. Estimation of the of the Euler equations using farm household consumption data provided estimates for the intertemporal elasticity of substitution and the coefficient of relative risk aversion. These parameters differ among the farm enterprises in which the households were engaged. Estimates for the intertemporal elasticity of substitution and the coefficient of relative risk aversion ranged from 0.158 to 0.351 and from 2.849 to 6.329, respectively. Results also provide further evidence that the LPIH is valid for modeling farm household consumption.

This paper aims to develop and utilize a conceptual framework to examine the impact of liquidity and solvency on cost efficiency for a sample of Kansas farms.

A standard cost-efficiency model is modified to incorporate liquidity and solvency ratios. Tobit regressions are used to determine the impact of farm characteristics on improvements in efficiency.

Results confirm that liquidity and solvency measures have a significant impact on improving cost efficiency. Farms with larger expenditures on purchased inputs relative to capital were less likely to improve efficiency when liquidity and solvency were considered.

To the authors' knowledge, the paper is the first to add liquidity and solvency ratios to the cost-efficiency model developed by Färe et al. for the analysis of farms.

The purpose of this paper is to examine the use of single vs multiple lenders by Kansas farms. Previous studies suggest that as the risk level of the firm changes, borrowers desire to enhance the probability of obtaining credit at the lowest possible cost may cause them to use multiple lenders.

A model is adopted from the banking literature to describe farm behavior in obtaining credit from a single vs multiple lenders. Using farm-level data from the Kansas Farm Management Association, an empirical model analyzes how farm characteristics affect the number of lending relationships. A model is developed to analyze the number of lending relationships effect on the profitability of the farm.

It is found that highly leveraged farms seek additional lending relationships supporting the theoretical model and that additional lending relationships correlate to a decrease in profitability. Roughly, 50 percent of Kansas farmers that borrow use a single lender. Roughly 48 percent use from two to four lenders, with the remaining 2 percent using more than four lenders.

Provides empirical results to support developed theoretical framework on the number of lending institutions. This study helps understand factors correlated to a farmer's decision to use multiple lenders. Analyzing the number of lending relationships helps understand how farmers manage their debt to maintain access to credit when needed at the lowest possible cost.

Understanding complex farming organizations is important in the USA given the rapid consolidation of the agricultural production sector. Multiple entity farms arise from a desire to enhance the ability to transfer the farm from one generation to the next, a desire to affect tax liability, and a desire to affect legal liability. To determine the extent of the multiple entity phenomena and the complications that multiple entities can cause in data collection, the purpose of this article is to address the importance of multiple entities in Kansas.

An overview of reasons leading to additional organizational complexity are discussed. Two case farms are presented to understand the depth of the complexity and how that complexity has implications for data collection.

The number of multiple entity farms is expected to continue to increase. Obtaining data through the Agricultural Resource Management Survey (ARMS) will become more difficult as production agriculture increases the use of multiple entities. ARMS must reconsider how multiple entity organizations are handled. Possible solutions include an alternative system for data collection of multiple entity farms. Documenting the prevalence of multiple entity organizations in the production sector and tracing through how those organizations are currently handled is critical to understanding potential impacts on the current data collected.

The National Research Council completed a review of ARMS that addressed challenges in keeping the survey relevant into the future. However, research that examines the construction of financial statements and other information had not been conducted since the early 1990s. This study fills part of that gap.

Farm level data are essential to accurate setting of crop insurance premium rates, but their time series tends to be too short to allow them to be the sole data source. County level data are available in longer time series, however. The purpose of this paper is to present a methodology to make full use of the information inherent in each of these data sets.

The paper uses a novel application of statistical tools for using farm and county level yield data to generate farm level yield densities that explicitly incorporate within county yield heterogeneity while accounting for systemic risk and other spatial or intertemporal correlations among farms within the county.

The empirical analysis shows that current approaches used by the Risk Management Agency to individualize premiums for a farm result in substantial mispricing of crop insurance premiums because they do not adequately capture farm yield variability and yield correlations between farms. The new premium setting method is empirically shown to substantially reduce government subsidies for crop insurance premiums.

The paper demonstrates how to extract more information from available data when setting crop insurance premiums, which allows the government to more closely tailor premiums to the farm than do current approaches.

The purpose of this paper is to determine the drivers of economic financial success of US cow-calf operations.

This research uses a system of equations (DuPont analysis) in conjunction with 2008 farm-level data from the US Department of Agriculture's Agricultural Resource Management Survey to evaluate the factors driving cow-calf profitability, namely net profit margins, asset turnover ratio, and asset-to-equity ratio.

The study finds that the main drivers of return on equity are region, number of harvested acres on the farm, diversification of the farm, operator off-farm work, spousal off-farm work, and adoption of technologies. Of these factors, those for which producers can make short-term adjustments include off-farm work decisions and adoption of technologies. Longer-term adjustments can be made for farm diversification.

To the authors’ knowledge, no existing research has used farm-level data across US production regions to examine the factors affecting returns to equity of US cow-calf operations. These research results may be used to identify strategies producers can use to improve their farm's economic viability, areas where extension services can assist farmers in making better financial decisions and economic factors that are likely to lead to structural changes in the beef industry.

The aim of this study is to use a financial approach based on the Du Pont expansion to investigate the impact of demographics, specialization, tenure, vertical integration, farm type, and regional location on the three levers of performance (ROE) – namely, net profit margins, asset turnover ratio, and asset‐to‐equity ratio.

This research uses a system of equations in conjunction with 1996‐2009 farm‐level data from the US Department of Agriculture's Agricultural Resource Management Survey (ARMS) to evaluate the factors driving farm‐level profitability, namely, net profit margins, asset turnover ratio, and asset‐to‐equity ratio. The methodology employed in this study corrects heterogeneity and uses repeated cross‐section estimation procedure to estimate the empirical models.

The study finds that key drivers of net profit margins are operator education, farm size and typology, specialization, and level of government payments. Key factors affecting the asset turnover ratio component of the Du Pont model include asset turnover ratio is driven by operator age, contracting, specialization, and receiving government payments. Finally, key factors affecting asset‐to‐equity ratio component of the Du Pont model are farm size, farm typology, contracting, and specialization drive asset‐to‐equity ratio.

Existing research does not examine the factors affecting returns to equity in faring at the farm‐level. Specifically, a micro‐level analysis of American farm's future structure and financial performance that accounts for the spatial and inter‐temporal dimensions of profitability has never been conducted.

The purpose of this paper is to explore the linkage between agricultural sector and macroeconomic factors with farm financial health. It considers whether agricultural lenders can more accurately anticipate changes in the credit quality of their portfolios by considering broad economic indicators outside the agriculture sector.

This paper examines firm, sector, and macroeconomic drivers of probability of default (PD) migrations from a sample of 153 grain farms of actual lender data from Farm Credit Mid-America’s portfolio. A series of ordered logit models are developed.

Farm-level and sector-level variables have the most significant impact on PD migrations. Equity to asset ratios, working capital to gross farm income ratios, and gross corn income per acre are found to be the most significant drivers of PD migrations. Macroeconomic variables are shown to unreliably forecast PD migrations, suggesting that agricultural lenders should emphasize firm and sector variables over macroeconomic factors in credit risk models.

This paper builds the literature on agricultural credit risk by testing a broader set of sector and macroeconomic variables than previous articles. Also, prior articles measured the direction but not magnitude of PD migrations; the ordered model in the analysis measures both.

The purpose of this paper is to determine the effects of the 2018–2020 China–US trade war on US farm bankruptcies as filed under Chapter 12. The key task is to identify the economic factors affecting farm bankruptcies generally, and to then control for the trade war impacts including the Market Facilitation Program (MFP), floods, agricultural conditions and the health of agricultural finance leading into the trade war.

Results were obtained using ordinary least square regression and panel fixed effect model using bankruptcy rates and number as the dependent variable. Independent variables included market effects, credit conditions, yield variation, trade impacts, 2019 flooding, macroeconomic conditions and regional fixed effects. The authors use cubic splines to interpolate annual and quarterly data to a monthly base.

Based on a fixed effect model, the authors find that all other things being equal the China–USA trade war would have had a significant impact on Chapter 12 farm bankruptcies, increasing the bankruptcy rate by 25.7%. The flooding in 2009 had minor effects of increasing the rate by only 0.05%. The overall impact will, however be substantially lower than the 25.7% because of the MFP. The MFP variables (binary) had mixed effects and its true impact is unknowable at this time; however, the authors also find that a 1% increase in the producer price index decreases bankruptcy rates by 2.62% and farm bankruptcy numbers by 3.70%. Likewise a 1% increase in GDP reduces bankruptcies by 3.25%. These suggest that the MFP program will have likely reduced farm bankruptcies considerably than what would have occurred in their absence. The authors also find that states heavily dependent on trade faced lower market uncertainty. Broader economic factors (net charge-offs of farm loans held by insured commercial banks, US real GDP, the average effective interest rate on nonreal estate farm loans) affect farm bankruptcy.

The authors use monthly bankruptcy statistics, however not all data were available in monthly measures requiring interpolation using cubic spline functions to approximate monthly changes in some variables. Although the MFP had mixed effects in the model, the mid- to longer-term effects may be more impactful. These longer-term effects (and even shorter-term effects through 2020) are complicated by the coronavirus disease 2019 (COVID-19) pandemic, which will require a different identification strategy than that employed in this paper.

The analysis and results of this paper are, to the authors' knowledge, the first to investigate the impact of the China–US trade war on Chapter 12 farm bankruptcy filings. The use of cubic splines in the interpolation of agricultural data is also a technical innovation.