Jennifer Ifft

The Impact of Pasture Insurance on Farmland Values

This study examines the impact of publicly supported insurance on agricultural land values. The analysis employs confidential, nationally representative panel data on field-level pastureland values and exploits a natural experiment provided by gradual introduction of the Pasture, Rangeland, and Forage Insurance Pilot Program. We use a field-level fixed-effects model that controls for several time-variant factors. We find that insurance availability is associated with an increase of at least 4 percent in pastureland values. This increase is comparable with increases generated by other government programs but is much smaller than total farmland value increases experienced in recent years.

Potential Farm-Level Effects of Eliminating Direct Payments

Since 2003, direct payments have accounted for a signifi cant portion of farm program payments. If direct payments were eliminated, many agricultural producers would be affected, both through the loss of income and potential declines in land values and rental rates. This report considers the potential contribution of direct payments to farm revenues and land values across farm commodities and regions and estimates the magnitude of the financial impact on participating farms should direct payments be eliminated. Direct payments are highest relative to crop revenues in parts of the Northern Plains, Southern Plains, Mountain, Delta, and Southeast regions, and the estimated effect of direct payments on cropland values also is relatively high in many of these regions. Overall, our analysis suggests that an abrupt end to the direct payment program could reduce the number of farms with a favorable financial status (profitable farms having relatively low debt burdens) by about 11,000 nationally, or about 2 percent of farms that received direct payments in 2009. The estimated effect varies regionally and is more pronounced in the Delta and Southeast regions, where direct payments per farm tend to be higher, on average, than elsewhere.

Does Federal Crop Insurance Lead to Higher Farm Debt Use? Evidence from the Agricultural Resource Management Survey

Purpose - – The purpose of this paper is to consider how the federal crop insurance (FCI) program influences farm debt use, one of the key financial decisions made by farm operators. Design/methodology/approach - – Using data from the nationally representative Agricultural Resource Management Survey, the paper implements a propensity score matching model of the impact of FCI participation on various measures of farm business debt use. To account for the simultaneity of financial decisions, the paper further tests this relationship using a seemingly unrelated regression model. Findings - – FCI participation is associated with an increase in use of short-term farm debt, but not long-term debt, consistent with risk balancing behavior and current trends in the farm sector. Research limitations/implications - – In addition to risk balancing, the results are also consistent with credit constraints or lender preferences. The paper cannot fully establish causality between crop insurance participation and short-term debt levels. Future research should address these limitations. Practical implications - – Agricultural lending standards are generally conservative and the farm sector as a whole currently has historically low leverage, which implies that an increase in debt use may not be a threat to the financial health of the farm sector. Social implications - – The results indicate that the reduction in total risk facing the farm sector is significantly less than the decline in risk provided by FCI, which is an important consideration for policymakers. Originality/value - – This is the first paper to use an econometric model to analyze the relationship between FCI and farm debt use decisions. This paper can inform future research on the FCI program and farm financial decisions.

Marketing Contracts and Crop Insurance

Contracts between farmers and intermediaries and crop insurers are important means for farmers to mitigate risks in modern U.S. agriculture. In this paper, we investigate the effect of crop insurance enrollment on contract terms and farmers’ participation in marketing contracts. Following Ligon (2003), we set up a mechanism design framework to demonstrate an intermediarys contract design problem, where farmers are assumed to be utility maximizing agents. We depict farmers’ optimal choices of insurance coverage using the specification developed by Babcock (2012). Our model shows that improved terms of crop insurance (lower premiums, higher subsidies) make contracts less appealing to farmers as mechanisms for mitigating risk. Therefore, intermediaries may revise their contract offers so that they are more attractive. However, improvements in contract terms are limited by their cost to the intermediaries and will not lead to expanded participation in contracts.

Alternative Strategies to Manage Weather Risk in Perennial Fruit Crop Production

Fruit producers in the Eastern United States face a wide range of weather-related risks that have the capacity to largely impact yields and profitability. This research examines the economic implications associated with responding to these risks for sweet cherry production in three different systems: high tunnels, revenue insurance, and weather insurance. The analysis considers a distribution of revenue flows and costs using detailed price, yield, and weather data between 1984 and 2013. Our results show that the high tunnel system generates the largest net return if significant price premiums exist for earlier and larger fruit.

Can machine learning improve prediction – an application with farm survey data

Businesses, researchers, and policymakers in the agricultural and food sector regularly make use of large public, private, and administrative datasets for prediction, including forecasting, public policy targeting, and management research. Machine learning has the potential to substantially improve prediction with these datasets. In this study we demonstrate and evaluate several machine learning models for predicting demand for new credit with the 2014 Agricultural Resource Management Survey. Many, but not all, of the machine learning models used are shown to have stronger predictive power than standard econometric approaches. We provide a cost based model evaluation approach for managers to analyze returns to machine learning methods relative to standard econometric approaches. While there are potentially significant returns to machine learning methods, research objectives and firm-level costs are important considerations that in some cases may favor standard econometric approaches.

Ethanol Plant Location and Land Use: A Case Study of CRP and the Ethanol Mandate

This study uses a county-level difference-in-difference framework to estimate the share of re-enrollment into the Conservation Reserve Program (CRP) in response to local ethanol production capacity after the Renewable Fuels Standard (RFS). Relatively more land remained in CRP in ethanol-intensive areas after the RFS. This seemingly counter-intuitive result can be explained by post-RFS changes to the CRP that favored ethanol-intensive areas. Both CRP design changes and production trends correlated with ethanol plant location pose challenges for empirical strategies that use ethanol plant location to study production or land use decisions. Changes to CRP policies can play an important role in participation and land use decisions.