Recipient Organization
UNIVERSITY OF FLORIDA
G022 MCCARTY HALL
GAINESVILLE,FL 32611
Performing Department
Food and Resource Economics
Non Technical Summary
The goal of this research is to gain a better understanding of how individual farmers and agribusiness firms make managerial decisions under uncertainty, including price uncertainty, production risk, and regulatory and policy uncertainty. We will investigate the risk management techniques that farmers and agribusiness firms use to manage and mitigate these risks including diversification, accessing financial services, and participating in crop insurance programs. In addition, we will investigate the effects of government support programs on farmers' access to financial services, farm asset values (particularly land), and credit terms (interest rates). More specifically, the effects of government policies on creditworthiness will be evaluated by analyzing liquidity, coverage ratios, and solvency as these are metrics used by lenders to determine creditworthiness. Both theoretical and empirical analyses will be pursued. Given the board research agenda, a variety of techniques will be employed. Various national, state-level, and farm-level USDA datasets will be used. Other datasets such as data collected by state governments will be employed when appropriate. While much of the research will focus on recent policy changes in the Agricultural Improvement Act of 2018 (2018 Farm Bill), other policies including Environmental Protection Agency (EPA) regulations will also be analyzed.
Animal Health Component
80%
Research Effort Categories
Basic
20%
Applied
80%
Developmental
(N/A)
Goals / Objectives
Evaluate the management strategies, capital needs, and policies impacting the financial performance and long-term sustainability of firms in the food and agribusiness sector.
Identify and analyze financial institutions and services that benefit agricultural producers and rural communities and expand agricultural markets, especially those producers that are beginning, young, and from socially disadvantaged groups.
Evaluate farmland and commodity markets and government policies that affect producers and the risk management and financial strategies producers use to mitigate risks and enhance profitability and sustainability of the agricultural sector.
Project Methods
Various theoretical models will be developed to explain the risk management behaviors of agricultural producers and agribusiness firms. Here are some examples.New support programs introduced in the Agricultural Act of 2014 (2014 farm bill) and continued (with some modifications) in the Agricultural Improvement Act of 2018 (2018 farm bill) willbe modeled. The 2014 farm bill introduced two new support programs for grain/program crops (corn, wheat, soybean, rice, oats, barely, peanuts, and sorghum): 1) a modified countercyclical payment program called Price Loss Coverage (PLC), which provides payments based on historic production (plantings during a set historic time period) when prices fall below target prices, and 2) Agriculture Risk Coverage (ARC), which provides payments when revenues falls below benchmark revenues. Producers must choose between these two programs. In addition, the 2018 farm bill expanded the existing subsidized crop insurance program and introduced two shallow-loss insurance programs: the Supplemental Coverage Option (SCO) program (available for cotton and other grain crops) and the STAX program, which is only available to cotton producers. We will develop models to gain a deeper understanding of how these programs affect farmers profitability, liquidity, input use, output and access to credit.The 2018 farm bill also expanded support for specialty crops. Specialty crops include fruits and nuts such as citrus, blueberries, strawberries, and wine grapes. Designing programs and setting premium rates for specialty crops are typically more challenging than setting rates for grain crops because production tends be more concentrated and these crops tend to have shorter production histories. As a result, the USDA's Risk Management Agency (RMA) has less data available to use for ratings. Furthermore, there are some perils that are crop specific. For example, citrus greening (Candidatus Liberibacter asiaticus or also known as Huanglongbing (HLB)) is a disease that infects citrus trees. While the infected trees eventually die from the disease, infected trees mature more slowly and hence have lower yields than uninfected trees. We will model citrus yield as a function of tree age, greening and hurricane events to identify improvements in rating techniques. We will support these models with empirical analyses. Smoke taint from wildfires is another example of a peril that uniquely affects wine grapes (Olen and Auld 2018). In recent year, wildfires in California occurring right before harvest have led to wine grapes being exposed to smoke. While wildfires are common in the Pacific Northwest, 2017 and 2018 were unusual in terms of the size and scope of the wildfires. Exposure to smoke from wildfires can lead to smoke taint in the grapes; however, the effect of smoke exposure varies depending on the length of exposure, concentration of smoke, and when exposure occurs during the growing season. Furthermore, measuring smoke taint is difficult. When grapes are exposed to smoke the compounds in the smoke bind to sugars to form glycosides (Prengaman and Courtney, 2018). These glycosides have no aromas and thus are hard to detect until they break down over time and are the smoke compounds are released into the wine (Prengaman and Courtney, 2018). As a result, smoke taint may affect the final expression of the wine even if it does not alter the taste of the grapes. Given the widespread fires in the Napa Valley region of California, many wine producers refused to take delivery of forward contracted grapes in 2018 fearing smoke taint (Mobley, 2018). Wineries feared producing smoke tainted wine would destroy their brands, eroding profits for years to come. While subsidized crop insurance is available, many wine grape growers do not have insurance. We will model the contractual and risk-sharing relationship between wine grape growers and wineries using contract theory and game theory to explore how this relationship is affected by smoke taint. Furthermore, we will model how participating in crop insurance programs affects that relationship. We will also model the benefits of these contractual relationships using standard capital budgeting techniques such as net present value.In addition, 2018 farm bill removed hemp (defined as Cannabis sativa L. with less than 0.3% THC) from the list of Schedule I controlled substances and making it an ordinary agricultural commodity. Farmers seeing hemp as a potentially lucrative commodity can now legally start producing it for fiber, seed, and flower. Given that hemp was previously illegal to grow, however, little is known about the demand for its products and its costs of production. Furthermore, producers are at risk of the plants that they grow "going hot". This occurs when the plants THC content exceeds the maximum allowable THC. If a plant goes hot, it must be destroyed. While the RMA offers some protection to hemp growers under the Whole-Farm Revenue Protection (WFRP), hemp having THC above the compliance level will not constitute an insurable cause of loss (USDA 2019). We propose developing a theoretical model supported with empirical evidence to model the risks associated with undertaking production of this newly legal yet highly regulated commodity.EPA Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA)requires agricultural chemical producers to register each chemical they producewith the EPA. The law allows "follow-on" registrants such as generic producers of the same chemical to use the original registrant's data for their registration as long as the follow-on registrant compensates the original registrant; however, the law does not state how compensation should be calculated. This as led to several court and arbitration cases. We will model how the regulation has led to societal welfare losses and support the claims using data from arbitration cases. Empirical analyses willbe conducted to support these theoretical models. USDA data will be the primary source of data however other national, state, and sub-state datasets will also be employed. The USDA's Agricultural Resource Management Survey (ARMS) dataset consists of annual cross-sectional farm-level data for the years 1996 to 2018. Prior to ARMS, the USDA collected similar data called the Farm Cost and Returns Survey (FCRS) from 1991 to 1995. Together these data form the richest farm-level dataset available to researchers interested in the financial structure (including government support) and production practices (including acreage, input use, and yields) of U.S. agricultural producers. The Risk Management Agency (RMA) maintains extensive datasets pertaining to crop insurance participation, causes of loss, and indemnities. The National Agricultural Statistics Service (NASS) publishes annual data on crop acreage and yields at the national, state, and sub-state levels. The Economic Research Service (ERS) publishes data on government support payments.We will use regression analysis to determine the factors that affect participation in government programs and to determine the extent to which government policies such as the elimination of direct payments, participation in crop insurance programs and programs targeted at disadvantaged and beginning farmers influence agricultural asset values (particularly land prices), liquidity, solvency, repayment capacity, access to credit and credit terms. While these topics have been the focus of many previous studies, the policy changes in the Agricultural Improvement Act of 2018, which build on policies introduced in the 2014 Agricultural Act, are likely to have large impacts on farm asset values, production decisions and access to credit.