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Summary: The uncertainty of the COVID-19 crisis is exposing community banks to a severe adverse shock that is arguably greater than the 2008 financial crisis. A stress test for the 1,671 agricultural banks and 2,261 non-agricultural banks in the United States is completed to provide an estimate of the potential effect of the COVID-19 crisis on bank financial status for 2000-2024. The stress test applies chargeoff rates derived from banks’ actual chargeoff rates for 2008 through 2012, which includes the recession and recovery following the financial crisis. The chargeoff rates applied to each bank in a local market are taken from the 90th percentile chargeoff rates for all banks in the market. Non-agricultural banks are projected by the simulation to be more adversely impacted than agricultural banks. A greater share of non-agricultural banks are expected to fail and to have lower profitability than the share of agricultural banks. A key assumption of the simulation is that agricultural loan chargeoff rates are less than non-agricultural loan chargeoff rates was the case during the 2008-2012 financial crisis. For this assumption to hold, it is implicitly assumed financial support will be provided to farmers and ranchers and the support when allocated and distributed will offset the negative impact from the COVID-19 shock experienced by farmers and ranchers. Without the support, more agricultural banks would be expected to have financial difficulties and fail. Moreover, if support is not allocated to match the parts of the agricultural sector most negatively impacted by the COVID-19 crisis, regional differences in the impact on agricultural banks are likely to emerge.

The study investigates whether vegetable price index insurance could be applied to the Beijing-Tianjin-Hebei (BTH) region of China and identifies farmers’ interest in the insurance. Vegetable price index insurance is growing rapidly in China and was recently introduced in the Shanghai region. Although the BTH region is an important region for vegetable production and consumption, no such insurance is currently available in the region. Survey data from 450 farmers in the BTH region were collected during July-August 2016. Factors influencing farmer demand for vegetable price index insurance in the region are identified by estimating a probit model. Four categories of factors are considered: location, demographics (gender, age, education, and experience), farm characteristics (size, organization membership, yield, and net income), and risk cognition (market price sensitivity, other insurance purchased, price risk type and recent loss experience from low price). The results should assist the Chinese government structure and promote a vegetable price insurance that will be an effective method to maintain vegetable prices in the region and promote vegetable production by farmers.

Many studies have used the U.S. Department of Agriculture’s (USDA) Agricultural Resource Management Survey (ARMS) to research various aspects involving the agricultural sector in the United States, including studies focused at the farm level. Since nonresponse and inaccurate reporting may cause significant bias in statistical analysis, research is conducted to estimate the magnitude of response error in the farm debt section of Phase III in ARMS. USDA Farm Service Agency (FSA) data on Farm Loan Program (FLP) borrowers with end of year debt are matched with ARMS respondents for years 2001, 2004, 2006, and 2007. A multinomial logit model is estimated to identify demographic, structural, and financial characteristics of FSA borrowers who refused to indicate if they had end of year farm debt, or who accurately or inaccurately classified their farm operations as having end of year farm debt on the ARMS. Additionally, estimates of the magnitude of response errors in ARMS for both FSA direct and guaranteed FLPs are estimated. The current study estimates that 12.9 percent of direct FLP respondents and 9.9% of guaranteed FLP respondents indicated “no” to the question on having end of year farm debt when they should have indicated “yes”. Also, those responding “no” are found to have their ARMS total debt outstanding less than their FSA total debt outstanding. Direct FLP operators are more likely to report “no” and, therefore, under-report end of year debt in the ARMS if they had a lower total FSA debt outstanding balance, had a greater value of crop production relative to total production, or had a lower gross cash farm income. Guaranteed FLP operators are more likely to under-report their debt in the ARMS if they had an operating line of credit loan, had a greater share of production from crops, or had a lower gross cash farm income. They are less likely to under-report their debt if they either had some college education, were eligible for socially disadvantaged loans, or were beginning farmer eligible. These results are of keen interest to those using ARMS data and those responsible for collecting ARMS data. The results allow researchers using ARMS data to appraise operator debt status to be better informed about potential data limitations. For example, estimates of the share of farm operations with little or no debt may be overestimated. Also, the results could assist the USDA National Agricultural Statistics Service in developing improved imputation techniques to estimate farm debt from the ARMS, especially for those respondents indicating “no” as to having end of year debt in the Farm Debt section of the ARMS.

A case study of an organic food company in the Slovak Republic involved in producing and sourcing inputs, food processing and distribution is presented. The case is based on a June 2014 “live” case study prepared for students in International MBA in Agribusiness programs at the Slovak University of Agriculture in Nitra, Warsaw University of Life Sciences and the National University of Life and Environmental Sciences of Ukraine, Kiev. The company was established in 2001 with the objective to bring organic food to health conscious consumers. The company grows organic spelt grain, wheat, rye, buckwheat, herbs and apples on its 156 ha and 400 ha of owned and rented farmland. The company further processes these crops into more than 40 finished products. Students are presented with company information and summaries of a company visit and discussions with management. Students perform PEST and SWOT analyses, identify a shortage of owned and leased land as a problem the company must address, conduct research and analysis,and recommend product specification contracts as a solution to the problem.

The global economy has continued to experience lingering effects of the global financial crisis that began in 2007. Although attention was initially given to the liquidity crisis and survival of some the world’s largest corporations and institutions, the financial crisis is likely to have long-lasting implications for agribusiness. As the world slowly recovers from the crisis, another round of problems are emerging as governments and international institutions attempt to unwind the positions they took in an effort to prevent the global economic bubble from bursting. Perhaps the most problematic factor for businesses is access to capital in sufficient amounts and at affordable rates. Governments and institutions, particularly in the United States (U.S.) and the European Union, have increased their financial obligations as the result of activities taken to curtail the economic crisis. These financial obligations and the associated financial risks place pressure on financial markets and tend to restrain the availability of capital and increase the cost of capital for businesses. However, the U.S. agricultural credit market has not experienced problems to the same extent as general business (commercial and industrial) and real estate credit markets have. In general, U.S. farm businesses have a strong balance sheet, adequate repayment capacity, sufficient amount of assets to offer collateral for loans, and reasonable profits. Thus, U.S. farm businesses have had an ample supply of credit at relatively low interest rates.

Regression models of Farm Service Agency borrower net worth, debt-to-asset ratio, and loan servicing actions over an average seven-year period following loan origination are estimated to identify determinants of borrower financial progress over time. Results show significantly increasing net worth and debt-to-asset ratio. The change in net worth increases and the change in debt-to-asset ratio decreases as time in loan program increases. Increasing origination net worth tends to decrease loan servicing actions. Socially disadvantaged and beginning farmer loans perform similarly to nonprogram loans. Borrowers originating limited resource loans experience significant net worth impacts relative to borrowers originating regular FSA loans.

Classical and count data regression models are estimated to predict improvement in three key financial indicators—net worth, debt-to-asset ratio and current ratio—as well as the number of loan restructurings and delinquencies. Data consist of Farm Service Agency direct loans originated in fiscal years 1994-1996. Models to predict outcomes vary by loan type. Models explaining variation in the financial measures have modest explanatory power but initial levels of debt-to-asset ratio and current ratio are significant in explaining changes in debt-to-asset ratios and current ratios, respectively. Models explaining number of restructurings and delinquencies for operating loans have satisfactory explanatory power. Increasing crop revenues to total farm revenues and increasing farm size lead to increased loan servicing actions

The USDA Farm Service Agency (FSA) direct farm loan program is designed to provide credit to family-sized farms unable to obtain credit from conventional sources at reasonable rates and terms despite having sufficient cash flow to repay and an ability to fully securitize the loan. FSA policy encourages borrowers to exit the program as soon as possible. This study uses Cox proportional hazard models in a competing risks framework to identify predictive factor of: (1) loan success or default, and (2) length of time to loan termination. Survey data from 1925 direct loans originated in federal fiscal years 1994-95 are used for analysis. Only data available to FSA at time of origination were collected. Since these data are all the information FSA has at time of loan origination, the competing risk models provide an alternative method for measuring priori relative riskiness indicated by borrower and loan characteristics. Results indicate that borrower financial strength, intensity of borrowers' current relationship with FSA and loan characteristics are significant measures of loan risk.

Farm Service Agency (FSA) direct loans are intended to provide transitory credit to creditworthy borrowers unable to obtain conventional credit at reasonable terms. Farm loan program (FLP) effectiveness is measured in part by how readily direct loan borrowers graduate to conventional credit. A survey of FSA borrowers originating direct loans during fiscal years 1994-1996 is utilized to estimate graduation rates. A majority of 1994-1996 loan originators did exit the direct FLP by November 2004. A multinomial logit model indicates financial strength at origination resulted in greater likelihood of farming without direct loans approximately nine years after loan origination.

This paper presents a detailed summary analysis of commercial bank, Farm Credit System (FCS), and other commercial lenders use of Farm Service Agency (FSA) guaranteed loan program over the time period fiscal 1993-2003. Key aspects of the guaranteed program are examined including the usage level of guaranteed loans by the various lenders and the intensity of guaranteed lending to socially disadvantaged (SDA) and beginning farmer groups. The usage of guaranteed loans is examined at state, regional, and national levels. Specific characteristics associated with banks using the program are identified.

The USDA's Farm Service Agency (FSA) serves as the nation's lender of last resort by providing direct loans to farmers unable to obtain credit at reasonable rates and terms. Annual loan losses have been substantial, averaging $576 million for fiscal 1994-2004. An econometric model using survey data from a sample of FSA loans originated in fiscal 1994-1996 is estimated to identify factors associated with loan losses. The results indicate previous debt settlement experience, loan type, farm type, farm size, and farm financial characteristics are important factors. This information may be used by FSA to adjust its underwriting standards in an effort to reduce loan losses and provide additional loans to farmers given its current funding.

The Farm Service Agency’s (FSA) interest assistance interest assistance program allows lenders to enter into an agreement with FSA to subsidize a guaranteed farm operating loan by reducing the interest rate charged to the borrower by up to four percentage points. With fiscal 1997-2003 data, an incidental truncation model framework is used to analyze: 1) commercial bank usage of the program; and 2) intensity of commercial bank usage. The results suggest bank characteristics, farm and non-farm financial characteristics, region, and time are important factors in determining bank usage of the interest assistance program and its intensity.

The three primary objectives of the Effectiveness Study are to: (1) identify groups being served by FSA direct farm loan programs, (2) examine the length of time borrowers remain in the programs and the proportion of borrowers who exit or 'graduate' from the programs, and (3) measure and identify ways of reducing loan subsidy rates. The study found that direct Farm Loan Programs (FLPs) appear to be serving their intended clientele. Recent FLP borrowers are more financially stressed than non-borrowers and would be generally considered as family farms. About 78 to 92 percent would qualify as small family farms using USDA's Small Farms Commission definition. FLP credit market penetration is relatively high among farms likely to be eligible for these credit programs, despite the fact that these programs represent a relatively small proportion of total outstanding agricultural debt. Increasing market penetration or the share of farms served by the program would require greater obligation funding and hence greater budgetary costs. Conversely, implementing more rigorous loan eligibility criteria would likely lower the number of operators receiving loans and hence loan loss occurrences and subsidy rates would likely fall. The majority of FSA Direct borrowers from FY 1994-1996 used FLPs as a transitional tool. At time of origination, FSA Direct borrowers had fewer years of farming experience than the farming population at large. More than half of these borrowers no longer had active FLP loans by the end of November 2004. So for the majority of borrowers, FLPs are not a lifetime credit source. FLPs are helping farmers move to commercial credit or aiding farmers who subsequently leave farming completely, as is common among U.S. farmers. Not surprisingly, farmers in stronger financial condition originating FSA Direct loans are more likely to exit and have fewer outstanding loans with FSA. FSA experiences higher loan loss rates than conventional agricultural lenders. This is to be expected because commercial lenders can be more selective in choosing borrowers and price loans to match risk profiles which FSA does not do. In essence, FSA's mission is to provide credit to riskier 'creditworthy' borrowers. The agency is accomplishing this goal. The natural consequence is that FSA loan loss rates are higher than for conventional lenders. Whether the current borrowers are too risky or should even riskier borrowers be included are policy questions. The analysis indicates that attempts to cut losses systematically would imply denying credit to some current borrowers.

The Farm Service Agency (FSA) direct farm loan programs provide credit to family-sized farms including beginning farmers and socially disadvantaged applicants. Approximately 37 percent of all U.S. farms are estimated to be eligible for FSA direct loans when farm size, credit needs, years of farming experience, and occupation are taken into account. Market penetration ratios for various cohorts of borrowers ranged from 0.75 to 5.13 for fiscal 2000-2003. Results suggest a substantial regional dispersion, which appears driven, in part, by the distribution of farmers eligible for FSA direct loans and the occurrence of natural-disasters.

Land leasing is a major source of the land input to production agriculture. Responses from a survey of landlords leasing crop land in Arkansas are analyzed to better understand those factors motivating landlords in the type of lease they select and the terms of those leases. Probit models are estimated to determine the relative importance of variables representing credit constraint, agency problem, and risk aversion factors. Regression models then estimate the impact of site, landlord, and tenant characteristics on contract terms – the percentage of crop and cost sharing arrangements between landlord and tenant. Probit results suggest credit constraint factors influence lease-type selection. Risk aversion, managerial ability, and social capital factors are also supported. Regression models show that land and crop characteristics are significant determinants of contract terms.

The Farm Security and Rural Investment Act of 2002 made permanent the interest assistance program for the Farm Service Agency's guaranteed loans, authorized a significant increase in funding for the program, and targeted funding for beginning farmers and ranchers. The research presented here provides a basic descriptive analysis of past use. In particular, borrower data for Federal fiscal years 1985 through 2002 are examined in several dimensions. These dimensions include geographic, borrower type, lender type, interest rate differentials, percent guarantee, and the status of the loan as to whether a loss claim was paid or the loan remained active. Even though the program has been in existence for more than 15 years, little is known about its impact and utilization. This research is an initial step in documenting usage of the program.

Regression analysis is used to estimate static and dynamic restructuring, direct and external effects of mergers from 1994 to 2001 on bank agricultural loan-to-asset ratios. Results indicate that mergers have a negative effect on agricultural loan ratios. The effect is less pronounced for smaller than larger bank mergers and more pronounced for mergers of banks affiliated with the same holding company than other merger types.

Landlord satisfaction levels with agricultural land-leasing agreements are examined with a 1998 sample of Arkansas landowners. Ordered probit models are estimated identifying which factors significantly affect satisfaction levels. Results indicate that the type of lease is not a significant determinant of landlord satisfaction levels. Proportion of landlord’s income from leasing, tenant educational background, social capital variables, presence of irrigation equipment, and perceptions about the FAIR Act were found to significantly affect lease satisfaction in at least one of the three satisfaction models estimated. A comparison with an earlier study of Arkansas tenants indicates landlords have generally higher satisfaction levels.

Financial, farm economy and bank factors are hypothesized to explain variation in FSA guaranteed loan principal outstanding. Regression models using state-level data (1990-1998) are estimated. Debt-to-asset ratios, percentage of agricultural revenues due to crops, interest rates, and off-farm income are significant variables for both farm ownership and operating loan principal.

This study tests cropland contract hypotheses utilizing a landlord data set. Ordered probit and classical regression models are estimated and presented identifying factors that affect the contract type selection and terms. Results suggest credit constraints are a viable land-leasing hypothesis. Risk aversion, managerial ability, and social capital are also supported.

The study identifies farm operator and economic characteristics explaining variation in FSA guaranteed loan loss claims rates. Regression models using state-level data are estimated. Debt-to-asset ratios, interest rates, off-farm income and bank loan-to-asset ratios explain FO loss rates. Farm size and bank loan-to-asset ratios are important to OL loss rates.