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The purpose of this paper is to identify determinants of feasible outcome events (expired with no loss, settled for loss, still performing) and time to event of Farm Service Agency (FSA) operating and farm ownership (FO) loan guarantees.

Data on 19,126 FSA guaranteed loans, which were made by various lenders to farmers who have limited ability to obtain loans from normal sources without the Federal guarantee, were collected. Cox proportional hazards models for operating loans (OLs) and FO loans are estimated to identify borrower characteristics, loan characteristics, lender types, and farm and macroeconomic environment factors that influence guarantee outcomes.

Loans with different characteristics (loan amount, loan term, lender type, region originated) and assistance programs (Beginning Farmer, Interest Assistance) have differing guarantee outcomes. Contemporaneous variables, in particular delinquency status, have a significant impact on guarantee outcomes.

All loans were originated in calendar years 2004 and 2005. Since FO loans may have as long as 40 year terms, results are not as robust for FO loans as for OLs.

Different loan characteristics and macroeconomic conditions significantly influence the occurrence of possible guarantee outcomes and time to the outcomes.

Guaranteed loans are the primary method of government credit assistance to US farm operators. Data on individual borrowers have been difficult to obtain for much of the life of the guaranteed program because loan applications are held privately. This study provides insight on how various factors drive guarantee performance which is useful to policy makers trying to increase guaranteed loan program efficiency.

The purpose of this paper is to examine credit usage by beginning farmers and ranchers (BFR). BFR credit usage is stratified by location (state) and by socially disadvantaged farmer and rancher (SDFR, also known as historically underserved) status. SDFR groups are defined to include women; individuals with Hispanic, Latino or Spanish Origin; individuals who identify as American Indian or Alaskan Native, Black or African American, Asian, Native Hawaiian or other Pacific Islander. Non-SDFR is defined as individuals who identify as non-Hispanic, White men.

The US Department of Agriculture’s Census of Agriculture, Agricultural Resource Management Survey (ARMS) is linked with Farm Service Agency (FSA) loan program administrative data to estimate shares of BFR operations using FSA credit. Census data provided information on population changes in total farms and BFR operations from 2012 to 2017 which are compared by SDFR status.

Results reveal differences among BFR operations active in agricultural credit markets by SDFR status and state. BFR were more common among SDFR groups as well as in regions where farms tend to be smaller, such as the Northeast, compared to a more highly agricultural upper Midwest. Among BFR, non-SDFR are more likely to utilize credit than SDFR, however, FSA appeared to be crucial in enabling BFR and especially beginning SDFR groups to access loans.

The results are timely and of keen interest to researchers, industry and policymakers and are expected to assist in developing and adjusting policies to effectively promote and improve BFR success in general and for beginning SDFR groups.

While progress has been made in the realm of teaching about sustainability to business students, integrating sustainability into experiential learning with a systemic mindset has been identified by leading scholars as an area for improvement. The purpose of this paper is to describe a pilot project in which students prepared a sustainability report for a client company and to answer the question of whether the experiment yielded the anticipated benefits.

The paper presents an initiative that was part of an MBA course delivered at the Warsaw University of Life Sciences in Poland by an international team of professors. The multinational group of students was confronted with the task of preparing an integrated sustainability report for a large corporation.

The initiative creates opportunities for both students and commercial organizations to understand large business commercial activities from a sustainability perspective. This paper identifies the next steps for others to build upon.

The paper explains the experiential learning opportunity that was created, describes how students rose to meet the challenge, discusses the benefits that accrued to students, professors and a commercial organization and shares some guidance for those seeking to emulate this practice.

The purpose of this paper is to examine changes in the structures of US farms and lenders and identify prospective implications for federal credit.

Data from US farm operations for 1996-2014 were adjusted to 2014 values using commodity price indices. Farm size groups were constructed by value of farm production to analyze changes in farm numbers, production, assets, debt, leverage, liquidity, profitability, land tenure, commodity type, contract production, organization type, and use of Farm Service Agency (FSA) direct and guaranteed loans by farm size. Bank, Farm Credit System (FCS), and FSA data from 1996 to 2015 were adjusted to 2014 values. Lender size groups were constructed to analyze changes in bank and association numbers, farm loans, and use of FSA guaranteed loans by lender size.

The greatest consolidation has been by farms with over $2 million in production. More farm debt is held by large, complex organizations, frequently with multiple operators, more variable income, and greater reliance on production contracts and operating and nonreal estate credit. Large farms have greater leverage, are more profitable, and have a larger share of household income from the farm. Banks and FCS institutions are fewer and larger, yet smaller institutions use FSA guarantees to a greater extent. Larger farms tend to be more reliant on both direct and guaranteed FSA loans and are likely to become more dependent on FSA credit.

Changing farm and lender structure together with softening farm income may require FSA farm loan program changes to meet any increase in loan demand. Policy alternatives are provided to meet changing demand for farm credit.

A potential farm policy concern is that if nontraditional (vendor/point-of-sale) financing represents increased risk, it may have an aggregate effect on sector-wide farm financial risk. This analysis examines the use of nontraditional lender credit among borrowers in the US Department of Agriculture (USDA)'s Farm Service Agency (FSA)'s direct farm loan programs.

Data source included the USDA FSA direct operating loan program for 2011–2020. A Cox proportional hazards model was used to estimate the occurrence of default over seven-year term direct operating loans.

Results indicated that point-of-sale financing has a significant and positive relationship with risk for FSA direct operating loan borrowers. The presence of intermediate point-of-sale financing (mostly from machinery and equipment vendors) is associated with an increased probability of default of 9%, and the presence of such loan balances in the amount of $50,000 or more had a higher probability of default of 21%. Short-term nontraditional financing (for example from fertilizer vendors) was found to be positively related to borrower risk of default as indicated by a 22–25% increase in the likelihood of loan default.

Through FSA Farm Business Plan data, the authors were able to distinguish specific vendors and their loan purpose, which advances the knowledge beyond what is currently available through survey data. Findings indicate a minor increase in borrower risk for those with intermediate-term nontraditional financing. However, borrowers with short-term nontraditional financing and having large balances or greater number of nontraditional loans had increases in risk of default by substantive amounts.

Federal farm credit programs currently administered by the USDA were initiated in the early 1900s to help the farm sector cope with natural disasters, and these programs have continued to evolve. There has been a rich history of research analyzing USDA farm credit programs and the effects they have had on farmers, ranchers, and credit markets. This paper highlights past research and offers a view of the future direction of research on federal farm credit programs.

The Farm Service Agency (FSA) direct farm loan program provides credit to family‐sized farms including those operated by beginning farmers and socially disadvantaged applicants. Approximately 37% of all U.S. farms are estimated to be eligible for FSA direct loans when farm size, credit needs, farming experience, and occupation are taken into account. However, market penetration rates for various borrower cohorts range from 0.8% to 4.6% for FY 2000S2003. In general, beginning farmers have weaker financial characteristics than non‐beginning farmers. Yet, the same result is not found when comparing socially disadvantaged farmers with non‐socially disadvantaged farmers, such that there are few significant differences or the differences in financial characteristics are mixed. Overall, results indicate FSA direct farm loan borrowers have weaker financial characteristics than eligible, non‐FSA direct farm loan borrowers, implying FSA is serving farmers likely to be denied credit by commercial lenders.

The purpose of this paper is to identify the characteristics of borrowers likely to benefit from loan modifications (restructuring) which includes concessions provided to the borrower from the lender.

Data were drawn from the US Department of Agriculture Farm Service Agency (FSA) for borrowers who had received an operating loan modification during 2005-2010. A logistic regression model is estimated to identify the characteristics associated with the likelihood of a borrower paying the modified loan as agreed or receiving a subsequent loan modification within seven years. Explanatory variables included financial condition, type and year of loan modification, farm type, organizational type, borrower demographics, and region.

Loans requiring more complex loan modifications and borrowers with previous loan restructuring, larger farms, little equity in loan collateral, little or no capital, and/or little to no liquidity are less likely to perform following loan restructuring, which could suggest a possible futility in providing concessions to these types of borrowers. Many of these borrowers ended up having a subsequent restructure within a short period of time. Most of the regional variability in loan performance appears to have been a result of land values and commodity prices and not jurisdictional laws.

FSA has followed a policy of providing loan modifications to the borrowers experiencing repayment problems for more than 25 years. Though farm financial conditions have remained relatively strong through 2016, a continuation of the low farm incomes and declining farm real estate values could increase farm loan repayment problems in upcoming years and increase interest in farm loan modifications from both lenders and policymakers. FSA’s experience provides a rich data source to examine and provide a better understanding of the costs and benefits associated with loan modifications.

The purpose of this paper is to estimate a three‐equation model of US commercial bank usage of the Farm Service Agency's (FSA) guaranteed operating loan and interest assistance programs. Also, to identify the key farm and banking variables that affect the decision to use loan guarantees and the volume of loans with interest assistance.

A triple hurdle, three‐equation system is estimated to model three decisions: to participate in the FSA operating loan program; whether to use interest assistance given the decision to participate in the operating loan program; and then the degree of participation in the interest assistance program. Statistical selection is modeled. Data on almost all commercial banks in the USA from 1995 to 2003 are used in the estimation sample.

Statistical selection is statistically significant so selection must be included in the models. Variables reflecting state‐level characteristics such as farm debt servicing ratio, individual bank loan‐to‐asset ratio, bank size and the general guaranteed loan and interest assistance environment are significant in all three equations. Intensity of interest assistance use varies markedly across states.

The interest assistance program has high subsidy costs and is an important source of support for financially marginal farmers. Scant prior research has investigated this program. The present study also shows that modeling interest assistance usage must be embedded in a larger model to give a complete specification.

The purpose of this research is to evaluate the financial performance measures calculated and reported by the Economic Resource Service (ERS) from Agricultural Resource Management Survey (ARMS) data. The evaluation includes the calculation method and the underlying assumptions used in obtaining the reported values. Recommendations for improving the information reported are proposed to ERS.

The financial measures calculated and reported are compared with those recommended by the Farm Financial Standards Council (FFSC). The underlying assumptions are identified by analyzing the software code used in calculating the values reported. The values reported by ERS are duplicated and alternative methods for calculating the financial performance measures are considered. The values obtained from the various calculation methods are compared and contrasted.

Recommendations for ERS include: calculate and report the financial measures recommended by FFSC, note values that are imputed, periodically update and validate assumptions used in calculating imputed values, review its policy for flagging estimates as statistically unreliable, report medians and other select percentiles, and consider reporting the percent of farm businesses that have values within critical zones.

A total of four methods for calculating financial performance measures are compared and contrasted. These are the aggregate mean, sample mean, sample median, and percentage of farm businesses with values in critical zones.