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Agricultural credit markets are dominated by two institutional retail lender groups, the cooperative Farm Credit System (FCS) and commercial banks. Analysis of farm loans made over the 1991S1993 and 2001S2002 periods indicates that FCS lenders were more likely to serve full‐time commercial farmers and farmers located in regions with less competitive credit markets. In contrast, commercial banks were more likely to serve small, part‐time, and hobby farmers. This segmentation of farm credit markets is consistent with federal regulations requiring the FCS to provide credit to “bona fide” farmers with a basis for credit.

Many associations in the Farm Credit System, which are financial cooperatives, pay their member‐borrowers a cash patronage payment based on the amount of loan volume with the association. In today's competitive lending environment, some Farm Credit associations have offered lower interest rates on new loans but these new member‐borrowers have to forgo their cash patronage payment to receive this new, lower‐interest rate loan. The purpose of this paper is to identify Farm Credit member‐borrowers' preferences for patronage refunds received as a cash payment versus lower fixed real estate interest rates.

Preferences for patronage refunds or lower fixed interest rates are elicited from Farm Credit Services of East Central Oklahoma member‐borrowers via conjoint analysis.

Results show that member‐borrowers strongly prefer patronage refunds compared to lower fixed interest rates.

This paper fulfills a need to better understand patronage refund programs within the Farm Credit System.

Farm Credit is a major provider of credit to agricultural producers in Oklahoma and nationally. The decision to place a new Farm Credit office reduces borrower search and travel costs and should increase loan volume. The purpose of this paper is to model the new loan volume as function of distance from east central Oklahoma county centroids to Farm Credit offices. The model is then used to predict the impact of placing new offices in underserved areas.

County aggregate new loan volume is regressed on distances to Farm Credit branch and field offices and other variables expected to impact agricultural loan volume. The estimated model is used to predict new loan volume impact of adding additional branch and field offices in counties that did not have these offices. Confidence intervals are used to measure the significance of predicted loan volumes.

Distances from county centroids to both branch and field offices were found to significantly reduce new loan volume. The results were used to simulate the addition of new branch and field offices. The simulation predicted the added annual new loan volume associated with office additions.

Using spatial models, Farm Credit of east central Oklahoma and other agricultural lenders can better plan for expansion (or consolidation). These models indicate counties where annual new loan volume will likely be higher (or lower for consolidation) than other nearby counties. The result can be improved borrower access and system financial performance.

While spatial modeling has been utilized in other sectors, little has been done relative to agricultural credit access and impact on loan volume. The model here explicitly models the impact that distance to Farm Credit offices have on annual new loan volume.

Beginning farmers have unique challenges securing credit because they are less likely to have established sales and collateral for secured loans. This article explores US beginning farmers’ financing strategies relative to those of established operations, with a focus on the source of financing and debt structure (short- vs long-term usage). Agricultural operations commonly use nontraditional financing tools and strategies to start, build and/or sustain their businesses. This article provides a comparative overview of financing strategies comparing established operators to operations with only beginning operators, as well as those multigenerational operations with at least one beginning operator.

The study uses 2013–2016 USDA Agricultural Resource Management Survey data to explore how various financing patterns vary across US beginning farmers and ranchers with a particular focus on understanding differences where (1) all operators are beginning, (2) there is a mix of beginning and established operators and (3) all operators are established.

This article explores how the nature of beginning farmer status, human capital resources and alternative marketing strategies may influence financial management strategies and lead to differential use of nontraditional financing sources for beginning farmers and ranchers.

Though exploratory, the authors hope that attention to patterns among US beginning farmers and ranchers of reliance on human capital resources including off-farm income and type of beginning farm operation, nontraditional government support programs and alternative marketing strategies can provide important information as to the role of nontraditional credit in the US farm economy.

USDA direct and guaranteed farm loan programs exhibit significant geographical variation in lending activity. County‐level estimations made using Tobit procedures indicate that use of Farm Service Agency (FSA) farm loan programs is greater in counties with lower per capita income and regions experiencing greater farm financial stress. Use of direct FSA loan programs was lower in counties with fewer private‐sector lenders. Guarantee loan program usage was found to decline when commercial agricultural lenders are absent from the county. FSA loan programs were more highly utilized in counties with an FSA loan service center and in states receiving greater FSA farm loan funding in past years.

A credit risk model suitable for agricultural lenders is identified. The model incorporates sector correlations and is applied to the loan portfolio of an agricultural credit association to create a distribution of loan losses. The distribution is used to derive the lender’s expected and unexpected losses. Results of the analysis indicate that the association is more than adequately capitalized based on 1997S2002 data. Since the capital position of the association is lower than that of most other associations in the Farm Credit System, this raises the issue of overcapitalization in the System.

Although growing Internet penetration in the hinterlands has attracted agribusinesses to promote digital platforms, farmers are sceptical about using them. The literature discusses agricultural platforms from the theoretical perspective of technological determinism, where the platforms are developed and promoted by firms in a top-down manner to be accepted by farmers. However, this approach results in poorly configured platforms with limited utility for farmers. It is evident from the existing literature that the mere creation of a platform business is not sufficient to guarantee adoption by users. Hence, this study explores how to make the agricultural platform more attractive for farmers.

The present study is based on a discrete choice experiment performed on 126 Indian farmers using agricultural platforms. The data were analysed using the conditional logistic regression method.

The study suggests that farmers expect government and cooperative entities to be also embedded with the platforms. Complementary features such as prompt service, competitive pricing and farm credit were identified as essential attributes. Further, the platforms should enable smallholders to trade farm produce by providing a mechanism for real-time online nudging and bargaining with buyers.

The study is based on the applications of random utility theory. The research has utility for Agtech managers, cooperative institutions and agricultural policymakers.

This is one of the first studies focussing on agricultural platform design from the farmers' perspective. The study implies that incorporating preferred attributes can help practitioners configure platforms to benefit farmers with prospects concerning farm management decisions.

This study examines agricultural lending by commercial banks and the competition they face from the Farm Credit System (FCS) and non-traditional lenders, including merchants, dealers and other input suppliers.

We construct a measure of commercial banks' perceived competition with FCS or non-traditional lenders using the individual responses to the Federal Reserve Bank of Chicago's Land Values and Credit Conditions Survey between 1999 and 2019. Through regression analysis of an unbalanced panel of survey responses, we present a number of stylized facts on the relationship between perceived competition and farm loan rate spreads, collateral requirements, loan delinquencies and expected lending volumes.

Our analysis shows that the two sources of competition have very different effects on commercial bank lending terms, loan portfolio riskiness and expected loan volumes. With these results in mind, we offer a number of suggestions for future research.

We leverage the unique characteristics of the Land Values and Credit Conditions Survey to examine the competition with non-traditional lenders that cannot be observed using administrative data.

The purpose of this paper is to examine the current farm economic downturn and credit restructuring by comparing it with the 1920s and 1980s farm crises from both economic and regulatory perspectives.

This paper closely compares critical economic and regulatory aspects of the current farm downturn with two previous farm crises in the 1920s and 1980s, and equally importantly, the golden eras that occurred before them. This study compares key aggregate statistics in land value, agricultural credit, lending regulations, and also evaluates the situations and impacts on individual farmer households by using three representative case studies.

The authors argue that there are at least three economic and regulatory reasons why the current farm downturn is unlikely to slide into a sudden collapse of the agricultural markets: strong, real income; growth in the 2000s, historically low interest rates; and more prudent agricultural lending practices. The current farm downturn is more likely a liquidity and working capital problem, as opposed to a solvency and balance sheet problem for the overall agricultural sector. The authors argue that the trajectory of the current farm downturn will likely be a gradual, drawn-out one like that of the 1920s farm crisis, as opposed to a sudden collapse as in the 1980s farm crisis.

The review provides empirical evidence for cautious optimism of the future trajectory of the current downturn, and argues that the current downturn is much more similar to the 1920s pattern than the 1980s crisis.

The authors aim to investigate what influences a Farm Credit System association to make a patronage refund payment. In particular, they seek to investigate what causes the regional heterogeneity in the patronage refund payment decision. It is unclear whether patronage refunds have been used more as a capital management tool or as a member recruitment and retention tool. This study aims to bring some clarity to this issue.

The authors use an empirical logistic model to estimate the probability of a positive patronage refund payment by a Farm Credit System association, controlling for variables related to the associations' balance sheet as reported in the associations' quarterly call reports.

The authors find there is evidence that Farm Credit Service associations use patronage refunds as a capital management tool, at least in part. However, they also find that there are still significant regional differences in the patronage refund payment decision even after controlling for variables affecting the associations' balance sheet. The authors conclude that this likely represents member heterogeneity in preferences for patronage refunds versus a discounted interest rate.

The present study is one of the few empirical papers to examine a broad panel of financial cooperatives. Because of this, the authors' paper provides valuable insight into the aggregate behavior of Farm Credit Service associations, particularly into whether they use patronage refunds as a capital management tool, or as a marketing and retention tool.