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Credit is essential in the farm business because it facilitates the adoption of productive technologies such as irrigation. However, access to credit remains a significant hurdle for sub-Saharan Africa, including Ghanaian farmers. Therefore, the authors assessed credit utilization and the intensity of borrowing by irrigated rice farmers in the Upper East region. In addition, how extension moderates the amount borrowed was analysed.

The multistage sampling approach was used in the study. The Tono and Vea irrigation schemes were purposively selected. Proportionally, 318 rice farmers were sampled from the Tono irrigation scheme and 159 from the Vea irrigation scheme. Cragg's double hurdle and moderation analysis were used.

It was uncovered that gender, age, years of farming, total farm size, rice farm size, contract farming and off-farm employment explain farmers' decision to borrow. On the other hand, the intensity of borrowing was influenced by gender, age, years of farming, rice farm size, contract farming and the number of extension contact. The moderation analysis revealed that extension contact improves the amount borrowed by farmers.

While there are irrigated rice farmers in other regions of Ghana, this study was limited to rice farmers under the Tono and Vea Irrigation schemes in the Upper East region.

This study investigated the moderating role of extension contact on amount borrowed in Ghana. This makes a modest addition to the limited literature on the moderating role of extension and credit access.

Nonfarm small businesses are an integral part of the US economy, and access to credit is crucial to their success. In rural America, a significant proportion of these businesses are owned by farm households. The purpose of this research is to compare farm households that operate a nonfarm business to other farm households as well as to rural and urban households operating a small business; and identify key factors that differentiate these businesses in their access to credit.

The paper uses a unique data set to draw comparisons between farm households (from Agricultural Resource Management Survey data) and rural and urban small businesses (from Survey of Small Business Finances data). Each of these data sets asks similar financial, demographic, and access to credit questions. Combining these data provide a unique way to analyze the financial health of farm households that operate nonfarm businesses.

The paper finds that farm households with a nonfarm business tend to have more household income and assets than other rural and urban small businesses and farm households without a nonfarm business. However, rural small business owners as well as farmers were able to access credit more freely than their urban counterparts.

Many studies have looked at the farmer's decision to work or invest off the farm. However, no study has considered the impact of owning a nonfarm business on the financial health and creditworthiness of a farm household.

The purpose of this paper is to analyse how farm production and input use (land, variable inputs, labour, and capital) is related to farm access to credit in the Central and Eastern Europe (CEE) transition countries.

Drawing on a unique farm level panel data set with 37,409 observations and employing a matching estimator, this paper analyses how farm access to credit affects farm input allocation and farm efficiency in the CEE transition countries. The large size of the FADN data set has an additional advantage. It allows the authors to employ a semi‐parametric estimator based on the propensity score matching. Using more than 37,409 observations assures that the loss in efficiency of semi‐parametric estimates, as compared to parametric ones, is not a problem. This is important for at least two reasons. First, applying a semi‐parametric propensity score matching (PSM) estimator allows to control for any heterogeneity in the relationship between farm performance and their observable characteristics (in particular access to credit). Second, matching estimators are robust in situations where farms having access to credit systematically differ from those that do not.

It is found that farms are asymmetrically credit constrained between inputs. The use of variable inputs and capital investment increases up to 2.3 percent and 29 percent, respectively, per 1,000 EUR of additional credit. The authors' estimates suggest also that farm access to credit increases the total factor productivity up to 1.9 percent per 1,000 EUR of additional credit, indicating that an improved access to credit results in adjusting the relative input intensities on farms. This finding is further supported by a negative effect of better access to credit on labour, suggesting that these two are substitutes. Interestingly, farms are found not to be credit constrained with respect to land.

To the best of the authors' knowledge, the present paper is the first to investigate the importance of access to credit for farm performance in the CEE region as a whole.

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.

The purpose of this paper is to examine the connections of agricultural productivity, access to credit and farm size in Africa using Ghana as a case study.

The paper employs mixed methods – quantitative and qualitative strategies for data collection and analyses. The hierarchical competitive model was used for the quantitative analyses supplemented with qualitative analyses using key informant interviews, focus group discussions and household case studies.

The results show that there is significant relationship between credit from formal and informal sources and agricultural productivity. Thus access to formal and informal credit increases farm household agricultural productivity by about 0.10 (p=0.05) and 0.45 (p < 0.01), respectively. The quadratic terms of formal and informal credit as well as farm size were found to significantly influence agricultural productivity. The implication of this is that the relationships between formal credit, informal credit and farm size on one hand and agricultural productivity on the other are non-linear in nature. The interactions of formal credit with informal credit; informal credit with farm size; and formal and informal credit with farm size have significant relationships with agricultural productivity. The amount of remittance received by farm households has negative and insignificant influence on agricultural productivity. Market access is also an insignificant determinant of agricultural productivity in Ghana.

This paper provides new insights on whether the scale of production (farm size as proxy) and access to financial services (credit as a proxy) matter in promoting agricultural productivity in Africa using Ghana as a case study. Thus the paper is of relevance to policy-makers and practitioners in Africa and Ghana in particular who are seeking to make informed policy decisions on effectively incorporating credit provision into the agricultural transformation agenda of the continent.

The problem of inaccessibility of finance for farm investment is a common phenomenon among farmers, especially the rural dwellers. Thus, there is a need to know how the accessibility of informal finance can be increased to increase farm investment. Therefore, this study evaluates farmers’ access to informal finance and its contribution to farm investment among rural farmers in Northcentral Nigeria.

A three-stage random sampling technique was employed to select 160 farmers. Primary data collected were analysed with descriptive statistics and the Heckman selection model.

The study revealed that cooperative society is the major informal means of loan acquisition used by the farmers followed by Rotational Savings and Credit Associations (RoSCAs). Informal loans contributed to agricultural investment through the various operational activities involved in production. Factors influencing farmers’ access to informal loans were the age, farm size and income of the farmers. Interest charged, farmers' age, farming experience, household size, education and loan duration were the drivers of the amount borrowed from the informal financing sector.

The findings of the study call for policies that will sustain informal financial institutions in developing economies, like Nigeria. Thus, the government through its regulatory agencies should assist informal finance providers with the necessary resources to achieve more goals. This is because the informal credit lenders help in bridging financial gaps created by formal financial institutions, such as commercial banks.

Unlike the previous research studies, this study investigated the driving factors of the amount borrowed from informal finance and its use in farm investment.

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.

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.

The purpose of this study is to assess the impact of credit constraints on agricultural labour productivity as well as the impact of credit constraints and agricultural labour productivity on rural households' consumption in Ghana.

This study uses the Ghana Living Standard Survey round six (GLSS 6) as the main source of data, which happens to be one of the most comprehensive household datasets in Ghana. Quantitative estimation techniques (namely: Endogenous Switching Regression and Two Stage Least Squares) are used to address possible endogeneity and selection into credit markets.

First, large households are prone to credit constraints while age (experience) and compliance with extension advice reduce credit constraints. Second, the determinants of agricultural labour productivity for both constrained and unconstrained households are age, sex, farm equipment, herbicide and farm size. Third, household size, education and livestock rearing influence agricultural labour productivity of constrained households. Fourth, credit constraints, irrespective of how they are measured, impede agricultural labour productivity while access to credit fosters labour productivity. Lastly, credit constraints robustly reduce consumption while agricultural labour productivity strongly enhances rural households' consumption.

The first contribution is that, unlike most previous studies, we do not focus on the widely used measure of productivity – output per unit land, but on agriculture labour productivity in particular. Secondly, unlike most previous studies which examine the effect of credit constraints either on productivity alone or consumption alone, our study examines the impact of credit constraints on both. Thirdly, unlike the existing literature which uses one or two measures of credit constraints, we use a wide range of measures of credit constraints – seven different measures of credit constraints. Lastly, our empirical strategy solves at least two critical econometric problems – sample selection bias and endogeneity.

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.