Agricultural Finance Review最新文献

Purpose

Long-term forecasts about commodity market indicators play an important role in informing policy and investment decisions by governments and market participants. Our study examines whether the accuracy of the multi-step forecasts can be improved using deep learning methods.

Design/methodology/approach

We first formulate a supervised learning problem and set benchmarks for forecast accuracy using traditional econometric models. We then train a set of deep neural networks and measure their performance against the benchmark.

Findings

We find that while the United States Department of Agriculture (USDA) baseline projections perform better for shorter forecast horizons, the performance of the deep neural networks improves for longer horizons. The findings may inform future revisions of the forecasting process.

Originality/value

This study demonstrates an application of deep learning methods to multi-horizon forecasts of agri-cultural commodities, which is a departure from the current methods used in producing these types of forecasts.

Purpose

The purpose of this article is to determine the regional economic factors and bank characteristics that significantly contribute to changes in bank liquidity. We also seek to identify regions that may be most susceptible to liquidity tightening.

Design/methodology/approach

For this article we use data on deposits from commercial banks, Federal Reserve survey data and indicators of regional and agricultural economic conditions. We specify a panel regression with fixed effects to model how liquidity at agricultural banks has changed and to identify the most significant drivers.

Findings

Our results suggest that small banks and banks with branch networks located in areas more concentrated in agricultural production bear the greatest risk of reduced liquidity.

Practical implications

Prior to the pandemic and more recently, lower deposit growth, combined with strong demand for agricultural loans, has led to reductions in liquidity at agricultural banks. Lower liquidity could reduce credit availability for farm borrowers and increase risks for banks that must rely on alternative sources of funding to meet loan demand.

Originality/value

Previous research has shown that exogenous shocks from other economic sectors, such as energy, can significantly affect bank liquidity, but research is limited on how agricultural bank liquidity is affected by downturns in the agricultural economy and other regional economic factors. Another contribution is this paper’s analysis of regional disparities in bank liquidity.

Purpose

This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Design/methodology/approach

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Findings

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

Originality/value

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

Purpose

Several farm safety net strategies are available to farmers as a source of financial protection against losses due to price instability, government policies, weather fluctuations and global market changes. Producers can employ these strategies combining crop insurance policies with countercyclical policies for several crops and production areas; however, less is known about the efficiency of these strategies in enhancing profit and reducing its variability. In this study, we examine the efficiency of these strategies at minimizing inter crop year farm profit variability.

Design/methodology/approach

We utilized relative mean of profit and coefficient of variation, to compare counterfactually calculated farm safety net strategies for a sample of 28,615 observations across 2,486 farms and four dryland crops (corn, soybean, sorghum and wheat) in Kansas spanning nine crop years (2014–2022). A no farm safety net strategy is used as the benchmark for every alternative strategy to ascertain whether a policy customization is statistically different from a no farm safety case.

Findings

The general pattern of the results suggests that program combination strategies that have a high-profit enhancement potential necessarily have low profit risk for dryland wheat and sorghum production. On the contrary, such a connection is absent for dryland corn and soybeans production. Low-cost farm safety net strategies that enhance corn and soybeans profits do not necessarily lower profit risks.

Originality/value

This paper is one of the first to use a large sample of actual farm-level observations to evaluate how combinations of safety net programs offered under the Title I (PLC, ARCCO and ARCIC) and XI (FCIP) of the U.S. Farm Bill rank in terms of profit level enhancement and profit risk reduction.

Purpose

The Farm Service Agency (FSA) conservation loan program was introduced in the 2008 Farm Bill to provide additional credit to assist producers implementing approved Natural Resources Conservation Service (NRCS) conservation projects. This paper explores why this program has been widely underutilized despite an overall increase in United States Department of Agriculture (USDA) Conservation Program participation.

Design/methodology/approach

The FSA administrative loan data are merged with NRCS program participation and payments data for 2010–2021. The share of project costs paid by producers and resulting savings achieved by farmers participating in both programs if their cost-share portion was paid by FSA loans are estimated, as well as the impact on farmer conservation spending under different estimates of increased participation.

Findings

A significant share of FSA farmers are likely to take advantage of NRCS programs, with the majority of participants paying under $25,000 in cost-share portions. These loans are less suited to guaranteed conservation loans and more appropriate for the discontinued direct conservation loan program. Few FSA borrowers participating in NRCS cost-share programs pay more than $50,000 in cost-share portions. These loans would receive the majority of benefits from interest reduction schemes under the current guaranteed loan program.

Practical implications

Our results and suggestions provide valuable information when discussing the Guaranteed Conservation Loan Program in the 2023 Farm Bill legislation.

Originality/value

No prior research has attempted to merge FSA guaranteed or direct loan data with conservation program participation and payment data, focused on producer cost-share levels or the FSA Guaranteed Conservation Loan Program in the last decade, making this study a valuable contribution to the literature.

Purpose

The study examines the synthesized influences of institutional governance and the scale effects of agricultural credits on the sustainability of microfinance institutions (MFIs) in Sub-Saharan Africa.

Design/methodology/approach

Using a sample of 840 MFIs (300 independent and 540 networked), the study applied Generalized Method of Moments (GMM) and Lewbel’s estimator.

Findings

Results indicate positive effects of financial structure, efficiency and agricultural credit scale on sustainability, with a non-linear U-shaped relationship between agricultural credit size and microfinance sustainability. Depending on institutional governance quality, a threshold is identified where agricultural credit scale significantly enhances the quality of Portfolio at Risk (lnPAR) in independent MFIs and Returns on Assets (lnROA) in networked MFIs.

Research limitations/implications

Study suggests strengthening governance for transparency and operating within optimal size for enduring sustainable performance. While focused on Sub-Saharan Africa, future research could expand to various economies or introduce additional variables for a comprehensive analysis.

Practical implications

MFIs can achieve sustainability by implementing management guided by better institutional norms, innovative financial transformations better suited to financing agricultural activities and techniques and an organizational structure more aligned with their performance targets.

Social implications

Broader and more reliable access to financial services, particularly in the agricultural sector, can stimulate production and alleviate poverty.

Originality/value

The study’s originality lies in its contribution to the literature by examining the role of institutional governance in microfinance institution performance and evaluating microfinance in a broader Sub-Saharan African context, proposing threshold limits where agricultural microcredit compromises performance.

Purpose

The growth of lending from nontraditional lenders may pose challenges for official US Department of Agriculture (USDA) farm sector debt estimates, but it is difficult to find data to assess official estimates. The purpose of this study is to examine whether debt provided by nontraditional lenders is accurately accounted for in official estimates.

Design/methodology/approach

We compare traditional and nontraditional lending data from farm equipment lien collateral values and the USDA Agricultural Resource Management Survey (ARMS). After analyzing trends in equipment lending implied by farm equipment lien data and ARMS, we estimate whether changes in farm equipment lien values predict changes in equipment debt reported in ARMS and whether lender type influences that relationship.

Findings

We find that credit provided by nontraditional lenders is likely underreported in ARMS. Our econometric model shows that equipment debt volumes for nontraditional lenders are consistently lower than traditional loan volumes in ARMS across a variety of model specifications. We also find that an increase in lien values for nontraditional lenders is less likely to predict an increase in ARMS equipment debt volumes than an increase for traditional lenders.

Practical implications

Official farm sector debt estimates may not fully account for nontraditional lenders.

Originality/value

This study demonstrates how the growth of nontraditional lending poses challenges for estimating US farm sector debt. We evaluate farm sector debt estimates and advance knowledge of the role of nontraditional lenders in farm equipment credit provision. The farm equipment lien dataset provides a rich source of novel data for research on local and national equipment debt and investment.

Purpose

This paper investigates the relationship between climate finance and climate ergodicity. More specifically the paper examines how climate ergodicity as measured by a mean-reverting Ornstein–Uhlenbeck process affects the value of climate-linked bonds.

Design/methodology/approach

Bond valuation is evaluated using Monte Carlo methods of the Ornstein–Uhlenbeck process. The paper describes climate risk in terms of the Hurst coefficient and derives a direct linkage between the Ornstein–Uhlenbeck process and the Hurst measure.

Findings

We use the Ornstein–Uhlenbeck mean reversion relationship in its OLS form to estimate Hurst coefficients for 5 × 5° grids across the US for monthly temperature and precipitation. We find that the ergodic property holds with Hurst coefficients between 0.025 and 0.01 which implies increases in climate standard deviation in the range of 25%–50%.

Practical implications

The approach provides a means to stress-test the bond prices to uncover the probability distribution about the issue value of bonds. The methods can be used to price or stress-test bonds issued by firms in climate sensitive industries. This will be of particular interest to the Farm Credit System and the Farm Credit Funding Corporation with agricultural loan portfolios subject to spatial climate risks.

Originality/value

This paper examines bond issues under conditions of rising climate risks using Hurst coefficients derived from an Ornstein–Uhlenbeck process.

Purpose

The United States Department of Agriculture Farm Balance Sheet forecasts provide important, timely information on the financial assets and debt in the U.S. farm sector. Despite their prominent role in policy and decision making, the forecasts have not been rigorously evaluated. This research examines the degree to which the USDA’s Farm Balance Forecasts are optimal predictors of subsequent official estimates.

Design/methodology/approach

Following prior studies of USDA’s farm income forecasts, archived asset and debt forecasts from 1986 through 2021 are used in regression-based tests of bias and efficiency.

Findings

Forecasts from 1986–2021 are found to be unbiased but inefficient. The forecasts have a tendency to over-react to new information early in the revision process.

Originality/value

These findings can be helpful for forecast users in adjusting their expectations and for forecasters in adjusting the current forecasting methods.

Purpose

A correlation has been shown in the literature between credit constraints and the adoption of agricultural technologies, technical efficiencies and measures for adapting to climate change. The relationship between credit constraints, risk management strategy adoption and income, however, is not well understood. Consequently, the purpose of this study was to investigate how credit constraints affect the income and risk management practices adopted by Northern Nigerian maize farmers.

Design/methodology/approach

Cross-sectional data were collected from 300 maize farmers in Northern Nigeria using a multi-stage sampling technique. Descriptive statistics, seemingly unrelated regression and double hurdle regression models were the analysis methods.

Findings

The results showed that friends and relatives, banks, “Adashe”, cooperatives and farmer groups were the main sources of credit in the study area. The findings also revealed that the sources of risk in the study area included production risk, economic risk, financial risk, institutional risk, technological risk and human risk. In addition, the risk management strategies used to mitigate observed risks were fertilizer application, insecticides, planting of disease-resistant varieties, use of herbicides, practising mixed cropping, modern planning, use of management tools as well as making bunds and channels. Furthermore, we found that interest rate, farm size, level of education, gender and marital status were significant determinants of statuses of credit constraints while the age of the farmer, gender, household size, primary occupation, access to extension services and income from maize production affected the choice and intensity of adoption of risk management strategies among the farmers.

Research limitations/implications

The study concluded that credit constrained status condition of farmers negatively affected the adoption of some risk management strategies and maize farmers’ income.

Practical implications

The study concluded that credit constrained status condition of farmers negatively affected the adoption of some risk management strategies and maize farmers’ income. It therefore recommends that financial service providers should be engaged to design financial products that are tailored to the needs of smallholder farmers in the study area.

Originality/value

This paper incorporates the role of constraints in influencing farmers’ decisions to uptake credits and subsequently their adoption behaviours on risk management strategies. The researcher approached the topic with a state-of-the-art method which allows for obtaining more reliable results and hence more specific contributions to research and practice.