Search results

The purpose of this paper is to compare the characteristics of farms who participate in farm management associations to the wider population of farms at the state level.

Farm-level records obtained from the USDA's Agricultural Resource Management Survey (ARMS) are compared to similar data obtained from farm management associations in three states: Illinois, Kansas, and Kentucky.

Data collected through farm management associations tend to represent larger farms and a greater share of crop producers as compared to livestock producers. Association data, however, capture a greater share of younger farm operators.

This is the first study to compare farm statistics from several farm management associations to ARMS, and the study confirms the findings of existing studies of prior USDA surveys.

The purpose of this article is to offer an introduction of the Agricultural Resource Management Survey (ARMS) for applied research in agricultural finance and farm management.

This article provides a brief overview of the history, design, use, and accessibility of the ARMS in government reporting and applied research.

The ARMS provides a number of unique advantages for addressing critical issues of the agricultural sector.

The paper provides an access point for researchers who are unfamiliar with the basic features of ARMS.

The purpose of this paper is to discuss the feasibility of preparing a statement of owner equity (SOE) and statement of cash flows (SOCF) for the agricultural sector. Also, the use of the Agricultural Resource Management Survey (ARMS) to collect data needed to supplement the US farm sector accounts to prepare a sector SOE and SOCF is discussed.

An SOE and SOCF for an individual producer was used to provide an example format for preparing an SOE and SOCF for the agricultural sector and to identify the data needed from the ARMS survey to supplement farm sector accounts.

The format and data needed to prepare a sector SOE and SOCF were identified and the feasibility of the collection of that data using current ERS/USDA survey collection methods would provide the data needed to prepare the statements. However, the use of two independent data collection authorities to collect the data would result in an agricultural sector SOE and SOCF that would not reconcile.

The paper initiates a dialog of possible alternatives available to the ERS/USDA and researchers concerning data needed and data sources available to prepare an agricultural sector SOE and SOCF, as well as the shortfalls and inaccuracies that would result.

The purpose of this paper is to consider how the federal crop insurance (FCI) program influences farm debt use, one of the key financial decisions made by farm operators.

Using data from the nationally representative Agricultural Resource Management Survey, the paper implements a propensity score matching model of the impact of FCI participation on various measures of farm business debt use. To account for the simultaneity of financial decisions, the paper further tests this relationship using a seemingly unrelated regression model.

FCI participation is associated with an increase in use of short-term farm debt, but not long-term debt, consistent with risk balancing behavior and current trends in the farm sector.

In addition to risk balancing, the results are also consistent with credit constraints or lender preferences. The paper cannot fully establish causality between crop insurance participation and short-term debt levels. Future research should address these limitations.

Agricultural lending standards are generally conservative and the farm sector as a whole currently has historically low leverage, which implies that an increase in debt use may not be a threat to the financial health of the farm sector.

The results indicate that the reduction in total risk facing the farm sector is significantly less than the decline in risk provided by FCI, which is an important consideration for policymakers.

This is the first paper to use an econometric model to analyze the relationship between FCI and farm debt use decisions. This paper can inform future research on the FCI program and farm financial decisions.

The purpose of this review article is to demonstrate how the quasi-experimental approach has been used to study environmental and natural resource issues related to agricultural production.

This review article first provides a short introduction to the quasi-experimental approach using the potential outcomes framework and then uses studies on the environmental sustainability of agricultural production to illustrate how quasi-experimental methods have been applied. Papers reviewed consist of studies that estimate the environmental externalities from agricultural production, evaluate agri-environmental and other related policies and programs, and demonstrate issues related to on-farm resource use and climate adaptation.

Difference-in-differences (DID) and two-way fixed effects methods that utilize the spatial and temporal variation in panel data are widely used to estimate the causal impact of changes in agricultural production and policy on the environment. Utilizing the discontinuities and limits created by agricultural policies and regulations, local treatment effects on land and other input use are estimated using regression discontinuity (RD) or instrumental variable (IV) methods with cross-sectional data.

Challenges faced by the food systems have made agricultural sustainability more critical than ever. Over the past three decades, the quasi-experimental approach has become the powerhouse of applied economic research. This review article focuses on quasi-experimental studies on the environmental sustainability of agriculture to provide methodological insights and to highlight gaps in the economics literature of agricultural sustainability.

The purpose of this paper is to examine the impact of changes in farm economic conditions and macroeconomic trends on US farm capital expenditures between 1996 and 2013.

A synthetic panel is constructed from Agricultural Resource Management Survey (ARMS) data. A dynamic system GMM regression model is estimated for farms as a whole and separately within farm typology categories. The use of farm typologies allows for comparison of the relative magnitudes of these estimates across farms by farm sales level and the operator’s primary occupation.

Changes in gross farm income levels, tax depreciation rates, and interest rates have a significant impact on crop farm investment, while changes in output prices, net cash farm income levels, tax depreciation rates, and farm specialization levels have significant impacts on livestock farm capital investment. The relative significance and magnitudes of these impacts differ within farm typologies. Significant differences include a greater responsiveness to change in tax policy variables for residential crop farms, greater responsiveness to changes in output prices and debt to asset ratios for intermediate livestock farms, and larger changes in commercial crop and livestock farm investment given equivalent changes in farm sales or the returns to investment.

These findings are of interest to agricultural economists when constructing farm investment models and employing pseudo panel methods, to those in the agricultural equipment and manufacturing sector when constructing models to manage inventories and plan for production needs across regions and over time, to those involved in drafting tax policy and evaluating the potential impacts of tax changes on agricultural investment, and for those in the agricultural lending sector when designing and executing agricultural capital lending programs.

This study uniquely identifies differences in the level of investment and the magnitude of investment responsiveness to changes in farm economic conditions and macroeconomic trends given differences in income levels and primary operator occupation. In addition, this study is one of the few which utilizes ARMS data to study farm capital investment. Utilizing ARMS data provides a rich panel data set, covering producers across many different crop production types and regions. Finally, employing pseudo panel construction methods contributes to efforts to effectively employ cross-sectional data and dynamic models to study farm behavior across time.

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.

The Agricultural Resource Management Survey (ARMS) conducted annually by the USDA's Economic Research Service collects data on US agriculture, ranging from production practices to the financial condition of farm and ranch enterprises and the farm household. The purpose of this article is to consider what could make ARMS useful from a farmer's point of view.

A Delphi method is used to gather input from a panel of experts.

Results show that increasing the usability of the ARMS to agricultural producers involves expanding the content and relevance of the data collected. Specific types of data needed are identified. Also, recommendations are made concerning how the usefulness and relevance of the data could be increased by refining the sample frame. Finally, it is argued here that after making some adjustments to the ARMS sample frame to create nationally representative data, the ARMS project could serve as a hugely important basis for reporting economic performance levels for American agriculture.

This study offers insights from agricultural finance experts on how the ARMS could be improved to expand the quality and usefulness of its output for both professionals and agricultural producers.

The aim of this study is to use a financial approach based on the Du Pont expansion to investigate the impact of demographics, specialization, tenure, vertical integration, farm type, and regional location on the three levers of performance (ROE) – namely, net profit margins, asset turnover ratio, and asset‐to‐equity ratio.

This research uses a system of equations in conjunction with 1996‐2009 farm‐level data from the US Department of Agriculture's Agricultural Resource Management Survey (ARMS) to evaluate the factors driving farm‐level profitability, namely, net profit margins, asset turnover ratio, and asset‐to‐equity ratio. The methodology employed in this study corrects heterogeneity and uses repeated cross‐section estimation procedure to estimate the empirical models.

The study finds that key drivers of net profit margins are operator education, farm size and typology, specialization, and level of government payments. Key factors affecting the asset turnover ratio component of the Du Pont model include asset turnover ratio is driven by operator age, contracting, specialization, and receiving government payments. Finally, key factors affecting asset‐to‐equity ratio component of the Du Pont model are farm size, farm typology, contracting, and specialization drive asset‐to‐equity ratio.

Existing research does not examine the factors affecting returns to equity in faring at the farm‐level. Specifically, a micro‐level analysis of American farm's future structure and financial performance that accounts for the spatial and inter‐temporal dimensions of profitability has never been conducted.

In recent years, prices for prime farmland have increased substantially, begging the question is the dramatic increase the result of a speculative bubble or consistent with market fundamentals with increases driven by increased global demand, low interest rates, and recent changes to US agricultural and energy policies. The purpose of this paper is to investigate the impacts of recent agricultural support policies and ethanol policies on farmland values and rental rates.

Farm-level Agricultural Resource Management Survey data collected by the United States Department of Agriculture (USDA) between 1998 and 2008 as well as county-level data collected by the USDA, US Census Bureau, and Bureau of Economic Analysis are used to determine the impacts of recent agricultural support policies and ethanol policies on farmland values and rental rates, while controlling for parcel characteristics and urban pressure. Specifically, weighted ordinary least squares and two-stage least squares are used to investigate the impact of various governmental agricultural support policies, corn ethanol facilities location, and local corn ethanol production capacity on farmland values and rental rates.

The results indicate that government payments, urban pressure, and the proximity of the parcel to an ethanol facility have a positive impact on both farmland values and rental rates. More specifically, parcels located in the same county as at least one corn ethanol facility are more valuable and command higher rental rates. In addition, county-level ethanol production capacity is positively associated with farmland values and rental rates. An inverse relationship between distance of the parcels from an ethanol facility and farmland values is also found; a similar result is found for rental rates.

The findings suggest that agricultural support payments and ethanol policies are capitalized into farmland values. These findings have important implications for the formulation of future farm policy. A limitation of the analyses is that farmland values are estimated by landowners; future research could utilize farmland transaction data to overcome potential biases generated by using landowner estimates. In addition, while our study period covers 11 years, future research could expand the time period further to analyze the effect of more recent agricultural and ethanol policies.

This paper extends prior research pertaining to factors influencing farmland values and rental rates by also examining the proximity of the parcel to an operating ethanol facility using a unique data set.