The U.S. Department of Agriculture (USDA) recognizes several groups of farmers who have been historically underserved by USDA and operates several programs and policies targeting these groups. Yet, there is limited information about the current financial health of the farms these producers operate, their credit and agricultural program use, which inhibits the measurement of progress toward more equitable outcomes. This report provides an overview of the financial characteristics of the farms operated by socially disadvantaged (individuals identifying as Black or African American, American Indian or Alaska Native, Hispanic or Latino, and Asian or Pacific Islander), women, and limited resource producers (farms with low sales and low household income), using data from the 2017-20 annual Agricultural Resource Management Survey.

Propensity score matching is used to estimate how direct -to -consumer (DTC) marketing influences farm survival and growth over 5 -year periods. Results show that beginning farms with DTC sales grow more slowly but are more likely to survive in business compared to similar farms without DTC sales. The study finds that DTC marketing is associated with lower financial performance and a greater likelihood of facing borrowing constraints, which might help explain the slower farm growth. DTC marketing is also associated with lower farm income volatility, which might help explain the higher survival rate.

Beginning farmers and ranchers represent an important segment of U.S. agriculture, yet they face financial challenges relative to more established operations. This article provides an overview of emerging research on the financial performance of beginning farm and ranch operations, with a lens toward implications for the 2023 Farm Bill. First, we use U.S. Department of Agriculture (USDA) Agricultural Resource Management Survey data to explore descriptive statistics relative to beginning farm and ranch usage of: local food markets, Federal crop insurance, and financing mechanisms. Subsequently, we leverage a farm-level panel dataset from the USDA Census of Agriculture and regression analysis to examine the relationship between key financial metrics and beginning farmer success. Results show: (a) beginning farm performance over time is associated with both increases in scale and productivity, as well as participation in agricultural programs, (b) access to credit is important, and that being credit constrained lowers the probability of survival, growth, and success for beginning operations, and (c) beginning operations are significantly less likely to use Federal crop insurance compared to established operations across all scales.

Using linked data from multiple years of the U.S. Census of Agriculture, this study identifies farm and operator characteristics associated with beginning farm survival, growth, and success. Success is defined as continuing in business for 5 years without a decline in farm real estate asset value. The results indicate which types of beginning farms and farmers are likely to survive and grow-information which could be useful in targeting program resources. By identifying policy-amenable variables that correlate with both farm survival and business expansion, the results also suggest possible mechanisms for increasing the success of beginning farms.

PurposeCredit may help farmers survive and grow by helping farm households cope with farm or off-farm income variation and by allowing farmers to adopt more efficient production technologies and take advantage of scale economies. This study estimates how credit constraints affect the survival and growth of beginning farms and explores how this effect varies depending on the age of the farm operator.Design/methodology/approachFarms businesses are classified as credit constrained using a measure of repayment capacity: the interest expense ratio (interest expenses relative to gross income). Linked data from consecutive Agricultural Censuses are used to track individual farms over time.FindingsResults show that beginning farms with a high interest expense ratio take on less new debt over the subsequent five years. These credit-constrained farms were found to have lower five-year survival and growth rates than similar unconstrained farms. The negative effect of being constrained on growth is greater for farms with operators younger than 40 years old.Practical implicationsThe finding that credit constraints impede the growth and survival of beginning farms supports a rationale for targeted loan programs designed to help beginning farmers. Results suggest that some of the benefits from these programs will be greater for farms with younger operators.Originality/valueThis study is the first to estimate the effect of credit constraints on the survival and growth of farm businesses. The expansive farm-level panel dataset, which includes almost all beginning farmers in the US, allows for precise coefficient estimates while controlling for numerous farm and operator characteristics.

Many farmers face borrowing limits that depend on their household income and net worth. Given such credit constraints, an increase in off-farm income should allow farmers to borrow more, thus influencing production decisions and productivity. To test this hypothesis, the education level of the farm operator's spouse is used to identify exogenous variation in off-farm income. Findings indicate that higher off-farm income leads to more borrowing, capital expenditures, capital input intensity, farm labor use, output, farm income, and productivity. Results suggest that Federal programs that promote access to credit for limited-resource farmers may increase farm investment and productivity.

Beginning farms (those on which all operators have no more than 10 years of farming experience) operate at a smaller scale, earn less farm income, and have more debt relative to their assets than more established farms. Beginning farm households work more off-farm and have less wealth than established farm households.

This article explores whether income underreporting for tax purposes can explain why the majority of U.S. farmers earn low or negative net farm income. Using 10 years of U.S. Department of Agriculture farm-level data, the extent of underreporting is estimated by exploiting the fact that farm households face an incentive to underreport farm income that varies with their reported off-farm income. Results indicate that 39% of total farm income is underreported. For large farms, the results imply a substantial discrepancy between reported and earned farm income. For small-scale operations, underreporting reduces but does not eliminate the gap between farm and off-farm wages.

Beginning farms (those on which all operators have no more than 10 years of farming experience) operate at a smaller scale, earn less farm income, and have more debt relative to their assets than more established farms. Beginning farm households work more off-farm and have less wealth than established farm households.

This study uses a newly created panel dataset drawn from the 1997 to 2013 Agricultural Resource Management Survey to provide the first national estimates of income volatility for commercial farm households in the United States. Results show that the income of commercial farm households is substantially more volatile than that of all U.S. households—though the volatility of farm income is not more volatile than income from nonfarm self-employment. Using a regression analysis, we identify operator, operation, and regional characteristics associated with higher income volatility, providing information that could improve targeting of riskmitigating programs. We find that farm income volatility has declined for farms specializing in program crops in recent decades, supporting the hypothesis that the expansion of the federal crop insurance program helped reduce farm income risk.

In recent decades, agricultural production in the U.S. has continued to shift to large-scale operations, raising concerns about the economic viability of small and midsized farms. To understand whether economies of size provided an incentive for the consolidation of production, the study estimates the total factor productivity (TFP) of five size classes of grain-producing farms in the U.S. Heartland (Corn Belt) region. Using quinquennial Agricultural Census data from 1982 to 2012 the study also compares TFP growth rates across farm sizes to gain insight into whether observed productivity differences are likely to persist. The finding of a strong positive relationship between farm size and TFP suggests that consolidation of production has contributed to recent aggregate productivity growth in the crop sector. The study estimates the extent to which sctoral productivity growth can be attributed to structural change versus other factors including technological change. The study also explores some tradeoffs associated with policies that raise the productivity of small versus large farms.

A much-touted policy tool to reduce nutrient pollution from livestock agriculture is the nutrient management plan (NMP). NMPs can be voluntary or required, and oblige farms to match the nutrients applied as manure or commercial fertilizer with the absorptive capacity of land and crops. However, little research examines whether these plans are implemented, even if farms have records of having plans. In this paper we use nationally-representative Agricultural and Resource Management Survey (ARMS) data on hog producers to compare the nutrient application practices of farms with and without NMPs to see whether having an NMP makes a farm less likely to over-apply nutrients, as well as to adopt other nutrient management practices. We also examine whether the effect of having an NMP on nutrient management is strengthened by state NMP requirements, proximity to urban areas, regional nutrient balance, and watershed water quality oversight. Our preliminary findings suggest that NMPs are effective in encouraging nutrient testing but not in reducing over application of nutrients to farmland; they have the most effect in states with more stringent regulation.

This study uses a newly created panel dataset drawn from the 1997 to 2013 Agricultural Resource Management Survey to provide the first national estimates of income volatility for commercial farm households in the United States. Results show that the income of commercial farm households is substantially more volatile than that of all U.S. households—though the volatility of farm income is not more volatile than income from nonfarm self-employment. Using a regression analysis, we identify operator, operation, and regional characteristics associated with higher income volatility, providing information that could improve targeting of risk-mitigating programs. We find that farm income volatility has declined for farms specializing in program crops in recent decades, supporting the hypothesis that the expansion of the federal crop insurance program helped reduce farm income risk.

Farm real estate (including land and the structures on the land) accounts for over 80 percent of farm sector assets. Farmland values have appreciated substantially since 2000, more than doubling from $1,483 per acre in 2000 to $3,060 per acre in 2015. Cropland appreciated faster than pastureland, while farmland in the Midwest appreciated faster than other areas of the country.

Farm income is highly variable, and this variability can affect household welfare, agricultural production, and environmental quality. Federal agricultural policies have long sought to shelter farmers from income fluctuations. The 2014 Farm Act focused attention on risk reduction by creating new programs tied to fluctuations in prices, yields, and revenues. ERS researchers use a large panel dataset created from 18 years of the USDA’s Agricultural Resource Management Survey (ARMS) to provide new information about the extent of farm household income variability. Analysis compares total income volatility of farm and nonfarm households; for farm households, it compares the volatility of farm and off-farm income and examines how income volatility differs across types of producers and farms of different sizes. A regression analysis explores the determinants of household income volatility and identifies trends in volatility over time. Researchers disaggregate total household income variability into farm, off-farm, and other components to trace how each component contributes to the overall volatility. Lastly, researchers look at the effects of U.S. Government programs on farm household income variability and estimate the risk-reducing benefits of these programs.

Farm income is highly variable, and this variability can affect household welfare, agricultural production, and environmental quality. Federal agricultural policies have long sought to shelter farmers from income fluctuations. The 2014 Farm Act focused attention on risk reduction by creating new programs tied to fluctuations in prices, yields, and revenues. ERS researchers use a large panel dataset created from 18 years of the USDA’s Agricultural Resource Management Survey (ARMS) to provide new information about the extent of farm household income variability. Analysis compares total income volatility of farm and nonfarm households; for farm households, it compares the volatility of farm and off-farm income and examines how income volatility differs across types of producers and farms of different sizes. A regression analysis explores the determinants of household income volatility and identifies trends in volatility over time. Researchers disaggregate total household income variability into farm, off-farm, and other components to trace how each component contributes to the overall volatility. Lastly, researchers look at the effects of U.S. Government programs on farm household income variability and estimate the risk-reducing benefits of these programs.

Farmers use antibiotics to treat, prevent, and control animal diseases and increase the productivity of animals and operations. However, there is concern that routine antibiotic use in livestock will contribute to antimicrobial-resistant pathogens, with repercussions for human and animal health. Given these concerns, pressure to limit antibiotic uses for purposes other than disease treatment is mounting. Changes in use will lead to a series of adjustments in animal agriculture as producers change production practices, with potential repercussions for prices and volumes in livestock markets. This report addresses the following questions: How widely are antibiotics used in the livestock industries? How could the current structure of the livestock industry influence the effects of restrictions on certain uses of antibiotics? How might the restriction of antibiotics affect production and costs at the animal and farm levels? How might those impacts affect production and prices in markets?

We evaluate whether expanding compliance to include nitrogen management could be an effective tool to reduce excess nitrogen applications in the Mississippi and Atchafalaya River Basin (MARB). Compliance requires farmers to meet some minimum standard of environmental protection on environmentally sensitive land as a condition for receiving federal farm program benefits. Using farm-level data on "excess" nitrogen applications and program benefits, we estimate the level of compliance and the reduction in excess nitrogen applications to cropland in the MARB. Extending compliance provisions to nitrogen management could reduce excess applications up to 60% under ideal conditions.