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The purpose of this paper is to investigate the factors (farm, operator and household characteristics, along with farm type and regional location of the farm) affecting financial performance of new and beginning farmers and ranchers.

Returns on assets (ROA), a measure of financial performance widely used in the farm management literature, is the ratio of net farm income plus interest payment to total assets. This measure has been used by Gloy and LaDue and Gloy et al. to measure financial performance of farmers in New York. ROA is hypothesized to be a function of operator/farm characteristics and management strategies used to manage the farm. The independent variables hypothesized to affect the farm's financial performance encompass the following three areas: farm operator characteristics, farm characteristics such as production and marketing efficiency measures, and management strategies. All standard errors were adjusted for heteroscedasticity using the Huber–White sandwich robust variance estimator based on algorithms contained in STATA.

Results from this study show that although there is an inverted U‐shaped relationship between age of the operator and financial performance, management strategies such as increasing the number of decision makers, engaging in value‐added farming, and having a written business plan can lead to higher financial performance.

More than 50 percent of current farmers are likely to retire in the next five years. US farmers over age 55 control more than half the farmland, while the number of new farmers replacing them has fallen since the Farm Crisis period, 1982‐1987. Paralleling this shift in production, agriculture is in a decline in overall farm numbers. Concern in many states arises because the loss adversely affects the future of family farms, the farm economy and healthy rural communities. Additionally, the rapid decline in the entry of new and young farmers is an indication of rising barriers to entry, resulting in calls from within the farming community for public policy measures designed to aid new and beginning farmers.

Dairy farms, along with livestock and specialty crop farms, face a tight labor supply and increasing labor costs. To overcome the challenging labor market, farm managers can increase labor-use efficiency through both human resource and capital investments. However, little is known about the relationship between such investments and farm profitability. The purpose of this paper is to examine the relationship between dairy farm financial performance and labor-use efficiency, as measured by labor productivity (milk sold per worker equivalent); labor costs (hired labor cost per unit of milk sold and hired labor cost per worker); and investment in labor-saving equipment.

Cluster analysis is applied to partition dairy farms into three performance categories (high/middle/low), based on farms’ rate of return on equity, asset turnover ratios and net dairy income per hundredweight of milk. Next, the annual financial rank is fitted into both random- and farm-level fixed-effects ordered logit and linear models to estimate the relationship between dairy farms’ financial performance and labor-use efficiency. This study also investigates the implications of using a single financial indicator as a measure of financial performance, which is the dominant approach in literature.

The study finds that greater labor productivity and cost efficiency (as measured by hired labor cost per unit of milk sold) are associated with better farm financial performance. No statistically significant relationship is found between farm financial performance and both hired labor cost per worker and advance milking systems (a proxy of capital investment in labor-saving technology). Future studies would benefit from better measurements of labor-saving technology. This study also demonstrates inconsistency in regression results when individual financial variables are used as a measure of financial performance. The greater labor-use efficiency on high-performing farms may be a combination of hiring more-skilled workers and managerial strategies of reducing unnecessary labor activities. The results emphasize the importance of managerial strategies that improve overall labor-use efficiency, instead of simply minimizing total labor expenses or labor cost per worker.

This study examines the importance of labor productivity and labor cost efficiency for dairy farm management. It also develops a novel approach which brings a more comprehensive financial performance evaluation into regression models. Furthermore, this study explicitly demonstrates the potential for inconsistent results when using individual financial variable as a measure of financial performance, which is the dominant measurement of financial performance in farm management studies.

Farm businesses in England are under pressure to intensify production sustainably while managing costs and meeting market demands. Commodity prices and support from Common Agricultural Policy (CAP) payments are important determinants of profitability. With the United Kingdom (UK) leaving the European Union (EU), revised policy will see farming more exposed to fluctuating commodity prices and financial support from Government more focused on encouraging environmental land management. The research reported here, investigated whether business management practices of farmers influences financial performance, and how policy could be tailored to better meet the needs of farm businesses.

Regression models were estimated for 862 Cereals, Dairy and Livestock farms in England using official data for 2011–2012, in order to assess whether different farm characteristics, business management practices (identified from a systematic review of 102 studies), knowledge acquisition indicators and manager experience had an effect on four different financial performance ratios. The financial performance of the top 25% of the sample was also compared to the bottom 25% in terms of use of business management practices.

The results show that business planning and benchmarking had a positive, statistically significant, effect on financial performance, as do business size and knowledge acquisition, albeit to a lesser extent.

The research reported here is the most extensive examination, to date, of the impact of management practices on the financial performance of farms. Thus, it sends strong policy recommendations.

The purpose of this research is to evaluate the financial performance measures calculated and reported by the Economic Resource Service (ERS) from Agricultural Resource Management Survey (ARMS) data. The evaluation includes the calculation method and the underlying assumptions used in obtaining the reported values. Recommendations for improving the information reported are proposed to ERS.

The financial measures calculated and reported are compared with those recommended by the Farm Financial Standards Council (FFSC). The underlying assumptions are identified by analyzing the software code used in calculating the values reported. The values reported by ERS are duplicated and alternative methods for calculating the financial performance measures are considered. The values obtained from the various calculation methods are compared and contrasted.

Recommendations for ERS include: calculate and report the financial measures recommended by FFSC, note values that are imputed, periodically update and validate assumptions used in calculating imputed values, review its policy for flagging estimates as statistically unreliable, report medians and other select percentiles, and consider reporting the percent of farm businesses that have values within critical zones.

A total of four methods for calculating financial performance measures are compared and contrasted. These are the aggregate mean, sample mean, sample median, and percentage of farm businesses with values in critical zones.

The purpose of this paper is to evaluate dairy farm financial performance over time utilizing farm financial ratios from three university business analysis programs. The evaluation includes measures of profitability, solvency, and liquidity by herd size.

Financial ratios to reflect profitability (rate of return on assets), solvency (debt to asset ratio), and liquidity (current ratio) were collected from Cornell University, Michigan State University, and the University of Wisconsin for dairy farms from 2000 to 2012. The distribution of farm financial performance using these ratios was examined over time and by herd size. Variance component methods are used to examine the percent of variation due to individual firm and industry aspects. A simple credit risk score is calculated to examine relative farm risk.

Dairy farm profitability performance is similar across herd sizes in poor years but larger herds realized significantly more profitability in good years. Findings were similar with respect to liquidity. Large herds consistently carried relatively more debt. Large herds’ financial performance was more uniform than across smaller herds. Larger herds had more financial risk as measured by credit risk scoring but recovered quickly to industry averages in profitable years.

The variation of dairy farm financial performance in an era of volatile milk and feed price is assessed. The results have important implications for farm financial management and benchmarking farm financial performance. In addition to helping to evaluate the efficacy of various price and income risk management tools, these results have important implications for understanding the benefits of the new federal Margin Protection Program for Dairy that is available to all US dairy farmers.

The purpose of this research is to understand US Upper Midwest dairy farm profitability performance over time and across herd size. Profitability is broken down into asset efficiency and operating profit margin. The primary objective is to determine how much information is required to accurately benchmark farm performance.

Financial ratios to measure profitability (rate of return on assets), profit margin (operating profit margin ratio), and asset efficiency (asset turnover) were collected from Michigan State University and the University of Wisconsin business analysis programs for dairy farms from 2000 through 2016. Financial ratio patterns were examined both across time and herd size. Annual distributions were divided into quartiles and the use of one to five-year averages were used to determine accuracy of quartile rank compared to true long-run farm profitability performance.

Financial performance across large herds was more uniform than across smaller herds. Small and large herd profitability performance converged in poor years but diverged in good years. Using three or more years performance greatly improved accuracy of benchmarking profitability.

The data utilized are very rich in the sense of the amount of variation across years and herd size. The results have important implications for farm financial management and benchmarking farm financial performance. Farm firms should benchmark multiple years of profitability before making major management changes to alleviate deficiencies.

The adoption of several basic financial management practices is examined for a group of New York dairy farms. The study provides estimates of the extent to which various business analysis and control, investment analysis and decision making, and capital acquisition practices have been adopted. Many practices, such as net present value analysis, are not widely adopted by farmers. The relationship between the adoption of financial management practices and farm profitability is also examined. Results suggest that the adoption of financial management practices, such as using investment analysis techniques, significantly impacts farm financial performance.

The Agriculture Improvement Act of 2018 directed the US Secretary of Agriculture to report on the profitability and viability of beginning farmers and ranchers. Many beginning operations use local food markets as they provide more control, or a premium over commodity prices, and beginning operations cannot yet take advantage of economies of scale and subsequently have higher costs of production. Little research assesses the relationship between beginning farmer profitability and sales through local food markets. In this paper, the profitability implications of sales through local food markets for beginning farmers and ranchers are explored.

The authors utilize 2013–2016 USDA agricultural resource management survey data to assess the financial performance of US beginning farmers and ranchers who generate sales through local food markets.

The results point to four important takeaways to support beginning operations. (1) Local food channels can be viable marketing opportunities for beginning operations. (2) There are differences when using short- and long-term financial performance indicators, which may indicate that there is benefit to promoting lean management strategies to support beginning operations. (3) Beginning operations with intermediated local food sales, on average, perform better than those operations with direct-to-consumer sales. (4) Diversification across local food market channel types does not appear to be an indicator of improved financial performance.

This article is the first to focus on the relationship beginning local food sales and beginning farmer financial performance. It incorporates short-term and long-term measures of financial performance and differentiates sales by four local food market type classifications: direct-to-consumer sales at farmers markets, other direct-to-consumer sales, direct-to-retail sales and direct-to-regional distributor or institution sales.

The purpose of this paper is to identify the factors associated with farm financial success for those farms known to produce for local supply chains. The analysis considers alternative measures of farm financial performance and considers the role of the local foods supply chain in the choice to market locally.

The paper uses a two-stage Heckman approach which addresses the possibility of sample selection bias. In the first stage, the choice model to engage in direct marketing is estimated. In the second stage, the authors estimate a model of the financial performance of those in the sample that direct marketed which includes an IMR term calculated from the parameters of the first stage equation. The analysis uses national farm-level data from the Agricultural and Resource Management Survey of the US Department of Agriculture and combines data from 2009 to 2012 to overcome the constraint of small samples.

Indicators of the development of a local foods supply were positively related to the choice to engage in direct marketing. Factors affecting farm financial performance varied significantly between a short-term and a long-term measure. The results emphasize the importance of considering multiple outcome measures, developing local supply chains and provide implications about beginning farms.

If a local foods system is going to thrive, the farms that market the agricultural products in the local food system must attain a certain level of profitability. The value of the analysis is an improved understanding of the financial performance of farms producing for a small, but growing segment of the food supply chain.

The purpose of this study is to examine how US farm sector performed in 2020, the first year of the pandemic. There were significant supply and demand shocks due to the pandemic. Furthermore, there was significant fluctuation in commodity prices and record high government payments in 2020. This study aims to examine the performance and position of US farm sector (financially) to system (and global economy) wide shocks.

The authors examine 2020 values for farm sector financial ratios before and after the onset of the Coronavirus (COVID-19) pandemic using the data from the United States Department of Agriculture to understand the financial position and performance of the US farm sector.

The authors find solvency ratios (which are indicators of the sector's ability to repay financial liabilities via the sale of assets) worsened in 2020 relative to pre-pandemic expectations. Efficiency ratios (which evaluate the conversion of assets into production and revenue) and liquidity ratios (which are indicators of the availability of cash to cover debt payments) showed mixed outcomes for the realized results in 2020 relative to the pre-pandemic forecasts. Four profitability ratios were stronger in 2020 relative to pre-pandemic expectations. All solvency, liquidity and profitability ratios plus 2 out of 5 efficiency ratios for 2020 were weaker than their respective average ratios obtained from 2000 to 2019 data.

This research is one of the first papers to use financial ratios to examine how the US farm sector performed in 2020 compared to expectations prior to the pandemic.