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Despite the abundance of small-scale farms in the USA and their importance for both rural economic development and food availability, the extensive research on small business management and entrepreneurship has mostly neglected the agricultural context, leaving many of these farms' business challenges unexplored. The authors focus on informing a specific decision faced by small farm managers: selling directly to consumers (i.e. farmer's markets) versus selling through aggregators. By collecting historical data and a series of interviews with industry experts, the authors employ simulation methodology to offer a framework that advises how small-scale farmers can allocate their product across these two channels to increase revenue in a given season. The results, which are relevant for operations management, small business management and entrepreneurship literature, can help small-scale farmers improve their performance and compete against their larger counterparts.

The authors rely on historical and interview data from key industry players (an aggregator and a small farm manager) to design a simulation analysis that determines which factors influence season-long farm revenue performance under varying strategies of channel allocation and commodity production.

The model suggests that farm managers should plan to evenly split their production between the two distribution channels, but if an even split is not possible, they should plan to keep a larger percentage in the nonaggregator (farmers' market/direct) channel. Further, the authors find that farmers can benefit significantly from a strong aggregator channel customer base, which suggests that farmers should promote and advertise the aggregator channel even if they only use it for a limited amount of their product.

The authors integrate small business management and operations management literature to study a widely understudied context and present practical implications for the performance of small-scale farms.

Recently the World Bank, aid donors, and others have shown greater interest in improving the income prospects of small farmers in LDC's. This interest springs in part from the fact that small farmers are at the bottom of the income ladder in many poor countries (though landless rural workers may be even worse off). Proposals to raise small farmer incomes have run the gamut from “wide‐spread” land reform to urban migration. Credit reallocation towards small and away from large farms has also been consistently advocated and has received renewed impetus due to the perceived failure of other alternative measures. Effective land reform has been found difficult to achieve short of full scale political revolution while enthusiasm for nonagricultural solutions has been tempered, at least until recently, by disappointing growth of high income employment opportunities in urban sectors.

The relationship between farm size and land productivity is a hotly debated issue in the study of agricultural economics and development economics. The purpose of this paper is to explore the causes leading to the inverse productivity relationship by examining the relationship between farm size and factor inputs.

With a large panel data set of farm households in China during 2010–2011, this study uses the factor demand models to examine the relationship between farm size and per-mu labor and non-labor inputs while employing a stochastic frontier production function in determining the difference of labor efforts in farming operation across farm sizes. Moreover, the models for value-added margins and profits are used to further determine producer behavior of small-size farms.

Results of this study show that, as compared to larger farms, smaller farms not only utilize more labor and non-labor inputs per mu, but also benefit from a higher labor effort. Moreover, smaller farms concentrate more on grain output and cash costs while focusing less on the family labor input costs in an effort to maximize value-added margins rather than profits. The higher yields on smaller farms are thus a result of the utilization of a relatively higher level of labor and non-labor inputs along with skilled-oriented precision farming technology. The inverse productivity relationship is explained by the behavior of small-size producers with employment constraints, leading to smaller farms generating a higher yield than larger farms.

While Sen (1966), Feder (1985), Eswaran and Kotwal (1986) and others have theoretically derived the causal relationship between the incomplete factor markets, especially incomplete labor markets, and the inverse productivity, empirical studies to test the causal relationships are limited. In particular, a solid foundation based on an empirical analysis is lacking when it comes to explaining the inverse productivity in China. Results of this study are expected to have significant policy implications in terms of the understanding of small-size producer behavior and the associated mechanism underlying the inverse relationship between farm size and land productivity.

The purpose of this paper is to investigate the impact of internet usage on financial performance of small farm business households in the USA. In particular, the authors want to assess the impact of internet usage on small farm businesses, where the owner’s main occupation is farming. Using a nationwide farm-level data in the USA and a non-parametric matching estimator, the study finds a significant positive impact of internet usage on gross cash income, total household income, off-farm income. The study further suggests that small farm businesses receive benefits from internet usage as it facilitates reduction in income risk through off-farm income sources, as well as a reduction in marketing and storage costs; households’ non-farm transportation and vehicle leasing expenses.

In this study, the authors use the “nearest neighbors” matching method in treatment evaluation, developed by Abadie and Imbens (2002). In this method, a weighting index is applied to all observations and “nearest neighbors” are identified (Abadie et al., 2004). Although matching estimation through the nearest neighbor method does not require probit or logit model estimation per se, the authors have estimated a probit model because it allows the authors to check the balancing property and to analyze the association of included variables with the likelihood of internet use.

The study suggests that small farm business households using the internet are better off in terms of total household income and off-farm income. As compared to the control group (which is counterfactual, representation of small farm businesses not using the internet), small farm businesses using the internet earn about $24,000-$26,000 more in total household income and about $27,000-$28,000 more in off-farm income. Also, small farm businesses using the internet earn about $4,100-$4,900 more in gross cash farm income compared to their counterpart. The estimate of ATT for NFI is not different from zero. However, gross cash farm revenue increased significantly.

To this end internet can provide an important role in information gathering. Internet is one of the convenient means to access and exchange information. Information and communication facilitation through internet have opened up new areas of commerce, social networking, information gathering, and recreational activities beyond a geographical bound. Producers and consumers can take advantages of internet in both collaborative and competitive aspects in economic activities as it can reduce the information asymmetries among economic agents.

Farmers will seek assistance in interpreting data and applying information to their farming operations, via the internet. Therefore, it is essential that land grant universities continue to improve the delivery of electronic extension and provide information in a clear and concise manner.

Studies in farm households have mainly investigated factors influencing internet adoption, purchasing patterns through internet, internet use, and applications. In most cases, impact analyses of communication and information technologies such as internet in agricultural businesses are discussed with references to large scale farm businesses. Thus, the authors know very little about access to the internet when it comes to small farm businesses and small farm households and about how it impacts well-being of small farm households.

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.

Small-scale farmers are highly heterogeneous with regard to their types of farming, levels of technology adoption, degree of commercialization and many other factors. Such heterogeneous types, respectively groups of small-scale farming systems require different forms of government interventions. This paper applies a machine learning approach to analyze the typologies of small-scale farmers in South Africa based on a wide range of objective variables regarding their personal, farm and context characteristics, which support an effective, target-group-specific design and communication of policies.

A cluster analysis is performed based on a comprehensive quantitative and qualitative survey among 212 small-scale farmers, which was conducted in 2019 in the Limpopo Province of South Africa. An unsupervised machine learning approach, namely Partitioning Around Medoids (PAM), is applied to the survey data. Subsequently, the farmers' risk perceptions between the different clusters are analyzed and compared.

According to the results of the cluster analysis, the small-scale farmers of the investigated sample can be grouped into four types: subsistence-oriented farmers, semi-subsistence livestock-oriented farmers, semi-subsistence crop-oriented farmers and market-oriented farmers. The subsequently analyzed risk perceptions and attitudes differ considerably between these types.

This is the first typologisation of small-scale farmers based on a comprehensive collection of quantitative and qualitative variables, which can all be considered in the analysis through the application of an unsupervised machine learning approach, namely PAM. Such typologisation is a pre-requisite for the design of more target-group-specific and suitable policy interventions.

An established paradigm in small business lending is segmented by bank size with large banks more likely to lend to large informationally transparent firms while small banks are more likely to lend to small informationally opaque firms. In light of banking consolidation, this market segmentation can have implications for credit availability. Federal loan guarantees, such as those provided by USDA's Farm Service Agency (FSA) may reduce the risks of lending to informationally opaque firms thereby mitigating the impacts of the bank size lending paradigm. This paper aims to discuss these issues.

This analysis utilized a binomial logit procedure to determine if there was any empirical evidence that smaller community banks served a unique clientele of farmers when making FSA-guaranteed loans. The analysis relied on a unique data set which incorporated detailed data on farm businesses receiving FSA-guaranteed loans, loan characteristics, as well as information about the originating bank and characteristics of the local credit markets.

Results were consistent with the bank size lending paradigm with smaller banks being less likely to engage in fixed-asset lending, and more likely to serve a riskier and less established clientele when making guaranteed loans.

Data limitations did not permit detailed analysis of banks larger than $250 million in total assets nor for consideration of non-bank lenders. An expansion by these lender groups into serving more informationally opaque borrowers could mitigate any adverse impacts arising from fewer small community banks.

The results suggested that Federal guarantees do not completely eliminate the relative informational advantages of large and small size banks. And, continued bank consolidation, such that there are fewer small community banks, could result in less credit availability among smaller, less creditworthy farm businesses.

While FSA guarantees may not enhance a large banks propensity to serve informationally opaque farm borrowers, they may enhance the ability of smaller community banks to serve groups specifically targeted through FSA lending programs; the provision of credit to family farmers who, despite being creditworthy, are unable to obtain credit at reasonable rates and terms.

The analysis examines relationship between bank size and the use of FSA guarantees using a unique data set which incorporated information on FSA-guaranteed loans, farm financial characteristics, along with characteristics of commercial banks which participated in the FSA-guarantee program.

The Food Safety Modernization Act (FSMA), designed to establish and enforce food-safety standards for foods from domestic and foreign origins, focuses federal regulation on the prevention of food contamination. Many concerns have been expressed about how FSMA-compliance costs will affect the economic viability of very small and small farms, which have higher average compliance costs than do larger farms, thus marginalizing their ability to compete in the marketplace. The purpose of this study is to estimate how FSMA will affect differently sized US and international tomato producers in the fresh-tomato industry. A simulation approach is applied for changes of quantities demanded, revenues, and profits for differently sized farms based on elasticities estimated using a differential approach. Our findings indicate that both domestic and foreign tomato producers with both very small and small farms are expected to see significant losses in profit after the adoption of FSMA. The practical implications of these findings are that the Food and Drug Administration (FDA) should be aware of, be concerned about, and take into consideration the adverse consequences of their regulatory decisions on food prices, food-industry costs, the structure of the food industry, and product diversity. In essence, the FDA needs to strike a balance between food safety (the primary objective of FSMA) and market performance.

The purpose of this paper is to identify the structural problem in the Chinese dairy sector. There exists a large number of low‐efficiency, small‐scale farms, and productivity inequality between small and large farms keeps increasing, which is a possible driving force behind the Melamine scandal in 2008.

Using the stochastic frontier production function, this paper estimates and compares the changes in technology and technical efficiency between backyard, small‐scale, medium‐scale and large‐scale dairy farms in China over the period between 2004 and 2008.

There are compensating effects between technology and technical efficiency. However, low yield for backyard farms is mainly caused by traditional low‐yield varieties, even though the technical efficiency is very high, which cannot compensate for the low technology.

The author put the assumption of constant return to scale mainly due to the data availability. Such an assumption implies that there are no scale‐effects between the different scales in productivity, and the productivity difference is explained by technology and technical efficiency.

In order to solve the structural problems, Chinese governments should help small‐scale farmers to adopt new high‐yield varieties, to subsidize small‐scale farmers, and to train farmers to master the complicated skills for raising high‐yield varieties.

The paper gives another possible explanation for the Melamine scandal of milk powder in 2008. If the structural problem cannot be solved, similar food safety scandals could happen once again.

The purpose of this paper is to present theoretical synopsis of risk balancing hypothesis (RBH) and estimate empirical models examining debt, savings and debt-to-equity use decisions of small US farms.

The authors use primary survey data from Tennessee and generalized linear models (GLMs).

The study’s findings suggest that the perceived higher business risk (BR) significantly increases the extent of debt use, savings use and debt-to-equity of small farmers. Moreover, results indicate that factors such as age and education of the operator, family involvement, incomes, land acreage, adoption of alternative on-farm enterprises and farmers' continuation plan significantly influence the financing decisions of small farm operations.

The authors investigated an essential empirical question examining the risk balancing behavior of small US farm operations. While risk balancing has been a theme of several studies, none of the previous studies have specifically looked at the behavior in the context of small US farms. The theoretical synopsis and empirical findings contribute to the literature of risk balancing, debt use and savings use decisions and the policy discussions on farm financial and support strategies.