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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.

Based on two time periods (1995 and 1999), this study examines how much of the variability in total farm household income can be attributed to the variability in net farm income and in off‐farm income sources (such as income from off‐farm businesses, wages and salaries, interest and dividends, and other off‐farm income). Comparisons are also made between participants and nonparticipants in federal commodity programs. Using a normalized variance decomposition approach and data from the Agricultural Resource Management Study (ARMS), variability in the total income of participating households is shown to originate primarily from farming. This is particularly true for large or super‐large farms, and for farms not located in the Northeast. The major source of income variability for nonparticipating households is income from off‐farm sources, especially for cash grain or “other livestock” producers, farms in the small or mid‐size range, and farms located in the South.

The agricultural sector in the USA has experienced significant structural changes. For accommodating farm business, households have diversified their operations adopting various strategies—agricultural, structural, environmental, and income strategies. The purpose of this study is to analyze the factors influencing farmer’s diversification strategies while taking into account the simultaneous decision-making process.

This study uses a nation-wide farm household data from the US. The diversification decisions are analyzed using multivariate probit regressions.

The study suggests that agricultural, structural, environmental, and income diversification strategies are interlinked. Specifically, results indicate that, on one hand, environmental and income diversification strategies are positively interlinked. On the other hand, agricultural and structural diversification strategies are positively interlinked. Additionally, the factors representing location, farm, and farmer characteristics, farm type, and financial condition of the farm are major determinants in the choice of farm diversification strategies.

In this paper, diversification activities are broadly classified under four strategies: agricultural, structural, environmental, and income. Depending on the context and country, the definition and strategy set may need revision.

Strong complementary between diversification strategies suggests that studies analyzing farm household decisions and strategies need to account for the simultaneous decision-making process. As decisions are interlinked, separately analyzing one specific strategy may lead to biased estimates. Farm business households need to develop multiple skills and flexible capacities to tackle farming-related issues, including structural changes, risk management, and income enhancing activities. Improving employment opportunities for the rural farming population can stimulate structural diversification.

This paper contributes to limited literature about diversification by analyzing factors influencing different diversification decisions and finds interlinkage between decisions.

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 examine the effect of falling commodity prices on farm debt usage of corn and soybean farms, and how this debt usage differs based on the financial leverage of the farm.

Using panel data on farms surveyed at least twice in the Agricultural Resource Management Survey (ARMS) from 1996 to 2015, this paper uses a difference-in-differences approach to measure the effect of low commodity price shocks on financially vulnerable farms. To account for the correlation in the error structure between the three dependent variables (real estate debt, non-real estate debt, and interest payments) we use a seemingly unrelated regression approach.

Following a commodity price shock, financially vulnerable farms (debt-to-asset ratio greater than 40 percent) were found to increase their non-real estate debt when compared with non-financially vulnerable farms. Off-farm business income was found to help farms reduce real estate debt and interest payments in the face of these shocks.

Data consist of corn and soybean farms surveyed more than once in the ARMS from 1996 to 2015 and are not representative of all US farms, but have similar characteristics to US commercial farms.

The results indicate that financially vulnerable commercial crop farms respond to lower prices by taking on non-real estate debt, increasing financial stress. Well-targeted federal programs could prevent further financial stress for this group.

This is the first paper to use unbalanced panel data from ARMS to examine how farm debt use responds to commodity prices. This paper can inform policymakers about the financial risks to farms resulting from the current low-price environment.

In the early stage of the COVID-19 pandemic, Vietnam imposed many drastic restrictions to curb the outbreak of this virus. Such restrictions interrupted the normal functioning of various economic sectors, including agriculture. This research examined disruptions to agricultural activities, income loss and perceived food insecurity among farm households during the pandemic, and then explored the relationships among these economic factors.

Household data from Vietnam and Generalized Structural Equation Model (GSEM) were used for empirical analysis.

Descriptive analyses found that only a small proportion of farm households suffered from the COVID-19 disruptions to their agricultural activities, a large percentage experienced income loss, and a medium number were worried about their food insecurity. GSEM results also revealed that the COVID-19 disruptions to agricultural activities significantly increased the likelihood of worrying about food insecurity, mediated by income loss.

Due to data limitations, the authors could not use better indicators to define and measure the variables of interest (e.g. COVID-19 disruptions to agricultural activities, income loss and food insecurity). Another similar concern was that our models did not account for unobservables, causing some estimation biases.

This research is among the first attempts that examined the direct and indirect (mediated by income loss) effects of the COVID-19 disruptions to agricultural activities on food insecurity.

The determinants of income of rural and urban farm households, with emphasis on the role of off-farm employment by farm household members and of farm size, are examined using data from the 2016 Agricultural Resource Management Survey (ARMS) and quantile regression procedure. The implemented quantile regression technique is extended to allow for the decomposition of the income gap between the two groups of farm households. Findings indicate, regardless of the location of the farm, a positive and significant impact of a previous year's participation in off-farm work by household members on the distribution of current household income. Having operated a larger-sized farm in the previous year is shown with a similar effect in the upper range of the income distribution for urban households and with a comparable impact but across the whole income distribution for rural farm households.

Data from the 2016 ARMS are used in conjunction with quantile regression in order for decomposition of the income gap between the two groups of farm households.

Findings show that urban farm households who in a previous year have participated in off-farm work and operated larger-sized farms tend to earn higher incomes. Results further indicate higher rates of return to education for “urban” farm households in comparison to “rural” farm households, particularly for those with a college education and beyond who are at the lower portion of the income distribution.

To the extent that the ARMS is an annual cross-sectional data, the temporal impacts of factors that potentially may influence the incomes of farm households in urban and rural areas cannot be measured.

Findings from this research indirectly support previous published research where higher earnings by urban US population were documented in comparison to rural population and where earnings tend to rise as a result of participation in off-farm work and in expanding the size of the farming operation; this is in addition to the procurement of higher education.

The results of a higher rate of return to education for “urban” farm households in comparison to “rural” farm households have important policy implications for policymakers.

This is the first paper in the agricultural economic literature that implements a method of assessing the rural–urban divide across all of the quantiles of income distribution.

Agriculture and associated services are central to the rural economy of the United Kingdom. Rural veterinary enterprises are important providers of services to livestock producers, but are now facing concerns over their future economic viability. The objectives of this chapter are to outline the changes occurring in the veterinary and agricultural sectors, and to explore the main issues affecting veterinary enterprises in a changing business environment.

This is a conceptual chapter contextualised mainly within the United Kingdom. As such, the methodological approach comprises a critical review of current academic literatures, as well as government reports and relevant media articles.

The findings demonstrate that the commercial success of rural veterinary enterprises is critical to ensuring the future provision of high-quality animal health services to both farmers and government. The current issues facing farmers are likely to affect their willingness and resources to invest in veterinary services. Furthermore, farmers may have doubts as to vets’ ability to provide business-focussed services that add value. In addition, many public services are outsourced to private veterinary enterprises, and forthcoming policy changes are expected to lead to reduced income from public sources for both vets and their livestock farming clients. While wider issues affecting agriculture are beyond the control of private veterinary practitioners, veterinary enterprises will need to ensure that they can deal with such challenges and, where required, adapt their services accordingly.

The chapter is based on a review of extant literatures, and the scope of the research is therefore limited to existing knowledge about the farm animal veterinary business landscape.

The chapter summarises current knowledge of the challenges facing rural veterinary enterprises. While some of the issues described are specific to the veterinary sector, many are also relevant to other rural SMEs providing knowledge-intensive services to farmers. The chapter also describes areas requiring further empirical research.

The present study aims at examining the determinants of occupational migration of unskilled labourer from domestic agriculture and their impact on farm business income (FBI) in Assam, India.

Primary data for this study were collected during June–November, 2019 from 224 farm (cultivator) households in two contiguous districts in central Brahmaputra valley of Assam. The study used three-stage least square (3SLS) estimation technique for jointly determining the factors influencing migration and remittances and their impact on FBI.

The result of this study confirms that occupational migration of unskilled labourer from domestic agriculture significantly reduced household FBI. In contrast to the inflow of remittances from migrants helped in increasing the FBI. The migration in the study area considerably influenced by household size, total value of assets holding, networking influence, distance to commercial bank and flood proneness of the village; while the number of migrants, number of dependents and age of migrants seen to be strong predictor of inflow of remittances. Findings of present study offer evidence in support of the new economics of labour migration (NELM) theory.

The study is restricted to a single crop (paddy) and constrained by the collection of longitudinal data with a revisit to the farm household pre and post-migration of the unskilled labourer from household agriculture.

This paper is based on a novel data set that has especially been collected to examine the determinants of occupational migration from agriculture and their impact on the FBI in Assam that has not been studied before.