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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.

This study aims to evaluate the effectiveness of crop diversification in increasing the income of farm households. In addition, this study introduces the impact of natural disasters in the analysis to determine how diversification helps mitigate the negative effect of disasters on farm income. More importantly, the study also analyses the effect of diversification on farm income by farm class to see where the benefits of diversification are concentrated.

This study uses a linear model, in which agricultural income is expressed as a function of diversification, natural disasters and several control variables. Diversification is measured using the Simpson index of diversification. The linear model is enhanced with the inclusion of an interaction term of natural disasters with the diversification index to shed light on the role of diversification in negating their harmful effect on agricultural income. Finally, to analyze the impact of institutional variables on farm income, the interactions of diversification with irrigation, insurance, usage of technical information and formal training are incorporated in the linear model.

The study highlights the importance of demographic, farm and institutional variables in raising farm income. The study suggests that an increase in education level, irrigation, usage of technical information and possession of Kisan Credit Card (KCC) have a positive impact on agricultural income. The study reveals that crop diversification has a positive impact on farm income and the benefits of diversification are conditioned by institutional factors. Thus, there is a need for policy intervention to ensure increased irrigation facilities along with extension services to provide information to the farm households. It has been found that small farmers gain more from crop diversification than larger farmers. Furthermore, the results show that natural disasters negatively impact farm income, but their impact can be mitigated by higher levels of diversification.

The results of the study are based on the recent unit-level data from the 77th Round of the National Sample Survey Office survey. The survey covers a large number of farm households and reports information for the year 2018–2019.

The purpose of this paper is to identify the factors associated with the adoption of agro-processing methods and to estimate their impact on farm income and farm diversification.

Using a large-scale sample of 12,122 special crop farm households drawn from the 2015 Agricultural Census Survey in Taiwan, the semiparametric multivalued treatment effect model was estimated.

The authors found that agro-processing farm households obtain higher farm incomes than non-agro-processing farm households. Among the agro-processing methods, self-processing generates higher farm income than outsourced-processing. Moreover, farm households that adopt either agro-processing method are more likely to diversify into agritourism and other agribusinesses than non-agro-processing farms.

The authors could only access data on farm income and not on agro-processing costs. Future studies may address the impact of agro-processing on farm profitability if relevant data are available.

Very few studies have examined the relationship between agro-processing, farm income and farm diversification. To the best of the authors’ knowledge, this is one of the first papers to examine the impact of different agro-processing practices on farm income and farm diversification.

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.

The purpose of this paper is to identify the structure of household income and examine the effects of non-farm incomes on the income distribution of farm households in a relatively developed rural area of the Malaysian rice bowl.

The non-farm incomes were disaggregated into different components to determine the contribution of each income source to total household income and overall inequality. The income distribution and decomposition was examined using the Gini decomposition method.

It was found that almost 71 percent of the households in the sample had at least one source of non-farm income. On average, non-farm incomes contributed about 33 percent to total household income. Non-farm wage employment was the dominant source of non-farm income, accounting for almost 26 percent of overall household income. The farm incomes, especially the paddy incomes were found to be the inequality-decreasing income source. The study also confirmed the proposition that the non-farm incomes were the inequality-increasing income source as they contributed up to 35 percent of the overall income inequality.

Previous studies have found that non-farm incomes have different effects on income inequality of rural communities, especially those in the rice granary areas situated in less developed states of Malaysia, where poverty is still a problem. This study is significant because it identifies the effect of certain incomes on the overall income inequality among farm households in the granary areas located in a relatively developed rural area. The studied areas are characterized by an intensive paddy production and a rapid development in business and industrial activities, and hence, providing non-farm employment opportunities to the rural farmers. Therefore, this study shows the income structure and how farm and non-farm incomes affect the overall income distribution of the paddy farmers.

The purpose of this paper is to identify the potential relationship between farm income variability and off-farm employment decisions in the short and medium term for the case of Irish farm operators.

Panel probit models of off-farm labour supply are estimated using Teagasc National Farm Survey data for Irish farms. The framework is based largely on standard expected utility but includes a constraint for recent employment history.

The analyses identifies some evidence of a positive association between farm income variability and off-farm employment in the medium term but no significant relationship in the short term. This suggests that off-farm employment is part of a wider portfolio decision but is not a strong solution to short-term farm income shocks.

European farmers increasingly face high income variability but financial risk management tools are not sufficiently developed or widely accessible to assist farmers in managing the associated risk. This deficiency can have negative implications for household economic welfare and future farm investments and hence the future farm income. Off-farm employment can form part of a wider medium-term portfolio strategy but more effective tools are also required for risk management particularly in dealing with short-term volatility and where off-farm employment is not a realistic endeavour given time constraints and/or demographics.

The estimation of farm income variability includes a detrending method thus reducing the likelihood of overestimating farm income variability for farms in deliberate expansion or decline. While previous research has typically focused on the short-term response of farmers to historical farm income variability, this research has distinguished between the short and medium term.

The recent fluctuations in farm income remind us of the boom-bust nature of the agricultural sector. To better understand these fluctuations in farm income, the purpose of this paper is to examine the relationship between farm income and influential factors from 1964 to 2010 allowing for structural breaks in the data.

The authors estimate error-correction models for an overarching model and several sub-models at different scales based on their relationship with farm income: micro, meso, and macro. The authors then provide a series of impulse response functions (IRFs) that combine short- and long-run impacts in a rigorous framework indicating the response of farm income to shocks from any of the explanatory variables.

Results indicate that prices paid (PP) and received by farmers, technological change, interest and exchange rates (ERs), gross domestic product (GDP) and land prices all influence farm income. Results using IRFs show how increases in farm income arise from shocks to prices received and GDP; while PP, interest rates, and land prices have a negative impact on farm income. Technological progress and ERs switch from having a negative short-run impact, to a positive long-run impact.

This paper takes a fresh look at the single, overarching model for farm income determinants. The authors break this model into three separate levels, with results indicating that these sub-groups perform better than the one overarching model of all variables.

The purpose of this paper is to examine the role of institutional factors in agricultural structural change in the European Union (EU) using the case study of land mobility in Ireland. A range of agricultural land use options are compared in order to examine the effect of domestic and EU policy instruments on land mobility.

Using socio-economic data from the Teagasc National Farm Survey, three hypothetical farms are created using a microsimulation approach to compare incomes across farm systems and land use options. Tax and subsidy policies are applied to derive returns for the hypothetical farms under a variety of land use scenarios.

The analysis finds that in comparing four hypothetical scenarios, leasing out agricultural land on a long-term basis can prove more profitable for cattle and tillage farmers than farming the land. Only dairy farmers derive consistently higher disposable incomes from farming their land as opposed to leasing it out. Changes in CAP rules can also negatively affect farmers taking advantage of Ireland’s tax-based leasing incentives.

A gap in the literature exists in terms of how institutional factors may act to prevent either land supply or demand channels from functioning properly. This paper addresses that gap, using Ireland as a case study.

This paper aims to investigate shrimp income losses of farmers in the four farming systems in the research areas of Ca Mau, Vietnam, and determine the vulnerability of shrimp farming income to climate change events.

Field research interviews were conducted with 100 randomly selected households across the four farming systems to access shrimp income status and vulnerability levels to climate change events. Four focus groups, each aligned to a particular farming system, were surveyed to categorise likelihood and consequences of climate change effects based on a risk matrix worksheet to derive levels of risk, adaptive capacity and vulnerability levels.

Shrimp farmers in the study areas have been facing shrimp income reduction recently and shrimp farming income is vulnerable to climate change events. There are some differences between farmers’ perspectives on vulnerability levels, but some linkages are evident among shrimp farmer characteristics, ramifications for each farming system, shrimp income losses and shrimp farmers’ perspectives on vulnerability levels of shrimp incomes. From an income perspective, farmers operating in intensive shrimp farming systems appear to be less vulnerable to existing and expected climate change effects relative to those in mixed production or lower density systems.

Having identified the vulnerability level of shrimp farming income to climate change events in different farming systems based on shrimp farmers’ perspectives, the paper adds new knowledge to existing research on vulnerability of the aquaculture sector to climate change. The research findings have implications for policymakers who may choose to encourage intensive shrimp farming to enhance shrimp farmer resilience to the effects of climate change as well as improving cultivation techniques for shrimp farmers. The findings could thus guide local government decision-making on climate change responses and residents of Ca Mau as well as within the wider Mekong Delta in developing suitable practical adaption measures.

The purpose of this paper is to examine the factors affecting the relative variability in farm and off‐farm income for Canadian farm operators.

Variability of farm and off‐farm income is analyzed using a dataset of 17,000 farm operators from 2001 to 2006. Relative ranking of the coefficients of variation (CV) for farm and off‐farm income are compared across farm types and are regressed against factors conditioning the variations.

Greater reliance on farm income results in lower (greater) relative variability in farm (off‐farm) income. Larger commercial operations experience larger farm income volatility because they are less risk averse or they can manage more risk. Diversification and off‐farm employment appear to be risk management strategies for commercial operations.

Government payments have a small, positive effect on farm and off‐farm income variability, indicating this support leads farmers to take on more risky activities and/or reduce the use of self‐insurance activities. Results could also be due to the lag between the time of the income reduction and the time in which the aid is received. Further research is necessary to decipher the effects of government support on farm decisions.

The results on relative variation in the farm and off‐farm income across farm type raises questions about whether government programs should target specific operations.

While income variation remains a focus of public policy, factors affecting its variability are not well‐understood. Studies have examined the level of farm income and the decision to participate in off‐farm employment but none has examined the variance in both income sources.