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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.

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 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 author argues that we must stop and take a look at what our insistence on human labour as the basis of our society is doing to us, and begin to search for possible alternatives. We need the vision and the courage to aim for the highest level of technology attainable for the widest possible use in both industry and services. We need financial arrangements that will encourage people to invent themselves out of work. Our goal, the article argues, must be the reduction of human labour to the greatest extent possible, to free people for more enjoyable, creative, human activities.

This study aimed to determine the impact of the COVID-19 pandemic on the oil palm smallholders' income, which includes both on-farm and off-farm resources.

This study used a simultaneous equations system for arranging the oil palm household economic model.

The results showed that the negative effect of demand disruption (decreasing of household income) is more than supply disruption (production declining). Declining household income due to COVID-19 caused farmer households to have no access to both basic need and other goods.

The samples for before-pandemic data differed from the situation during COVID-19 in both the location and the person due to technical constraints in research sites.

The main contribution of this study was providing an empirical understanding of how the COVID-19 pandemic influences the economic behavior of the most vulnerable entities in the Indonesian palm oil industry (oil palm smallholder farmers' households). This study would provide baseline information on the impact of the COVID-19 pandemic on the economy of oil palm smallholder's household income.

The purpose of this paper is to evaluate the effects of access to formal credit on rural household technical efficiency in China.

Based on the rural household survey data in Weifang city, Shandong province in northern China, the authors apply recent developed bootstrapped DEA approach to investigate rural technical efficiency at the household level under the consideration of off-farm activities. Rural households are then identified as credit constrained and classified as supply-side and demand-side credit constraints by applying direct elicitation method. Finally, the authors apply a tobit regression to examine the effects of credit constraints on household technical efficiency.

Rural households in China not only suffer supply-side credit constraints, but also demand-side credit constraints resulted from the transaction costs and risk rationing. The tobit regression discloses that demand-side credit constraints impose significant negative impacts on household technical efficiency.

The authors clarify the definition of credit constraints and classify the credit constraints into supply-side and demand-side credit constraints. The results of this paper have significant policy implications for rural finance policies in China.

The purpose of this paper is to examine how households’ engagement in concurrent business (CB), which is measured by the contribution of off-farm income to household income, affects the farm size–technical efficiency (TE) relationship in Northern China.

This paper applies a stochastic frontier analysis method to analyze data on 1,006 rural households collected from four major wheat-producing provinces in Northern China, adopting a translog specification for the underlying production function.

The analysis yields three findings. First, the farm size–TE relationship is inverted U-shaped for all CB engagement levels higher than 5 percent, and the most technically efficient farm size increases with the level of household CB engagement. Second, how TE varies with the level of CB engagement depends on farm size: an inverted-U relationship for relatively small farms (<10μ), a positive relationship for middle-size farms (10–20μ), and a negative relationship for large farms (>20μ). Finally, the overall TE score, 0.88, suggests that wheat output can be increased by 12 percent in Northern China if technical inefficiency were eliminated.

Unlike most previous studies that examine the impacts of farm size and households’ off-farm business involvement separately, this paper examines how these two factors interact with each other.

In every industry there are resources. Some are moving, others more fixed; some are technical, others social. People working with the resources, for example, as buyers or sellers, or users or producers, may not make much notice of them. A product sells. A facility functions. The business relationship in which we make our money has “always” been there. However, some times this picture of order is disturbed. A user having purchased a product for decades may “suddenly” say to the producer that s/he does not appreciate the product. And a producer having received an order of a product that s/he thought was well known, may find it impossible to sell it. Such disturbances may be ignored. Or they can be used as a platform for development. In this study we investigate the latter option, theoretically and through real world data. Concerning theory we draw on the industrial network approach. We see industrial actors as part of (industrial) networks. In their activities actors use and produce resources. Moreover, the actors interact − bilaterally and multilaterally. This leads to development of resources and networks. Through “thick” descriptions of two cases we illustrate and try to understand the interactive character of resource development and how actors do business on features of resources. The cases are about a certain type of resource, a product − goat milk. The main message to industrial actors is that they should pay attention to that products can be co-created. Successful co-creation of products, moreover, may require development also of business relationships and their connections (“networking”).

The Sertão, located in the Northeastern region of Brazil, is the most populous semi-arid region in the world. The region also faces the highest rates of poverty, food insecurity and climate risks in this country. Basic economic activities, such as extensive livestock and dairy farming, tend to be mainly affected by the increasing temperatures and recurrent droughts taking place in the past decades. This paper aims to analyze farmers’ responses to climatic variability in the Sertão.

Analyses are based on farm-level data of the Agricultural Census 2006 and on historical climate data gathered by meteorological stations. The climate impacts and the effectiveness of adaptive strategies are compared between three groups of farms, which discriminate different levels of social and environmental vulnerability. Four production functions are modeled (milk, cattle, goat and sheep) accounting for sample selectivity bias.

In response to increasing temperatures, farmers tend to shift their activities mainly to cattle and dairy farming. But the overall productivity tends to reduce with the recurrence of droughts. Decreasing precipitation affects mainly the production of milk of smallholder family farmers and the cattle herd of non-family farmers.

Analyses do not account for short- and medium-run productive impacts of extreme droughts, which usually have devastating socioeconomic effects in the region.

Smallholder family farmers are the most vulnerable group who deserve more social and technical intervention, as they lack basic social and technological resources that can greatly improve their productivities and overcome the impacts of decreasing precipitation.