Farm Households Research Articles

Investigation of the differences in characteristics of Welsh farming households with and without off-farm income

ObjectiveTo examine the characteristics of farming households that have no off-farm income, and how they compare to farming households containing at least one person employed in a non-farming occupation. ApproachThis research uses the AD|ARC dataset for Wales held in the SAIL databank at Swansea University. This Research Ready Dataset, created by the research team, is comprised of population census data and existing agricultural datasets. Individuals were split into two groups, those in households where all individuals were primarily employed in farming or unemployed, and those in households where at least one individual was in a non-farming occupation. ResultsComparing these groups, our findings show: There are 8135 farming households with at least one person in a non-farming occupation, and 5415 households with no off-farm income. Households with off-farm income are on average 65% larger than households with no off-farm income. 33% of farming only households consist of a single person; 73% of these farmers are male, and the average age is 62. The average age for farming-only households is 45, and the average age of households with non-farming occupations is 36. We will present detailed findings on the socio-economic and farm business characteristics of these groups. ConclusionUnderstanding differences between households with and without off-farm income provides insights on farm types and households most impacted by changes in agricultural policies and subsidies. This can inform policy design and help authorities deliver commitments on just transitions in a targeted and efficient manner.

AD|ARC (Administrative Data Agri-Research Collection): Linking Individual, Household and Farm Business Data for Sociodemographic Research on Farming Households

ObjectiveTo understand the socio-demographic characteristics and structure of farming households in Wales who receive subsidies compared to non-farming rural households. Approach We utilised the AD|ARC dataset, a comprehensive resource we created within the SAIL databank by linking de-identified records of farms receiving farming subsidies in Wales with socio-demographic information from the Census 2011. A control group was created by matching each farming household with up to three households in the 2011 Census from a similar geographical area that did not contain a farmer or household member in receipt of farming subsidies. We used Census 2011 variables to compare household size, family structure and age for 18,135 farming households with a control group of 53,365 rural non-farming households. Results We found little difference between the age and gender distribution between cohorts, however we found significant differences in the household structure. Farming households had a larger average size compared to non-farming households. Single-occupancy farming households had a significantly higher proportion of males, farming households were more likely to have couples with children and a higher percentage of these couples were married compared to non-farming households. Implications The agriculture sector in Wales is facing a range of challenges linked to social, economic, and environmental sustainability as it establishes a new direction after UK withdrawal from the EU. Understanding the sociodemographic characteristics of farming communities is essential for developing effective policies and interventions that address the specific needs and challenges faced by these groups.

The Impact of Forestry Technological Innovation on the Welfare of Farm Households Managing Jujube Forests (Ziziphus jujuba Mill.) in the Lüliang Mountains of the Yellow River’s Middle Reaches

Jujube (Ziziphus jujuba Mill.) makes up a traditional characteristic industry with ecological significance in the Lüliang Mountain of middle reaches of the Yellow River (LMMRYR). However, low economic efficiency has reduced local farm households’ willingness to continue jujube cultivation, threatening the sustainable maintenance and development of jujube forests and the ecological environment. In response, Lüliang City implemented a technological innovation program, that is, the Jujube Forest High Grafting and Optimization Program (JFHGOP), in 2018. Based on survey data from 302 local farm households, an empirical analysis using propensity score matching and ordinary least squares methods revealed that the program significantly enhanced the economic, ecological, and social benefits for participating farm households, improving their overall welfare. Robustness tests confirmed these findings, and a heterogeneity analysis showed varied impacts across different dimensions. The program improved welfare through government support and cooperatives’ assistance. To further promote green development and farm households’ welfare, recommendations include advancing forestry innovation technology, supporting small farm households with policy, capital, and technology, optimizing subsidy mechanisms, supporting new business entities, and promoting cooperation and benefit-sharing among stakeholders.

AD|ARC (Administrative Data | Agricultural Research Collection): Characteristics of farm households participating in agri-environment schemes in Wales, UK.

Objectives Describe the sociodemographic and farm business characteristics of farming households in Wales that have agri-environment contracts (voluntarily opting-in) without primary farm subsidies (voluntarily opting-out). Investigate the sociodemographic and farm business characteristics of farming households in Wales by the number and type of farming subsidies received. ApproachWe utilised the AD|ARC dataset comprising of Census 2011 variables, linked to agricultural and business variables for all farming households in Wales. We analysed variables relating to the individual (e.g. age, gender), household (e.g. household size, structure), farm business (e.g. farm type, diversification activities) and rural payments data (subsidy type, amount paid). We conducted comparative analyses between three groups of farming households: households engaged in agri-environmental schemes but not primary subsidies, households engaged with both subsidy types, and households claiming primary subsidies only. ResultsWe will present a detailed description of the sociodemographic and business characteristics of farming households by the number and type of subsidies claimed and engagement with agri-environmental schemes. Using business, personal, and household characteristics, we will discuss different farming household typologies in relation to participation in agri-environmental schemes, and outline the relationship between farming households and positive environmental action. Full findings will be presented at the conference but are currently subject to information governance restrictions. With an increasing interest in climate change and supporting the natural environment, our findings provide insight into the characteristics of farming households and their relationship to engagement with agri-environmental schemes. Our results will inform policy-makers seeking to support farming and the rural environment.

AD|ARC (Administrative Data Agri-Research Collection): Comparative analysis of highest educational qualification in Welsh households receiving a farming subsidy with rurality-matched control households, using 2011 UK Census data.

Objective To investigate highest level of educational attainment in farming households compared to rural non-farming households in Wales. Approach We used the AD|ARC dataset of households in Wales receiving farming subsidies in 2010. We included farming households containing at least one member reporting an Agricultural Main Occupation (AMO) and households where all members reported Non-Agricultural Main Occupations (Non-AMO) but farming occurs. Utilising Census 2011 variables relating to age, gender, education and occupation we conducted comparative analyses of educational attainment within AMO, Non-AMO and non-farming rural households at both an individual and household level. ResultsFindings will be presented comparing the educational attainment for AMO individuals with matched individuals within Non-AMO and non-farming rural households. At the household level, we calculated the highest level of education attained within each household and will present results comparing the highest household level of education between each group. We will present detailed results regarding the structure and educational attainment within these households. Full results and findings will be presented at the conference but are currently subject to information governance restrictions. ConclusionOne indicator of resilience amongst farmers and farm households is ability to respond to economic and non-economic pressures by adopting new practices and techniques or reducing reliance on farming via diversification or off-farm employment. As educational attainment is closely correlated to acquisition of skills and is a gateway to employment, understanding relative and absolute levels of attainment is important in identifying need and devising policies which support agricultural and rural communities.

Determinants of Perception, Adaptation Strategy and Measuring Composite Vulnerability of Agricultural Households to Climate Change in the Desert State Rajasthan, India

The study aims to assess agricultural households’ vulnerability, perception and adaptation to climate change in the desert state of Rajasthan, India. A data set of 600 households was collected using a purposive random sampling technique from six districts: Ajmer, Barmer, Jodhpur, Jaipur, Dausa and Tonk. While using the binary logit model, factors affecting the perception and adaptation of agricultural households were assessed, and a composite livelihood vulnerability index was constructed to determine the households’ susceptibility to climate change. The findings suggest that the majority of the households are moderately vulnerable. Of the total households surveyed, 89 per cent of the agricultural households perceive climate change. 83.3 per cent of perceived households used adaptation strategies such as mixed cropping, crop rotation and farm ponds. Socioeconomic variables like the educational status of the household head, farming experience, type of agriculture financial support, agricultural training, land size and access to agricultural institutions influence farmers’ adaptation decisions. Further, lack of information, extension services, access to credit, improved seeds and water, insufficient capital, infertile soil and a labour shortage are barriers to adaptive strategies. Hence, the study recommends effective government policies to enhance credit availability, financial support and agricultural mechanisation for agricultural households. JEL Codes: Q54, Q12, Q18

Factors Influencing Farmers’ Willingness to Pay for NPK Fertilizer in Specialty Coffee: Insights from Pagar Alam, Indonesia

Robusta coffee cultivation in Pagar Alam City has not been carried out optimally because most farmers do not practice precision farming patterns in the use of production inputs to increase coffee plant productivity, including the use of unsubsidised coffee-specific fertilisers. In order to be accepted by farmers, fertiliser companies must pay attention to the willingness to pay of farmers for these fertilisers. For this reason, the study aims to: (1) determine farmer perceptions of nutrient content, ease of use and quality of fertiliser, (2) determine the value of farmers’ willingness to pay for unsubsidised NPK fertiliser for coffee in Pagar Alam City, and (3) determine the effect of nutrient content, ease of use and quality of fertiliser and other factors on farmers' WTP in Pagar Alam City. The research location was determined purposively with the consideration that Pagar Alam City is one of the highest coffee producing areas in Indonesia. A total of 100 coffee farmers were used as samples and interviewed using a prepared questionnaire. Data on farmers' willingness to pay was obtained by using the contingent valuation method (CVM). Descriptive analysis was used to determine the level of farmers' perception of nutrient content, ease of use and quality of fertiliser. Multiple linear regression methods was used to analyze the effect of nutrient content, ease of use and fertiliser quality and other factors on farmers' WTP. The results showed that the average value of farmers' WTP for unsubsidised NPK fertiliser for coffee was IDR 11,160 per kg which is lower than the price of unsubsidised NPK fertiliser commonly paid by coffee farmers. The study found that fertiliser quality, ease of fertiliser uses and fertiliser nutrient content, farm income level, land size, age, farming experience, household size and farmer membership status in farmer groups significantly affect the farmers’ WTP.

Determinants of multiple maize technology package adoption in Ethiopia: evidence from the Sidama region

The adoption of improved agricultural technology packages is vital in Ethiopia, as the expansion of cultivable land appears nearly exhausted and population size has skyrocketed. However, the country has shown a low adoption rate. Thus, this study aimed to explore the factors that hinder or facilitate the adoption of multiple maize technology packages and intensity of adoption in the northern Sidama zone of Ethiopia. A multistage sampling procedure was applied to gather cross-sectional data from 424 farm households owning 545 maize plots. A multivariate probit and two-limit Tobit models were applied to address the study objectives. The conditional probability results confirmed that maize technology packages are complementary (positive relationship). This infers that agriculture-focused policies that influence the adoption of a single component of technology packages can have a reinforcing advantage over the adoption of other technologies. Furthermore, the results from the models showed that farmers with greater experience, family size, plot size, livestock and oxen ownership, number of maize plots owned, off-farm income, access to credit, extension services and membership in institutions are more likely to adopt at least one of the improved technology packages and achieved a better status of intensity of adoption. Therefore, it is crucial to reinforce and deliver quality extension services, provide credit access, motivate youth to be involved in farming activities, inspire membership and ease the system to access inputs and technologies for broader adoption of technology packages.

The Role of Procedural Fairness: Land Titling Programs and Agricultural Investment in China

This study examines the moderating role of procedural fairness between land titling programs and agricultural investment. We constructed a theoretical model that introduces perceived security of land tenure and procedural fairness into the traditional “property rights-investment incentives” analytical framework. Moreover, we empirically analyze the impact of land titling and its procedural fairness on agricultural investment using data from the “Thousands of People, Hundreds of Villages” survey held in 2018 among 9596 households in China. The empirical analyses were conducted by using the ordinary least squares (OLS), probit, and instrumental variable methods. Our analysis showed that land titling in China significantly promotes agricultural investment by farm households and that procedural fairness has a significant positive moderating role in the investment incentive effect of land titling as well as significantly improving the institutional credibility of land titling and enhancing farmers’ perceived land tenure security.

Effect of Non-Farm Income on Income Inequality in Pakistan

The portion of farm households' income outside the farm, such as non-farm salaries and wages, pensions, and interest income, is referred to as non-farm income. In Pakistan, non-farm profit is an essential source of income. "income inequality" describes the inequitable income distribution between people or families in a community or economy. Income inequality is a significant issue worldwide, including in Pakistan. This research estimated the effect of non-farm income on rural and urban areas and analyzed the relationship between non-farm income and income inequality within households. Secondary data from the Pakistan Social and Living Standard Measurement Survey Household data (2019-20) was used. Multinomial Logistic Regression was applied to the data, and income inequality was calculated using the Gini coefficient. The population was divided into quartiles to measure income distribution. The research found that non-farm income raises income inequality, while farm income is an income equalizer among the population. The reason behind this is that a major portion of income or wealth is accessed by the upper society, with only a minor portion reaching the lower levels of the population. Specifically, the Gini coefficient for non-farm income was 0.65, indicating higher inequality, compared to 0.45 for farm income. In urban areas, non-farm income provides large opportunities, while rural areas predominantly depend on the farm sector. There is a need to raise awareness about the non-farm sector in rural sector. The study's policy implications include the following: In order to improve agricultural households' income and low-income disparity, non-farm income activities have to be promoted. The study also suggests that Inequalities within rural communities should be addressed as part of an equal development strategy, in addition to the rural-urban gap.

Poverty status and income inequality among farming households in Dutse Local Government Area of Jigawa State, Nigeria

Poverty status and income inequality among farming households in Dutse Local Government Area of Jigawa State, Nigeria

A Review on Financial Literacy for Farmer-educate to Empower

The volume of farming community in the world ranges upto 30% [1]. India is a significant global contributor in the agriculture industry. Agriculture and allied sectors are the main source of livelihood to about 55% of India’s population. Of the country's population engaged in agriculture and related sectors, 69% live in rural areas, with the majority of these people being farmers [2]. Although, India is having top standings in terms of highest milk production, livestock population etc., but on the ground reality farmers face many constraints hindering the sustainability in production and productivity of agriculture sector. Among several constraints exist, financial literacy poses to be one of the major constraints hindering farmers growth. Financial inclusion and, eventually, financial stability were seen as much aided by financial literacy in India. Among all farming households, about 27-37% are financially literate and have access to official credit sources [3]. Recognizing the importance of agriculture and allied sector in India’s development, the Government, Reserve Bank of India (RBI) and many financial research institutes in collaboration with farmer-oriented universities like agriculture, veterinary and allied universities have played an important role for catering to the increasing awareness on financial literacy and access to credit services among farmers as there exist visible lacunae in achieving improved farmers economy [4]. The financial literacy was found to have direct impact on socio-economic factors and hence protocols containing suitable delivery services that addressing financial concepts results in producing beneficial effect and long-term sustainability on farming communities [5].

Estimating the impact of wildlife damage compensation policy on farmers’ incomes

At present, the conflict between humans and wildlife in China is becoming more serious, and the wildlife damage compensation policy, as an important part of the ecological compensation system, has become an important means to alleviate the contradiction between wildlife protection and farm household development. This study empirically examines the mechanism of action and analyses the impact of the pilot wildlife damage compensation policy on farmers’ incomes, using Chinese provincial panel data from 2004 to 2021, and employing synthetic control methods, double difference models and moderated effects models. The results show that: (1) At the provincial level, the wildlife damage compensation policy can significantly increase farmers’ income by 16.7 %, which has been validated through a series of robustness tests. (2) Further analysis using micro-survey data shows that the wildlife compensation policy significantly increases farmers’ income by 22.5 %. (3) Mechanism analysis reveals that the wildlife damage compensation policy can increase rural agricultural employment by 4.3 %, thereby increasing farmers’ income. (4) Heterogeneity analyses indicate that wildlife damage compensation policy has different effects on farmers’ income. Among these, the impact on low-income farmers is more pronounced. Understanding the impact and heterogeneity of wildlife damage compensation policy on farmers’ income can facilitate the design of targeted compensation policies to address the mismatch between conservation and development, thereby achieving the policy objective of promoting a symbiotic coexistence between wildlife conservation and farmers’ economic interests.

Livelihood strategies of farm households of lower Brahmaputra valley zone of Assam: An empirical study

Livelihood strategies of farm households of lower Brahmaputra valley zone of Assam: An empirical study

Impact of Institutional and Non-institutional Agricultural Credit on Farmers-income and Constraint Analysis in Telangana, India

The present study is to analyse Impact of institutional and non-institutional Agricultural credit on farmers income and Constraint analysis. The study was conducted in 3 districts, 6 mandals, 12 villages and 120 sample farmers were selected for the study. Cost of cultivation (cost concepts) and income measures were used to analyse impact of agricultural credit on farm household income. Garette’s ranking technique was used to analyse the constraints faced by farmers in availing Agricultural credit. The cost of paddy cultivation was ₹78212.78 for institutional farmers and ₹80,726.78 for non-institutional farmers. ₹62050.96 is the Net Return for institutional farmers, whereas ₹54879.66 is for non-institutional farmers. Institutional farmers had a return on investment per rupee of 1.81, while non-institutional farmers had a return of 1.69. The major constraint faced by farmers is delays in loan disbursement. Agricultural credit has a positive impact on farm income. Therefore, the increase in institutional loans has improved the borrower farmers' economies. Timely disbursement of loans, simplifying the application procedure/documentation procedure and educating farmers about agricultural loans are suggested to improve crop loans availing in institutions.

ICT adoption, commercial orientation and productivity: Understanding the digital divide in Rural China

This study investigates the impact of Chinese smallholders’ adoption of Information and Communication Technologies—the use of smartphones and computers connected to the internet—on their commercial orientation, land, and labor productivity. Commercial orientation is the share of farm output for sales in the market. We used a control function approach and a selectivity-corrected model. The study uses national survey data from rural sample households, the China Household Database, and the China Household Finance Survey and Research Center. Findings reveal that the adoption of information and communication technologies by Chinese farmers increased the commercial orientation of farming. Furthermore, adopting information and communication technologies increases land and labor productivity by about 21.3 % and 28.2 %, respectively. Farm households’ commercial orientation improved labor productivity by about 35.9 %. Heterogeneity indicates that the adoption of information and communication technologies has a more significant effect on improving productivity for young household heads and small farmers. Policymakers should establish information and communication technologies training programs, develop digital infrastructure, and promote smallholder commercial production to increase agricultural productivity.

Gendered distributional impacts of ownership of mobile money account on farm input expenditures: A micro perspective from rural maize farmers in Ghana

AbstractThis article investigates the distributional impact of mobile money account ownership on farm input expenditures among maize farmers in southern Ghana. Using cross‐sectional data from 1044 farm households, we employed an instrumental variable quantile regression to account for endogeneity and selection bias. We find that socioeconomic, spatial location, and institutional factors significantly influence male and female‐headed household decisions to own mobile money accounts. The empirics show that male‐headed households spend more on fertilizer and pesticide relative to female‐headed households. Mobile money account ownership increases fertilizer expenditure in male‐headed households more than female‐headed households. Male‐headed families with mobile money accounts spend 13.9% and 6.5% more on fertilizer at the 40th and 60th quantiles. High 80th quantile female‐headed households spend 4.3% more on fertilizer. For male‐headed households, farming experience and education positively influence mobile money account ownership on fertilizer expenditure, while off‐farm activity at the 40th quantile positively influences female‐headed households. Our results distill useful policy implications that call for concerted efforts targeted at digital financial inclusion with an eye to bridging differential gender gaps.

Analysis of Income Inequality of Farm Households Using the Gini Coefficient

Analysis of Income Inequality of Farm Households Using the Gini Coefficient

Climate-smart agricultural practices and its implication in Ethiopia: a systematic review

Purpose The purpose of this paper is to assess the adoption of climate-smart agriculture (CSA) and its implication on improving the farming household food security status, their resilience and livelihood risk management of farmers. Design/methodology/approach This systematic review has followed procedures to accomplish the review, including literature searches, screening studies, data extraction, synthesis and presentation of the data. Findings Based on the result of the review, the determinants of CSA adoption can be categorized into five categories, including demographic factors (age, sex, family size, dependency ratio, education), economic factors (land size, household income, livestock ownership), institutional factors (extension services, training access, credit services, farm input, market distance), environmental factors (agroecology, change in precipitation, slope of land) and social factors (cooperatives membership, farmers perception). The result also shows that applying CSA practices has an indispensable role on increasing productivity, food security, income, building resilient livelihoods, minimizing production risk and alleviating poverty. This concluded CSA practice has a multidimensional role in the livelihood of agrarian population like Ethiopia, yet its adoption was constrained by several factors. Originality/value This review mainly emphasizes on the most commonly practiced CSA strategies that are examined by different scholars.

The Dynamics of Urbanization and Its Effects on Rural Livelihoods: Evidence from Adama City and Adama Rural District, Ethiopia

Rapid urbanization in cities brings about both opportunities and challenges for national development. This study focuses on investigating the dynamics of urbanization and its impact on livelihoods using land use/land cover change in Adama City, located in the Oromia Regional State. Various datasets from the U.S. Geological Survey (USGS), including Landsat Thematic Mapper (LTM), Landsat Enhanced Thematic Mapper Plus (ETH[Formula: see text]), and Operational Land Imager (OLI), were utilized. Geographic Information System (GIS) and remote sensing tools were employed to extract relevant information from these datasets for years from 1986 to 2022. In addition, findings pertain to the impact of urbanization on income diversification and the overall welfare of farm households near Adama City was aligned with the study. The findings reveal that built-up areas and agricultural land exhibit the most significant changes over the examined period. However, these changes occur in opposite directions. The built-up area has experienced a substantial increase of 60% from 1986 to 2022, while agricultural land has decreased by 40% during the same timeframe. The conversion of farmland to built-up areas negatively impacts farm households by reducing farming activities and increasing reliance on non-farm activities, resulting in lower consumption expenditures for rural households near urban areas compared to those farther from the urban center. The result underscores the need for policies that promote appropriate land management and a well-planned urbanization process to ensure sustainable urban development. Furthermore, it is vital to enhance understanding of the spatial and temporal dynamics of urban growth and its impacts on farm households residing near urban areas. By addressing these issues, sustainable food security and poverty reduction objectives can be better achieved in peri-urban areas of the region.