Farm Heterogeneity Research Articles

How does digital technology application empower specialty agricultural farmers? Evidence from Chinese litchi farmers

IntroductionSpecialty agricultural farmers have widely adopted digital technologies in all aspects of their specialty agricultural operations. However, the effect of digital technology application (DTA) on specialty agricultural farmers’ income has not been fully evaluated. Moreover, it remains unclear whether DTA enhances farmers’ income (FI) by improving their varied capabilities. To fill this gap, we analyzed the intrinsic relationship and mechanism between DTA, farmers’ capability (FC), and specialty agricultural farmers’ income.MethodsUsing field survey data from 635 litchi farmers in China, we employed OLS regression models and mediation effect models to empirically investigate DTA’s direct and indirect impacts on litchi farmers’ income. Additionally, we examined the group heterogeneity and regional heterogeneity.Results and discussionThe empirical results show that DTA can promote specialty agricultural farmers’ income by enhancing their production capacity (PC) and transaction capacity (TC). Heterogeneity analysis showed that the empowering effect of DTA is especially significant for disadvantaged farmers, and farmers who are in specialty agricultural resources-rich regions.ConclusionThis study contributes to the body of research on the empowerment of specialty agricultural farmers by examining the effect of DTA from a capability perspective. Consequently, to better play the empowering effect of DTA for specialty agricultural farmers, policymakers should account for regional disparities in promoting digital agriculture, and enhance heterogeneous farmers’ DTA capability.

Understanding the Dairy Sector in Slovenia: A Modeling Approach for Policy Evaluation and Decision Support

This study investigates the dairy sector in Slovenia, focusing on farm heterogeneity, efficiency in resource utilization, and policy implementations. Through a modeling approach, we explore the differences among dairy farms, considering factors such as herd size, farm management, natural conditions, and production potential. Based on 32 typical dairy farms, representing the entire dairy sector, composed of 6400 dairy farms, the analysis was performed using the farm model (SiTFarm). We emphasize the importance of accurate assessments, given the variability of policy impacts across farm types. While medium-to-large, specialized farms dominate milk production, smaller farms, particularly in less favored areas, hold social and environmental importance despite facing competitive challenges. Addressing environmental sustainability could involve promoting practices that improve milk yield and include grazing, as this tends to lower greenhouse gas emissions per kilogram of milk (−5%). Dairy farms contribute about one-third of the generated revenue in Slovene agriculture, of which a good half goes to farms located in less favored areas. They manage a good quarter of permanent grassland in Slovenia, and it is certainly the sector that can achieve the highest return on these areas. In 75% of the farms, the gross margin is higher than 1756 EUR/ha and using best practices they exceed 3400 EUR/ha. The model results indicate that the average hourly rate on dairy farms during the observed period falls within the range of EUR 7.3 to 17.4 of gross margin for most farms, with the top-performing ones exceeding 24 EUR/h. However, due to the significant reliance on budgetary payments (on average, 58% of the gross margin), the implementation of the common agricultural policy strategic plan generally leads to a deterioration in the economic indicators of dairy farms. This impact is particularly pronounced on medium-sized and larger farms, increasing the effect on income due to the substantial fixed costs involved. Our findings underscore the interplay between policy interventions, farm characteristics, and sectoral outcomes, offering valuable insights for policy-makers and stakeholders.

Analysis of the impact of farm machinery outsourcing services on farmers’ cropping structure and grain production: an empirical evidence from Henan Province, China

IntroductionThe impact of farm machinery outsourcing services (FMOS) on grain production has been extensively studied in the literature. However, whether FMOS would adjust cropping structures and ensure food security is little known. To address this research gap, we analyzed the impact of FMOS on the cropping structure adjustment and grain production of farmers.MethodsWe estimate the impact of FMOS on grain production by using data collected from a survey of 3,863 wheat farmers in China. We employ the Extended Regression Model (ERM) to address selectivity bias.Results and discussionThe empirical results show that FMOS can adjust the cropping structure and increase the proportion of household food cultivation. Among all segments of FMOS, fertilizer application services drive the increase in grain acreage more significantly. We also find that large-scale households prefer to self-purchase farm machinery rather than FMOS, leading to an insignificant impact of FMOS on cropping structure. Therefore, in the process of promoting the development of FMOS, we should focus on the coordinated development of various types of FMOS, strengthen the popularization and application of fertilization services, and formulate a differentiated subsidy policy for heterogeneous farmers in terms of scale to better play the role of agricultural machinery in promoting grain production.

What Affects the Willingness of Farmers to Participate in Forest Ticket Trading? Empirical Analysis Based on Incomplete Information Theory

Forest tickets refer to a type of forest resource usufruct certificate characterized by “cooperative operation, quantification of rights and interests, free circulation, and guaranteed dividends”. It is an important means to build a market-oriented mechanism for realizing the value of ecological resources. Incomplete information, based on field survey data from thirteen villages in eight townships (towns) in Sanming City, Fujian Province, China, and a binary logit model were used to explore the moderating effects of factors affecting farmers’ willingness to participate in forest ticket trading, the heterogeneity of farmers, and social capital. We found the following: In an environment with incomplete information, farmers’ willingness to participate in forest ticket trading is influenced by heterogeneity expectations, social capital, government propaganda, and individual family characteristics. There are certain differences in the influencing factors and degree of farmers’ willingness to participate in forest ticket trading among different groups of farmers with different levels of education and part-time employment. Social capital can strengthen the positive impact of income expectations and policy sustainability expectations, and alleviate the negative impact of risk expectations.

Groundwater Extraction for Irrigation Purposes: The Case of Asymmetric Players

In this paper, we address the problem of groundwater exploitation by heterogeneous farmers for irrigation purposes. In particular, we study the possible inefficiencies that can arise in this type of common resource problem by considering the dynamic and strategic interactions between groundwater users. To this end, we build a two-player differential game in which two types of farmers (or many farmers grouped into two types, with a representative farmer for each group) display different characteristics related to their agricultural activity. More precisely, they can have different water demand functions, extraction costs, crop productivity, land types and time-preferences. Conditions are studied for the existence and uniqueness of the cooperative and non-cooperative solutions asymptotically converging to a steady state. The model is then applied to the case study of the Western La Mancha aquifer. Effects of the different heterogeneities on the degree of inefficiency of non-cooperative solutions with respect to cooperative solutions are analyzed. Numerical results show that cooperation is always beneficial for the environment and for the agents: It results in higher levels of groundwater stock and total welfare. Moreover, considering heterogeneous time preferences is crucial for reducing the inefficiency of non-cooperation with respect to cooperation, regardless of the other asymmetries between farmers.

A study on heterogeneous farmers’ forestry production behavior, performance and differences in southern collective forest areas: based on 7 years of continuous observation data in Hunan Province

A study on heterogeneous farmers’ forestry production behavior, performance and differences in southern collective forest areas: based on 7 years of continuous observation data in Hunan Province

Heterogeneity in farmers’ stage of behavioural change in intercropping adoption: an application of the Transtheoretical Model

Despite its potential economic and environmental benefits, intercropping adoption remains limited in Europe. Drawing upon the Transtheoretical Model, this paper views adoption decision as stages of behavioural change. The paper aims to investigate socioeconomic, behavioural, and policy factors associated with stage of change in intercropping adoption in Sweden. Exploratory factor analysis and generalized ordered logit regression were performed on data from a nation-wide farmer survey conducted in 2021 with 388 usable replies. Results show that farmers with better knowledge of intercropping, a higher evaluation of financial benefits and ease of intercropping, and ley growers were more likely to progress to higher stages of the adoption process. Farmers who have higher perceived seed separation costs, a lower education level, and are older tend to remain at lower stages. Perceived environmental benefits of intercropping, household income, and instrumental values of farming could turn non-adopters into either potential adopters or actual adopters. We found no significant association between policy support and stage of change in intercropping adoption. Policy implications aimed at fostering intercropping adoption were discussed.

The business case for carbon farming in the USA

U.S. agricultural producers are increasingly able to participate in private voluntary carbon initiatives that compensate their efforts to sequester CO2, reduce GHG emissions, and provide ecosystem services through eligible conservation practices. This study examines the potential effects of alternative private payment regimes (per practice vs. per output), prices paid to farmers relative to out-of-pocket costs (low vs. high), and the availability of information on CO2 sequestration (limited vs. full), on the adoption of cover crops and no-till in the United States, the resulting CO2 sequestration, and changes in farmers’ net returns. The analysis relies on a highly stylized model of heterogeneous farms calibrated with county-level agronomic data, and simulated for current estimates of GHG impacts of cover crop planting and no-till under different scenarios. Our results indicate that agricultural carbon markets can be profitable for U.S. farmers, although with substantial geographic variability, and that annual carbon sequestration could range between 17 and 75 million mtCO2e. Payments per output would incentivize higher carbon sequestration than payments per practice, but the former regime would be less favored by farmers as a unified group than the latter (due to lower aggregate net returns). However, if operators of farms with high carbon sequestration potential could decide the payment regime to be implemented, they would choose the payment per output regime (due to higher net returns per enrolled hectare). Total projected net changes in GHGs under payments per practice, based solely on county-average net GHG effects of cover crops and no-till, over-estimate actual total GHG sequestration (based on the entire distribution of net effects by county) by 2.1 and 14.2 million mtCO2e, or 18% and 21%, respectively.

Farm heterogeneity and leveraging federal crop insurance for conservation practice adoption

AbstractCurrent and proposed policies aim to leverage the Federal Crop Insurance Program (FCIP) to promote conservation practices. This study uses corn farm‐level survey data to inform the effectiveness of targeting FCIP participants. We implement an unsupervised machine learning algorithm to assess what conservation practices are most common among participants. We find that farms that use crop insurance have higher adoption rates for practices that are generally profit‐maximizing. We also calculate nitrogen balance, a yield‐scaled measure of nitrogen fertilizer's potential environmental impact. We find a positive relationship between crop insurance and nitrogen balance closer to the optimum.

Tailoring interventions through a combination of statistical typology and frontier analysis: a study of mixed crop-livestock farms in semi-arid Zimbabwe

Summary An innovative methodological approach combining statistical typologies and stochastic frontier analysis was applied to data collected from 1840 mixed crop-livestock farms in six districts of Zimbabwe, representative of semi-arid areas of the country. The average annual cereal production was 362 kg farm–1, and the average annual livestock offtake was 0.64 ± 1.32 Tropical Livestock Units (TLU) farm–1. Our results demonstrate there is scope to increase cereal and livestock production by 90.7% and 111.9% relative to current production levels, respectively, with more efficient use of existing resources and technologies. Rainfall was found to have a strong effect on cereal production, highlighting the need for climate-smart practices. Livestock mortality (0.59 ± 1.62 TLU farm–1) was found to be in the same order of magnitude as livestock offtake (0.64 ± 1.32 TLU farm–1). Cereal production was supported by livestock, demonstrating the importance of crop-livestock interactions in these mixed farming systems. Three farm types were identified in our analysis. Crop-oriented mixed farms (31%) are likely to be the ones most responsive to crop-specific interventions e.g., crop rotation and integrated pest management. Livestock-oriented mixed farms (34%) are likely to benefit the most from livestock-specific interventions, e.g., home feed. Mixed farms dependent on off-farm activities (36% of the sample) may require nutrition-sensitive and labour-saving sustainable intensification technologies to benefit from their limited resources. Reducing cattle mortality is a priority for all three farm types. The method proposed here could be adapted to other contexts characterized by heterogeneous farming populations to target interventions.

Curbing methane emissions from Italian cattle farms. An agroeconomic modelling simulation of alternative policy tools

Methane (CH4) emissions from cattle farms have been prioritised on the EU agenda, as shown by recent legislative initiatives. This study employs a supply-side agroeconomic model that mimics the behaviour of heterogeneous individual farms to simulate the application of alternative economic policy instruments to curb CH4 emissions from Italian cattle farms, as identified by the 2020 Farm Accountancy Data Network survey. Simulations consider increasing levels of a tax on each tonne of CH4 emitted or of a subsidy paid for each tonne of CH4 curbed with respect to the baseline. Individual marginal abatement costs are also derived. Besides, to consider possible technological options to curb emissions, a mitigation strategy is simulated, with different levels of costs and benefits to appraise the potential impacts on the sector.Relevant reductions in operating income are foreseen, the most substantial in farm types and size classes characterised by lower levels of carbon productivity. The introduction of the mitigation strategy shows that the outcome in terms of mitigation potential, without undermining production level, highly depends on the implementation costs, but can also vary widely due to heterogeneous farms’ economic performances. Policy implications are also derived.

Socially responsible retailer’s service provision strategies for poverty and inequity alleviation in the supply chain

This paper examines the effects of a retailer’s value-added services on poverty alleviation where the retailer engages in corporate social responsibility (CSR) by considering consumer surplus. Considering two heterogeneous farmers (i.e., a poor farmer from an impoverished area with cost disadvantages and a normal farmer from a non-poverty area) selling their products through a common retailer, we develop a model to determine when a retailer bestows a uniform or differential service provision to farmers. We also examine the impact of cost disadvantage, per-unit service cost, and consumer surplus concern on supply chain participants’ profits and social welfare distribution under different service provision strategies. We find that supply chain participants’ preferences for service provision strategy are compatible when cost disadvantage and per-unit service cost are below a certain threshold. Retailer-provided services effectively increase each participant’s profit, but this may result in unequal proportions of their profits in social welfare, potentially impacting shared prosperity. The cost disadvantage negatively (positively) impacts the proportion of the poor (normal) farmer’s profit in the overall social welfare equation. However, when the retailer provides services solely to the anti-poverty (normal) product, the per-unit service cost positively affects the proportion of the normal (poor) farmer’s profit in social welfare while consistently improving the retailer’s profit proportion. In addition, higher retailer concern for consumers fosters greater compatibility in service strategy preferences. When the per-unit service cost is low and the ratio of services for the normal product is small, increasing the ratio of services can increase the profits of the poor farmer, the normal farmer, the retailer, and the consumer surplus simultaneously. The research sheds light on the potential tradeoffs and considerations required to create a more equitable and prosperous society.

Revisiting the impact of decoupled subsidies on farm performance: a counterfactual analysis using microdata

ABSTRACT The 2003 reform of the Common Agricultural Policy, which decoupled farm subsidies from production, was expected to increase farmers’ market orientation and to positively impact farm productivity. This theoretical effect of decoupling on farm performance has been verified in a few ex-post analyses. However, these studies lack important aspects of farm-level policy impact evaluations. First, they do not use a well-defined counterfactual scenario, second they do not account for farm heterogeneity when measuring performance and third they do not assess farm performance in a comprehensive manner. We address these shortcomings by combining quasi-experimental empirical methods with a latent-class production function. Using UK and French farm-level data, we show that farms indeed operate with distinct production technologies and that decoupling had positive and significant effects on productivity. Our results further show that under decoupling, farmers tend to diversify their businesses while keeping environmental pressure at a similar level as with coupled support.

Improving agricultural field parcel delineation with a dual branch spatiotemporal fusion network by integrating multimodal satellite data

Accurate spatial information for agricultural field parcels is important for agricultural production management and understanding agro-industrialization and intensification. However, traditional remote sensing methods that rely on single-modal or single-date data struggle to identify heterogeneous field parcels, particularly in regions dominated by smallholder farming systems. To address this challenge, we proposed a Dual branch Spatiotemporal Fusion Network (DSTFNet) that integrated very high-resolution (VHR) images and medium-resolution satellite image time series (MRSITS) to extract agricultural field parcels over various landscapes. The DSTFNet consisted of two branches: a spatial branch that extracted spatial features from VHR images and a temporal branch that explored seasonal spectral dynamics from MRSITS data by using ConvLSTM units and an attention module. We evaluated the DSTFNet in four regions across China by using GF-2 and Sentinel-2 data. The results showed that DSTFNet performed well in delineating agricultural field parcels, achieving the highest Matthew’s correlation coefficient (MCC) = 0.823 for the field extent, the highest F1-score of edge (Fedge) = 0.865 for field boundary, and the lowest errors of segmentation evaluation index (SEI) = 0.191 for the vectorized field parcels in Hubei province. In addition, DSTFNet significantly outperformed two single-branch models that used VHR or MRSITS alone, as well as existing BsiNet, ResUNet_a, UNet and RAUNet models. DSTFNet also showed good spatial transferability in distinct regions without training data (on average, MCC = 0.728, Fedge = 0.729, and SEI = 0.281 for three target regions). Using limited training data to fine-tune the DSTFNet can further improve its ability to delineate field parcels over complex regions. The visualization analysis of temporal attention weights demonstrated that DSTFNet can well capture cropland spectral dynamics, making it advantageous in extracting diverse cropland parcels. By exploiting important spectral, spatial and temporal information from multimodal satellite data, DSTFNet provided an effective, robust, and transferable solution for accurately delineating agricultural field parcels across heterogeneous farming systems.

Eliciting farmers’ preferences towards agriculture education in Northern Ireland

ABSTRACT Purpose Agriculture education promotes farmers and societal welfare through a more efficient use of resources and the adoption of sustainable farming practices. Nevertheless, farmers’ educational attainment is limited across countries. This study explores farmers’ intention to participate in formal agriculture education. Design/methodology/approach A discrete choice experiment survey with 363 farmers exploring their preferences for different specifications within a diploma in agriculture was employed. We focus on Northern Ireland, a country with a relatively large agricultural sector and were farmers have a low level of formal education. Findings Our results show that farmers exhibited a preference for short courses delivered in the evenings, with 75% of the teaching time delivered online, favouring educational topics linked to farm performance against those related to socio-environmental outcomes. Part-time younger farmers, operating in small farms located in less favoured areas, reported the highest willingness to pay for the course. However, farmers that had previously participated in a Young Farmer Payment Scheme reported the lowest interest in undertaking the course. Practical implications The study provides useful information to education providers in relation to the design of agriculture courses to better meet farmers’ preferences and therefore encourage levels of uptake. Secondly, the study can help policymakers to make informed decisions about policy instruments incentivising farmers to engage in education. Theoretical implications By drawing from random parameter choice models, the study highlights that the heterogeneity of farmers’ preferences should be considered when modelling their decision to engage in agriculture education. Originality/value This is the first study that elicits farmers’ preferences towards agriculture education via a discrete choice experiment. Secondly, the study explores preference heterogeneity based on farmers and farms’ characteristics.

State‐contingent production technology formulation: Identifying states of nature using reduced‐form econometric models of crop yield

AbstractConducting experiments can be time consuming and expensive, and may not always be reasonable. Therefore, empirical research often derives structural parameters based on observational data and reduced‐form econometric models. The state‐contingent approach presents a consistent conceptual framework for analyzing producer decisions under uncertainty. However, application of this structural modeling approach has been hampered by data constraints, particularly the lack of information for mapping producers' stochastic outputs onto a set of the states of nature representing different uncertain events. Consistent mapping of uncertainty is particularly critical in the context of multiple output production where weather shocks often have different effects across crops and in microeconometric analyses when unobserved farm heterogeneity may confound the effect of uncertainty. Our study demonstrates how the application of reduced‐form approaches can overcome constraints of structural econometric modeling associated with the lack of relevant data and presents an approach for identifying states of nature in the context of multiple output production using reduced‐form econometric models of crop yield. In an empirical application based on Hungarian farm accountancy data, we demonstrate that the proposed approach allows a consistent mapping of production uncertainty in crop farming, utilizes panel data structure, and controls for potential endogeneity due to unobserved farm heterogeneity. We anticipate the presented approach to be useful for developing further the state‐contingent approach and to stimulate further studies combining the strengths of structural approaches and reduced‐form models.

An Impact Assessment of GHG Taxation on Emilia-Romagna Dairy Farms through an Agent-Based Model Based on PMP

The aim of this work is to assess the structural, production, environmental, and economic impact of an increasing tax on climate change gas emissions related to milk production under the current CAP payment system. The analysis is performed using an Agent-Based Model (ABM) based on Positive Mathematical Programming (PMP). The integration between ABM and PMP makes it possible to simulate farmers’ strategies considering the interaction between them, the territorial specificity, and the heterogeneity of farms in the presence of little information on production costs. It also makes it possible to add a social and cultural perspective to the economic factors. The model is calibrated using FADN data for the Emilia-Romagna region (Italy) from the year 2020. The results show that farmers belonging to different age groups make decisions based on economic profitability, but also on their social and cultural background. To maximise their utility functions, farmers can opt for more efficient agricultural management practices that may result in the exchange of production factors, especially land. The overall impact penalises less efficient farms and agricultural production with higher negative externalities.

Farmland Transfer and Income Distribution Effect of Heterogeneous Farmers with Livelihood Capital: Evidence from CFPS

Farmland transfer is a crucial aspect of modernizing China’s agriculture, which has a significant impact on farmers’ income distribution. With rapid urbanization, farmers’ livelihood has changed significantly, and the income effects of farmland transfer will differ among heterogeneous farmers. Based on the China Family Panel Studies (CFPS) data, this study examines the impact of farmland transfer on farmers’ income growth and income disparity, as well as the income differences in farmland transfer among farmers with different types of livelihood capital. An endogenous switching regression model and unconditional quantile treatment effects are used to correct the selection bias and farmer heterogeneity. The results show that (1) farmland transfer increases farmers’ income. Specifically, farmland transfer-in increases farmers’ income by 21.15%, while transfer-out increases it by 43.33%. (2) The impact of farmland transfer on farmers’ income has a “Matthew effect” and will widen the income gap between farmers. (3) Moreover, farmland transfer exhibits diverse income effects on heterogeneous farmers with livelihood capital. Capital-rich farmers experience the largest income-growth effect from farmland transfer-in, while capital-balanced farmers experience the largest income-growth effect from farmland transfer-out. The policy implications are to further improve the farmland transfer market and enable low-income farmers to participate by improving their qualifications and abilities.

Can Joining the Agricultural Industry Chain Alleviate the Problem of Credit Rationing for Farmers?

To solve the problem of credit rationing for farmers that shackles the transformation and upgrading of the agricultural industry and to improve the effect of agricultural industry chains in helping farmers, this paper empirically examines the effect of joining agricultural industry chains on supply-based and demand-based credit rationing for farmers, using the probit and tobit models based on the microscopic research data of 991 farmers in Shaanxi Province, China, and explores its specific effect mechanism. The study finds that agricultural industry chains can increase farmers’ credit by reducing the information asymmetry between farmers and banks, reducing agricultural business risks, and forming effective collateral and guarantee mechanisms, and that they have significant mitigating effects on farmers’ supply-based quantity rationing and demand-based price rationing, risk rationing and transaction cost rationing. Credit enhancement mechanisms such as information systems, commercial credit, orders, guarantees, production services and government support in agricultural industry chains have significant effects on alleviating farmers’ credit rationing, while insurance has limited effects on alleviating farmers’ credit rationing. There are differences in the effects of agricultural industry chains on alleviating various types of credit rationing of heterogeneous farmers in terms of scale, and the effects of joining agricultural industry chains on alleviating supply-based and demand-based credit rationing of large-scale farmers are more obvious than those of small farmers. Compared with the existing studies, this paper incorporates the credit rationing produced from farmers’ own reasons into the analysis framework, and introduces credit-enhancing variables of agricultural industry chains to test their specific mechanisms on farmers’ credit rationing. The findings of this study can provide a theoretical basis for promoting the innovation of financial models of agricultural industry chains, which is of great practical significance for improving the system of agricultural industry chains in developing countries, strengthening the role of industrial chains in absorbing small farmers, and promoting the modernization of agricultural industries with financial assistance.

Farmland Transfer Participation and Rural Well-Being Inequality: Evidence from Rural China with the Capability Approach

Participation in the farmland transfer market provides farmers with diversified opportunities which would generate a range of social and economic benefits to improve their overall well-being. However, the previous studies have disproportionately focused on the income increase of farmers’ participation while ignoring its impact on other dimensions significantly contributing to the well-being gap of farmers with different social and economic characteristics. Based on the capability approach, this study constructed a multi-dimensional well-being assessment framework and employed the fuzzy method to evaluate the well-being level of rural households. By employing the national rural survey data from China, this paper examined the impact of farmland transfer participation on the well-being of farmers with different livelihoods. Further, it explored the influence of farmland transfer participation on the well-being gap of farmers by using the quantile regression method. Our results suggest that farmland transfer participation played a positive role in increasing farmers’ well-being, while farmland transfer participation has no significant impact on farmers’ well-being inequality. Meanwhile, the heterogeneity of farmers’ livelihoods is the key factor for the differentiation of farmers’ well-being.