Search Results (461)

Agricultural production trusteeship (APT) represents an innovative model of agricultural socialized services in which specialized service organizations provide scaled operations to smallholders without transferring land rights. It can effectively address rural labor shortages while contributing to China’s grain security and agricultural modernization by improving production efficiency and promoting more sustainable resource allocation. Using survey data from farm households across six Shandong cities, this study applies an Ordered Probit model to examine how off-farm employment influences farmers’ adoption of APT, together with its underlying mechanisms and heterogeneous patterns. The results show that a higher share of household labor engaged in off-farm employment is associated with a higher probability of APT adoption. This association is stronger under flexible employment model and weaker as the location of off-farm work becomes more distant. Mechanism analysis shows capital endowment mediates the association between off-farm employment and APT adoption, with physical capital having the strongest effect and economic capital the weakest. Higher risk perception weakens the positive effect of off-farm employment on farmers’ adoption of APT. These findings provide novel evidence and practical policy implications for improving APT implementation and facilitating smallholders’ effective participation in modern agricultural production systems. Full article

Whether and how Agricultural Socialized Services (ASS) are associated with grain-growing smallholders’ general self-reported subjective well-being (SWB) remains unclear. Using 894 survey records collected in Sichuan Province, China, from 21 to 31 August 2023, we estimate ordered probit and instrumental variable two-stage least-squares models. ASS Adoption Breadth is positively associated with SWB. One additional service category is associated with a 2.73-percentage point increase in the probability of reporting the highest SWB category. The IV estimate remains positive and statistically significant (0.077, p < 0.05), conditional on the maintained instrument assumptions. ASS Adoption Breadth is also positively associated with Relative Income Comparison, Log Hired Labor Expenditure, and Plant-protection Drone Adoption. These measured behavioral indicators provide evidence consistent with the hypothesized channels of Perceived Relative Economic Gain, Market-based Labor Substitution, and technology-enabled reduction in direct occupational exposure. They do not directly measure physical workload, pesticide exposure, or health outcomes. The findings inform policies that expand smallholders’ access to agricultural services, but the mechanism interpretations remain provisional. Full article

Agricultural socialized service has expanded rapidly within China’s smallholder farming system, yet evidence on their relationship with land productivity remains limited. Using three waves of data from the China Land Economic Survey between 2020 and 2022, this study examines the association between agricultural production outsourcing and land productivity. The results show that outsourcing participation is associated with 4% to 5% higher land productivity, with the positive association remaining broadly robust across alternative estimators and identification strategies. Heterogeneity analysis indicates that this association is stronger on land with better soil fertility, among older-generation farmers, and among small-scale farmers. Further analysis suggests that outsourcing participation is associated with higher fertilizer application intensity, greater mechanization, and lower household agricultural labor input, providing evidence on potential channels linking outsourcing to land productivity. These findings highlight the potential role of specialized agricultural services in relaxing production constraints faced by smallholders, while also suggesting that their productivity implications depend on household and land characteristics. Full article

This study protocol presents a pre-specified framework for assessing whether Farm Machinery-as-a-Service (FMaaS) can support inclusive access to Agriculture 4.0 in fragmented agrarian systems. The aim of the protocol is to define the conceptual framework, research questions, hypotheses, target population, sampling strategy, data collection procedures and planned analytical approach for a future empirical study using Poland as a case of a fragmented, family-farm-based agricultural system in which many small and medium-sized farms face difficulties in keeping pace with Agriculture 4.0. The manuscript does not report empirical results or human-subject data. The planned study will use a sequential mixed-methods design in which each component has a specific role. Secondary data analysis will diagnose farm structural conditions, machinery-use patterns, productivity gaps and innovation gaps. Expert and stakeholder interviews will refine the typology of FMaaS arrangements and identify relevant service attributes. A farm survey and a discrete choice experiment will assess farmers’ willingness to use FMaaS, perceived transaction costs, trust, digital readiness and preferences for specific service characteristics. Farm-level economic modeling and Monte Carlo simulation will evaluate the viability of ownership-based and service-based modernization scenarios under uncertainty. Structural equation modeling will examine social-sustainability mechanisms, including adaptive capacity, perceived resilience, perceived inclusion in digital agriculture and the perceived ability to continue farming under technological change. The expected output of the protocol is an integrated analytical framework for evaluating the economic, institutional, resource-use and social conditions under which FMaaS may become a viable and socially acceptable pathway to Agriculture 4.0. By combining transaction-cost theory, market-design reasoning, stated-preference methods, farm-level modeling and social-sustainability assessment, the protocol provides a transparent basis for future empirical research on access-based digital mechanization. Full article

Common prosperity constitutes a fundamental goal of Chinese modernization, and digital rural development has emerged as an important pathway for advancing sustainable rural transformation. Based on balanced panel data covering 61 counties in Hubei Province from 2014 to 2023, this study empirically investigates the impact of digital rural development on county-level common prosperity and explores its underlying mechanisms. The entropy weight method is employed to construct the Digital Rural Development Index and the County-Level Common Prosperity Index. A fixed-effects model is adopted for baseline estimation, supplemented by mediation analysis, moderation analysis, and a series of robustness tests. The results reveal that digital rural development significantly promotes county-level common prosperity. Significant heterogeneity exists across county types, with stronger effects observed in Type II counties due to their stronger compatibility between agricultural foundations and digital transformation. Furthermore, financing constraint alleviation serves as an important mediating mechanism, while industrial agglomeration positively moderates this relationship by enhancing counties’ capacity to transform digital resources into inclusive development outcomes. These findings underscore the importance of strengthening rural digital infrastructure, expanding inclusive digital financial services, and implementing differentiated regional strategies to improve the effectiveness of digital rural development and promote county-level common prosperity. Full article

Adjusting cropping structure is a key way smallholder agriculture adapts to changing labor availability, production costs, mechanization, and agricultural services. Most studies explain such change through macro-level factors—prices, policies, and resource endowments—and pay less attention to how farmers perceive these changes and adjust their crop choices over time. Taking Xiajin County, Shandong Province, China, as a case, this study combines sown-area data for major crops from 1980 to 2023, provincial cost–benefit records for wheat, maize, and cotton, and retrospective semi-structured interviews with 57 respondents to examine how changing crop choice criteria shaped county-level cropping structure transformation. The county shifted from cotton dominance to wheat–maize dominance, with rising structural concentration. Farmers’ criteria also changed: early decisions reflected drought tolerance, subsistence grain, and cash income, whereas later ones emphasized labor saving, income stability, mechanization compatibility, and outsourcing feasibility. Under labor out-migration, rural aging, rising labor opportunity costs, and service expansion, wheat and maize became more attractive than cotton because their key stages were easier to mechanize and outsource. County-level transformation thus emerged from the accumulation of farmers’ repeated crop choice adjustments under changing constraints, rather than from external change alone. Grain-oriented concentration should be balanced with crop diversity and sustainable farming systems. Full article

Farmland abandonment has become an increasingly important challenge for the sustainable use of cultivated land worldwide. Although agricultural cooperatives can help mitigate farmland abandonment, their effectiveness has historically been limited in China due to their relatively weak organizational capacity and irregular development. This study argues that the County-wide Cooperative Quality Improvement Policy (CCQIP) improves the operational performance of agricultural cooperatives, thereby strengthening their capacity to mitigate farmland abandonment. Using geospatial data, we identify farmland abandonment at the county level in China and develop and test an analytical framework based on two mechanisms: increasing agricultural returns and improving service provision. The results show that the CCQIP significantly reduces the area of farmland abandonment. This effect is primarily achieved through three channels: promoting agricultural product branding, increasing the use of agricultural socialized services, and enhancing the vitality of the farmland transfer market. Heterogeneity analysis further indicates that the policy has stronger effects in major grain-producing regions and in rice-dominant areas. Furthermore, the inhibitory effect of the CCQIP on farmland abandonment becomes stronger as the region’s comparative advantage in agriculture increases. Full article

Underutilised indigenous crops (UICs) are increasingly recognised for their potential to contribute to climate-resilient agriculture, dietary diversification, and household food security among smallholder farmers in South Africa. However, evidence regarding farmers’ knowledge, attitudes, and practices (KAP) toward these crops remains limited. This study examined the KAP of smallholder farmers toward UICs and assessed factors associated with their adoption in selected municipalities of South Africa. A quantitative cross-sectional survey was conducted among 205 smallholder farming households using a structured questionnaire. Respondents were selected through a multi-stage sampling procedure involving purposive, snowball, and stratified sampling. Data were analysed using descriptive statistics, Ordinary Least Squares (OLS) regression, binary logistic regression, and bootstrap mediation analysis. Results showed that smallholder farmers generally possessed positive knowledge and attitudes toward UICs, particularly regarding their nutritional value, adaptability to local climatic conditions, and contribution to household food security. However, utilisation practices remained uneven across households. Age, farming experience, water availability, extension support, and market access were significantly associated with adoption. Formal bootstrap mediation analysis (1000 simulations) did not support attitudinal mediation of the knowledge-adoption relationship (ACME = −0.007, 95% CI: −0.079 to 0.059, p = 0.802), suggesting that knowledge operates through mechanisms other than attitudinal change. The study concludes that UICs possess significant potential to contribute to food security and climate adaptation in the study areas, but stronger institutional support, extension services, and market development are required for wider adoption. Full article

Digital infrastructure (DI) and agricultural socialized services (ASSs) are key drivers of agricultural value chain efficiency (AVCE), yet their causal effects remain confounded by endogeneity bias. This study constructs an integrated SFA-Tobit-DML framework using 31-province Chinese panel data (2011–2023). SFA estimates provincial technical efficiency; Tobit provides associational benchmarks; and Double Machine Learning (DML) with K = 5 cross-fitting identifies causal average treatment effects (ATEs). The DML results validate the Tobit benchmark for DI (LassoCV ATE: 0.810, 95% CI [0.656, 0.963]; Tobit: 0.826), while revealing Tobit overestimates ASSs by approximately 37% (LassoCV: 0.850 [0.599, 1.101]; Tobit: 1.347). Bootstrap mediation tests confirm that agricultural R&D investment mediates 61.58% and 47.45% of DI and ASS effects. Heterogeneity analysis shows that urbanization amplifies DI marginal returns (ATE: 1.401 in low-urban vs. 0.711 in high-urban regions), while government intervention exhibits conditional crowding-out effects. These findings imply that policies should shift from uniform investment toward regionally differentiated strategies that account for local absorptive capacity. Full article

Based on microscopic survey data of 10,057 kiwifruit growers from the Shaanxi, Guizhou, and Sichuan provinces in China, this study takes the initial and sustained adoption of low-carbon agricultural technology by farmers as dependent variables. A comprehensive evaluation system is constructed based on the digital capability of farmers. The system covers four dimensions, namely, digital access, digital acquisition, digital processing, and digital sharing. Multiple empirical models, including Lasso regression, extreme gradient boosting, random forest, and back propagation neural network are adopted to compare the fitting and predictive performance of different approaches. The model comparison results demonstrate that the random forest model presents the optimal fitting performance among all alternative specifications. To identify the core influencing factors and explore the underlying mechanisms, this study employs the SHAP method to quantitatively evaluate the marginal contribution of each variable. The empirical findings reveal that the digital capability of farmers significantly and positively promote both initial and sustained adoption of low-carbon agricultural technology with distinct dimensional heterogeneity. Specifically, digital access and digital acquisition capability act as core driving factors for the initial adoption of technologies by farmers, while digital processing and digital sharing capability play a decisive role in facilitating sustained low-carbon technology application. In addition, farmers’ education level, health status, household income, cooperative participation, and household labour scale are crucial characteristic variables affecting the behaviours of farmers in the adoption of low-carbon technology across different stages. Accordingly, this study proposes targeted policy implications for low-carbon agricultural development. On the basis of promoting digital rural construction and improving the systems of digital skill training and digital agricultural technical services, it is essential to strengthen dynamic tracking surveys of farmers, accurately identify their technology adoption stages, and implement differentiated supporting policies. These targeted measures can effectively consolidate and assess the low-carbon production behaviours of farmers and promote the long-term and sustainable development of low-carbon agriculture. Full article

The accelerating aging of China’s rural labor force—characterized by an ‘earlier and more severe’ trajectory in rural areas compared to urban ones—poses a fundamental challenge to agricultural modernization, yet its implications for the development of agricultural productive services remain poorly understood. This study addresses a central question for agricultural sustainability: does rural labor aging act as a ‘catalyst’ or a ‘stumbling block’ for the development of agricultural productive services? To address this question, we employ fixed-effects, panel threshold regression, and mediation models using provincial panel data from 2011 to 2022, systematically investigating the nonlinear effects and transmission mechanisms. A significant single threshold is identified at an aging rate of 8.17%. Below this threshold, labor aging promotes productive service development via a demand-pull effect; above it, the promoting effect weakens substantially due to an adoption-inhibition effect driven by human capital decline. Mechanism analysis reveals a negative mediating role of cultivated land scale management: land concentration induced by labor aging, without corresponding human capital upgrades, is associated with “inefficient scale management” as reflected in the suppression of service development. While our empirical measure captures operational scale rather than efficiency per se, this negative mediation pattern is consistent with the presence of an inefficiency trap. Heterogeneity analysis shows the strongest inhibitory effect in northeast China, while eastern regions with strong factor substitution capacity are less affected. As most provinces had crossed the threshold by 2022, policies should shift from leveraging the dividend period to addressing deep-aging challenges, including breaking the trap of inefficient scale management and adopting region-specific strategies. These findings highlight that achieving agricultural sustainability in an aging society requires shifting from relying on the demographic dividend to proactively overcoming the structural lock-in of deep aging. Our proposed policies could contribute to fostering a more resilient and sustainable agricultural system in China, and may also offer valuable references for other rapidly aging economies with appropriate contextual adaptation. Full article

Agricultural productive services (APS) play an important role in improving agricultural efficiency and ensuring food security. Existing studies have widely recognized that labor scarcity induced by rural labor transfer stimulates farmers’ demand for APS and promotes the expansion of agricultural service markets. Yet non-farm employment in developing countries is often characterized by instability, temporariness, and informality, which may generate volatile service demand and undermine the sustainable provision of APS. Based on survey data from 684 maize-producing households in China, this study constructs a multidimensional index of non-farm employment quality and investigates how it influences APS adoption intensity and transaction stability. Our results confirm that improvements in non-farm employment quality significantly increase APS adoption intensity, and the findings remain stable under alternative measurements and estimation methods. Mechanism analysis reveals that higher employment quality promotes APS adoption intensity by reducing farmers’ risk perception, while this effect is moderated by social capital. Heterogeneity analysis shows that the positive effect of non-farm employment quality is concentrated among growth-stage households, lower-land-fragmentation households, small-scale households, and single-type employment households. Further analysis indicates that higher employment quality contributes to more stable and long-term contractual relationships between farmers and service providers. By highlighting the importance of non-farm employment quality in stabilizing agricultural service demand and fostering stable transaction relationships, this study contributes to ongoing discussions on rural labor transformation and the sustainable development of APS markets under smallholder farming systems in developing countries. Full article

Digital agricultural service platforms are transforming rural financial systems by converting public data into actionable credit intelligence, yet the mechanisms underlying this value creation process remain under-theorized. This study employs grounded theory to investigate Longjiang Nongtou in China, with comparative insights from CropWizard in the United States. Based on semi-structured interviews with platform operators, technical staff, bank loan officers, smallholder farmers, and government officials, supplemented by secondary data, we develop a three-stage value creation model comprising data aggregation, credit profiling, and value integration. Our findings reveal that reducing transaction costs through sequential stages—information asymmetry, search and evaluation costs, and contracting and enforcement costs—constitutes the core mechanism enabling rural financial inclusion. A cross-national comparison demonstrates that divergent institutional environments generate two distinct value pathways: credit empowerment for smallholders versus knowledge-based agronomic services. This study offers a processual theoretical framework for data-driven agricultural platforms, extends transaction cost theory to digital agriculture by specifying sequential mechanisms of cost reduction, and provides practical implications for platform operators and policymakers seeking to advance sustainable rural development. It should be noted that CropWizard is examined as a contrasting secondary case based on publicly available documentation, rather than as an equally grounded empirical case. Full article

This study examines whether agricultural mechanization promotes rural income growth in China. Using provincial panel data from 2000–2023, a two-way fixed effects model and a Spatial Durbin Model (SDM) are employed to estimate the direct, conditional, and spatial effects of mechanization. The results show that the positive association between mechanization and rural income becomes statistically insignificant after controlling for agricultural production, while agricultural production remains significantly associated with rural income. Mechanization exhibits diminishing returns as production levels increase and appears to be more effective in grain-oriented agricultural systems. Spatial analysis further reveals that agricultural production generates significant spillover effects, whereas mechanization itself does not. This study contributes to the literature by distinguishing the income effect of mechanization from its production-related and spatial effects. The findings suggest that policies should focus on strengthening production capacity, improving agricultural service systems, and better aligning mechanization investment with regional production structures. Full article

Tropical riparian ecosystems are among the most threatened biomes on Earth, yet decade-scale, spatially explicit documentation of vegetation loss and landscape fragmentation in community-based ecotourism corridors remains scarce. This study investigates whether agricultural intensification associated with ecotourism development has driven measurable vegetation decline and landscape fragmentation in the Ledok Amprong riparian corridor (Poncokusumo District, Malang Regency, East Java; 8.21 ha, 800–900 m a.s.l.), a mixed-canopy riparian system dominated by Ficus benjamina, Albizia chinensis, and bamboo (Phyllostachys spp.) interspersed with smallholder coffee (Coffea canephora) agroforestry, over the period 2015–2024. We applied multi-temporal Sentinel-2 (10 m) imagery, NDVI analysis, supervised Maximum-Likelihood classification (Overall Accuracy: 89.75%, Kappa: 0.86), and FRAGSTATS-derived landscape metrics to quantify vegetation dynamics and land cover transition. Results show that agricultural land expanded by 157.51% (1.14 to 2.93 ha), while vegetation cover declined by 28.45% (6.27 to 4.49 ha). A strong negative correlation between agricultural expansion and vegetation loss (r = −0.96, p < 0.001) confirmed agriculture as the primary driver of ecosystem degradation. Landscape fragmentation intensified markedly: patch density increased by 49.6%, the connectivity index declined by 23.5%, and the fragmentation index increased by 54.3%. These findings establish the first quantitative, decade-scale spatiotemporal baseline for this ecotourism corridor and demonstrate that community-based ecotourism development, without adequate spatial governance, accelerates rather than mitigates riparian degradation—a paradox with broad implications for tropical riparian conservation governance worldwide. Evidence-based management recommendations include 10–30 m dynamic buffer zones, a 2.5 ha targeted restoration programme, and a Payment for Ecosystem Services (PES) mechanism, offering a transferable framework for sustainable riparian ecotourism management across rapidly urbanizing tropical watersheds. Full article