[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-54":3,"related-54":79},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":73,"slug":76,"view_count":13,"doi":77,"paper":8,"created_at":78},54,"农业生产驱动力的结构转型：基于农户数字能力异质性的分析（辽宁1208户葡萄种植户证据）","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1867048\u002Ffull","基于辽宁省1208户葡萄种植户微观调查数据，应用PAM聚类无监督机器学习方法将农户按数字能力分为高、中、低三组，考察农业生产驱动力的结构转型。结果显示：农户数字能力存在显著异质性；物质投入仍是基础，但其相对重要性随数字能力提高而下降，劳动投入在各组中对产出均无显著贡献；新要素特别是智能装备仅在数字能力高的农户群体中产生显著的生产率提升。表明农业技术升级的有效性取决于与数字人力资本的匹配，政策应从统一技术供给转向加强农户数字能力建设。",null,"[](https:\u002F\u002Fwww.frontiersin.org\u002F)\n\n[![Image 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Sustain. Food Syst.\n\nSec. Agricultural and Food Economics\n\nVolume 10 - 2026 |  doi: 10.3389\u002Ffsufs.2026.1867048\n\n[Published in ![Image 6: Frontiers in Sustainable Food Systems](https:\u002F\u002Fd2csxpduxe849s.cloudfront.net\u002Fmedia\u002FE32629C6-9347-4F84-81FEAEF7BFA342B3\u002FF3B1CE16-45F3-4D96-BE72BE0491F0DAF2\u002Fwebimage-08DFEB67-EB21-43D0-995D8A1DFF53DB18.png) Frontiers in Sustainable Food Systems * #### Agricultural and Food Economics * **4.1** impact factor * **6** citescore](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems)\n\n### Reviewers\n\nReviewed by\n\n![Image 7: 2 Anonymous reviewers](https:\u002F\u002Fstatic2.frontiersin.org\u002Fstatic-resources\u002Fimages\u002Fdefault_profileimage.webp)\n\n2 Anonymous reviewers\n\nOutline\n\n*    [Abstract](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1867048\u002Ffull#h1)\n\n## ORIGINAL RESEARCH article\n\nFront. Sustain. Food Syst.\n\nSec. Agricultural and Food Economics\n\n# Structural Transformation of Agricultural Production Drivers: An Analysis Based on Farmers' Digital Capability Heterogeneity\n\n*   [![Image 8: Xueying Wang](https:\u002F\u002Floop.frontiersin.org\u002Fimages\u002Fprofile\u002F3488138\u002F74) Xueying Wang 1](https:\u002F\u002Floop.frontiersin.org\u002Fpeople\u002F3488138)\n*   [![Image 9: Wang Yanfeng](https:\u002F\u002Floop.frontiersin.org\u002Fimages\u002Fprofile\u002F2217558\u002F74) Wang Yanfeng 2](https:\u002F\u002Floop.frontiersin.org\u002Fpeople\u002F2217558)\n*   E M \nEndong Mu 1 \n*   [![Image 10: Xiaoli Yang](https:\u002F\u002Floop.frontiersin.org\u002Fimages\u002Fprofile\u002F3452314\u002F74) Xiaoli Yang 1](https:\u002F\u002Floop.frontiersin.org\u002Fpeople\u002F3452314)\n\n*   1. School of Economics and Management, Shenyang Agricultural University, Shenyang, China\n\n*   2. Institute of Rural Development, Liaoning Academy of Social Sciences, Shenyang, China\n\nArticle metrics\n\n[View details](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1867048\u002Ffull#metrics)\n\n73\n\nViews\n\nThe final, formatted version of the article will be published soon.\n\nNotify me\n\nSelect one of your emails\n\nYou have multiple emails registered with Frontiers:\n\nNotify me\n\nNotify me on publication\n\nPlease enter your email address:\n\nEmail \n\nIf you already have an account, please [login](https:\u002F\u002Fwww.frontiersin.org\u002Flogin?returnUrl=\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1867048\u002Fabstract)\n\nYou don't have a Frontiers account ? You can [register here](https:\u002F\u002Fwww.frontiersin.org\u002Fregister?returnUrl=\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1867048\u002Fabstract)\n\nNotify me\n\n## Abstract\n\nThe transformation of agricultural production drivers is essential for achieving sustainable food sy","Frontiers in Sustainable Food Systems · 2026-07-24","2026-07-24T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,4,1,"基于辽宁1208户葡萄种植户数据的实证研究，探讨农户数字能力异质性对农业生产驱动力转型的影响，方法新颖、数据规模大，发表于国际期刊，但发表于2026-07-24，已过4天，时效性较低。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业信息化","智能装备","数字能力","农业生产驱动力","结构转型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,58,63,68],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},11,true,[48],"葡萄",[48],[],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":57},"I2",3,[],[56],"机器学习",[],{"code":59,"score":21,"supported":14,"title":60,"abstract":61,"tags":62},"F5",[],[],[27],{"code":64,"score":21,"supported":14,"title":65,"abstract":66,"tags":67},"I1",[],[],[26],{"code":69,"score":21,"supported":14,"title":70,"abstract":71,"tags":72},"I6",[],[],[28],[74,75],"农业生产驱动力 农业信息化 数字能力 智能装备","农业生产驱动力 农业信息化","农业生产驱动力农业信息化数字能力智能装备-54","10.3389\u002Ffsufs.2026.1867048\u002Ffull","2026-07-28T23:53:31.295182Z",{"total":80,"page":21,"page_size":80,"items":81},6,[82,136,173,256,296,367],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":8,"content":87,"source_name":88,"source_url":8,"published_at":89,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":90,"score_detail":91,"sources":96,"tags":98,"primary_topic":103,"secondary_topics":104,"classification":110,"search_phrases":131,"slug":134,"view_count":53,"doi":8,"paper":8,"created_at":135},380,"农户数字能力对低碳农业采纳的影响——基于陕黔川10057户猕猴桃种植户的机器学习分析","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088","基于陕西、贵州、四川三省10057名猕猴桃种植户的微观调查数据,西安建筑科技大学向雯与西北农林大学高建中等于Sustainability发表论文。以农户对低碳农业技术的初始与持续采纳为因变量,构建涵盖数字接入、数字获取、数字处理和数字共享4个维度的数字能力综合评价体系。比较Lasso回归、极端梯度提升、随机森林和反向传播神经网络等多种实证模型,结果显示随机森林模型表现最优。采用SHAP方法定量评估各变量边际贡献,研究发现农户数字能力显著正向促进低碳农业技术的初始与持续采纳,且具有明显的维度异质性:数字接入和获取能力是初始采纳的核心驱动因素,数字处理和共享能力对持续采纳起决定性作用。","## 1. Introduction\n\nIn light of the ongoing decline in atmospheric quality and the momentous deterioration of the ecological environment, the pursuit of low-carbon development has emerged as an imperative response to these challenges. The sustainable development model of low-carbon development, which is characterised by energy conservation, reduced consumption, and pollution reduction, has become a crucial avenue for addressing environmental issues [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B1-sustainability-18-08088),[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B2-sustainability-18-08088),[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B3-sustainability-18-08088),[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B4-sustainability-18-08088)]. In response to the specific conditions of its own development, China has put forth proposals for carbon peaking and carbon neutrality programmes. In 2020, China formally proposed a double carbon goal, committing to the international community to peak its carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060 [[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B5-sustainability-18-08088)]. It is widely acknowledged that agriculture represents a significant global source of greenhouse gas emissions. Consequently, it has become evident that farmland has an integral part to play in achieving the double carbon goal [[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B6-sustainability-18-08088),[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B7-sustainability-18-08088)].\n\nNevertheless, for an extended period, the direct cost of agricultural production has been the sole factor considered, with the cost of resource and environmental losses incurred during the production process remaining unaccounted for. The excessive and inefficient use of agricultural resources, such as pesticides and chemical fertilisers, in agricultural production has a direct impact on the quality and safety of agricultural products, the ecological environment, and human health. The negative externalities of this practice are becoming increasingly apparent [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B8-sustainability-18-08088),[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B9-sustainability-18-08088),[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B10-sustainability-18-08088)].\n\nOver the past few decades, China’s traditional agricultural production and cultivation patterns have undergone significant expansion, resulting in a considerable accumulation of high carbon emissions and a multitude of ecological damage issues [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B11-sustainability-18-08088),[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B12-sustainability-18-08088),[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B13-sustainability-18-08088)]. In recent years, the central No. 1 document has consistently emphasised the necessity of promoting environmentally sustainable agricultural practices. This entails accelerating the promotion and application of agricultural input reduction and efficiency technology, integrating water and fertiliser management, and establishing and enhancing the collection and utilisation of straw, agricultural film, pesticide packaging waste, livestock and poultry manure, and other agricultural waste collection and treatment systems. Furthermore, General Secretary Xi Jinping emphasised the necessity of developing ecological and low-carbon agricultural practices, asserting that environmentally sustainable practices are the foundation of modern agriculture.\n\nGreen and low-carbon agriculture has the potential to save energy and reduce emissions, facilitate the sequestration of carbon in an ecological manner, enhance the utilisation of agricultural resources, produce safe and high-quality agricultural products, and provide robust support for the realisation of China’s double carbon target [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B14-sustainability-18-08088),[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B15-sustainability-18-08088)]. The foundation of low-carbon agricultural production is the utilisation of cutting-edge low-carbon agricultural technology [[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B16-sustainability-18-08088),[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B17-sustainability-18-08088)]. It is for this reason that research and development, promotion, and application of low-carbon agricultural technology have become pivotal measures to achieve carbon sequestration and emission reduction in agricultural production. The contemporary understanding of low-carbon agricultural technology is primarily concerned with the minimisation of inputs, including energy, fertilisers, pesticides and water, in agricultural production. This approach ultimately aims to curtail greenhouse gas emissions from agricultural sources. In particular, low-carbon agricultural technology encompasses a range of techniques and practices, including formula fertilisation technology, commercial organic fertiliser application, farm fertiliser application, no-till technology, and water-saving irrigation technology.\n\nThe implementation of the household contract responsibility system in China has resulted in the farming household becoming the primary entity engaged in production and management activities within rural areas, and is thus the most fundamental decision-making unit. The uptake of low-carbon agricultural technology by farmers is contingent upon the comprehensive promotion and implementation of ecological low-carbon agriculture in China [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B18-sustainability-18-08088)]. The decision to adopt technology is a behavioural choice made by an individual, influenced by the opinions of others and based on an evaluation of the advantages offered by the technology and their own suitability for it [[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B19-sustainability-18-08088)]. The dissemination and uptake of low-carbon agricultural technology can be conceptualised as a process of technology diffusion [[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B20-sustainability-18-08088)]. The process can be divided into two distinct stages: the initial stage concerns farmers making the decision to adopt low-carbon agricultural technology; the subsequent stage pertains to farmers deciding to continue the behaviour after adopting low-carbon agricultural technology [[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B21-sustainability-18-08088)]. In accordance with the conventional perspective on economics, a considerable number of scholars have examined the internal and external elements influencing the initial adoption of low-carbon agricultural technology by farmers at both the theoretical and empirical levels. Internal factors include gender, age, health, education, peer effects, ecological awareness, household income, non-farm labour migration, time preference, and risk perception [[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B22-sustainability-18-08088),[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B23-sustainability-18-08088),[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B24-sustainability-18-08088),[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B25-sustainability-18-08088),[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B26-sustainability-18-08088),[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B27-sustainability-18-08088),[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B28-sustainability-18-08088)]. The adoption of low-carbon agricultural technology by farmers is influenced by several external factors, including training, social capital, market incentives, government regulation, subsidies, agricultural insurance, participation in cooperatives, and machinery outsourcing services [[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B29-sustainability-18-08088),[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B30-sustainability-18-08088),[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B31-sustainability-18-08088),[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B32-sustainability-18-08088),[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B33-sustainability-18-08088),[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B34-sustainability-18-08088)]. A review of the literature reveals a paucity of studies investigating the sustained adoption of low-carbon agricultural technology by farmers. Many scholars have employed sustained use modelling to examine the influence of various factors, including technology cost and efficacy, technology complexity, government technology promotion, and policy subsidies, on the sustained adoption of low-carbon agricultural technology by farmers [[35](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B35-sustainability-18-08088),[36](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B36-sustainability-18-08088),[37](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B37-sustainability-18-08088)]. A consensus was reached on the fundamental premise that the continued adoption of low-carbon agricultural technology by farmers represents an extension of the technology adoption process. This extension is based on the evaluation of the actual results of technology adoption in relation to individual expectations of the effects of low-carbon agricultural technology. It is frequently observed that the performance of a technology is at its lowest during the initial adoption phase. It is only through continued utilisation of the technology that its intrinsic value can be fully appreciated. Despite the ascendance of green agriculture in China in the context of production and development, a discrepancy persists between the initial and sustained adoption of green and low-carbon agricultural technologies by farmers. Nevertheless, the majority of existing studies focus on the initial decision-making process surrounding the adoption of low-carbon technology, neglecting to consider the dynamic characteristics of technology adoption behaviour across different stages. Furthermore, there is a tendency to conflate initial adoption with sustained adoption.\n\nThe digital economy is particularly adept at optimising the low-carbon agricultural industry landscape [[38](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B38-sustainability-18-08088)]. The advent of the digital economy has given rise to a plethora of novel technologies, industries, and business models. Moreover, the digitisation of knowledge and information has become a primary factor in the production process [[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B39-sustainability-18-08088)]. The integration of modern information technology with agriculture, including the internet, the Internet of Things, cloud computing, big data, and artificial intelligence, enables real-time collection of agricultural data, real-time monitoring of the agricultural ecological environment, precise input of agricultural production factors, quantitative decision-making in the agricultural production process, and intelligent regulation and control of agricultural production. These functions collectively constitute a new low-carbon agricultural production model. It has been demonstrated that the digital economy plays a notable role in the implementation of low-carbon agricultural technology by farmers [[40](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B40-sustainability-18-08088)]. Concurrently, the digital economy has considerable expectations regarding the digital capability of farmers. Those with superior digital abilities are more adept at navigating digital technology and are more likely to receive a premium for their skills in the context of the advancement of the digital economy.\n\nThe disparate digital capability of farmers gives rise to variations in the digital divide and the digital dividend. This raises the question of whether digital capability has a significant positive effect on the initial adoption of low-carbon agricultural technology by farmers. Does digital capability also have a significant positive effect on the sustained adoption of low-carbon agricultural technology? Given the established link between initial and sustained adoption of technology, it is pertinent to inquire whether the factors influencing the initial behaviour of farmers in the adoption of low-carbon agricultural technology also exert an influence on their sustained adoption behaviour. This study proposes an innovative perspective to study the gap between the initial and sustained adoption of existing low-carbon agricultural technology based on a comprehensive analysis of the preceding findings. The objective of this study is to achieve an accurate match between low-carbon agricultural technology and farmers.\n\nFurthermore, it is worthwhile to mention that the conventional methodology utilised in agricultural policy research typically entails the gathering of data, the application of analytical models to elucidate the interrelationships between variables, and the formulation of policy recommendations. A review of the existing literature reveals that the majority of studies have formulated hypotheses and conducted regression analyses, primarily utilising econometric methods such as binary probit, propensity score matching, and double difference methods. Nevertheless, the inherent bias in establishing linear relationships between indicators in explanatory modelling results in insufficient discriminatory precision, which may lead to a wide range of policy recommendations that are disparate. The conventional techniques employed in statistical analysis are gradually being shown to have certain inherent limitations.\n\nIn recent years, machine learning methods have been employed extensively in agri-environmental and economic research due to their ability to effectively address complex non-linear problems. The field of machine learning is predicated on the analysis and learning of the mapping of existing data to expert classification models. Techniques for processing data and information are developed through algorithms that facilitate the stable and reliable classification of unknown categorical data. In the context of artificial intelligence and big data technology, the computational analysis method of machine learning can be applied to discover data laws or implicit models from multi-dimensional data that are difficult to perceive and discover. The computational analysis method offers several advantages, including high accuracy, rapid training speed, and straightforward implementation and interpretation. The method enhances the existing research paradigms and is extensively employed in diverse classification and regression tasks [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B41-sustainability-18-08088)].\n\nIn light of the aforementioned evidence, this study proceeded to devise and administer a questionnaire survey comprising 10,057 kiwifruit farmers situated in the provinces of Shaanxi, Guizhou, and Sichuan, which collectively represent the three leading kiwifruit-producing regions in China. The current status and challenges associated with the initial and sustained adoption of low-carbon agricultural technology by kiwifruit growers were further elucidated. The ordered logit model, which has been demonstrated to offer a robust explanatory framework for behavioural decisions, was selected as a reference for the analysis of the results obtained from other machine learning methods. The dataset was divided into training and testing sets based on the results of the experimental analysis, which employed a range of machine learning techniques. The optimal model is selected on the basis of the expected goodness of fit. Subsequently, the SHAP value interpretive method is implemented to ascertain the contribution rate of each digital capability dimension to the initial and sustained adoption of low-carbon agricultural technology by farmers, thereby facilitating the effective identification of the most significant influencing factors. The influence of digital capability and its constituent elements on the initial and sustained adoption of low-carbon agricultural technology by farmers is subjected to rigorous analysis, and recommendations for pertinent policy measures are put forth.\n\nThe contributions of this study are mainly reflected in the following three aspects: (1) Research Theme: This study investigates the characteristics of a sample of farmers and their initial and sustained adoption of low-carbon agricultural technology through micro-field research. In accordance with the findings of static technology adoption studies, further consideration is given to the continued adoption of technology. Accordingly, the objective of this study is to address the existing gap in the literature on the sustained adoption of low-carbon agricultural technology by farmers. (2) Research Content: This study provides a comprehensive examination of the composition of the digital capability of farmers, with the utilisation of factor analysis to construct and measure the digital capability index from four dimensions: digital access capability, digital acquisition capability, digital processing capability, and digital sharing capability. This offers a reference paradigm for the construction of indicators for subsequent related studies. Concurrently, it furnishes novel evidence to elucidate the influence of the digital capability of farmers and their multifaceted dimensions on the initial and sustained adoption of low-carbon agricultural technology by farmers. (3) Research Method: This study offers a comprehensive integration of the strengths inherent to both traditional econometric research methods and machine learning methods. In order to more accurately capture the impact of digital capability on the initial and sustained adoption of low-carbon agricultural technology by farmers, and to provide more scientific policy recommendations for the decision-making community, an ordered logit model, a Lasso regression method, a random forest method, an XGBoost model, and a BP neural network model were used.\n\n## 2. Theoretical Analysis and Research Hypotheses\n\n### 2.1. Theoretical Analysis Framework\n\nThe behaviours of farmers in the adoption of green and low-carbon agricultural technologies can be divided into two progressive stages: initial trial and long-term sustained application. However, there exist significant discrepancies in the behavioural constraint mechanisms between the two stages [[42](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B42-sustainability-18-08088)]. The former stage is primarily restricted by decision-making uncertainty arising from insufficient information, whereas the latter stage hinges on perceived technical benefits and long-term production preferences. As a core endowment enabling farmers to adapt to the digital transformation of modern agriculture, digital capability can systematically resolve practical dilemmas hindering the adoption of low-carbon technology by farmers through two pathways: alleviating endogenous decision-making constraints and optimising exogenous production mechanisms. Digital capability effectively drives the behavioural transition of farmers from passive wait-and-see to proactive trials, and from one-off usage to regular, in-depth implementation. On the one hand, grounded in the bounded rationality decision-making theory and farmer behaviour theory, comprehensive digital capability integrated by multi-dimensional capability including digital access, information acquisition, data processing, and information sharing can break the ingrained cognitive habits shaped by the traditional farming experiences of farmers, expand supply channels of agricultural technical information, correct farmers’ biased risk perceptions of low-carbon technology, and reshape their long-term income expectations. From the endogenous cognitive dimension, it thereby facilitates farmers’ acceptance of innovative low-carbon production modes. On the other hand, drawing upon the sustained technology application theory, a sound d","Sustainability 2026-08-08","2026-08-08T00:00:00Z",75,{"impact":17,"substance":92,"depth":17,"authority":93,"freshness":94,"relevant":21,"comment":95},20,12,7,"基于大规模农户数据的机器学习研究，探讨数字能力对低碳农业采纳的影响，方法新颖，数据量大，结论可靠，对农业信息化与低碳发展有参考价值。",[97],{"name":88,"url":85},[28,99,100,101,102],"低碳农业","SHAP","随机森林","Sustainability",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[105],{"code":52,"slug":106,"name":107,"path":108,"parent_code":109},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":111},{"candidates":112,"agricultural_evidence":46},[113,118,122,127],{"code":32,"score":45,"supported":46,"title":114,"abstract":116,"tags":117},[115],"猕猴桃",[115],[],{"code":52,"score":13,"supported":46,"title":119,"abstract":120,"tags":121},[56],[],[100,101],{"code":123,"score":21,"supported":14,"title":124,"abstract":125,"tags":126},"H4",[],[],[99],{"code":69,"score":21,"supported":14,"title":128,"abstract":129,"tags":130},[],[],[28],[132,133],"低碳农业 数字能力 随机森林 Sustainability","低碳农业 数字能力","低碳农业数字能力随机森林Sustainability-380","2026-08-10T23:54:46.360428Z",{"id":137,"title":138,"url":139,"summary":140,"summary_zh":8,"content":141,"source_name":142,"source_url":8,"published_at":143,"category":144,"cover_url":8,"hotness":13,"is_selected":46,"score":145,"score_detail":146,"sources":148,"tags":150,"primary_topic":153,"secondary_topics":154,"classification":155,"search_phrases":167,"slug":170,"view_count":171,"doi":8,"paper":8,"created_at":172},182,"科学家解析果皮微生物密码，建立荔枝货架期预测模型","https:\u002F\u002Fnews.yunnan.cn\u002Fsystem\u002F2026\u002F08\u002F03\u002F034106498.shtml","中国农业科学院农业基因组研究所团队解析采后荔枝果皮微生物群落动态，建立可依据微生物变化判断新鲜度和剩余储存时间的预测模型。研究比较了耐储性较强的“怀枝”和不耐储的“糯米糍”，为绿色、安全、精准保鲜技术提供新思路。","原标题：科学家解析果皮微生物密码，建立货架期预测模型——夏日荔枝，保鲜有术（科技瞭望）\n\n荔枝是深受消费者喜爱的夏季水果，然而“一日色变，二日香变，三日味变”这句流传已久的俗语，道出了荔枝采后保鲜的难题。刚从枝头摘下的荔枝晶莹饱满、香气浓郁，但经过运输、储存，果皮很快失去鲜红色泽，出现褐变、流水，甚至腐烂。如何延长荔枝的鲜活时间，一直是采后保鲜领域的重要课题。\n\n近日，中国农业科学院农业基因组研究所的科研团队破解了这一痛点。他们通过解析采后荔枝果皮微生物群落的动态变化规律，建立了荔枝货架期预测模型，能够根据微生物群落变化判断荔枝的新鲜程度和剩余储存时间。这项研究相当于为荔枝安装了一个“新鲜度预测时钟”，为开发绿色、安全的采后保鲜技术提供了新策略。","人民日报海外版 \u002F 中国农业科学院","2026-08-02T21:15:00Z","报道",84,{"impact":92,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":147},"国家级科研机构在荔枝保鲜领域的创新成果，具有产业应用价值，报道详实，时效性强。",[149],{"name":142,"url":139},[26,151,152],"中国农科院","农产品保鲜",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":156},{"candidates":157,"agricultural_evidence":46},[158,163],{"code":32,"score":45,"supported":46,"title":159,"abstract":161,"tags":162},[160],"荔枝",[160],[],{"code":64,"score":21,"supported":14,"title":164,"abstract":165,"tags":166},[],[],[26],[168,169],"中国农科院 农业信息化 农产品保鲜","中国农科院 农业信息化","中国农科院农业信息化农产品保鲜-182",13,"2026-08-02T23:54:40.202456Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":8,"source_name":179,"source_url":176,"published_at":180,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":181,"score_detail":182,"sources":187,"tags":189,"primary_topic":195,"secondary_topics":196,"classification":203,"search_phrases":225,"slug":228,"view_count":229,"doi":230,"paper":231,"created_at":255},5547,"Apple quality and soil biological diversity in long-term organic and IPM apple orchards","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1943487","Introduction While several studies have compared fruit quality in organic and conventional apple production, integrated assessments of fruit quality and soil biological diversity remain limited. Majority of previous studies have been based on relatively short-term experimental comparisons, although responses of soil biota and soil properties often require many years of consistent management to become fully expressed. Methods This study compared yield, fruit quality, and soil biological diversity in long-term organic (ORG) apple orchard and long-term integrated pest management (IPM) orchard under Nordic conditions. The study was conducted in 2022–2024 at the Polli Horticultural Research Centre (Estonia) using the cultivars “Katre”, “Krista,” and “Alesja”. Apple, yield, fruit quality attributes (soluble solids and titratable acids content and their ratio, ascorbic acid content and total polyphenols, total anthocyanins, fruit firmness, fruit colour, fruit mineral elements content), soil chemical parameters, earthworm and springtail communities were analysed. Results and Discussion Higher total and fresh-market yields were observed in the IPM orchard in two of the three cultivars, whereas the proportion of fresh-market fruit did not differ consistently between the two orchards. When averaged across cultivars, apples from ORG orchard exhibited more intense peel coloration. Differences in taste-related traits and antioxidant composition between cultivation systems were cultivar- and year-dependent rather than consistently associated with cultivation system. Total polyphenol content was consistently higher under ORG management only in “Katre,” while “Alesja” exhibited the highest total polyphenol and anthocyanin contents irrespective of cultivation system, highlighting the dominant role of genotype. Soil in the ORG orchard had higher pH and greater phosphorus and calcium contents, supporting a higher proportion of anecic earthworms, particularly Aporrectodea longa . Total earthworm abundance was higher in the IPM orchard, springtail abundance did not differ significantly between systems. These results suggest that the conditions in the long-term ORG were more favourable for ecologically important earthworm groups without consistently affecting internal fruit quality compared to IPM orchard.","引言 尽管已有若干研究比较了有机与常规苹果生产中的果实品质，但结合果实品质与土壤生物多样性的综合评价仍较为有限。以往研究大多基于相对短期的试验比较，而土壤生物区系和土壤性质的响应往往需要多年持续一致的管理才能充分显现。方法 本研究在北欧条件下比较了长期有机（ORG）苹果园与长期综合病虫害管理（IPM）苹果园的产量、果实品质及土壤生物多样性。研究于2022—2024年在爱沙尼亚Polli园艺研究中心开展，供试品种为“Katre”“Krista”和“Alesja”。分析了苹果产量、果实品质指标（可溶性固形物与可滴定酸含量及其比值、抗坏血酸含量、总多酚、总花青素、果实硬度、果实色泽、果实矿质元素含量）、土壤化学参数、蚯蚓和跳虫群落。结果与讨论 在3个品种中的2个品种上，IPM果园的总产量和鲜食市场产量较高，而鲜食市场果实比例在两种果园间并无一致差异。按品种平均，ORG果园苹果果皮色泽更深。栽培系统间口感相关性状和抗氧化成分的差异因品种和年份而异，而非与栽培系统稳定相关。总多酚含量仅在“Katre”中于ORG管理下持续较高，而“Alesja”无论栽培系统如何均表现出最高的总多酚和花青素含量，凸显了基因型的主导作用。ORG果园土壤具有较高的pH以及较高的磷和钙含量，支持了较高比例的内栖型蚯蚓，尤其是长穴居蚯蚓（Aporrectodea longa）。IPM果园蚯蚓总丰度较高，跳虫丰度在系统间无显著差异。这些结果表明，与IPM果园相比，长期ORG的条件更有利于生态上重要的蚯蚓类群，但并未对果实内部品质产生一致影响。","Frontiers in Sustainable Food Systems","2026-10-21T00:00:00Z",66,{"impact":183,"substance":92,"depth":184,"authority":171,"freshness":185,"relevant":21,"comment":186},8,16,9,"北欧长期定位试验对比有机与IPM苹果园的果实品质与土壤生物多样性，结论有新意但属区域尺度研究，公共影响有限。",[188],{"name":179,"url":176},[190,191,192,193,194],"长期定位试验","有机果园","土壤生物多样性","苹果品质","IPM",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[197],{"code":198,"slug":199,"name":200,"path":201,"parent_code":202},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":204},{"candidates":205,"agricultural_evidence":46},[206,214,219],{"code":32,"score":207,"supported":46,"title":208,"abstract":210,"tags":213},17,[209],"apple",[211,212,209],"苹果","orchard",[],{"code":198,"score":19,"supported":46,"title":215,"abstract":216,"tags":218},[199],[217,199],"土壤",[],{"code":220,"score":53,"supported":46,"title":221,"abstract":222,"tags":224},"H6",[],[223],"生物多样性",[],[226,227],"爱沙尼亚 Polli 苹果 有机 IPM","苹果园 蚯蚓 跳虫 土壤","爱沙尼亚Polli苹果有机IPM-5547",0,"10.3389\u002Ffsufs.2026.1943487",{"doi":230,"openalex_id":232,"authors":233,"venue":179,"cited_by_count":229,"oa_url":176,"card":248,"direction":252,"ingested_from":254},"W7223104118",[234,237,240,242,245],{"name":235,"orcid":236},"Ulvi Moor","https:\u002F\u002Forcid.org\u002F0000-0002-8808-8494",{"name":238,"orcid":239},"Annely Kuu","https:\u002F\u002Forcid.org\u002F0000-0002-0335-6243",{"name":241,"orcid":8},"K. Kahu",{"name":243,"orcid":244},"P. Põldma","https:\u002F\u002Forcid.org\u002F0000-0002-7906-823X",{"name":246,"orcid":247},"Reelika Rätsep","https:\u002F\u002Forcid.org\u002F0000-0002-8673-9978",{"tldr":249,"method":250,"finding":251,"direction":252,"opportunity":253},"比较长期有机与IPM苹果园的果实品质和土壤生物多样性。","在爱沙尼亚长期试验园测定三个品种的产量、品质及蚯蚓和跳虫群落。","有机园果皮色泽更深、土壤pH和磷钙更高，但果实内在品质差异多随品种和年份变化。","农业绿色发展与碳","可结合长期定位试验，量化土壤动物群落与果实品质的关联机制，并拓展多区域验证。","openalex","2026-10-22T11:20:15.557635Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":8,"content":261,"source_name":262,"source_url":8,"published_at":263,"category":144,"cover_url":8,"hotness":13,"is_selected":14,"score":264,"score_detail":265,"sources":267,"tags":269,"primary_topic":274,"secondary_topics":275,"classification":276,"search_phrases":291,"slug":294,"view_count":229,"doi":8,"paper":8,"created_at":295},5516,"上海农科院葡萄团队赴银川参加第47届世界葡萄与葡萄酒大会","https:\u002F\u002Fwww.saas.sh.cn\u002Fhzjl\u002Fgjhz\u002Fcontent_57128","10月12日至16日第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员作专题报告，阐明疏穗与环剥处理对果实糖、花青素及脱落酸积累的源库调控机制。团队还观摩酿酒葡萄AI综合作业机器人成果展示，与研发单位围绕葡萄智慧装备与果园智能化管理深入交流。","2026年10月12-16日，第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员一行赴会交流。团队围绕“提升葡萄种植生产韧性与可持续发展能力”主题，向全球同行展示最新科研成果。\n\n会上，奚晓军作“Changes in sugar, anthocyanin and ABA accumulation in response to cluster thinning and girdling in ‘Jumeigui’grape”专题报告。报告围绕葡萄疏穗与环剥处理开展研究，阐明源库比对果实糖、花青素及脱落酸积累的调控机制，为葡萄优质栽培技术提供理论支撑，报告内容引发国内外园艺专家广泛研讨。参会期间，团队前往银川国际会展中心观摩酿酒葡萄 AI 综合作业机器人成果展示，详细了解该装备在葡萄园智能作业方面的技术特点与应用前景，并与研发单位围绕葡萄智慧装备研发、果园智能化管理等方向开展深入交流。\n\n本次参会是葡萄团队持续推进科研国际化、对标国际前沿开展研究的重要实践。","上海市农业科学院 2026-10-19","2026-10-19T00:00:00Z",65,{"impact":184,"substance":207,"depth":171,"authority":171,"freshness":80,"relevant":21,"comment":266},"国际学术会议交流并涉及葡萄AI作业机器人观摩，有实质科研内容与行业参考价值，但属团队参会动态，影响面有限。",[268],{"name":262,"url":259},[270,271,48,272,273],"智慧农业","智能农机","栽培技术","国际交流",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":277},{"candidates":278,"agricultural_evidence":46},[279,283,287],{"code":32,"score":93,"supported":46,"title":280,"abstract":281,"tags":282},[48],[48],[48],{"code":64,"score":20,"supported":46,"title":284,"abstract":285,"tags":286},[],[270],[270],{"code":59,"score":21,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[271],[292,293],"上海农科院 葡萄 银川","世界葡萄与葡萄酒大会 奚晓军","上海农科院葡萄银川-5516","2026-10-22T00:07:04.061911Z",{"id":297,"title":298,"url":299,"summary":300,"summary_zh":301,"content":8,"source_name":179,"source_url":299,"published_at":302,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":303,"score_detail":304,"sources":307,"tags":309,"primary_topic":315,"secondary_topics":316,"classification":317,"search_phrases":336,"slug":339,"view_count":229,"doi":340,"paper":341,"created_at":366},5468,"Farm-level technical efficiency in temperate horticulture: evidence from apple, pear, and cherry growers","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1928386","Improving resource-use efficiency is fundamental to enhancing agricultural productivity and promoting sustainable horticultural development, particularly in regions where opportunities for expanding cultivated land and increasing input use are limited. This study evaluates the technical efficiency of apple, pear, and cherry growers in the Kashmir Valley and examines the socioeconomic, institutional, and regional factors associated with farm-level technical efficiency. Primary data were collected from 418 horticultural households during 2023 using a multi-stage sampling approach. Technical efficiency was first estimated separately for apple, pear, and cherry growers using an input-oriented Data Envelopment Analysis (DEA) framework under both Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) assumptions. Scale efficiency was subsequently estimated, while a pooled DEA model was employed to generate farm-level technical efficiency scores for the second-stage analysis. The determinants of technical efficiency were examined using Tobit regression, supported by a fractional logit model as a robustness check, and regional disparities were assessed using Kruskal–Wallis and Mann–Whitney non-parametric tests. The results indicate that apple growers achieved the highest overall technical efficiency, followed by pear and cherry growers. The VRS estimates reveal substantially higher levels of pure technical efficiency, whereas the scale efficiency analysis suggests that a considerable proportion of technical inefficiency arises from farms operating below their optimal production scale. The second-stage analysis identifies family size, education, irrigation facilities, access to credit, and cold storage infrastructure as significant positive factors associated with farm-level technical efficiency, while significant regional disparities are observed across the Kashmir Valley. The robustness analysis confirms the consistency of the estimated relationships. These findings highlight that improving horticultural performance requires not only efficient farm-level resource management but also stronger institutional support, investments in irrigation and post-harvest infrastructure, improved access to rural credit, and region-specific development strategies. The study contributes to the literature by integrating crop-specific and pooled efficiency analyses with socioeconomic and regional assessments, providing policy-relevant evidence for promoting resource-efficient and sustainable horticultural production.","提高资源利用效率是提升农业生产力、促进园艺产业可持续发展的基础，在耕地扩张和投入增加机会有限的地区尤为如此。本研究评估了克什米尔谷地苹果、梨和樱桃种植者的技术效率，并考察了与农场层面技术效率相关的社会经济、制度和区域因素。研究采用多阶段抽样方法，于2023年从418个园艺农户收集了一手数据。首先，在规模报酬不变（CRS）和规模报酬可变（VRS）两种假设下，运用投入导向的数据包络分析（DEA）框架分别估算苹果、梨和樱桃种植者的技术效率。随后估算规模效率，并采用混合DEA模型生成农场层面技术效率得分用于第二阶段分析。技术效率的决定因素通过Tobit回归进行检验，并以分数logit模型作为稳健性检验加以支持，区域差异则采用Kruskal–Wallis和Mann–Whitney非参数检验进行评估。结果表明，苹果种植者总体技术效率最高，梨和樱桃种植者次之。VRS估计显示纯技术效率水平显著更高，而规模效率分析表明，相当比例的技术无效率源于农场经营规模低于最优生产规模。第二阶段分析识别出家庭规模、教育、灌溉设施、信贷获取和冷库基础设施是与农场层面技术效率显著正相关的因素，同时在克什米尔谷地观察到显著的区域差异。稳健性分析证实了所估计关系的一致性。这些发现表明，提升园艺产业绩效不仅需要有效的农场层面资源管理，还需要更强的制度支持、对灌溉和采后基础设施的投资、改善农村信贷获取以及因地制宜的发展策略。本研究通过将作物特定效率分析与混合效率分析同社会经济和区域评估相结合，为促进资源利用效率提升提供了具有政策相关性的证据。","2026-10-20T00:00:00Z",73,{"impact":93,"substance":305,"depth":17,"authority":171,"freshness":185,"relevant":21,"comment":306},21,"基于418户果农一手数据，用DEA与Tobit回归分析技术效率，方法规范、结论有政策参考价值，但属区域性研究，影响范围有限。",[308],{"name":179,"url":299},[310,311,312,313,314],"可持续农业","园艺产业","苹果种植","技术效率","数据包络分析",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":318},{"candidates":319,"agricultural_evidence":46},[320,324,331],{"code":32,"score":19,"supported":46,"title":321,"abstract":322,"tags":323},[209],[211,209],[],{"code":325,"score":80,"supported":46,"title":326,"abstract":327,"tags":330},"H3",[],[328,329],"灌溉","irrigation",[],{"code":332,"score":21,"supported":14,"title":333,"abstract":334,"tags":335},"H8",[],[],[310],[337,338],"数据包络分析 可持续农业 园艺产业 技术效率","数据包络分析 可持续农业","数据包络分析可持续农业园艺产业技术效率-5468","10.3389\u002Ffsufs.2026.1928386",{"doi":340,"openalex_id":342,"authors":343,"venue":179,"cited_by_count":229,"oa_url":299,"card":360,"direction":364,"ingested_from":254},"W7223025732",[344,346,349,352,355,358],{"name":345,"orcid":8},"Seerat Sajjad",{"name":347,"orcid":348},"Khursheed Hussain Dar","https:\u002F\u002Forcid.org\u002F0000-0002-3925-0758",{"name":350,"orcid":351},"Anam Ul Haq Ganie","https:\u002F\u002Forcid.org\u002F0000-0002-4404-5011",{"name":353,"orcid":354},"Ghadda Mohamed Awad Yousif","https:\u002F\u002Forcid.org\u002F0000-0003-4183-2957",{"name":356,"orcid":357},"Mohd Salim","https:\u002F\u002Forcid.org\u002F0000-0003-0162-8433",{"name":359,"orcid":8},"Mahdy S. Othman",{"tldr":361,"method":362,"finding":363,"direction":364,"opportunity":365},"评估克什米尔谷地苹果、梨、樱桃种植户的技术效率及其影响因素。","418户调查数据，DEA测效率，Tobit回归与分数logit分析影响因素。","苹果效率最高，规模效率不足是主因；灌溉、信贷、冷库等显著提升效率。","数字乡村与农业信息化","可结合遥感与物联网数据构建果园投入产出实时效率监测，弥补自报数据局限。","2026-10-21T23:30:07.145395Z",{"id":368,"title":369,"url":370,"summary":371,"summary_zh":372,"content":8,"source_name":179,"source_url":370,"published_at":302,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":373,"score_detail":374,"sources":376,"tags":378,"primary_topic":383,"secondary_topics":384,"classification":385,"search_phrases":410,"slug":413,"view_count":229,"doi":414,"paper":415,"created_at":429},5464,"From symbolic to substantive: How subsidy design shapes cooperative roles and high-quality rural industrial development?","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1949418","Introduction Government subsidies do not necessarily produce high-quality rural industrial development (HQRID), as different subsidy designs elicit different behavioral responses from cooperatives and smallholders. This study examines how subsidy design shapes the strategies of local governments, cooperatives, and farmers. Methods Drawing on the Dingzhuang Grape Cooperative in Jurong, China, this study builds a tripartite evolutionary game model to examine how creation support and function support shape the strategies of the local government, cooperative, and farmer. Results The results distinguish two contrasting pathways. Creation support tends to induce symbolic compliance, in which cooperatives adopt formal structures without providing training, quality control, or market access, leading the system toward a low-quality equilibrium. Function support aligns cooperative incentives with verifiable performance and encourages substantive services that connect smallholders to higher-value markets. Three conclusions follow. First, the transition to substantive service requires function support that gives cooperatives sufficiently strong incentives. Second, substantive services lower the quality-premium share that farmers require, strengthening the economic case for high-quality production. Third, function support is bounded by fiscal capacity, and excessive performance-linked payments can reverse the transition. Discussion Policy should therefore tie support to measurable gains in farmer income and product quality. The study contributes to induced institutional change theory by specifying that external inducements produce substantive institutions only when rewards are tied to verifiable performance rather than to registration, a boundary condition that explains why supported organizations may remain hollow.","引言 政府补贴并不必然带来高质量的农村产业发展（HQRID），因为不同的补贴设计会引发合作社与小农户不同的行为反应。本研究考察补贴设计如何塑造地方政府、合作社与农户的策略。方法 本研究以中国句容市丁庄葡萄合作社为案例，构建三方演化博弈模型，考察创建支持与功能支持如何塑造地方政府、合作社与农户的策略。结果 结果区分出两条截然不同的路径。创建支持倾向于诱发象征性遵从，即合作社采取正式结构却不提供培训、质量控制或市场准入，使系统趋向低质量均衡。功能支持使合作社激励与可验证绩效相一致，并鼓励将小农户连接至更高价值市场的实质性服务。由此得出三点结论。第一，向实质性服务的转变需要功能支持，使合作社获得足够强的激励。第二，实质性服务降低了农户所需的质量溢价份额，强化了高质量生产的经济合理性。第三，功能支持受财政能力约束，过度的绩效挂钩支付可能逆转这一转变。讨论 因此，政策应将支持与农户收入和产品质量的可测量改善挂钩。本研究对诱致性制度变迁理论作出贡献，明确指出外部诱因只有在奖励与可验证绩效而非与登记注册挂钩时才会产生实质性制度，这一边界条件解释了受支持组织为何可能保持空壳化。",77,{"impact":184,"substance":18,"depth":17,"authority":171,"freshness":183,"relevant":21,"comment":375},"以句容葡萄合作社为案例的三方演化博弈研究，区分创建型与功能型补贴对合作社行为的影响，结论对补贴政策设计有实质参考价值。",[377],{"name":179,"url":370},[379,380,381,382],"乡村产业","合作社","小农户","补贴设计",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":386},{"candidates":387,"agricultural_evidence":46},[388,393,399,405],{"code":32,"score":80,"supported":46,"title":389,"abstract":390,"tags":392},[],[48,391],"grape",[],{"code":394,"score":395,"supported":46,"title":396,"abstract":397,"tags":398},"G2",5,[],[381],[380,381],{"code":400,"score":53,"supported":46,"title":401,"abstract":402,"tags":404},"G6",[],[403],"农户收入",[],{"code":406,"score":21,"supported":14,"title":407,"abstract":408,"tags":409},"G4",[],[],[379],[411,412],"句容 丁庄 葡萄合作社","补贴设计 合作社 小农户","句容丁庄葡萄合作社-5464","10.3389\u002Ffsufs.2026.1949418",{"doi":414,"openalex_id":416,"authors":417,"venue":179,"cited_by_count":229,"oa_url":370,"card":424,"direction":364,"ingested_from":254},"W7222997429",[418,420,422],{"name":419,"orcid":8},"Man Huang",{"name":421,"orcid":8},"蒋礼文",{"name":423,"orcid":8},"Jincai Zhuang",{"tldr":425,"method":426,"finding":427,"direction":364,"opportunity":428},"用三方演化博弈分析补贴设计如何影响合作社行为与农村产业高质量发展。","构建政府、合作社、农户三方演化博弈模型，基于句容丁庄葡萄合作社案例。","创建型补贴诱发象征性合规，功能型补贴促成实质性服务并降低农户质量溢价要求。","可将补贴绩效挂钩机制与可验证数据采集结合，研究数字化考核如何抑制合作社空心化。","2026-10-21T23:30:06.732822Z"]