[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3433":3,"related-3433":74},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":59,"slug":62,"view_count":20,"doi":8,"paper":63,"created_at":73},3433,"《数字农业发展的信贷融资效应——来自江苏省家庭农场的证据》","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fzgncgc\u002Fmulu\u002F647040.html","周月书、葛云杰使用江苏省家庭农场实地问卷调查数据考察数字农业发展的信贷融资效应及其作用机制。研究发现：数字农业发展显著提升了家庭农场的信贷可得性与信贷融资规模；机制上通过缓解信息不对称程度、降低信贷交易成本和提升风险抵御能力促进家庭农场融资；异质性分析发现数字农业发展对于所在地农业技术发展环境较好、农技人员服务水平较高的家庭农场，以及家庭农场主资本禀赋较低、金融认知能力较低以及种植粮食作物的家庭农场具有更显著的融资增进作用。",null,"《中国农村观察》\u002F南京农业大学","2026-09-20T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,6,1,"基于江苏家庭农场实地调研的实证研究，机制与异质性分析扎实，对数字普惠金融与新型经营主体融资有参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"数字农业","家庭农场","江苏","农村金融","信贷融资",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,49,54],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":48},12,true,[26],[26],[26],{"code":50,"score":44,"supported":45,"title":51,"abstract":52,"tags":53},"I1",[25],[25],[25],{"code":55,"score":20,"supported":13,"title":56,"abstract":57,"tags":58},"G3",[],[],[28],[60,61],"江苏 家庭农场 数字农业 信贷","周月书 数字农业 融资","江苏家庭农场数字农业信贷-3433",{"doi":8,"openalex_id":8,"authors":64,"venue":8,"cited_by_count":65,"oa_url":8,"card":66,"direction":70,"ingested_from":72},[],0,{"tldr":67,"method":68,"finding":69,"direction":70,"opportunity":71},"基于江苏家庭农场调查，研究数字农业发展对信贷融资的影响及机制。","江苏省家庭农场实地问卷调查数据，实证分析信贷效应与机制。","数字农业显著提升信贷可得性与规模，通过缓解信息不对称、降成本、提风险抵御力实现。","数字乡村与农业信息化","可探究数字农业融资效应在不同区域和作物类型间的差异，及金融科技的中介作用。","agent","2026-09-25T00:09:33.572434Z",{"total":19,"page":20,"page_size":19,"items":75},[76,144,210,263,327,379],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":81,"content":8,"source_name":82,"source_url":79,"published_at":83,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":84,"score_detail":85,"sources":89,"tags":91,"primary_topic":96,"secondary_topics":97,"classification":98,"search_phrases":126,"slug":129,"view_count":65,"doi":130,"paper":131,"created_at":143},4601,"Digital Agriculture and Sector Transformation in South Africa","https:\u002F\u002Fdoi.org\u002F10.36096\u002Fijbes.v8i4.1274","Digitalisation is increasingly promoted as a catalyst for agricultural productivity, climate resilience, and market integration. However, there is limited systematic analysis of how digital agricultural transformation unfolds within South Africa’s institutional and socio-economic context. This study examines the trajectory, drivers, and governance implications of digitalisation in South African agriculture. Anchored in an integrated Socio-Technical Systems and Digital Agriculture Ecosystem framework, the study applies a structured qualitative document analysis of 82 academic and policy documents published between 2013 and 2025. The findings reveal highly uneven digital adoption patterns that reflect a structural dualism between capital-intensive commercial agriculture and resource-constrained smallholder systems. While South Africa has developed a relatively advanced digital policy architecture, implementation fragmentation, infrastructure constraints, and limited capacity for extension impede inclusive digital outcomes. The study contributes by integrating socio-technical and ecosystem perspectives to analyse digital transitions in emerging economies and by providing a transparent analytical protocol for document-based research. Policy implications highlight the need for coordinated governance, investment in rural connectivity, and targeted support mechanisms to ensure equitable digital transformation.","数字化日益被视为提升农业生产力、增强气候韧性和促进市场整合的催化剂。然而，对于数字农业转型如何在南非的制度与社会经济语境中展开，尚缺乏系统性分析。本研究考察南非农业数字化的演进轨迹、驱动因素及治理意涵。研究以社会技术系统与数字农业生态系统的整合框架为依托，对2013年至2025年间发表的82份学术与政策文件进行了结构化定性文献分析。研究发现，数字技术采纳模式高度不均衡，反映出资本密集型商业农业与资源受限的小农体系之间的结构性二元分化。尽管南非已建立起相对先进的数字政策架构，但实施碎片化、基础设施制约以及推广能力有限等因素阻碍了包容性数字成果的实现。本研究的贡献在于整合社会技术与生态系统视角以分析新兴经济体的数字转型，并为基于文献的研究提供了透明的分析方案。政策启示强调，需通过协调治理、农村互联互通投资以及有针对性的支持机制，确保公平的数字转型。","International Journal of Business Ecosystem and Strategy (2687-2293)","2026-10-09T00:00:00Z",80,{"impact":16,"substance":17,"depth":16,"authority":86,"freshness":87,"relevant":20,"comment":88},13,9,"基于82份文献的系统性分析，揭示南非数字农业的结构性二元分化与治理碎片化，对新兴经济体数字乡村研究有参考价值。",[90],{"name":82,"url":79},[25,92,93,94,95],"智慧农业","小农户","南非农业","农村数字治理",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":99},{"candidates":100,"agricultural_evidence":45},[101,107,113,120],{"code":50,"score":102,"supported":45,"title":103,"abstract":105,"tags":106},16,[104],"digital agriculture",[25,104],[25,92],{"code":31,"score":108,"supported":45,"title":109,"abstract":110,"tags":112},4,[],[111],"smallholder",[93],{"code":114,"score":115,"supported":45,"title":116,"abstract":117,"tags":119},"H6",3,[],[118],"农业生态",[],{"code":121,"score":115,"supported":45,"title":122,"abstract":123,"tags":125},"H7",[],[124],"climate resilience",[],[127,128],"南非 数字农业 转型","南非 小农户 数字鸿沟","南非数字农业转型-4601","10.36096\u002Fijbes.v8i4.1274",{"doi":130,"openalex_id":132,"authors":133,"venue":82,"cited_by_count":65,"oa_url":79,"card":137,"direction":70,"ingested_from":142},"W7221113328",[134],{"name":135,"orcid":136},"Mokgadi Julia Ngoepe-Ntsoane","https:\u002F\u002Forcid.org\u002F0000-0001-7995-4824",{"tldr":138,"method":139,"finding":140,"direction":70,"opportunity":141},"系统分析南非农业数字化轨迹、驱动因素与治理影响，揭示数字鸿沟与政策碎片化。","社会技术系统与数字农业生态框架，对82份2013-2025年文献政策进行结构化质","数字采用高度不均，商业农业与小农二元分化；政策先进但实施碎片化、基建与推广能力不足阻碍包容性转型。","可延伸至新兴经济体小农数字包容性治理机制与农村连接性投资评估的实证研究。","openalex","2026-10-10T23:30:34.991942Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"content":8,"source_name":150,"source_url":147,"published_at":151,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":152,"score_detail":153,"sources":156,"tags":158,"primary_topic":163,"secondary_topics":164,"classification":165,"search_phrases":178,"slug":181,"view_count":20,"doi":182,"paper":183,"created_at":209},4203,"Moderate-scale operation of family farms in China: underlying logic, practical constraints, and optimization pathways for sustainable food systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1937247","The transformation of sustainable food systems must reconcile production efficiency and supply resilience with the protection of smallholder livelihoods, and for economies with large populations and limited arable land this reconciliation rests heavily on how the scale of family-farm operation is understood and organized. The academic community has long held theoretical disagreements on this issue between static efficiency standards and dynamic equilibrium perspectives, and no integrated explanatory framework that combines economic logic, social context and temporal dimensions has yet been established. Drawing on new institutional economics, economic sociology, family life-cycle theory and intergenerational transmission theory, and proceeding through theoretical deduction, institutional context analysis and secondary evidence from policy documents, official statistics and literature, this paper develops a triple-constraint dynamic equilibrium framework for the moderate-scale operation of family farms in China; its purpose is explanatory rather than estimative, since no uniform optimal scale is defensible across heterogeneous regional endowments, crop systems, service conditions and family structures. The framework holds that moderate scale is not a fixed value set externally but a dynamic equilibrium interval of operating scale: the economic-efficiency constraint sets its lower bound, the social-embeddedness constraint sets its upper bound, and the intergenerational-sustainability constraint operates as an independent temporal condition that also moderates the position and width of the interval as the family life cycle and succession arrangements change. Seen thus, the factor-market, organisational-coordination and temporal-dimension failures observable in practice appear as three structural tensions that jointly compress the feasible interval and constrain the contribution of family farms to sustainable food-system goals. The contribution lies in bringing social embeddedness and intergenerational sustainability into the analysis of moderate scale, moving beyond a single-efficiency account and offering an integrated explanatory framework for the evolution of family-farm scale in transition economies with fragmented landholdings and household-centred production. The framework applies directly to family farms and other family-centred operations; its three constraints are analytically general, whereas their relative binding strength, the interval's position and the associated policy instruments remain conditional on the institutional environment, particularly China's collective land ownership and household contract responsibility system, so that its reference value for other transition economies is conditional rather than universal.","可持续食物系统的转型必须协调生产效率与供给韧性同小农生计保护之间的关系，而对于人口众多、耕地有限的经济体而言，这种协调在很大程度上取决于如何理解与组织家庭农场的经营规模。学术界长期以来在这一问题上存在理论分歧，表现为静态效率标准与动态均衡视角之间的对立，且尚未建立起一个将经济逻辑、社会情境与时间维度相结合的综合解释框架。本文借鉴新制度经济学、经济社会学、家庭生命周期理论与代际传承理论，通过理论推演、制度情境分析以及来自政策文件、官方统计与文献的二手证据，构建了中国家庭农场适度规模经营的三重约束动态均衡框架；其目的在于解释而非估算，因为在异质的区域禀赋、作物体系、服务条件与家庭结构之间，并不存在一个统一的最优规模。该框架认为，适度规模并非由外部设定的固定数值，而是经营规模的一个动态均衡区间：经济效率约束设定其下限，社会嵌入性约束设定其上限，而代际可持续性约束则作为独立的时间条件发挥作用，并随着家庭生命周期与继承安排的变化调节该区间的位置与宽度。由此观之，实践中可观察到的要素市场、组织协调与时间维度失灵，表现为三种结构性张力，它们共同压缩了可行区间，并制约了家庭农场对可持续食物系统目标的贡献。本文的贡献在于将社会嵌入性与代际可持续性纳入适度规模分析，超越单一效率解释，并为土地细碎化、以家庭为中心生产的转型经济体提供家庭农场规模演变的综合解释框架。该框架直接适用于家庭农场及其他以家庭为中心的经营活动；其三重约束在分析上具有一般性，而它们的相对约束强度、区间的位置以","Frontiers in Sustainable Food Systems","2026-10-05T00:00:00Z",73,{"impact":44,"substance":154,"depth":16,"authority":86,"freshness":12,"relevant":20,"comment":155},20,"理论性论文，提出三重约束动态均衡框架，对家庭农场适度规模经营有解释力，但偏学术、政策落地性有限。",[157],{"name":150,"url":147},[26,159,160,161,162],"可持续食物系统","土地制度","适度规模经营","代际传承",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":166},{"candidates":167,"agricultural_evidence":45},[168,173],{"code":31,"score":169,"supported":45,"title":170,"abstract":171,"tags":172},7,[],[26,111],[26],{"code":174,"score":20,"supported":13,"title":175,"abstract":176,"tags":177},"E2",[],[],[159],[179,180],"家庭农场 适度规模经营","中国 家庭农场 可持续食物系统","家庭农场适度规模经营-4203","10.3389\u002Ffsufs.2026.1937247",{"doi":182,"openalex_id":184,"authors":185,"venue":150,"cited_by_count":65,"oa_url":147,"card":204,"direction":70,"ingested_from":142},"W7219629671",[186,189,191,193,195,198,200,202],{"name":187,"orcid":188},"Ning Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5618-6359",{"name":190,"orcid":8},"Jingjing Wang",{"name":192,"orcid":8},"Yong Xia",{"name":194,"orcid":8},"Fuhong Wang",{"name":196,"orcid":197},"Guirong Wang","https:\u002F\u002Forcid.org\u002F0009-0000-3917-003X",{"name":199,"orcid":8},"Yi Lyu",{"name":201,"orcid":8},"Ziyang Wang",{"name":203,"orcid":8},"Yijuan Yang",{"tldr":205,"method":206,"finding":207,"direction":70,"opportunity":208},"构建三重约束动态均衡框架，解释中国家庭农场适度规模经营逻辑与优化路径。","新制度经济学、经济社会学、家庭生命周期与代际传递理论，结合政策与统计数据。","适度规模是动态均衡区间：经济效率定下限，社会嵌入定上限，代际可持续为时间条件。","可实证测度三重约束的区间边界，并探究数字技术如何缓解要素市场与组织协调失灵。","2026-10-05T23:30:07.088111Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":8,"source_name":150,"source_url":213,"published_at":216,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":217,"sources":219,"tags":221,"primary_topic":226,"secondary_topics":227,"classification":228,"search_phrases":241,"slug":244,"view_count":245,"doi":246,"paper":247,"created_at":262},3875,"Farmland management right mortgage loan participation and agricultural return migration: evidence from Ningxia, China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1912009","China’s farmland management right mortgage loan (FMRML) arrangement allows farmland management rights to serve as collateral for agricultural production finance, yet household-level evidence linking it to agricultural return migration remains limited. Using 2024 survey data from 532 rural households in FMRML implementation areas of Ningxia, this study examines agricultural return migration, defined as the return of a household member with migrant-employment experience who remained in the rural area for at least 3 months and engaged in agriculture or agriculture-related activities. In the fully adjusted Probit model, participation is associated with a 15.9-percentage-point higher predicted probability of agricultural return migration, conditional on household characteristics, land and agricultural operating foundations, and local financial-service conditions. The association persists when a demand-side proxy is used and across alternative model and control specifications, county\u002Fdistrict and township fixed effects, propensity-score matching, and a heteroskedasticity-based instrumental-variable sensitivity analysis. The baseline coefficient also lies outside the random-label permutation distribution. Mechanism-consistent models associate participation with a larger net change in operated agricultural area per household member and a larger log change in long-term agricultural productive-asset stock; both components are positively associated with agricultural return migration conditional on participation and the baseline controls. Bootstrap inference for Karlson–Holm–Breen decompositions yields positive descriptive indirect associations for each component and jointly. Separate probability-scale comparisons indicate larger associations for trained than untrained households, for households operating contiguous rather than noncontiguous plots, and for households whose heads had at least six rather than fewer years of schooling; differences by terrain and contracted farmland area are not statistically discernible. The association is substantially larger among households embedded in socialized agricultural service organizations. These findings connect the financial mobilization of farmland management rights with household labor re-engagement and local agricultural operating conditions and suggest that household capabilities, plot configuration, and organizational services are important complementary conditions. Because the core variables share a common cross-sectional reference period, temporal ordering remains unresolved; the estimates represent conditional and mechanism-consistent associations. Policy design should coordinate agricultural finance with inclusive technical and organizational services, voluntary plot-adjustment support, and borrower safeguards.","中国农地经营权抵押贷款（farmland management right mortgage loan, FMRML）制度允许农地经营权作为农业生产融资的抵押物，但其与农业回流迁移之间关联的农户层面证据仍然有限。本研究利用2024年宁夏FMRML实施地区532户农户的调查数据，考察农业回流迁移，将其定义为具有外出务工经历的家庭成员返回农村地区并至少停留3个月且从事农业或农业相关活动。在完全调整的Probit模型中，在控制农户特征、土地与农业经营基础以及当地金融服务条件的情况下，参与FMRML与农业回流迁移的预测概率高出15.9个百分点相关。当使用需求侧代理变量以及在不同模型和控制设定、县\u002F区与乡镇固定效应、倾向得分匹配以及基于异方差的工具变量敏感性分析下，这一关联依然稳健。基准系数也位于随机标签置换分布之外。机制一致性模型表明，参与FMRML与人均经营农地面积的较大净变化以及长期农业生产性资产存量的较大对数变化相关；在控制参与变量和基准协变量的条件下，这两个组成部分均与农业回流迁移正相关。Karlson–Holm–Breen分解的Bootstrap推断显示，各组成部分及联合均存在正向的描述性间接关联。分概率尺度比较表明，受过培训的农户比未受培训的农户关联更大，经营连片地块的农户比经营非连片地块的农户关联更大，户主受教育年限至少六年的农户比少于六年的农户关联更大；地形和承包耕地面积方面的差异在统计上不可辨别。嵌入农业社会化服务组织的农户中，这一关联显著更大。这些发现将农地经营权的金融化动员与农户劳动力再参与及当地农业经营条件联系起来，并表明农户能力、地块配置和组织化服务是重要的互补条件。由于核心变量共享共同的横截面","2026-09-30T00:00:00Z",{"impact":102,"substance":17,"depth":16,"authority":86,"freshness":87,"relevant":20,"comment":218},"基于宁夏532户调查的实证研究，方法稳健、机制清晰，为农村金融与劳动力回流政策提供参考，但属区域截面证据、时效与影响层级有限。",[220],{"name":150,"url":213},[28,222,223,224,225],"农业社会化服务","农户调查","土地经营权","返乡务农",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":229},{"candidates":230,"agricultural_evidence":45},[231,237],{"code":31,"score":232,"supported":45,"title":233,"abstract":234,"tags":236},5,[],[235],"社会化服务",[222,223],{"code":55,"score":20,"supported":13,"title":238,"abstract":239,"tags":240},[],[],[28],[242,243],"宁夏 土地经营权抵押贷款 返乡务农","农地经营权抵押 农户 回流","宁夏土地经营权抵押贷款返乡务农-3875",2,"10.3389\u002Ffsufs.2026.1912009",{"doi":246,"openalex_id":248,"authors":249,"venue":150,"cited_by_count":65,"oa_url":213,"card":257,"direction":70,"ingested_from":142},"W7214986053",[250,252,254],{"name":251,"orcid":8},"Guanyu Qin",{"name":253,"orcid":8},"Yun Yu",{"name":255,"orcid":256},"Tao Li","https:\u002F\u002Forcid.org\u002F0000-0001-7470-5036",{"tldr":258,"method":259,"finding":260,"direction":70,"opportunity":261},"基于宁夏532户调查，研究农地经营权抵押贷款参与是否促进农业回流迁移。","2024年宁夏532户问卷，Probit模型、PSM、工具变量与KHB分解。","参与抵押贷款使农业回流迁移概率提高15.9个百分点，且受培训、地块连片和服务组织强化。","可探究金融可得性通过资产与规模路径影响劳动力回流，并引入数字普惠金融或服务组织的中介机制。","2026-10-01T23:30:07.372962Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":8,"content":8,"source_name":268,"source_url":8,"published_at":269,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":270,"score_detail":271,"sources":274,"tags":276,"primary_topic":280,"secondary_topics":281,"classification":282,"search_phrases":314,"slug":317,"view_count":87,"doi":318,"paper":319,"created_at":326},3327,"Digitalization of smallholder agriculture: Adoption and impacts——特刊导论（Sun et al. 2026）","https:\u002F\u002Fwww.sciopen.com\u002Flocal\u002Farticle_pdf\u002F10.1016\u002Fj.jia.2026.04.021.pdf","《Journal of Integrative Agriculture》2026年专刊导论，IAMO孙占力博士、华中农大熊航教授、北大易红梅教授等担任客座编辑。本专刊收录Liu et al. (2026)研究——使用面板数据和多种估计方法调查数字技术使用如何影响中国易地搬迁农户收入稳定性，发现数字技术显著改善收入水平和稳定性；Hu et al. (2026)使用来自5省1,833个农场的倾向得分匹配数据，发现网络信息使用实际上增加了小农户的化肥支出，反思了农业数字化的复杂性；Bai et al. (2026)研究数字素养通过改善农民对气候灾害风险的感知促进气候适应性生产行为；Amolegbe et al. (2026)研究尼日利亚数字知识与电商估值。","《Journal of Integrative Agriculture》2026专刊","2026-09-17T00:00:00Z",79,{"impact":16,"substance":17,"depth":272,"authority":18,"freshness":19,"relevant":20,"comment":273},19,"国际期刊专刊导论，汇集多国小农户数字化实证研究，方法扎实、结论具反思性，对农业信息化研究与实践有参考价值。",[275],{"name":268,"url":266},[25,277,278,93,279],"农村电商","数字素养","数字鸿沟",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":283},{"candidates":284,"agricultural_evidence":45},[285,289,295,300,305,310],{"code":31,"score":44,"supported":45,"title":286,"abstract":287,"tags":288},[111],[93],[93],{"code":290,"score":115,"supported":45,"title":291,"abstract":292,"tags":294},"G6",[],[293],"农户收入",[],{"code":121,"score":115,"supported":45,"title":296,"abstract":297,"tags":299},[],[298],"气候适应",[],{"code":301,"score":245,"supported":13,"title":302,"abstract":303,"tags":304},"I6",[],[],[278,279],{"code":306,"score":20,"supported":13,"title":307,"abstract":308,"tags":309},"E5",[],[],[277],{"code":50,"score":20,"supported":13,"title":311,"abstract":312,"tags":313},[],[],[25],[315,316],"Journal of Integrative Agriculture 数字农业 专刊","易地搬迁农户 数字技术 收入稳定性","JournalofIntegrativeAgriculture数字农业专刊-3327","10.1016\u002Fj.jia.2026.04.021.pdf",{"doi":318,"openalex_id":8,"authors":320,"venue":8,"cited_by_count":65,"oa_url":8,"card":321,"direction":70,"ingested_from":72},[],{"tldr":322,"method":323,"finding":324,"direction":70,"opportunity":325},"该专刊导论综述小农户农业数字化的采纳与影响，涵盖中国及尼日利亚的多项实证研究。","基于面板数据、倾向得分匹配等方法的专刊导论，整合多国小农户实证研究。","数字技术可提升收入稳定性，但也可能增加化肥支出，数字化影响具有复杂性。","可深入探究数字技术对不同农户群体的异质性影响及化肥增投的机制与调控路径。","2026-09-24T00:04:03.016677Z",{"id":328,"title":329,"url":330,"summary":331,"summary_zh":332,"content":8,"source_name":150,"source_url":330,"published_at":333,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":334,"sources":338,"tags":340,"primary_topic":345,"secondary_topics":346,"classification":347,"search_phrases":358,"slug":361,"view_count":232,"doi":362,"paper":363,"created_at":378},2035,"Family farm development, agricultural technical efficiency and farmers’ income","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1880985","This study examines how family farm development is associated with farmers’ income using prefecture-level panel data from China spanning 2013 to 2024. We employ a two-way fixed-effects framework with standard errors clustered at the prefecture-level city, and measure regional agricultural technical efficiency using data envelopment analysis under variable returns to scale (DEA–VRS). The results show a significant positive association between family farm development and farmers' income, though the magnitude is modest. Instrumental variable estimates confirm the robustness of this association, with coefficients significantly larger than ordinary least squares results, revealing a pattern of negative selection in which family farms develop more rapidly in areas with weaker agricultural conditions. Mechanism tests indicate that agricultural technical efficiency serves as a partial mediator, while direct channels play the dominant role. Interaction-based heterogeneity analysis shows that the income-enhancing association is significantly stronger in western, non-major-grain-producing, and economically less-developed regions, consistent with diminishing marginal returns to organizational innovation. The findings provide empirical evidence for understanding the regional income effects of family farm development and inform the design of regionally differentiated policies.","本研究利用2013—2024年中国地级市面板数据，考察家庭农场发展与农民收入之间的关联。我们采用双向固定效应框架，并将标准误聚类到地级市层面，同时使用可变规模报酬下的数据包络分析（DEA–VRS）测度区域农业技术效率。结果表明，家庭农场发展与农民收入之间存在显著正相关，但幅度较为有限。工具变量估计证实了该关联的稳健性，且系数显著大于普通最小二乘结果，揭示出一种负向选择模式，即家庭农场在农业条件较弱的地区发展更快。机制检验表明，农业技术效率发挥部分中介作用，而直接渠道占主导地位。基于交互项的异质性分析显示，这种增收关联在西部、非粮食主产区和经济欠发达地区显著更强，这与组织创新的边际收益递减规律相一致。研究结论为理解家庭农场发展的区域收入效应提供了经验证据，并为区域差异化政策设计提供了参考。","2026-09-09T00:00:00Z",{"impact":16,"substance":335,"depth":336,"authority":86,"freshness":87,"relevant":20,"comment":337},21,17,"基于2013—2024年地级面板数据与DEA-VRS、工具变量法的实证研究，结论扎实且具区域差异化政策含义，值得进入每日精选。",[339],{"name":150,"url":330},[341,26,342,343,344],"农民增收","农业政策","区域差异","农业技术效率",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":348},{"candidates":349,"agricultural_evidence":45},[350,354],{"code":31,"score":108,"supported":45,"title":351,"abstract":352,"tags":353},[],[26],[26],{"code":290,"score":20,"supported":13,"title":355,"abstract":356,"tags":357},[],[],[341],[359,360],"农业技术效率 农业政策 农民增收 区域差异","农业技术效率 农业政策","农业技术效率农业政策农民增收区域差异-2035","10.3389\u002Ffsufs.2026.1880985",{"doi":362,"openalex_id":364,"authors":365,"venue":150,"cited_by_count":65,"oa_url":330,"card":373,"direction":70,"ingested_from":142},"W7212082704",[366,369,371],{"name":367,"orcid":368},"Lin Fang","https:\u002F\u002Forcid.org\u002F0009-0001-1710-8144",{"name":370,"orcid":8},"Jingyu YIN",{"name":372,"orcid":8},"Zhou Fangzhou",{"tldr":374,"method":375,"finding":376,"direction":70,"opportunity":377},"基于中国地级市面板数据，检验家庭农场发展对农民收入的关联及农业技术效率的中介作用。","2013-2024年地级市面板数据，双向固定效应、DEA-VRS测效率、工具变量","家庭农场发展与农民收入显著正相关但幅度有限，技术效率仅部分中介，直接渠道主导，西部和欠发达地区效应更","可探究数字技术\u002F信息化服务如何增强家庭农场对农民收入的直接渠道，并关注欠发达地区的异质性机制。","2026-09-10T23:30:07.795645Z",{"id":380,"title":381,"url":382,"summary":383,"summary_zh":8,"content":384,"source_name":385,"source_url":8,"published_at":386,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":387,"score_detail":388,"sources":390,"tags":392,"primary_topic":398,"secondary_topics":399,"classification":400,"search_phrases":415,"slug":418,"view_count":232,"doi":8,"paper":8,"created_at":419},539,"ICT-Based Versus Human-Based Climate Information: Implications for Agronomic Decisions Among Smallholder Farmers in the Eastern Cape, South Africa","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114","基于南非东开普省 217 户小农户的横断面调查与多阶段抽样,采用 logistic 回归识别 ICT 气候信息采纳决定因素,并用倾向得分匹配(PSM)评估其对农事决策的影响。结果显示 65% 农户通过 ICT 平台获取气候信息,35% 依赖人力渠道;ICT 采纳驱动因素包括教育、数字素养、移动网络可靠性、实时更新感知价值与智能手机持有;PSM 估计显示基于 ICT 的气候信息在影响种植决策、投入品使用与农事时机方面比人力渠道效果低 10%—15%,提示数字接入并不自动转化为有效使用。","Logical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[![Image 1: sustainability-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Fsustainability-logo.png?3798e4e58c765aed)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fsustainability)\n\n## Article Menu\n\nFont Type:\n\n_Arial_ _Georgia_ _Verdana_\n\nFont Size:\n\nAa Aa Aa\n\nLine Spacing:\n\n__ __ __\n\nColumn Width:\n\n__ __ __\n\nBackground:\n\nOpen Access Article\n\nby \nJabulile Zamokuhle Manyike\n\n *[](mailto:jmanyike@ufh.ac.za)[![Image 2: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0001-6529-9574) and \nYanga-Inkosi Nocezo\n\n[](mailto:yanganocezo12@gmail.com)[![Image 3: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0002-7607-495X)\n\nDepartment of Agricultural Economics, Extension, and Agri-Business, Faculty of Science and Agriculture, University of Fort Hare, 1 King William’s Town Road, Alice 5700, South Africa\n\n*\n\nAuthor to whom correspondence should be addressed.\n\nSubmission received: 3 February 2026 \u002F Revised: 7 March 2026 \u002F Accepted: 8 March 2026 \u002F Published: 9 August 2026\n\n## Abstract\n\nSmallholder farmers in South Africa face increasing climate variability, yet their agronomic decision-making depends on timely and reliable climate information. Although digital ICT platforms are expanding, limited evidence exists on how their effectiveness compares to human-based advisory systems. The study addresses this gap by examining the determinants of ICT-based climate information adoption using logistic regression and assessing its influence on agronomic decisions through propensity score matching (PSM). A cross-sectional survey and multistage sampling were used to collect data from 217 smallholder crop farmers in the Eastern Cape. The results indicate that 65% of farmers accessed climate information through ICT platforms, while 35% relied on human-based sources. The adoption of ICT-based information is driven by education, digital literacy, mobile network reliability, the perceived value of real-time updates, and smartphone ownership, whereas habitual dependence on traditional channels hinders digital uptake. PSM estimates show that ICT-based climate information is 10–15% less effective than human-based sources in shaping planting decisions, input use, and the timing of farm operations, likely due to digital literacy and infrastructure constraints. The study demonstrates that access to digital tools does not automatically translate into effective use and recommends a hybrid information model integrating digital platforms with trusted human intermediaries to strengthen climate resilience and agronomic decision-making.\n\n## 1. Introduction\n\nAgriculture remains central to rural livelihoods in South Africa, particularly in provinces such as the Eastern Cape, where smallholder farmers form the majority of agricultural producers [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B1-sustainability-18-08114),[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B2-sustainability-18-08114),[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B3-sustainability-18-08114)]. Despite its importance, this sector is highly vulnerable to climate-related risks, as smallholder farmers often operate with limited resources, weak extension support, and poor access to climate adaptation tools [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B4-sustainability-18-08114),[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B5-sustainability-18-08114),[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B6-sustainability-18-08114),[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B7-sustainability-18-08114)]. Increasingly frequent droughts, delayed rainfall, rising temperatures, and shifting pest and disease pressures threaten food security and economic stability in the region [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B8-sustainability-18-08114),[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B9-sustainability-18-08114),[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B10-sustainability-18-08114)]. Timely, reliable climate information is therefore critical for guiding agronomic decisions such as planting dates, crop choices, water management, and input application [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B11-sustainability-18-08114),[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B12-sustainability-18-08114),[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B13-sustainability-18-08114),[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B14-sustainability-18-08114),[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B15-sustainability-18-08114)]. Access to such information enhances farmers’ preparedness for climate extremes and strengthens their adaptive capacity [[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B15-sustainability-18-08114),[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B16-sustainability-18-08114),[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B17-sustainability-18-08114),[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B18-sustainability-18-08114)]. Climate information services typically include rainfall onset and cessation, seasonal duration, temperature trends, soil moisture, wind patterns, and early warnings for droughts or floods [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B18-sustainability-18-08114),[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B19-sustainability-18-08114),[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B20-sustainability-18-08114),[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B21-sustainability-18-08114),[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B22-sustainability-18-08114)]. Traditionally, this information has been disseminated through human-based channels—extension officers, farmer groups, NGOs, and community networks [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B14-sustainability-18-08114),[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B22-sustainability-18-08114),[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B23-sustainability-18-08114),[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B24-sustainability-18-08114)]. These sources are valued for their contextual relevance and interpersonal trust [[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B21-sustainability-18-08114)]. However, in South Africa, high extension-to-farmer ratios, inconsistent service delivery, communication gaps, and sociocultural barriers, including gendered access to advisory support, limit the effectiveness of these channels [[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B25-sustainability-18-08114),[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B26-sustainability-18-08114),[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B27-sustainability-18-08114),[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B28-sustainability-18-08114),[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B29-sustainability-18-08114)].\n\nAt the same time, the expansion of digital infrastructure and mobile technologies has enabled Information and Communication Technologies (ICTs) such as mobile weather alerts, agricultural apps, WhatsApp groups, and voice-based advisory platforms to play an increasingly prominent role in climate information delivery [[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B23-sustainability-18-08114),[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B30-sustainability-18-08114),[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B31-sustainability-18-08114),[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B32-sustainability-18-08114),[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B33-sustainability-18-08114),[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B34-sustainability-18-08114),[35](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B35-sustainability-18-08114),[36](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B36-sustainability-18-08114)]. ICT-based tools have the potential to provide rapid, localized, and scalable climate information [[37](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B37-sustainability-18-08114)]. However, their adoption remains uneven due to persistent digital barriers, including poor network coverage, low digital literacy, high data costs, and language mismatches [[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B33-sustainability-18-08114),[38](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B38-sustainability-18-08114),[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B39-sustainability-18-08114),[40](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B40-sustainability-18-08114)]. Moreover, farmers often continue to trust interpersonal sources more than digital platforms, creating a dual system where ICT-based and human-based information coexist but are not equally utilized or valued [[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B30-sustainability-18-08114),[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B39-sustainability-18-08114)]. These dynamics raise important questions regarding why some farmers adopt ICT-based climate information while others rely on traditional channels, and whether ICT-based information is as effective as human-based sources in influencing agronomic decisions. Addressing these questions is essential for designing climate information systems that are both accessible and impactful.\n\nThis study, therefore, seeks to bridge this gap by (i) identifying the factors that influence smallholder farmers’ adoption of ICT-based climate information over human-based sources, and (ii) evaluating the impact of ICT-based climate information on agronomic decision-making compared to human-based sources. Focusing on the Eastern Cape Province, where both ICT infrastructure and extension systems are active but unevenly distributed, the study provides timely insights into how information flows influence climate adaptation at the farm level. By shedding light on these dynamics, the study contributes to policy discussions on how to design integrated climate information services that are inclusive, accessible, and actionable. It also offers practical recommendations for strengthening both digital and interpersonal communication channels, ensuring that no farmer is left behind in the shift toward climate-resilient agriculture.\n\n## 2. Materials and Methods\n\n### 2.1. Description of the Study Area\n\nThe study was conducted in Elundini Local Municipality, located within the Joe Gqabi District of the Eastern Cape. The Municipality is made up of three towns, Mount Fletcher in the north, Maclear in the centre, and Ugie in the South. [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#fig_body_display_sustainability-18-08114-f001) illustrates their locations along with the distribution of sampled households across the study area. The municipality is located near Mthatha [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B41-sustainability-18-08114)], the third largest city within the province [[42](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B42-sustainability-18-08114)]. Elundini’s economy is driven by agriculture, social services, and retail trade, with the agricultural sector comprising commercial, emerging, and subsistence farming [[43](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B43-sustainability-18-08114)]. Farmers commonly grow maize, potatoes, and cabbages, and keep both large and small ruminants [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B41-sustainability-18-08114)]. The area receives an annual average rainfall of approximately 1200 mm, mostly received in the summer planting season [[44](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B44-sustainability-18-08114)], but experiences significant climate variability, including erratic rainfall and periodic droughts that heighten production risks [[43](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B43-sustainability-18-08114)]. Furthermore, Elundini has many natural resources and a good climate that makes it possible for households to engage in agriculture [[45](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B45-sustainability-18-08114)]. However, its agricultural performance remains below its potential, constrained by limited market access, infrastructure, and technology uptake—conditions that contrast sharply with highly mechanized and well-serviced agricultural zones [[46](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B46-sustainability-18-08114)]. In the area, government support is available through extension services and farmer assistance programmes such as the provision of seedlings, livestock facilities, and fencing materials [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B41-sustainability-18-08114),[46](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B46-sustainability-18-08114)]. Nonetheless, the extent of their reach is limited due to high farmer-to-officer ratios and communication constraints [[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B25-sustainability-18-08114),[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B26-sustainability-18-08114),[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B27-sustainability-18-08114),[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B28-sustainability-18-08114),[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B29-sustainability-18-08114)].\n\n**Figure 1.** Map of study area. Source: Department of GIS, University of Fort Hare. Note: Numbers 1–17 show the municipal wards; red dots mark the locations of the sampled households.\n\nICT infrastructure is provided through public libraries equipped with computers and internet access [[46](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B46-sustainability-18-08114)], though network reliability and digital literacy remain barriers for many community members. Given the combination of high agricultural potential, exposure to climate-related risks, uneven technological advancement, and mixed advisory and ICT service environments, Elundini provides an ideal setting for analyzing differences in the adoption and effectiveness of ICT-based versus human-based climate information sources.\n\n### 2.2. Research Design\n\nThis study adopted a quantitative cross-sectional research design to investigate the adoption and impact of ICT-based climate information among smallholder crop farmers in the Eastern Cape Province of South Africa. A cross-sectional approach was deemed appropriate for capturing data at a single point in time, enabling an assessment of current patterns of ICT use, climate information access, and related agronomic decisions.\n\n### 2.3. Sampling Procedure and Sample Size\n\nA multistage sampling technique was employed to ensure representativeness and practicality in data collection. In the first stage, Elundini municipality was purposively selected. Within the municipality, eight wards were randomly selected based on their agricultural relevance and reported farming activity levels. Within each ward, one or two villages were randomly selected, leading to a total of twelve participating villages. Finally, within each village, a random sampling technique was used to select 217 smallholder farmers. [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#table_body_display_sustainability-18-08114-t001) presents the distribution of sampled farmers across the three towns. From the total sample, 167 farmers indicated that they had access to some form of climate information, either through ICT-based platforms or human-based sources, while 50 had no access, and were excluded from the impact assessment component of the analysis. Additionally, among the 167 farmers included, 69 were from Maclear, and 65 from Mount Fletcher, with Ugie represented by 33 farmers.\n\n**Table 1.** Sampling size per location.\n\n### 2.4. Data Collection\n\nData was collected in May 2022 using a structured questionnaire, administered through face-to-face interviews to ensure comprehension and minimize non-response. The questionnaire consisted of questions on farmer’s socio-demographic characteristics (age, education, income, household size, and gender), sources of climate information (e.g., extension agents, radio, mobile apps, WhatsApp groups, local farmer associations), ICT access and use (mobile phone ownership, digital literacy, access to internet or network), and agronomic decision-making (e.g., planting time, crop choices, irrigation practices, input application). The instrument was pre-tested with a small group of farmers outside the sample area to ensure clarity and relevance, and necessary adjustments were made before final data collection. During the data collection phase, authors ensured that verbal consent was obtained from farmers, they were treated with respect and allowed to refuse to be part of the research or withdraw from it at any given time, and that their information was kept confidential and used for academic purposes only. All of these requirements were guided by the ethical approval letter [MAN011SNOC01], which was obtained from the department of agricultural economics and extension, faculty of Science and Agriculture at the University of Fort Hare.\n\n### 2.5. Data Analysis\n\nData was first analyzed using descriptive statistics to summarize the characteristics of the sample, including age distribution, education levels, climate information sources, and ICT use. To identify the factors influencing the adoption of ICT-based climate information, a binary logistic regression model was employed. Stata\u002FSE 15.1 (Stata Corp LLC) was used for the analysis.\n\n#### 2.5.1. Logistic Regression Model\n\nThe dependent variable was a binary indicator of whether a farmer accessed climate information via ICT (1) or through human-based sources (0). The probability (\n\n$P_{i}$\n) that farmers receive climate information via ICT platforms is represented as\n\n$$\nY_{i } = \\beta_{0} + \\sum_{i = 1}^{n} \\beta_{i} X_{i ,}\n$$\n\n(1)\n\nThe equation represents a binary choice, which involves the estimation of the probability of receiving climate information via ICT platforms (Y) as a function of independent variables (X).\n\n$\\beta_{0 }$\nis constant and\n\n$Y_{i }$\nis equal to one 1 when farmers receive ICT-based climate information and 0 if through human-based sources. The logit model uses a logistic cumulative distributive function to estimate, P given by\n\n$$\nP = \\left(\\right. Y = \\frac{1}{X} \\left.\\right) = \\frac{e^{y}}{1 + e^{y}}\n$$\n\n(2)\n\n$$\nP = \\left(\\right. Y = \\frac{0}{X} \\left.\\right) = 1 - \\frac{e^{y}}{1 + e^{y}}\n$$\n\n(3)\n\n$$\nY = \\mathsf{\\beta}_{1} X_{1} + \\mathsf{\\beta}_{2} X_{2} + \\ldots + \\mathsf{\\beta}_{k} X = \\sum_{i = 1}^{k} \\mathsf{\\beta}_{i} X_{i ,}\n$$\n\n(4)\n\nwhere k represents the number of explanatory predictors that are to be included in the analysis. The model is as follows:\n\n$$\nY = \\mathit{Ln} \\left(\\right. \\frac{P}{1 - P} \\left.\\right) = \\beta_{0} + \\beta_{k} + \\epsilon ,\n$$\n\n(5)\n\nwhere Y = farmer accessed climate information via ICT; Ln\n\n$\\left(\\right. \\frac{P}{1 - P} \\left.\\right)$\nthe ratio of probability of accessing climate information via ICT (p) to receiving it through human-based sources (1 − P); β = slope of coefficient;\n\n$X_{k}$\n= vector of the independent variables presented in [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#table_body_display_sustainability-18-08114-t002); ε = error term.\n\n**Table 2.** Independent variables and expected signs in ICT adoption model.\n\n#### 2.5.2. Measuring Agronomic Decision\n\nThe Agronomic Decision Index (ADI) was employed to assess the extent of agronomic decisions made by smallholder farmers. This index serves as a tool to quantify the degree to which each household adopts key agronomic practices. While the existing literature commonly measures agronomic decision-making by simply noting whether a farmer made a decision or not [[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B17-sustainability-18-08114)], this approach does not capture the depth of adoption. In contrast, ADI provides a more comprehensive measure. Each agronomic decision was coded as 1 if undertaken by the farmer and 0 otherwise. The index for each farmer was then calculated as shown in Equation (6):\n\n$$\nA D I = \\frac{D_{1} + D_{2} + D_{3} + D_{4} + D_{5} + D_{6} + D_{7}}{7}\n$$\n\n(6)\n\nwhere D1 to D7 include decisions on land preparation, planting date decis","Sustainability","2026-08-08T22:00:00Z",64,{"impact":44,"substance":154,"depth":16,"authority":44,"freshness":245,"relevant":20,"comment":389},"论文聚焦南非小农气候信息获取，对农业信息化有参考价值，但地域性强，时效性低。",[391],{"name":385,"url":382},[25,393,394,93,395,396,279,397],"数字乡村","农业信息化","气候信息","ICT采纳","发展中国家",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":401},{"candidates":402,"agricultural_evidence":45},[403,407,411],{"code":31,"score":44,"supported":45,"title":404,"abstract":405,"tags":406},[111],[93],[93],{"code":50,"score":245,"supported":13,"title":408,"abstract":409,"tags":410},[],[],[394,25],{"code":301,"score":245,"supported":13,"title":412,"abstract":413,"tags":414},[],[],[393,279],[416,417],"农业信息化 发展中国家 数字乡村 数字农业","农业信息化 发展中国家","农业信息化发展中国家数字乡村数字农业-539","2026-08-15T00:02:55.675693Z"]