[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3434":3,"related-3434":70},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":55,"slug":58,"view_count":20,"doi":8,"paper":59,"created_at":69},3434,"《农业数字供应链金融何以助力小农户采纳农业新质技术?》","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F165468.html","刘彦君、申云基于园区交易信任视角从理论与实证维度考查以现代农业产业园为载体的农业数字供应链金融何以助力小农户采纳农业新质技术。研究发现：农业数字供应链金融显著促进园区内信任机制的形成，有效激励农户采纳农业新质技术，不仅提升采纳率而且显著改善应用水平与推广效果；机制上通过农业供应链整合、信息渠道数字化及同群效应三条路径发挥作用；该促进效应在市级及以上等级园区、近郊型园区以及高学历农户群体中更为突出。",null,"《农业经济与管理》2026年第2期","2026-09-20T00:00:00Z","论文",10,false,75,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,22,18,14,5,1,"核心期刊实证研究，揭示数字供应链金融促进小农户采纳新质技术的机制，对数字乡村与农业科技推广有参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"数字乡村","小农户","现代农业产业园","数字供应链金融","农业新质技术",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},12,true,[27],[27],[27],{"code":51,"score":21,"supported":13,"title":52,"abstract":53,"tags":54},"I6",[],[],[26],[56,57],"农业数字供应链金融 小农户 新质技术","现代农业产业园 园区信任 技术采纳","农业数字供应链金融小农户新质技术-3434",{"doi":8,"openalex_id":8,"authors":60,"venue":8,"cited_by_count":61,"oa_url":8,"card":62,"direction":66,"ingested_from":68},[],0,{"tldr":63,"method":64,"finding":65,"direction":66,"opportunity":67},"研究农业数字供应链金融如何通过园区交易信任促进小农户采纳农业新质技术。","基于现代农业产业园调研数据，构建理论与实证模型检验信任机制与中介路径。","数字供应链金融显著提升农户新质技术采纳率与应用水平，通过供应链整合、信息数字化和同群效应实现。","数字乡村与农业信息化","可探究不同园区类型与农户禀赋下数字供应链金融的异质性作用及信任机制的长期演化。","agent","2026-09-25T00:09:33.652154Z",{"total":71,"page":21,"page_size":71,"items":72},6,[73,118,167,247,292,359],{"id":74,"title":75,"url":76,"summary":77,"summary_zh":8,"content":78,"source_name":79,"source_url":8,"published_at":80,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":81,"score_detail":82,"sources":86,"tags":88,"primary_topic":92,"secondary_topics":93,"classification":94,"search_phrases":105,"slug":108,"view_count":109,"doi":8,"paper":110,"created_at":117},5537,"农业产业园促进小农户融入数字化供应链：协同机制与制度逻辑","https:\u002F\u002Fkyy.nwafu.edu.cn\u002Fkjjz\u002Fxdkjjz\u002F557ffd662c8e4af98e46fbf90f35d91f.htm","西北农林科技大学阮俊虎教授团队联合德国哥廷根大学于晓华教授，以大荔县国家现代农业产业园等为典型案例，从交易成本理论出发围绕技术赋能、组织整合、规则治理与价值分配四个机制开展案例内分析与跨案例比较，揭示农业产业园作为中国特色制度平台促进小农户融入数字化供应链的深层逻辑。","近日，经济管理学院“数智农业运营管理团队”阮俊虎教授联合德国哥廷根大学于晓华教授在《中国农村经济》发表题为“农业产业园促进小农户融入数字化供应链：协同机制与制度逻辑”的研究论文，通过多案例研究揭示农业产业园促进小农户融入数字化供应链的协同机制，并阐释机制形成与运行的制度逻辑。博士研究生刘晨欢为第一作者，阮俊虎教授为通讯作者，王月博士和于晓华教授为合作学者。\n\n数字技术正深刻重构农产品供应链格局，大型电商平台等供应链核心主体加速构建覆盖生产源头至消费终端的数字化供应链体系，该体系不仅体现为数字技术在生产、销售、物流等环节的应用，更强调不同环节与经营主体之间的系统协同与数据贯通，小农户亟须有效融入数字化供应链以应对市场竞争格局的深刻变化。然而，“大国小农”国情农情下，小农户经营规模较小、土地细碎化程度较高，难以独立对接数字化、规模化的现代供应链体系。促进小农户深度融入数字化供应链，进而实现小农户和现代农业发展有机衔接，成为农业现代化进程中值得关注的重要议题。农业产业园作为区域农业发展的综合性制度平台，兼具资源集聚、组织协同与制度保障三重优势，能够为地方政府及园区经营管理主体协同整合制度资源并协调多元主体关系提供平台支撑，但将资源集聚、组织协同和制度供给优势转化为小农户融入数字化供应链的现实能力，其内在机理尚未得到系统性揭示。\n\n本文立足“农业产业园促进小农户融入数字化供应链的内在机理”这一科学问题，从交易成本理论出发，以大荔县国家现代农业产业园等为典型案例，围绕技术赋能、组织整合、规则治理和价值分配四个机制开展案例内分析与跨案例比较分析，揭示不同园区作用机制的共性、差异及情境适配特征，并从制度层面阐释农业产业园作为中国特色制度平台，促进小农户融入数字化供应链的深层逻辑。研究旨在为农业经营呈现小规模、分散化特征的国家和地区促进小农户和现代农业发展有机衔接提供理论参考与实践借鉴。\n\n该研究得到国家社会科学基金重大项目“我国农产品供应链数字化转型的发展战略与实施路径研究”（23&ZD123）的资助。\n\n论文链接：\n\n[https:\u002F\u002Fzgncjj.ajcass.com\u002F?jumpnotice=201606270007#\u002FissueDetail?contentId=123722&year=2026&issue=10&title=%E5%BD%93%E6%9C%9F%E7%9B%AE%E5%BD%95](https:\u002F\u002Fzgncjj.ajcass.com\u002F?jumpnotice=201606270007#\u002FissueDetail?contentId=123722&year=2026&issue=10&title=%E5%BD%93%E6%9C%9F%E7%9B%AE%E5%BD%95)\n\n编辑：张晴 终审：刘玉峰","中国农村经济 2026-10-21","2026-10-21T00:00:00Z",82,{"impact":18,"substance":17,"depth":18,"authority":83,"freshness":84,"relevant":21,"comment":85},15,9,"核心期刊论文，多案例揭示农业产业园促进小农户融入数字化供应链的协同机制与制度逻辑，理论新意足、政策参考价值高，值得进入每日精选。",[87],{"name":79,"url":76},[26,27,89,90,91],"制度逻辑","数字化供应链","农业产业园",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":95},{"candidates":96,"agricultural_evidence":46},[97,101],{"code":32,"score":45,"supported":46,"title":98,"abstract":99,"tags":100},[27],[27],[27],{"code":51,"score":21,"supported":13,"title":102,"abstract":103,"tags":104},[],[],[26],[106,107],"农业产业园 小农户 数字化供应链","大荔县 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小农户 数字赋能","CLDS 互联网使用 农业生产","华南农业大学小农户数字赋能-4972",{"doi":8,"openalex_id":8,"authors":160,"venue":8,"cited_by_count":61,"oa_url":8,"card":161,"direction":66,"ingested_from":68},[],{"tldr":162,"method":163,"finding":164,"direction":66,"opportunity":165},"基于CLDS数据实证检验互联网使用如何助推小农户融入现代农业发展。","有限理性与信息不对称理论，CLDS 2018农户微观数据实证。","互联网使用促进小农户机械化、规模化和生产性投资，中老年及中部东北农户效应更显著。","可探究数字素养培训与数字基础设施如何协同提升小农户数字赋能效果。","2026-10-15T00:06:09.096458Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":8,"source_name":173,"source_url":170,"published_at":174,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":175,"score_detail":176,"sources":179,"tags":181,"primary_topic":185,"secondary_topics":186,"classification":193,"search_phrases":226,"slug":229,"view_count":71,"doi":230,"paper":231,"created_at":246},3512,"AI-Driven Agricultural Advisory and Diagnostic Systems for Smallholder Farming: Technical Architectures, Evidence and Deployment Priorities for North-East India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Farja\u002F2026\u002Fv19i4919","Artificial intelligence (AI) is being introduced into agricultural advisory services through machine learning, computer vision, conversational large language models, retrieval-augmented generation and multimodal interfaces. For smallholder farming, the central question is not whether these technologies can produce technically plausible outputs, but whether they can provide locally correct, actionable and safe recommendations under heterogeneous agronomic, linguistic and connectivity conditions. This critical narrative review integrates evidence on digital extension, AI-enabled agricultural advice, image-based diagnosis and responsible digital agriculture, with particular reference to North-East India. Literature published from 1 January 2010 to 17 July 2026 was considered, with emphasis on peer-reviewed field evaluations, technical validation studies, reviews and regionally relevant research. Evidence from digital extension provides the strongest causal baseline: mobile and personalised advisory services frequently improve information recall, agronomic knowledge and adoption of recommended practices, yet effects on yield, profit and welfare are inconsistent. Recent generative-AI studies show that large language models can produce useful agricultural responses, but site-specific rates, timing and local practice remain recurrent failure points. Retrieval grounding and expert feedback improve local relevance, although multi-season farm-level effectiveness evidence remains scarce. Image-based plant-disease systems achieve high accuracy in curated datasets, but performance can deteriorate sharply under field domain shift, class novelty and variable image quality. North-East Indian studies of mobile advisory systems in Meghalaya, Nagaland and Tripura demonstrate a valuable institutional foundation based on interactive voice response, local expert networks and user-centred service design; they do not, however, establish the effectiveness of autonomous AI. The most defensible deployment model is therefore an offline-tolerant, multilingual, multimodal and human-supervised architecture that grounds recommendations in curated regional knowledge, represents uncertainty, preserves provenance and escalates high-risk or out-of-distribution cases. Future research should prioritise prospective district- and season-spanning evaluations that connect model quality to farmer decisions, agronomic outcomes, equity, safety and cost-effectiveness.","人工智能（AI）正通过机器学习、计算机视觉、对话式大语言模型、检索增强生成和多模态界面被引入农业咨询服务。对于小农户而言，核心问题不在于这些技术能否产生技术上看似合理的输出，而在于它们能否在异质的农艺、语言和网络连接条件下提供本地正确、可操作且安全的建议。本批判性叙事综述整合了数字推广、AI赋能的农业建议、基于图像的诊断和负责任数字农业方面的证据，并特别关注印度东北部。本文考察了2010年1月1日至2026年7月17日期间发表的文献，重点关注同行评议的田间评估、技术验证研究、综述及区域相关研究。来自数字推广的证据提供了最强的因果基线：移动化和个性化咨询服务经常改善信息记忆、农艺知识和对推荐措施的采纳，但对产量、利润和福利的影响并不一致。近期生成式AI研究表明，大语言模型能够产生有用的农业回答，但针对具体地点的用量、时机和本地实践仍是反复出现的失败点。检索 grounding 和专家反馈可提高本地相关性，但多季农场层面的有效性证据仍然稀缺。基于图像的植物病害系统在精选数据集上达到高准确率，但在田间域偏移、类别新颖性和图像质量多变的情况下，性能可能急剧下降。印度东北部在梅加拉亚邦、那加兰邦和特里普拉邦开展的移动咨询系统研究展示了基于交互式语音应答、本地专家网络和以用户为中心的服务设计的宝贵制度基础；然而，这些研究并未确立自主AI的有效性。因此，最可辩护的部署模式是一种容忍离线、多语言、多模态且有人工监督的架构，该架构将建议建立在精选的区域知识之上，表征不确定性，保留来源信息，并对高风险或分布外案例进行升级处理。未来研究应优先开展前瞻性的跨区县和跨季节评估，将模型质量与农户决策、农艺结果、公平性、安全性和成本效益联系起来。","Asian Research Journal of Agriculture","2026-09-24T00:00:00Z",80,{"impact":18,"substance":17,"depth":18,"authority":177,"freshness":84,"relevant":21,"comment":178},13,"系统综述AI农业咨询与诊断系统在印度东北小农场景的技术架构与落地证据，指出人机协同、离线多语言与检索增强是可行路径，对智慧农业落地有参考价值。",[180],{"name":173,"url":170},[26,182,183,184,27],"智慧农业","农业人工智能","农业技术推广",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[187],{"code":188,"slug":189,"name":190,"path":191,"parent_code":192},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":194},{"candidates":195,"agricultural_evidence":46},[196,201,210,217,222],{"code":32,"score":83,"supported":46,"title":197,"abstract":199,"tags":200},[198],"smallholder",[27,198],[27],{"code":188,"score":12,"supported":46,"title":202,"abstract":203,"tags":209},[],[204,205,206,207,208],"机器学习","计算机视觉","machine learning","artificial intelligence","computer vision",[183],{"code":211,"score":109,"supported":46,"title":212,"abstract":213,"tags":216},"I1",[],[214,215],"数字农业","digital agriculture",[182],{"code":218,"score":21,"supported":13,"title":219,"abstract":220,"tags":221},"G1",[],[],[184],{"code":51,"score":21,"supported":13,"title":223,"abstract":224,"tags":225},[],[],[26],[227,228],"印度东北部 农业AI 小农户","农业智能诊断 多语言 离线","印度东北部农业AI小农户-3512","10.9734\u002Farja\u002F2026\u002Fv19i4919",{"doi":230,"openalex_id":232,"authors":233,"venue":173,"cited_by_count":61,"oa_url":170,"card":239,"direction":66,"ingested_from":245},"W7214205238",[234,236],{"name":235,"orcid":8},"Pravangkar Boruah",{"name":237,"orcid":238},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":240,"method":241,"finding":242,"direction":243,"opportunity":244},"综述AI农业咨询与诊断系统，聚焦印度东北小农，提出人监督多模态部署架构。","批判性叙述综述，整合2010-2026年数字推广、生成式AI与图像诊断证据。","AI输出技术可行但本地化、安全与田间效果证据不足，需人监督与检索增强。","农业人工智能与决策模型","可开展跨区跨季前瞻评估，连接模型质量与农户决策、产量、公平及成本效益。","openalex","2026-09-25T23:30:39.745514Z",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":8,"content":8,"source_name":252,"source_url":8,"published_at":253,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":254,"score_detail":255,"sources":258,"tags":260,"primary_topic":263,"secondary_topics":264,"classification":265,"search_phrases":280,"slug":283,"view_count":146,"doi":8,"paper":284,"created_at":291},2739,"Bridging the Extension Gap: Mobile Phones and Agricultural Information Seeking among Smallholder Farmers in Kenya","https:\u002F\u002Fijsrm.net\u002Findex.php\u002Fijsrm\u002Farticle\u002Fview\u002F8204","肯尼亚Kericho郡Belgut分区小农户移动电话与农业信息寻求研究。调查显示,农业培训信息访问频率最高(39.6%),其次是投入品信息(19.8%),种植信息(14.4%)、病虫害管理(13.5%)、一般市场信息(13.5%)、商品价格信息(10.8%)和天气预报(9.9%)。整体利用率偏低表明,虽然移动电话具有作为能力建设工具的潜力,但系统性障碍(如数字素养、成本、本地化内容缺乏和对传统推广结构的依赖)阻碍其作为综合咨询机制的采用。","IJSRM 2026年9月13日","2026-09-13T00:00:00Z",59,{"impact":256,"substance":18,"depth":83,"authority":45,"freshness":71,"relevant":21,"comment":257},8,"基于肯尼亚小农户调查的一手数据，揭示移动电话在农技信息获取中的结构性障碍，对数字乡村与农技推广信息化有参考价值，但属境外区域研究、影响层级有限。",[259],{"name":252,"url":250},[26,132,27,261,262],"农技推广","移动农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":266},{"candidates":267,"agricultural_evidence":46},[268,272,276],{"code":32,"score":45,"supported":46,"title":269,"abstract":270,"tags":271},[198],[27],[27],{"code":51,"score":127,"supported":13,"title":273,"abstract":274,"tags":275},[],[],[26,132],{"code":218,"score":21,"supported":13,"title":277,"abstract":278,"tags":279},[],[],[261],[281,282],"农技推广 数字乡村 数字素养 移动农业","农技推广 数字乡村","农技推广数字乡村数字素养移动农业-2739",{"doi":8,"openalex_id":8,"authors":285,"venue":8,"cited_by_count":61,"oa_url":8,"card":286,"direction":66,"ingested_from":68},[],{"tldr":287,"method":288,"finding":289,"direction":66,"opportunity":290},"研究肯尼亚小农户用手机获取农业信息的现状及障碍。","对肯尼亚Belgut分区小农户进行问卷调查与信息类型统计。","手机农业信息利用率整体偏低，受数字素养、成本、本地化内容缺乏等系统性障碍制约。","可探索低成本本地化内容与数字素养培训如何提升手机农业咨询的实际采纳率。","2026-09-17T00:04:41.088694Z",{"id":293,"title":294,"url":295,"summary":296,"summary_zh":297,"content":8,"source_name":298,"source_url":295,"published_at":253,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":299,"score_detail":300,"sources":302,"tags":304,"primary_topic":307,"secondary_topics":308,"classification":309,"search_phrases":331,"slug":334,"view_count":318,"doi":335,"paper":336,"created_at":358},2516,"Scaling climate-smart agriculture through multi-channel advisory services: economic evaluation of weather information systems in sub-Saharan Africa","https:\u002F\u002Fdoi.org\u002F10.1080\u002F14735903.2026.2731746","Smallholder agriculture in sub-Saharan Africa is 95% rain-fed and exposed to climate variability. This post-implementation evaluation applies activity-based costing to weather advisory services delivered by PlantVillage in Kenya and Burkina Faso from 2022 to 2024 across television, radio, short message service (SMS), and community-based channels. A combined investment of USD 551,966 reached 9.41 million farmers at confirmed exposure and an estimated 14.5 million under a moderate-reach scenario (USD 0.038 per farmer). Because extended reach relies on literature-derived multipliers rather than direct measurement, we report a sensitivity range of USD 0.034 to 0.058; even at the conservative end, the cost advantage over Farmer Field Schools (USD 36 to 70 per participant) remains a factor of 620 to 1,200. Mass media achieved the lowest unit cost (USD 0.027 to 0.031), with evidence limited to confirmed exposure, whereas SMS supported intensive engagement at USD 0.60. In a convenience sample of 4,272 registered iShamba users in Kenya, 83.7% (n = 3,577) reported that advisories influenced their decisions, predominantly planting timing (51%) and farm-activity planning (23%). No comparable data were collected in Burkina Faso, and channel contributions appear complementary rather than demonstrably synergistic. Yields and income were not measured; causal impact requires experimental and longitudinal designs.","撒哈拉以南非洲的小农农业95%依赖雨养，易受气候变率影响。本项实施后评估采用作业成本法，对PlantVillage于2022至2024年间在肯尼亚和布基纳法索通过电视、广播、短信服务（SMS）及社区渠道提供的天气咨询服务进行成本核算。合计投资551,966美元，在确认触达口径下覆盖941万农民，在中度触达情景下估计覆盖1,450万农民（每农民0.038美元）。由于扩展触达依赖文献推导的乘数而非直接测量，我们报告0.034至0.058美元的敏感性区间；即使在保守端，相较于农民田间学校（每位参与者36至70美元）的成本优势仍达620至1,200倍。大众媒体的单位成本最低（0.027至0.031美元），但证据仅限于确认触达；短信则以每农民0.60美元支持深度参与。在肯尼亚4,272名注册iShamba用户的便利样本中，83.7%（n = 3,577）报告咨询信息影响了其决策，主要是种植时间（51%）和农事活动规划（23%）。布基纳法索未收集可比数据，各渠道的贡献似乎互补，而非可证明的协同。产量和收入未予测量；因果影响需要实验和纵向设计。","International Journal of Agricultural Sustainability",78,{"impact":18,"substance":17,"depth":18,"authority":177,"freshness":109,"relevant":21,"comment":301},"基于成本核算的天气信息服务大规模评估，单位成本数据与渠道对比结论扎实，对农业气象信息化推广有参考价值。",[303],{"name":298,"url":295},[26,182,305,27,306],"撒哈拉以南非洲","农业气象服务",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":310},{"candidates":311,"agricultural_evidence":46},[312,317,322,326],{"code":211,"score":84,"supported":46,"title":313,"abstract":315,"tags":316},[314],"smart agriculture",[],[182],{"code":32,"score":318,"supported":46,"title":319,"abstract":320,"tags":321},4,[],[198],[27],{"code":51,"score":21,"supported":13,"title":323,"abstract":324,"tags":325},[],[],[26],{"code":327,"score":21,"supported":13,"title":328,"abstract":329,"tags":330},"J3",[],[],[305],[332,333],"撒哈拉以南非洲 农业气象服务 数字乡村 智慧农业","撒哈拉以南非洲 农业气象服务","撒哈拉以南非洲农业气象服务数字乡村智慧农业-2516","10.1080\u002F14735903.2026.2731746",{"doi":335,"openalex_id":337,"authors":338,"venue":298,"cited_by_count":61,"oa_url":351,"card":352,"direction":357,"ingested_from":245},"W7212902218",[339,342,345,347,349],{"name":340,"orcid":341},"Rimnoma S. Ouedraogo","https:\u002F\u002Forcid.org\u002F0000-0003-3339-300X",{"name":343,"orcid":344},"Bipana Paudel Timilsena","https:\u002F\u002Forcid.org\u002F0000-0003-3584-0563",{"name":346,"orcid":8},"Nebnoma R. Tiendrebeogo",{"name":348,"orcid":8},"Derek Morr",{"name":350,"orcid":8},"D M Hughes","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F14735903.2026.2731746?needAccess=true",{"tldr":353,"method":354,"finding":355,"direction":66,"opportunity":356},"评估撒哈拉以南非洲多渠天气信息服务成本与农户决策影响。","采用活动成本法，分析2022-2024年肯尼亚和布基纳法索四类渠道数据。","人均成本仅0.038美元，远低于农民田间学校，83.7%用户称信息影响决策。","需实验与纵向设计验证天气信息对产量收入的因果影响及渠道协同效应。","智慧农业 \u002F 农业物联网","2026-09-15T23:30:10.644934Z",{"id":360,"title":361,"url":362,"summary":363,"summary_zh":8,"content":364,"source_name":365,"source_url":8,"published_at":366,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":367,"score_detail":368,"sources":371,"tags":373,"primary_topic":379,"secondary_topics":380,"classification":381,"search_phrases":396,"slug":399,"view_count":20,"doi":8,"paper":8,"created_at":400},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":45,"substance":369,"depth":18,"authority":45,"freshness":127,"relevant":21,"comment":370},20,"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。",[372],{"name":365,"url":362},[214,26,374,27,375,376,377,378],"农业信息化","气候信息","ICT采纳","数字鸿沟","发展中国家",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":382},{"candidates":383,"agricultural_evidence":46},[384,388,392],{"code":32,"score":45,"supported":46,"title":385,"abstract":386,"tags":387},[198],[27],[27],{"code":211,"score":127,"supported":13,"title":389,"abstract":390,"tags":391},[],[],[374,214],{"code":51,"score":127,"supported":13,"title":393,"abstract":394,"tags":395},[],[],[26,377],[397,398],"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"]