[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3434":3,"related-3434":46},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":36,"created_at":45},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",[32,33],"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",0,{"doi":8,"openalex_id":8,"authors":37,"venue":8,"cited_by_count":35,"oa_url":8,"card":38,"direction":42,"ingested_from":44},[],{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"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":47,"page":21,"page_size":47,"items":48},6,[49,76,107,151,189,215],{"id":50,"title":51,"url":52,"summary":53,"summary_zh":8,"content":54,"source_name":55,"source_url":8,"published_at":56,"category":57,"cover_url":8,"hotness":12,"is_selected":13,"score":58,"score_detail":59,"sources":65,"tags":67,"search_phrases":71,"slug":74,"view_count":35,"doi":8,"paper":8,"created_at":75},3374,"数字乡村开启高质量深耕新周期——《行动计划》扭转'重数量轻质量'旧模式","https:\u002F\u002Fwww.rmzxb.com.cn\u002Fc\u002F2026-09-22\u002F3979434.shtml","人民政协报评论指出：历经数年深耕，我国数字乡村建设已完成基础设施全面铺建，但部分地方陷入'建好不用、用而不实'的尴尬：智慧村务平台闲置、涉农系统重复填报、智能设备沦为应付检查的'摆设'。新一轮《行动计划》正式推动数字乡村从'有没有'的普及阶段迈入'好不好'的高质量深耕阶段，跳出'一刀切'固化思维，坚持因地制宜、分类施策、精准赋能。","村村通5G、户户通网络、田间智慧大屏落地……历经数年深耕，我国数字乡村建设已完成基础设施全面铺建。但深入乡村基层不难发现，不少地方的数字化建设陷入“建好不用、用而不实”的尴尬局面：智慧村务平台常年闲置，各类涉农系统重复填报，田间智能设备沦为应付检查的“摆设”。看似遍地开花的数字化场景，始终悬浮于乡土之上，没能真正转化为助农增收、乡村提质的实效。\n\n针对这些基层痛点与发展堵点，《数字乡村高质量发展行动计划（2026—2030年）》正式出台，为未来五年乡村数字化发展锚定全新方向。相较于前期重基建、重覆盖、重样板的粗放式建设，这份新政彻底扭转建设逻辑，推动数字乡村全面告别“重数量、轻质量，重投入、轻运营”的旧模式，正式从“有没有”的普及阶段，迈入“好不好”的高质量深耕阶段，核心要义就是让技术适配乡土、让数据赋能产业、让红利惠及农民。\n\n长期以来，很多人将数字乡村简单等同于直播带货、网络入户，部分地区建设也陷入“城市模板生硬下乡”的误区。不顾乡村产业禀赋差异、城乡发展差距，照搬统一设备、统一系统、统一标准，平原农田、山区果园、水乡养殖千村一面，空心村、薄弱村、富裕村同质化建设。最终导致大量数字化项目“水土不服”，看似光鲜亮眼，却贴合不了农事需求、解决不了基层难题，沦为脱离实际的“面子工程”。\n\n当下数字乡村建设的核心矛盾，早已不是网络不通、设备不足，而是“数据悬浮”与“数字鸿沟升级”。海量的田间生产、乡村治理数据采集后便沉睡后台，部门间信息壁垒森严、数据孤岛突出。基层干部深陷多头填报、重复台账的繁琐工作，数字化反而增添基层负担；广大小农户无法接触、运用数字资源，难以对接市场、规避风险，城乡差距从基础的“网络接入鸿沟”，演变为更深层的“数字应用鸿沟”“素养鸿沟”。\n\n本次行动计划最大的突破，就是跳出“一刀切”的固化建设思维，坚持因地制宜、分类施策、精准赋能。新政明确摒弃同质化建设路径，立足不同乡村的产业特色、资源禀赋、人口结构，定制本土化数字化发展方案。如，粮食主产区聚焦智慧耕种、精准植保、稳产提质；特色村镇深耕农产品溯源、数字电商、乡村文旅数字化；治理薄弱村庄重点推进智慧村务、数字减负、基层提效，真正实现让技术适配农民、贴合乡土，而非让乡村迁就城市标准。\n\n赋能产业提质增收，是本轮数字乡村升级的核心落脚点。新政彻底打破“数字乡村=直播带货”的浅层认知，聚焦培育农业新质生产力，用数字化重构农业全产业链。从春耕土壤监测、无人机智能植保，到田间病虫害预警、生长态势智能管控，再到秋收产量数据分析、农产品全程溯源，以及后端产销精准对接、村级物流网点全覆盖，数字技术贯穿耕、种、管、收、销全链条，推动传统农业彻底告别“看天吃饭、凭经验种地”的粗放模式，迈入“用数据种田、靠精准增效”的智慧新范式。\n\n更具普惠性的是，本轮数字化升级不再是种养大户、新型经营主体的“专属红利”，而是全力向普通小农户倾斜。通过下沉公共数字服务、普及免费数字化工具、开放产销大数据，帮助分散经营的小农户精准对接大市场，有效降低生产成本、规避市场波动风险，让最广泛的农民群体共享数字发展红利，让数字产业成为乡村长效增收的核心引擎。\n\n除了赋能产业发展，数字化升级更聚焦减负基层、惠及民生，重塑乡村治理与公共服务体系。过去，繁杂的线上系统、冗余的台账报表，占用基层干部大量时间精力，让干部深陷线上事务、脱离田间一线。同时，偏远乡村医疗、教育、文化资源薄弱，城乡公共服务不均等问题长期存在。\n\n网线纵横连乡野，数字赋能启新程。未来五年，乡村数字化发展不再比拼硬件数量、样板亮点，而是聚焦实干实效、民生福祉、长效运营。摒弃悬浮化建设、模板化发展的固有弊端，让数据真正扎根阡陌田野，使数字技术深度赋能“三农”发展。\n\n可以说，这场全方位、深层次的数字乡村升级变革，将持续激活乡村沉睡资源、培育农业新质生产力、畅通城乡要素双向流动，为乡村全面振兴、农业强国建设注入持久强劲的数字动能。（记者 李元丽）","人民政协网 2026-09-22","2026-09-22T00:00:00Z","报道",82,{"impact":60,"substance":61,"depth":16,"authority":62,"freshness":63,"relevant":21,"comment":64},26,20,13,7,"全国性数字乡村新政解读，直指数据悬浮与数字鸿沟痛点，政策信号明确，但属评论性报道、缺乏条款细节。",[66],{"name":55,"url":52},[26,68,69,27,70],"智慧农业","农业新质生产力","智慧村务",[72,73],"数字乡村 数据孤岛 基层减负","农业新质生产力 数字乡村 智慧农业 智慧村务","数字乡村数据孤岛基层减负-3374","2026-09-25T00:09:26.569518Z",{"id":77,"title":78,"url":79,"summary":80,"summary_zh":8,"content":8,"source_name":81,"source_url":8,"published_at":82,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":83,"score_detail":84,"sources":89,"tags":91,"search_phrases":95,"slug":98,"view_count":35,"doi":8,"paper":99,"created_at":106},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":85,"substance":18,"depth":86,"authority":87,"freshness":47,"relevant":21,"comment":88},8,15,12,"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。",[90],{"name":81,"url":79},[26,92,27,93,94],"数字素养","农技推广","移动农业",[96,97],"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":100,"venue":8,"cited_by_count":35,"oa_url":8,"card":101,"direction":42,"ingested_from":44},[],{"tldr":102,"method":103,"finding":104,"direction":42,"opportunity":105},"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":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":8,"source_name":113,"source_url":110,"published_at":82,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":114,"score_detail":115,"sources":117,"tags":119,"search_phrases":122,"slug":125,"view_count":35,"doi":126,"paper":127,"created_at":150},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":62,"freshness":63,"relevant":21,"comment":116},"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。",[118],{"name":113,"url":110},[26,68,120,27,121],"撒哈拉以南非洲","农业气象服务",[123,124],"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":126,"openalex_id":128,"authors":129,"venue":113,"cited_by_count":35,"oa_url":142,"card":143,"direction":148,"ingested_from":149},"W7212902218",[130,133,136,138,140],{"name":131,"orcid":132},"Rimnoma S. Ouedraogo","https:\u002F\u002Forcid.org\u002F0000-0003-3339-300X",{"name":134,"orcid":135},"Bipana Paudel Timilsena","https:\u002F\u002Forcid.org\u002F0000-0003-3584-0563",{"name":137,"orcid":8},"Nebnoma R. Tiendrebeogo",{"name":139,"orcid":8},"Derek Morr",{"name":141,"orcid":8},"D M Hughes","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F14735903.2026.2731746?needAccess=true",{"tldr":144,"method":145,"finding":146,"direction":42,"opportunity":147},"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","openalex","2026-09-15T23:30:10.644934Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":8,"source_name":157,"source_url":154,"published_at":158,"category":11,"cover_url":8,"hotness":159,"is_selected":13,"score":160,"score_detail":161,"sources":165,"tags":169,"search_phrases":173,"slug":176,"view_count":21,"doi":177,"paper":178,"created_at":188},2312,"MridAI: Autonomous Edge-to-Conversational IoT for Democratising Precision Agriculture via Neuro-Symbolic Telemetry","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724805","While sensor-guided precision agriculture improves water efficiency and curtails chemical run-off, adoption across smallholder farm land in the Global South remains under 1%. Commercial telemetry systems are constrained by high capital acquisition costs (>$300) and complex, dashboard-centric mobile applications that impose heavy cognitive burdens on low-literacy farmers. This paper proposes MridAI, a low-cost (\u003C$45 \u002F ₹3,420 COGS) autonomous in-ground agro-telemetry node coupled with a cloud-based neuro-symbolic artificial intelligence advisory pipeline. The physical layer integrates a multi-parameter Modbus RS485 sensor, an ESP32-C3 micro-controller, an Indian-band 4G LTE Cat-1 modem, and an energy harvesting subsystem within an IP67-rated solar stem. To eliminate measurement errors in high-clay tropical soils (Vertisols), an on-device digital signal-processing pipeline applies an adapted Topp dielectric correction alongside a one-dimensional discrete Kalman filter. To overcome rural telecommunication instability, the firmware implements an asynchronous, non-volatile store-and-forward ring buffer. At the cloud layer, an analytical solver calculates deterministic FAO-56 evapotranspiration deficits and enforces Indian Council of Agricultural Research (ICAR) chemical boundaries, strictly isolating numerical computation from an instruction-tuned Large Language Model (LLM). The generative model functions solely as a linguistic translator, delivering actionable, dialect-adapted recommendations directly via the Meta WhatsApp Cloud API without requiring third-party application downloads. Empirical power-budget modelling confirms indefinite operational autonomy (>240 days without solar irradiance), establishing a scalable paradigm for digital agriculture.","尽管传感器引导的精准农业提高了用水效率并减少了化学品径流，但在全球南方小农农田中的采用率仍不足1%。商业遥测系统受制于高昂的资本购置成本（超过300美元）以及复杂的、以仪表盘为中心的移动应用程序，后者给低识字率农民带来了沉重的认知负担。本文提出MridAI，一种低成本（物料清单成本低于45美元\u002F3，420印度卢比）的自主地下农业遥测节点，并耦合基于云的神经符号人工智能咨询流水线。物理层将多参数Modbus RS485传感器、ESP32-C3微控制器、印度频段4G LTE Cat-1调制解调器以及能量收集子系统集成于IP67防护等级的太阳能杆体内。为消除高黏土热带土壤（变性土）中的测量误差，设备端数字信号处理流水线采用适配的Topp介电校正与一维离散卡尔曼滤波器。为克服农村电信不稳定性，固件实现了异步、非易失性的存储转发环形缓冲区。在云层，分析求解器计算确定性的FAO-56蒸散亏缺，并执行印度农业研究理事会（ICAR）的化学品边界，将数值计算与指令微调的大语言模型（LLM）严格隔离。生成模型仅充当语言翻译器，通过Meta WhatsApp Cloud API直接提供可操作的、适配方言的建议，无需下载第三方应用程序。实证功率预算建模证实了无限期运行自主性（无太阳辐照下超过240天），为数字农业建立了一种可扩展的范式。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-12T00:00:00Z",25,85,{"impact":17,"substance":162,"depth":18,"authority":62,"freshness":163,"relevant":21,"comment":164},23,9,"面向全球南方小农户的低成本自主土壤遥测节点与神经符号AI咨询管线，方法新颖、成本与能耗数据具体，对农业信息化与普惠数字农业有实质参考价值。",[166,167],{"name":157,"url":154},{"name":157,"url":168},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724804",[26,68,170,171,27,172],"农业人工智能","农业物联网","精准灌溉",[174,175],"农业人工智能 农业物联网 数字乡村 智慧农业","农业人工智能 农业物联网","农业人工智能农业物联网数字乡村智慧农业-2312","10.5281\u002Fzenodo.22724805",{"doi":177,"openalex_id":179,"authors":180,"venue":157,"cited_by_count":35,"oa_url":154,"card":183,"direction":148,"ingested_from":149},"W7212357737",[181],{"name":182,"orcid":8},"Pranit Kamble",{"tldr":184,"method":185,"finding":186,"direction":148,"opportunity":187},"提出低成本自主地下物联网节点与神经符号AI咨询管道，让小农户用WhatsApp获取精准农业建议。","ESP32-C3与Modbus传感器、Topp校正和卡尔曼滤波、FAO-56与L","系统成本低于45美元，可独立运行超240天，无需下载应用即可通过WhatsApp获得方言化建议。","可探索低资源语言与方言适配的LLM农业咨询，以及神经符号系统在更多作物和土壤类型中的泛化验证。","2026-09-13T23:30:14.323767Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":8,"content":194,"source_name":195,"source_url":8,"published_at":196,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":197,"score_detail":198,"sources":201,"tags":203,"search_phrases":210,"slug":213,"view_count":21,"doi":8,"paper":8,"created_at":214},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":87,"substance":61,"depth":18,"authority":87,"freshness":199,"relevant":21,"comment":200},2,"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。",[202],{"name":195,"url":192},[204,26,205,27,206,207,208,209],"数字农业","农业信息化","气候信息","ICT采纳","数字鸿沟","发展中国家",[211,212],"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",{"id":216,"title":217,"url":218,"summary":219,"summary_zh":8,"content":8,"source_name":220,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":221,"sources":224,"tags":226,"search_phrases":229,"slug":232,"view_count":35,"doi":8,"paper":233,"created_at":240},3450,"《数智技术赋能农业新质生产力：内在机理、驱动要素与实现进路》","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fjianghuailuntan\u002Fmulu\u002F678750.html","孙壮珍从数智技术赋能农业新质生产力内在机理入手，分析数智技术赋能农业新质生产力驱动要素，并从主体-组织-制度全域视角提出通过构建利益导向机制、培育新型载体平台、推进制度调适的实现进路。文章认为农业新质生产力有高度的渗透性与广泛的链接性，能够进一步拓展农业的生产空间与效率边界，变革农业生产的工艺、技术与流程，加速我国农业强国建设的进程。","《江淮论坛》\u002F西南科技大学",{"impact":18,"substance":61,"depth":222,"authority":19,"freshness":47,"relevant":21,"comment":223},17,"核心期刊论文，从机理、要素到实现进路系统论述数智技术赋能农业新质生产力，理论增量明确，但属学术探讨而非政策落地，时效性一般。",[225],{"name":220,"url":218},[26,68,69,227,228],"农业强国","数智技术",[230,231],"数智技术 农业新质生产力","江淮论坛 农业新质生产力","数智技术农业新质生产力-3450",{"doi":8,"openalex_id":8,"authors":234,"venue":8,"cited_by_count":35,"oa_url":8,"card":235,"direction":42,"ingested_from":44},[],{"tldr":236,"method":237,"finding":238,"direction":42,"opportunity":239},"分析数智技术赋能农业新质生产力的内在机理、驱动要素与实现进路。","理论分析，从主体-组织-制度全域视角提出机制、平台与制度调适路径。","农业新质生产力具高渗透性与广链接性，可拓展生产空间与效率边界，加速农业强国建设。","可实证检验数智技术赋能农业新质生产力的机制与制度调适效果，弥补纯理论分析不足。","2026-09-25T00:09:34.845621Z"]