[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3005":3,"related-3005":44},{"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,"search_phrases":29,"slug":32,"view_count":33,"doi":8,"paper":34,"created_at":43},3005,"数字乡村建设能否提升县域经济韧性——基于双重机器学习与县级面板数据","https:\u002F\u002Fgcxb.gufe.edu.cn\u002FCN\u002FPDF\u002F9619","郑州轻工业大学陈昱等基于2014—2022年县级面板数据采用双重机器学习模型探究数字乡村建设对县域经济韧性的影响效应及作用机制，并进一步考察不同层级数字鸿沟产生的异质性影响。研究表明：数字乡村建设显著提升县域经济韧性经稳健性检验后结果依然成立；数字乡村建设以技术为核心通过破除市场壁垒、优化产业结构、提升非农创业活力提升县域经济韧性；但数字乡村建设对县域经济韧性的促进作用受到三级数字鸿沟制约，导致其在数字发展低水平地区产生抑制效应。",null,"贵阳学院学报(社会科学版)2026","2026-09-16T00:00:00Z","论文",10,false,79,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,13,8,1,"基于县级面板数据与双重机器学习方法，实证揭示数字乡村建设对县域经济韧性的提升效应及数字鸿沟的制约机制，方法新颖、结论可靠，对数字乡村政策具有参考价值。",[23],{"name":9,"url":6},[25,26,27,28],"数字乡村","双重机器学习","数字鸿沟","县域经济韧性",[30,31],"郑州轻工业大学 数字乡村 县域经济韧性","县域经济韧性 双重机器学习 数字乡村 数字鸿沟","郑州轻工业大学数字乡村县域经济韧性-3005",0,{"doi":8,"openalex_id":8,"authors":35,"venue":8,"cited_by_count":33,"oa_url":8,"card":36,"direction":40,"ingested_from":42},[],{"tldr":37,"method":38,"finding":39,"direction":40,"opportunity":41},"基于县级面板数据，用双重机器学习检验数字乡村建设对县域经济韧性的因果效应与机制。","2014—2022年县级面板数据，双重机器学习因果推断模型。","数字乡村建设显著提升县域经济韧性，但三级数字鸿沟会削弱甚至逆转该效应。","数字乡村与农业信息化","可探究数字鸿沟的微观形成机制及弥合路径，或分区域设计差异化数字乡村政策。","agent","2026-09-20T00:03:08.550563Z",{"total":45,"page":20,"page_size":45,"items":46},6,[47,94,122,151,180,205],{"id":48,"title":49,"url":50,"summary":51,"summary_zh":52,"content":8,"source_name":53,"source_url":50,"published_at":54,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":55,"score_detail":56,"sources":62,"tags":64,"search_phrases":68,"slug":71,"view_count":33,"doi":72,"paper":73,"created_at":93},2959,"Divide or bridge? The heterogeneous effects of digital village participation on rural residents’ happiness","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpsyg.2026.1893504","Introduction Enhancing rural residents’ happiness is a key objective of China’s rural revitalization strategy. Methods Using data from the China Rural Revitalization Survey (CRRS), this study empirically examines the relationship between digital village participation on rural residents’ happiness. Results The results show that digital village participation is positively associated with rural residents’ happiness. Dimensional-specific analyses further reveals that digital infrastructure, digital governance, and the digitalization of daily life exert significant positive effects on rural residents’ happiness, whereas the effect of rural economic digitalization is statistically insignificant. The mechanism analysis indicates that digital village participation improves rural residents’ happiness by alleviating credit constraints and increasing social interaction. The heterogeneity analysis reveals that rural residents with higher digital literacy are better able to translate digital participation into gains in happiness, whereas the effect is insignificant among those with lower levels of digital literacy. Discussion This study provides empirical evidence for narrowing the rural digital divide and offers policy implications for promoting targeted and differentiated digital village development.","引言 提升农村居民幸福感是中国乡村振兴战略的重要目标。方法 本研究利用中国乡村振兴调查（CRRS）数据，实证检验了数字乡村参与与农村居民幸福感之间的关系。结果 结果表明，数字乡村参与与农村居民幸福感呈正相关。分维度分析进一步显示，数字基础设施、数字治理和日常生活数字化对农村居民幸福感具有显著正向影响，而农村经济数字化的影响在统计上不显著。机制分析表明，数字乡村参与通过缓解信贷约束和增加社会互动提升农村居民幸福感。异质性分析显示，数字素养较高的农村居民更能将数字参与转化为幸福感的提升，而在数字素养较低的群体中该效应不显著。讨论 本研究为缩小农村数字鸿沟提供了实证依据，并为推进有针对性、差异化的数字乡村发展提供了政策启示。","Frontiers in Psychology","2026-09-18T00:00:00Z",76,{"impact":57,"substance":58,"depth":59,"authority":18,"freshness":60,"relevant":20,"comment":61},16,21,17,9,"基于全国性农户调查的实证研究，揭示数字乡村参与对农民幸福感的异质性影响，对数字乡村政策有参考价值。",[63],{"name":53,"url":50},[25,65,66,27,67],"数字素养","乡村振兴","农户幸福感",[69,70],"中国乡村振兴调查 CRRS 数字乡村","农户幸福感 乡村振兴 数字乡村 数字素养","中国乡村振兴调查CRRS数字乡村-2959","10.3389\u002Ffpsyg.2026.1893504",{"doi":72,"openalex_id":74,"authors":75,"venue":53,"cited_by_count":33,"oa_url":50,"card":87,"direction":40,"ingested_from":92},"W7213536926",[76,78,80,83,85],{"name":77,"orcid":8},"Ren Zhou",{"name":79,"orcid":8},"Cancan Zhang",{"name":81,"orcid":82},"Jingbo Li","https:\u002F\u002Forcid.org\u002F0000-0001-5960-806X",{"name":84,"orcid":8},"Yao MengYuan",{"name":86,"orcid":8},"Long Fan",{"tldr":88,"method":89,"finding":90,"direction":40,"opportunity":91},"基于CRRS数据实证检验数字乡村参与对农村居民幸福感的影响及异质性。","中国乡村振兴调查（CRRS）数据，实证回归与机制、异质性分析。","数字乡村参与提升幸福感，通过缓解信贷约束和增加社交互动，但经济数字化不显著。","可探究数字素养门槛下数字乡村参与对幸福感的非均衡影响及弥合数字鸿沟的干预路径。","openalex","2026-09-19T23:30:45.100468Z",{"id":95,"title":96,"url":97,"summary":98,"summary_zh":8,"content":8,"source_name":99,"source_url":8,"published_at":100,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":101,"score_detail":102,"sources":104,"tags":106,"search_phrases":109,"slug":112,"view_count":33,"doi":113,"paper":114,"created_at":121},2856,"数字技术与农村收入不平等:来自中国微观数据的证据——Frontiers in Sustainable Food Systems","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1937169\u002Fabstract","基于中国微观数据,论文探讨了数字技术应用与农村收入不平等之间的关系。研究发现,数字技术应用(尤其是电子商务)对农村收入差距的影响存在异质性,与人力资本、农村基础设施水平等条件密切相关。","Frontiers in Sustainable Food Systems","2026-09-14T00:00:00Z",77,{"impact":16,"substance":58,"depth":59,"authority":18,"freshness":19,"relevant":20,"comment":103},"基于中国微观数据实证数字技术与农村收入不平等的关系，结论具政策参考价值，但属学术论文且时效略滞后，可入精选。",[105],{"name":99,"url":97},[107,25,108,27],"农村电商","农民增收",[110,111],"农村电商 农民增收 数字乡村 数字鸿沟","农村电商 农民增收","农村电商农民增收数字乡村数字鸿沟-2856","10.3389\u002Ffsufs.2026.1937169\u002Fabstract",{"doi":113,"openalex_id":8,"authors":115,"venue":8,"cited_by_count":33,"oa_url":8,"card":116,"direction":40,"ingested_from":42},[],{"tldr":117,"method":118,"finding":119,"direction":40,"opportunity":120},"基于中国微观数据，研究数字技术应用与农村收入不平等的关系。","中国微观数据，分析数字技术（尤其电商）对收入差距的影响。","数字技术对农村收入差距影响异质，与人力资本和基础设施水平相关。","可探究数字技术缩小收入差距的阈值条件及不同区域异质性机制。","2026-09-18T00:03:30.921679Z",{"id":123,"title":124,"url":125,"summary":126,"summary_zh":8,"content":8,"source_name":127,"source_url":8,"published_at":128,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":129,"score_detail":130,"sources":133,"tags":135,"search_phrases":138,"slug":141,"view_count":33,"doi":142,"paper":143,"created_at":150},2740,"数字赋能乡村全面振兴的价值与路径探究","https:\u002F\u002Fxb.ynau.edu.cn\u002Fjwk_sk\u002Fcn\u002Farticle\u002Fdoi\u002F10.12371\u002Fj.ynau(s).202601056","保山学院杨忠平、苏州大学方文从激活数字价值—弥合数字鸿沟—促进数字监督—增强数字幸福感的价值链条探讨数字赋能乡村全面振兴。研究表明:数字赋能与乡村全面振兴的内在耦合点主要体现在目标导向、动能创新、全面性优势、发展破题四个维度。从乡村的数字教育问题入手,通过数字手段盘活乡村文化的数字基因,实现对乡村社会的数字治理。","云南农业大学学报 2026年9月11日","2026-09-11T00:00:00Z",68,{"impact":57,"substance":16,"depth":131,"authority":18,"freshness":45,"relevant":20,"comment":132},15,"核心期刊论文，从数字价值、鸿沟、监督、幸福感四维构建数字赋能乡村振兴的价值链条，理论框架有新意，但属学术探讨、缺乏实证数据与政策落地细节，时效性一般，可作主题页聚合素材而非每日精选头条。",[134],{"name":127,"url":125},[25,66,27,136,137],"数字教育","乡村数字治理",[139,140],"乡村数字治理 乡村振兴 数字乡村 数字教育","乡村数字治理 乡村振兴","乡村数字治理乡村振兴数字乡村数字教育-2740","10.12371\u002Fj.ynau(s).202601056",{"doi":142,"openalex_id":8,"authors":144,"venue":8,"cited_by_count":33,"oa_url":8,"card":145,"direction":40,"ingested_from":42},[],{"tldr":146,"method":147,"finding":148,"direction":40,"opportunity":149},"探讨数字赋能乡村全面振兴的价值链条与实现路径。","理论分析，从数字教育、文化、治理等维度构建价值链条。","数字赋能与乡村振兴在目标、动能、全面性和破题四维度耦合。","可实证检验数字教育、数字文化等具体路径对乡村振兴的因果效应。","2026-09-17T00:04:41.156718Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":8,"content":8,"source_name":156,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":157,"score_detail":158,"sources":162,"tags":164,"search_phrases":168,"slug":171,"view_count":33,"doi":8,"paper":172,"created_at":179},2736,"弥合数字鸿沟:数字经济参与能否提升农村家庭发展韧性?","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F165479.html","华中农业大学陈万红等基于2012—2022年中国家庭追踪调查(CFPS)农村面板数据,运用双重差分、倾向得分匹配、工具变量法等计量手段,实证检验数字经济参与对农村家庭发展韧性的作用。结果表明:数字经济参与能够显著提升农村家庭发展韧性;该赋能效应存在区域、家庭生命周期异质性,华北、华东、华中南、西北地区以及抚养期、负担期、稳定期家庭提升效果显著。","农业经济与管理 2026年第3期",74,{"impact":16,"substance":17,"depth":16,"authority":159,"freshness":160,"relevant":20,"comment":161},14,2,"基于CFPS十年面板数据的实证研究，方法规范、结论有新意，但时效性偏弱，适合主题聚合而非每日精选。",[163],{"name":156,"url":154},[25,165,27,166,167],"数字经济","CFPS","农村家庭韧性",[169,170],"农村家庭韧性 数字乡村 数字经济 数字鸿沟","农村家庭韧性 数字乡村","农村家庭韧性数字乡村数字经济数字鸿沟-2736",{"doi":8,"openalex_id":8,"authors":173,"venue":8,"cited_by_count":33,"oa_url":8,"card":174,"direction":40,"ingested_from":42},[],{"tldr":175,"method":176,"finding":177,"direction":40,"opportunity":178},"基于CFPS农村面板数据，实证检验数字经济参与对农村家庭发展韧性的提升作用。","2012—2022年CFPS农村面板数据，双重差分、倾向得分匹配、工具变量法。","数字经济参与显著提升农村家庭发展韧性，且存在区域和家庭生命周期异质性。","可探究数字经济参与提升韧性的具体机制，并关注数字鸿沟下弱势家庭的差异化干预策略。","2026-09-17T00:04:40.873674Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":8,"content":185,"source_name":186,"source_url":8,"published_at":187,"category":188,"cover_url":8,"hotness":12,"is_selected":13,"score":189,"score_detail":190,"sources":193,"tags":195,"search_phrases":200,"slug":203,"view_count":33,"doi":8,"paper":8,"created_at":204},2550,"补短板让农业生产更聪慧 经济日报评论智慧农业发展","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7684041568088556072\u002F","今年以来智慧农业发展迅速融入各地农业生产诸环节。当前智慧农业主要表现：生产方式与数据关系日益紧密(如寿光智能温室)、产业体系向链条融合升级(如浙江未来农场)、资源配置由要素驱动向创新驱动转变(如四川丘陵山区北斗导航智能农机)、经营主体从传统农民向新农人发展。但仍面临核心技术自主性不强、标准体系不健全、城乡数字鸿沟明显、复合型人才短缺等短板。","## 补短板让农业生产更“聪慧”\n\n2026-09-11 06:45·[中国经济网](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAX48WhqFh4ZlMkKgEZnGBoXUx0zeI5BcDWJ4OzPJGSPc\u002F?source=tuwen_detail)\n\n今年以来，智慧农业发展迅速，融入各地农业生产诸环节，在助力乡村振兴中持续发挥重要作用。作为数字技术与农业深度融合的产物，智慧农业是我国农业从传统粗放型发展向新型集约化发展转型的核心引擎，更是夯实乡村振兴基石、实现共同富裕的重要路径。\n\n当前，我国智慧农业的主要表现特征如下：\n\n一是生产方式与数据的关系日益紧密。智慧农业能够运用物联网、大数据等，实时收集环境因素和作物生长状况等信息，并把它们转化为科学生产决策。例如，在山东省寿光市，智能温室大棚已成为农业生产标配，农民在“云”上即可实现农事操作。\n\n二是产业体系向链条融合升级。智慧农业将产、加、销、售等方面紧密融合在一起，推动一二三产业深度融合发展。比如，浙江努力建设的“未来农场”，实现了自动化生产、智能化管理、在线化服务，并借助农产品电商平台向消费者直接销售农产品。\n\n三是资源配置由要素驱动向创新驱动转变。智慧农业正在借助技术手段为乡村振兴化解“土地零散化”“劳动力流失”等问题，从而探索创新土地、劳动力、资金、数据生产要素的优化组合模式。比如在地形比较复杂的四川丘陵山区，利用北斗导航的智能农机，实现对不规则地块厘米级的插秧、收割作业，弥补乡村劳动力缺乏劣势，提高土地利用率。\n\n四是经营主体从传统农民向“新农人”发展。智慧农业的发展深刻触及了农业生产经营的核心主体——人，“新农人”更像是一位农业经理人，将促进农业经营主体和服务体系多元化。\n\n尽管潜力巨大，但智慧农业在深化应用中依然面临核心技术自主性不强、标准体系不健全、城乡“数字鸿沟”明显、复合型人才短缺等短板。因此，接下来应加强系统谋划，增强政策引导。\n\n强化顶层设计与标准引领。加快落实《全国智慧农业行动计划（2024—2028年）》等规划部署，完善国家层面智慧农业数据接口、设备兼容、应用服务等关键标准，破除“数据烟囱”和“数据孤岛”。支持各地因地制宜制定发展规划，推动发展智慧种业、智慧畜牧、智能农机等重点领域，持续推进数字乡村发展行动，缩小城乡“数字鸿沟”差距。\n\n聚焦关键核心技术自主攻关。坚持问题和产业需求导向，集中攻克高端农业传感器、动植物生长模型与核心算法、智能决策系统、重型智能农机装备等“卡脖子”难题。鼓励产学研用紧密结合，形成自主可控、稳定高效的技术体系。\n\n培育壮大新型主体、数字农民。加快培育一批智慧农业龙头企业、专精特新企业，引领一大批生产经营主体、示范园区等推广应用新技术、新模式、新理念，进一步发挥技术集成优势。加大教育培训力度，面向广大农民、农民合作社带头人、返乡青年，大力普及智慧农业技能技术，培育一大批爱农业、懂技术、会经营的“新农人”，让他们成为乡村振兴中最活跃的因素。\n\n创新普惠共享的利益联结机制。防止技术鸿沟演变为“收入鸿沟”，积极探索“订单农业”“收益分红”等多种形式的利益联结模式，引导智慧农业服务公司为小农户提供价格实在、方便快捷的智慧农业数字化解决方案。借助大数据等技术打造“常态化精准帮扶”机制，确保尽快让贫困地区的贫困户、欠发达地区的农户也能抓住智能时代共享智慧农业发展红利的良机。（作者：冯娅妮 来源：经济日报）","中国经济网 2026-09-11","2026-09-10T23:00:00Z","报道",78,{"impact":191,"substance":16,"depth":57,"authority":159,"freshness":45,"relevant":20,"comment":192},24,"央媒评论系统梳理智慧农业四大特征与短板，并提出标准、技术、人才、利益联结四方面对策，政策参考价值较高，但属观点综述而非新增政策或数据。",[194],{"name":186,"url":183},[25,196,197,198,27,199],"智慧农业","智能农机","新农人","农业数据标准",[201,202],"农业数据标准 数字乡村 数字鸿沟 智慧农业","农业数据标准 数字乡村","农业数据标准数字乡村数字鸿沟智慧农业-2550","2026-09-16T00:03:44.886414Z",{"id":206,"title":207,"url":208,"summary":209,"summary_zh":8,"content":210,"source_name":211,"source_url":8,"published_at":212,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":213,"score_detail":214,"sources":218,"tags":220,"search_phrases":227,"slug":230,"view_count":20,"doi":8,"paper":8,"created_at":231},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":215,"substance":216,"depth":16,"authority":215,"freshness":160,"relevant":20,"comment":217},12,20,"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。",[219],{"name":211,"url":208},[221,25,222,223,224,225,27,226],"数字农业","农业信息化","小农户","气候信息","ICT采纳","发展中国家",[228,229],"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"]