[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4175":3,"related-4175":83},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":22,"tags":24,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":82},4175,"Integrating Agricultural Technology Adoption and Financial Inclusion as a Strategy to Improve Micro-Scale Farm Productivity in Rural Areas of South Sumatra","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26sep241","Micro-scale farming faces various constraints in improving productivity, particularly in relation to farmers' ability to adopt technology and access financing that meets their business needs. Agricultural technology can improve the efficiency of input use, access to information, farm management, and decision-making. However, technology adoption among smallholder farmers is still influenced by digital literacy, infrastructure, economic conditions, and institutional factors. On the other hand, access to credit can improve farm productivity and technical efficiency by enabling farmers to obtain production inputs and make productive investments. This study aims to: (1) analyze the effect of agricultural technology adoption on micro-scale farm productivity; and (2) analyze the effect of financial inclusion access on micro-scale farm productivity. The study employs a quantitative explanatory approach using multiple linear regression analysis. A total of 300 farm observations are used as simulated data to demonstrate the proposed analytical framework; therefore, the numerical results do not represent findings from an actual field survey. The independent variables consist of an agricultural technology adoption index and a financial inclusion access index, while farm productivity serves as the dependent variable. Farmers' age, education level, and land area are included as control variables. The results indicate that agricultural technology adoption and financial inclusion access have positive and statistically significant effects on farm productivity. The model explains a substantial proportion of the variation in productivity and provides an initial basis for further empirical investigation into the role of technology and financing in micro-scale farming. These findings are illustrative in nature and should be validated using primary data collected from farmers.","微观农业在提高生产力方面面临各种制约，尤其是农民采用技术以及获取满足其经营需求的融资能力方面的制约。农业技术可以提高投入品使用效率、信息获取、农场管理和决策效率。然而，小农户的技术采用仍受到数字素养、基础设施、经济条件和制度因素的影响。另一方面，获得信贷可以通过使农民能够获取生产投入并进行生产性投资来提高农场生产力和技术效率。本研究旨在：(1) 分析农业技术采用对微观农场生产力的影响；(2) 分析普惠金融获取对微观农场生产力的影响。本研究采用定量解释性方法，运用多元线性回归分析。共使用300个农场观测值作为模拟数据来展示所提出的分析框架；因此，数值结果不代表实际实地调查的发现。自变量包括农业技术采用指数和普惠金融获取指数，农场生产力为因变量。农民年龄、教育水平和土地面积作为控制变量纳入分析。结果表明，农业技术采用和普惠金融获取对农场生产力具有正向且统计显著的影响。该模型解释了生产力变异的相当大比例，为进一步实证研究技术和融资在微观农业中的作用提供了初步基础。这些发现具有示例性质，应使用从农民处收集的一手数据加以验证。",null,"International Journal of Innovative Science and Research Technology (IJISRT)","2026-10-03T00:00:00Z","论文",10,false,41,{"impact":17,"substance":13,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":21},8,6,9,1,"主题相关但数据为模拟生成、期刊权威性一般，仅具方法参考价值，不建议进入每日精选。",[23],{"name":10,"url":6},[25,26,27,28],"农业信息化","小农户","普惠金融","农业技术采纳",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49,54],{"code":30,"score":43,"supported":44,"title":45,"abstract":46,"tags":48},7,true,[],[26,47],"smallholder",[26],{"code":50,"score":20,"supported":14,"title":51,"abstract":52,"tags":53},"G1",[],[],[28],{"code":55,"score":20,"supported":14,"title":56,"abstract":57,"tags":58},"I1",[],[],[25],[60,61],"南苏门答腊 小农户 生产力","农业技术采纳 普惠金融","南苏门答腊小农户生产力-4175",2,"10.38124\u002Fijisrt\u002F26sep241",{"doi":64,"openalex_id":66,"authors":67,"venue":10,"cited_by_count":74,"oa_url":6,"card":75,"direction":79,"ingested_from":81},"W7218524136",[68,71],{"name":69,"orcid":70},"Lisa Hermawati","https:\u002F\u002Forcid.org\u002F0009-0001-5824-7052",{"name":72,"orcid":73},"Ema Pusvita","https:\u002F\u002Forcid.org\u002F0009-0002-2813-5567",0,{"tldr":76,"method":77,"finding":78,"direction":79,"opportunity":80},"用模拟数据回归分析农业技术采纳与金融普惠对微规模农场生产力的影响。","多元线性回归，300条模拟数据，构建技术采纳与金融普惠指数。","技术采纳和金融普惠均显著正向提升微规模农场生产力。","数字乡村与农业信息化","需用真实农户调查数据验证，并探究数字素养与信贷可得性的交互机制。","openalex","2026-10-04T23:30:30.503599Z",{"total":18,"page":20,"page_size":18,"items":84},[85,170,232,311,355,417],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":90,"content":9,"source_name":91,"source_url":88,"published_at":92,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":93,"score_detail":94,"sources":100,"tags":102,"primary_topic":106,"secondary_topics":112,"classification":114,"search_phrases":145,"slug":148,"view_count":149,"doi":150,"paper":151,"created_at":169},3330,"Beyond awareness: accounting for information exposure bias in adoption and income effects of Orange-Fleshed Sweet Potato technology among smallholder farming households in Nigeria","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1907286","This study examined the role of information exposure in the adoption of Orange-Fleshed Sweet Potato (OFSP) technology and its effect on the income of smallholder sweet potato farming households in Nigeria. Using cross-sectional primary data collected from 320 smallholder sweet potato farming households in Osun and Kwara States through a two-stage sampling procedure, the study accounted for heterogeneous awareness exposure and actual adoption behavior. The Heckman probit selection model was employed to distinguish between awareness exposure and adoption conditional on awareness, while the Endogenous Treatment Effect (ETE) and Inverse-Probability-Weighted Regression Adjustment (IPWRA) models were used to estimate the income effects of OFSP adoption while correcting for potential endogeneity and selection bias. The findings showed that awareness exposure was significantly influenced by age, marital status, household size, and membership in farmer associations, whereas adoption conditional on awareness was significantly associated with education, farming experience, and access to credit. Descriptively, OFSP adopters reported more favorable perceptions of the technology’s productivity, climate resilience, and nutritional benefits than non-adopters. Both ETE and IPWRA estimates further indicated that OFSP adoption had a positive and significant effect on household income. The study concludes that awareness and adoption are distinct stages influenced by different factors. Policies aimed at promoting OFSP adoption should therefore strengthen targeted information dissemination, extension support, farmer education, and access to credit to enhance adoption, household income, food security, nutrition, and rural livelihoods.","本研究考察了信息接触在橙肉甘薯（Orange-Fleshed Sweet Potato, OFSP）技术采纳中的作用及其对尼日利亚小农户甘薯种植家庭收入的影响。研究采用两阶段抽样程序，从奥孙州和夸拉州320户小农户甘薯种植家庭收集了横截面原始数据，并考虑了异质性认知接触与实际采纳行为。研究运用Heckman probit选择模型区分认知接触与在认知条件下的采纳行为，同时采用内生处理效应（Endogenous Treatment Effect, ETE）模型和逆概率加权回归调整（Inverse-Probability-Weighted Regression Adjustment, IPWRA）模型估计OFSP采纳的收入效应，并校正潜在的内生性和选择偏差。研究结果表明，认知接触显著受年龄、婚姻状况、家庭规模和农民协会成员身份的影响，而在认知条件下的采纳行为则与教育程度、种植经验和信贷可得性显著相关。描述性统计显示，OFSP采纳者对技术生产力、气候适应性和营养益处的感知优于非采纳者。ETE和IPWRA估计结果均表明，OFSP采纳对家庭收入具有显著的正向影响。研究结论认为，认知和采纳是受不同因素影响的 distinct 阶段。因此，旨在促进OFSP采纳的政策应加强针对性信息传播、推广支持、农民教育和信贷可得性，以提高采纳率、家庭收入、粮食安全、营养水平和农村生计。","Frontiers in Sustainable Food Systems","2026-09-24T00:00:00Z",73,{"impact":95,"substance":96,"depth":97,"authority":98,"freshness":13,"relevant":20,"comment":99},12,21,17,13,"以信息接触偏差为切入，方法规范、数据扎实，对农业技术推广信息化有参考价值，但属区域国别研究，公共影响有限。",[101],{"name":91,"url":88},[25,26,103,104,105],"甘薯","尼日利亚","信息传播",{"code":107,"slug":108,"name":109,"path":110,"parent_code":111},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[113],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":115},{"candidates":116,"agricultural_evidence":44},[117,123,127,134,141],{"code":107,"score":118,"supported":44,"title":119,"abstract":121,"tags":122},15,[120],"potato",[103,120],[103],{"code":30,"score":118,"supported":44,"title":124,"abstract":125,"tags":126},[47],[26,47],[26],{"code":128,"score":18,"supported":44,"title":129,"abstract":130,"tags":133},"A8",[],[131,132],"粮食安全","food security",[],{"code":135,"score":18,"supported":44,"title":136,"abstract":137,"tags":140},"H7",[],[138,139],"气候适应","climate resilience",[],{"code":55,"score":20,"supported":14,"title":142,"abstract":143,"tags":144},[],[],[25],[146,147],"尼日利亚 橙肉甘薯 小农户","信息接触 技术采纳 收入效应","尼日利亚橙肉甘薯小农户-3330",4,"10.3389\u002Ffsufs.2026.1907286",{"doi":150,"openalex_id":152,"authors":153,"venue":91,"cited_by_count":74,"oa_url":88,"card":164,"direction":79,"ingested_from":81},"W7214099591",[154,157,159,161],{"name":155,"orcid":156},"Adetomiwa Kolapo","https:\u002F\u002Forcid.org\u002F0000-0002-0808-1293",{"name":158,"orcid":9},"Melusi Sibanda",{"name":160,"orcid":9},"Nolwazi Z. Khumalo",{"name":162,"orcid":163},"Temitope O. Ojo","https:\u002F\u002Forcid.org\u002F0000-0002-3517-0435",{"tldr":165,"method":166,"finding":167,"direction":79,"opportunity":168},"研究尼日利亚小农户信息接触偏差对橙肉甘薯技术采纳及收入的影响。","320户两阶段抽样调查，用Heckman probit、ETE和IPWRA模型。","信息接触与采纳受不同因素影响，采纳显著提高农户家庭收入。","可延伸研究信息传播渠道差异如何影响技术采纳阶段转化与收入分配。","2026-09-24T23:30:06.870179Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":9,"source_name":176,"source_url":173,"published_at":177,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":182,"tags":184,"primary_topic":188,"secondary_topics":189,"classification":190,"search_phrases":206,"slug":209,"view_count":63,"doi":210,"paper":211,"created_at":231},2925,"From tradition to smart agriculture: how value and technology readiness shape farmers’ adoption decisions","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fjstpm-03-2025-0084","Purpose This study aims to investigate the slow adoption of smart farming technologies (SFTs) in developing regions, where farmers continue relying on traditional methods despite modernization policies. It examines how farmers’ value perceptions and technology readiness (TR) shape adoption intentions by integrating the value-based adoption model (VAM) with the theory of consumption values (TCV). Specifically, this study develops a value-centric framework in which TR moderates the link between overall perceived value (OPV) and adoption intention (INT). Design\u002Fmethodology\u002Fapproach Data were collected from 351 smallholder farmers through self-administered surveys and analyzed using partial least squares-structural equation modeling. Findings OPV emerged as the strongest predictor of adoption, driven by perceived efficiency, facilitating conditions and financial incentive policy. Among OPV dimensions, conditional value outweighed functional and monetary values, underscoring the importance of enabling conditions such as financial aid, training and infrastructure. Contrary to expectations, perceived cost increased OPV, while perceived complexity and uncertainty were insignificant, suggesting that familiarity or outsourcing may reduce traditional barriers. TR moderated the link between OPV and INT, where motivated farmers move toward adoption regardless, but reluctant farmers require stronger OPV to ease concerns. Originality\u002Fvalue To the best of the authors’ knowledge, this study is among the first to integrate VAM and TCV to explain the adoption of SFTs, establishing the value-centric perspective as a powerful lens for understanding technology acceptance in agriculture. It also demonstrates that TR differentially shapes how farmers rely on OPV to drive adoption.","目的 本研究旨在探究发展中地区智能农业技术（SFTs）采用缓慢的问题，尽管有现代化政策推动，农民仍继续依赖传统方法。研究通过将价值基础采用模型（VAM）与消费价值理论（TCV）相结合，考察农民的价值感知和技术准备度（TR）如何塑造采用意愿。具体而言，本研究构建了一个以价值为核心的框架，其中TR调节整体感知价值（OPV）与采用意愿（INT）之间的关系。设计\u002F方法\u002F路径 通过自填式问卷从351名小农户处收集数据，并采用偏最小二乘结构方程模型进行分析。研究发现 OPV是采用行为最强的预测因子，受感知效率、便利条件和财政激励政策驱动。在OPV各维度中，条件价值超过了功能价值和货币价值，凸显了财政援助、培训和基础设施等使能条件的重要性。与预期相反，感知成本增加了OPV，而感知复杂性和不确定性则不显著，表明熟悉度或外包可能降低了传统障碍。TR调节了OPV与INT之间的关系，有积极性的农民无论如何都会趋向采用，而犹豫不决的农民则需要更强的OPV来缓解顾虑。原创性\u002F价值 据作者所知，本研究是首批整合VAM和TCV来解释SFTs采用的研究之一，确立了以价值为核心的视角作为理解农业技术接受的有力透镜。研究还表明，TR以差异化方式塑造了农民依赖OPV来推动采用的过程。","Journal of Science and Technology Policy Management","2026-09-18T00:00:00Z",75,{"impact":180,"substance":96,"depth":97,"authority":98,"freshness":17,"relevant":20,"comment":181},16,"该研究整合VAM与TCV模型，基于351户小农户调查揭示价值感知与技术准备度对智慧农业技术采纳的影响，方法新颖、结论对发展区域推广政策有参考价值，但属学术论文、影响范围偏细分领域。",[183],{"name":176,"url":173},[185,26,28,186,187],"智慧农业","农业政策激励","技术准备度",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":191},{"candidates":192,"agricultural_evidence":44},[193,198,202],{"code":55,"score":19,"supported":44,"title":194,"abstract":196,"tags":197},[195],"smart agriculture",[],[185],{"code":30,"score":43,"supported":44,"title":199,"abstract":200,"tags":201},[],[26,47],[26],{"code":50,"score":20,"supported":14,"title":203,"abstract":204,"tags":205},[],[],[28],[207,208],"智慧农业技术 小农户 采纳","价值感知 技术准备度 农户","智慧农业技术小农户采纳-2925","10.1108\u002Fjstpm-03-2025-0084",{"doi":210,"openalex_id":212,"authors":213,"venue":176,"cited_by_count":74,"oa_url":9,"card":225,"direction":230,"ingested_from":81},"W7213541758",[214,217,220,223],{"name":215,"orcid":216},"Hazem Yusuf Osrof","https:\u002F\u002Forcid.org\u002F0000-0001-9622-5025",{"name":218,"orcid":219},"Cheng Ling Tan","https:\u002F\u002Forcid.org\u002F0000-0002-3759-6763",{"name":221,"orcid":222},"Sook Fern Yeo","https:\u002F\u002Forcid.org\u002F0000-0002-8060-5872",{"name":224,"orcid":9},"Kim Hua Tan",{"tldr":226,"method":227,"finding":228,"direction":79,"opportunity":229},"整合VAM与TCV模型，研究农户价值感知与技术准备度如何影响智慧农业技术采纳意愿。","对351户小农户问卷调查，采用PLS-SEM结构方程建模分析。","总体感知价值是采纳最强预测因子，条件价值作用最大，技术准备度起调节作用。","可探索不同区域与文化背景下价值维度差异，及技术准备度干预对采纳的长期效应。","智慧农业 \u002F 农业物联网","2026-09-19T23:30:10.915432Z",{"id":233,"title":234,"url":235,"summary":236,"summary_zh":9,"content":9,"source_name":91,"source_url":235,"published_at":237,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":238,"score_detail":239,"sources":243,"tags":245,"primary_topic":248,"secondary_topics":253,"classification":261,"search_phrases":286,"slug":289,"view_count":290,"doi":291,"paper":292,"created_at":310},1244,"Determinants of soil testing decisions among smallholder maize farmers in western Kenya","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1813147","Depletion of soil fertility over time remains a major challenge limiting agricultural productivity in Kenya, particularly for maize, the country’s staple crop. Despite the novelty of soil testing in enhancing agricultural productivity, adoption remains limited. Therefore, this study was conducted to identify the factors influencing smallholder maize farmers’ decision to conduct soil testing in western Kenya. The study used descriptive statistics to summarize the data, and a logistic regression model on a sample of 1,592 to evaluate the determinants of soil testing among farmers. Descriptive statistics show that less than 8% of farmers had conducted soil tests on their farms, and about 7% knew where to obtain soil testing services. The logistic results show that access to extension services ( p \u003C 0.01), age of the household head ( p \u003C 0.01), group membership ( p \u003C 0.01), farmers’ awareness about where to obtain soil testing services ( p \u003C 0.05), land size ( p \u003C 0.05), perceived fertility of the soil ( p \u003C 0.05), economically active household members ( p \u003C 0.10) are statistically significant in influencing the decision to conduct soil testing. This study recommends promoting access to soil testing services and enhancing farmer awareness through targeted communication strategies to increase soil testing.","2026-08-25T00:00:00Z",62,{"impact":13,"substance":240,"depth":241,"authority":95,"freshness":63,"relevant":20,"comment":242},20,18,"研究肯尼亚小农土壤检测采纳影响因素，数据详实，对农业技术推广有参考价值。",[244],{"name":91,"url":235},[26,246,247,28],"非洲农业","土壤检测",{"code":249,"slug":250,"name":251,"path":252,"parent_code":111},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize",[254,255],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"code":256,"slug":257,"name":258,"path":259,"parent_code":260},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":37,"locked":14,"version":38,"method":39,"evidence":262},{"candidates":263,"agricultural_evidence":44},[264,268,272,277,281],{"code":30,"score":95,"supported":44,"title":265,"abstract":266,"tags":267},[47],[47],[26],{"code":256,"score":95,"supported":44,"title":269,"abstract":270,"tags":271},[257],[257],[247],{"code":249,"score":273,"supported":44,"title":274,"abstract":275,"tags":276},11,[250],[250],[],{"code":50,"score":20,"supported":14,"title":278,"abstract":279,"tags":280},[],[],[28],{"code":282,"score":20,"supported":14,"title":283,"abstract":284,"tags":285},"J3",[],[],[246],[287,288],"农业技术采纳 土壤检测 非洲农业 小农户","农业技术采纳 土壤检测","农业技术采纳土壤检测非洲农业小农户-1244",3,"10.3389\u002Ffsufs.2026.1813147",{"doi":291,"openalex_id":293,"authors":294,"venue":91,"cited_by_count":74,"oa_url":304,"card":305,"direction":79,"ingested_from":81},"W7204172699",[295,297,299,301],{"name":296,"orcid":9},"Fido Orawo",{"name":298,"orcid":9},"Dickson Okello",{"name":300,"orcid":9},"John Olwande",{"name":302,"orcid":303},"Kindie Tesfaye","https:\u002F\u002Forcid.org\u002F0000-0002-7201-8053","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1813147\u002Fpdf",{"tldr":306,"method":307,"finding":308,"direction":79,"opportunity":309},"研究肯尼亚西部小农玉米种植户土壤检测决策的影响因素。","描述统计和逻辑回归模型，样本1592户。","土壤检测采用率低，推广服务、年龄、组织成员等显著影响决策。","可探索数字工具提升土壤检测服务可及性和农民意识，填补推广服务不足的空白。","2026-09-01T04:03:09.420598Z",{"id":312,"title":313,"url":314,"summary":315,"summary_zh":9,"content":316,"source_name":317,"source_url":9,"published_at":318,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":319,"score_detail":320,"sources":322,"tags":324,"primary_topic":331,"secondary_topics":332,"classification":333,"search_phrases":349,"slug":352,"view_count":353,"doi":9,"paper":9,"created_at":354},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":95,"substance":240,"depth":241,"authority":95,"freshness":63,"relevant":20,"comment":321},"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。",[323],{"name":317,"url":314},[325,326,25,26,327,328,329,330],"数字农业","数字乡村","气候信息","ICT采纳","数字鸿沟","发展中国家",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":334},{"candidates":335,"agricultural_evidence":44},[336,340,344],{"code":30,"score":95,"supported":44,"title":337,"abstract":338,"tags":339},[47],[26],[26],{"code":55,"score":63,"supported":14,"title":341,"abstract":342,"tags":343},[],[],[25,325],{"code":345,"score":63,"supported":14,"title":346,"abstract":347,"tags":348},"I6",[],[],[326,329],[350,351],"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",5,"2026-08-15T00:02:55.675693Z",{"id":356,"title":357,"url":358,"summary":359,"summary_zh":360,"content":9,"source_name":361,"source_url":358,"published_at":362,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":363,"score_detail":364,"sources":366,"tags":368,"primary_topic":372,"secondary_topics":373,"classification":380,"search_phrases":396,"slug":399,"view_count":74,"doi":400,"paper":401,"created_at":416},5592,"Evaluation of Concept-Rank Collapse Audits for Computer Vision Agriculture Disease Classifiers Under Low-Label Regimes: An Interpretability Capacity Bound for Smallholder Fungicide Selectivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545193","Concept-based interpretability methods for plant-disease classifiers are typically evaluated by whether their concept activations correlate with human-annotated labels, but this criterion is silent on a prior question: whether the classifier's internal representation retains enough concept-rank structure for any audit to be informative. the present analysis formalises that prior question as an interpretability capacity bound. A classifier is modelled as a concept-rank operator whose singular values govern how much concept-level signal survives the projection from pixel space to class logits, and a collapse audit is defined as the ratio of retained concept-rank energy to total concept energy. Under a low-label regime, where concept annotations are scarce and the audit must be estimated from few labelled examples, the bound predicts that audit reliability degrades as a function of the concept-rank spectrum and the label budget. The analysis is a theoretical and methodological contribution: it derives the bound, states the conditions under which a collapse audit is identifiable, and characterises the operating characteristics of the audit as a decision procedure under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 52 clusters were required for 5 percentage points at 80% power. The bound reframes concept audits as capacity tests rather than fidelity tests, with consequences for how smallholder fungicide-selectivity decisions should be supported by interpretable disease classifiers.","面向植物病害分类器的基于概念的可解释性方法，通常以其概念激活是否与人工标注标签相关作为评价标准，但该标准对一个更先前的问题保持沉默：分类器的内部表征是否保留了足够的概念秩结构，从而使任何审计具有信息量。本分析将该先前问题形式化为一个可解释性容量上界。分类器被建模为概念秩算子，其奇异值支配着从像素空间到类别logits的投影中概念级信号得以保留的程度，并将坍缩审计定义为保留的概念秩能量与总概念能量之比。在低标签情形下，概念标注稀缺且审计必须由少量标注样本估计，该上界预测审计可靠性会随概念秩谱和标签预算的函数而退化。本分析是一项理论和方法论贡献：推导出该上界，陈述坍缩审计可识别的条件，并在声明的方差结构（簇间SD = 6.0，残差SD = 4.5，组内相关系数 = 0.64）下刻画审计作为决策程序的操作特征。在2000个设计单元中，针对簇数为8、16、24和32以及效应量为3、5和8个百分点的情形估计了功效；在80%功效下达到5个百分点需要52个簇。该上界将概念审计重新框定为容量检验而非保真度检验，并对可解释病害分类器应如何支持小农杀菌剂选择性决策具有启示意义。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",69,{"impact":17,"substance":96,"depth":241,"authority":95,"freshness":13,"relevant":20,"comment":365},"理论方法论文，提出可解释性容量上界并给出审计功效分析，对智慧农业病害识别可信度有参考价值，但属小众方法学研究、缺乏田间实证，暂不宜进入每日精选。",[367],{"name":361,"url":358},[185,369,26,370,371],"农业人工智能","植物病害识别","可解释性",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[374],{"code":375,"slug":376,"name":377,"path":378,"parent_code":379},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":37,"locked":14,"version":38,"method":39,"evidence":381},{"candidates":382,"agricultural_evidence":44},[383,387,392],{"code":30,"score":95,"supported":44,"title":384,"abstract":385,"tags":386},[47],[47],[26],{"code":375,"score":13,"supported":44,"title":388,"abstract":390,"tags":391},[389],"computer vision",[],[369,370],{"code":55,"score":20,"supported":14,"title":393,"abstract":394,"tags":395},[],[],[185],[397,398],"农业人工智能 植物病害识别 可解释性 智慧农业","农业人工智能 植物病害识别","农业人工智能植物病害识别可解释性智慧农业-5592","10.5281\u002Fzenodo.23545193",{"doi":400,"openalex_id":402,"authors":403,"venue":361,"cited_by_count":74,"oa_url":358,"card":410,"direction":414,"ingested_from":81},"W7223137323",[404,406,408],{"name":405,"orcid":9},"Akshay Ramgoolam",{"name":407,"orcid":9},"Priya Bundhoo Appadoo",{"name":409,"orcid":9},"Jean-Noël LEspérance",{"tldr":411,"method":412,"finding":413,"direction":414,"opportunity":415},"提出可解释性容量上界，评估低标签下概念秩崩溃审计的可靠性。","将分类器建模为概念秩算子，推导容量上界并做2000格功效模拟。","审计可靠性随概念秩谱与标签预算下降；5个百分点效应需52个聚类达80%功效。","农业人工智能与决策模型","可将容量上界用于小农户杀菌剂选择性决策的可解释模型审计与标签预算设计。","2026-10-22T11:21:10.425290Z",{"id":418,"title":419,"url":420,"summary":421,"summary_zh":422,"content":9,"source_name":361,"source_url":420,"published_at":362,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":423,"score_detail":424,"sources":427,"tags":429,"primary_topic":432,"secondary_topics":433,"classification":435,"search_phrases":450,"slug":453,"view_count":74,"doi":454,"paper":455,"created_at":469},5590,"Evaluation of Concept-Probe Temporal-Stability Audits for Computer Vision Agriculture Disease Classifiers Under Seasonal Phenology Drift: An Interpretability Decay Bound for Smallholder Fungicide Timing","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545234","Concept-probe audits are increasingly used to certify that a crop-disease classifier's internal representations track the pathological concepts a smallholder would act on, but a probe certified at one point in the season may silently decouple from those concepts as phenology shifts the visual statistics of the field. the present analysis treats that decoupling as a formal object. It defines a temporal-stability audit as a sequence of concept-probe accuracies measured across defined phenological windows, and it derives an interpretability decay bound: the rate at which probe accuracy can fall before the fungicide-timing decision it is meant to support becomes unreliable. The bound is expressed as a function of the probe's initial margin, the drift in concept prevalence between windows, and the classifier's sensitivity to the drifted features, and it yields a certified audit interval rather than a single-point certificate. The paper then characterises the operating characteristics of the audit design under a declared variance structure, using a replication-based evaluation of the before-after-control-impact estimator with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64. Power was estimated across 2000 design cells over cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 54 clusters were required for 5 percentage points at 80% power. The contribution is a decision-theoretic account of when a concept-probe certificate expires, and a design rule for scheduling re-audits across the season.","概念探针审计（concept-probe audit）日益被用于验证作物病害分类器的内部表征是否追踪小农户会据以采取行动的病理学概念，但在生长季某一时点获得认证的探针，可能随着物候变化改变田间的视觉统计特征而悄然与这些概念脱钩。本分析将这种脱钩视为一个形式化对象。它将时间稳定性审计（temporal-stability audit）定义为在既定物候窗口内测量的一系列概念探针准确率，并推导出一个可解释性衰减界限（interpretability decay bound）：即探针准确率在其所支持的杀菌剂施用时机决策变得不可靠之前所能下降的速率。该界限表示为探针初始裕度、窗口间概念流行率漂移以及分类器对漂移特征敏感度的函数，并给出一个经认证的审计区间而非单点认证。随后，本文在声明的方差结构下刻画该审计设计的运行特征，采用基于重复的前后对照影响估计量评估，其中组间标准差 = 6.0、残差标准差 = 4.5、组内相关系数 = 0.64。功效在 2000 个设计单元上估计，聚类数取 8、16、24 和 32，效应量取 3、5 和 8 个百分点；在 80% 功效下达到 5 个百分点需要 54 个聚类。本文的贡献在于从决策论角度阐明概念探针认证何时失效，并给出在整个生长季内安排重新审计的设计规则。",70,{"impact":17,"substance":425,"depth":241,"authority":95,"freshness":13,"relevant":20,"comment":426},22,"该论文提出概念探针审计的时序稳定性框架与可解释性衰减界，方法新颖、统计设计扎实，但属小众方法论研究，产业影响有限，适合作为专业精选而非头条。",[428],{"name":361,"url":420},[185,369,26,430,431],"作物病害识别","模型审计",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[434],{"code":375,"slug":376,"name":377,"path":378,"parent_code":379},{"status":37,"locked":14,"version":38,"method":39,"evidence":436},{"candidates":437,"agricultural_evidence":44},[438,442,446],{"code":30,"score":118,"supported":44,"title":439,"abstract":440,"tags":441},[47],[26,47],[26],{"code":375,"score":13,"supported":44,"title":443,"abstract":444,"tags":445},[389],[],[430,369],{"code":55,"score":20,"supported":14,"title":447,"abstract":448,"tags":449},[],[],[185],[451,452],"概念探针 时序稳定性 作物病害分类器","杀菌剂 施药时机 可解释性衰减","概念探针时序稳定性作物病害分类器-5590","10.5281\u002Fzenodo.23545234",{"doi":454,"openalex_id":456,"authors":457,"venue":361,"cited_by_count":74,"oa_url":420,"card":464,"direction":414,"ingested_from":81},"W7223088030",[458,460,462],{"name":459,"orcid":9},"Abdi-Basid Idriss Farah",{"name":461,"orcid":9},"Amina Abdillahi Guedi",{"name":463,"orcid":9},"Moussa Omar Youssouf",{"tldr":465,"method":466,"finding":467,"direction":414,"opportunity":468},"提出概念探针时间稳定性审计与可解释性衰减界，判断何时需重新审计。","定义跨物候窗口探针精度序列，推导衰减界，用BACI复制评估统计功效。","5个百分点效应需54个聚类达80%功效，审计证书会随物候漂移过期。","可将衰减界与再审计调度规则嵌入小农户杀菌剂时机决策支持系统，并验证跨作物泛化。","2026-10-22T11:21:08.978348Z"]