[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1746":3,"related-1746":90},{"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":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":89},1746,"Digitization in Agriculture and Food Security in Sub-Saharan Africa: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.70382\u002Fajbar.v13i1.032","Digitization is increasingly recognized as a transformative force in Sub-Saharan Africa’s (SSA) agricultural sector, with significant implications for productivity enhancement and food security improvement. This systematic review examines the role of digital technologies in agriculture, focusing on their contributions to food availability, accessibility, utilization, and stability across SSA. The study adopts a systematic literature review (SLR) methodology, drawing evidence from peer-reviewed journals and reports published by institutions such as the World Bank, FAO, IFAD, and the African Development Bank between 2015 and 2025. Findings reveal that digital agriculture interventions, including mobile advisory systems, precision agriculture tools, digital financial services, and e-market platforms, significantly improve agricultural productivity, reduce transaction costs, strengthen market integration, and enhance farmers’ access to financial services. However, adoption remains uneven due to infrastructural deficits, digital illiteracy, gender inequality, and weak institutional frameworks. The review concludes that while digitization holds strong potential to transform food systems in SSA, its effectiveness depends on enabling environments such as infrastructure development, inclusive policies, and capacity building. The study recommends strengthening digital infrastructure, improving literacy programs, and expanding public–private partnerships to accelerate inclusive digital transformation in agriculture.","数字化日益被视为撒哈拉以南非洲（SSA）农业部门的变革性力量，对提高生产力和改善粮食安全具有重大意义。本系统综述考察了数字技术在农业中的作用，重点关注其对撒哈拉以南非洲粮食可得性、可及性、利用和稳定性的贡献。研究采用系统文献综述（SLR）方法，借鉴了2015年至2025年间同行评审期刊以及世界银行、联合国粮农组织（FAO）、国际农业发展基金（IFAD）和非洲开发银行等机构发布的报告中的证据。研究结果表明，包括移动咨询系统、精准农业工具、数字金融服务和电子市场平台在内的数字农业干预措施，显著提高了农业生产力，降低了交易成本，加强了市场整合，并增强了农民获取金融服务的机会。然而，由于基础设施不足、数字文盲、性别不平等和制度框架薄弱，数字技术的采用仍不均衡。本综述得出结论：尽管数字化在改变撒哈拉以南非洲粮食系统方面具有巨大潜力，但其有效性取决于有利环境，如基础设施发展、包容性政策和能力建设。研究建议加强数字基础设施建设，改善扫盲计划，并扩大公私伙伴关系，以加速农业领域的包容性数字化转型。",null,"Journal of Biotechnology and Agricultural Research","2026-09-04T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,12,2,1,"系统综述非洲数字农业对粮食安全的影响，证据扎实，但时效性低，适合精选。",[24],{"name":10,"url":6},[26,27,28,29],"数字农业","系统综述","粮食安全","非洲农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,58],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},15,true,[47],"food security",[28,47],[28],{"code":51,"score":13,"supported":45,"title":52,"abstract":53,"tags":57},"I1",[],[54,26,55,56],"精准农业","precision agriculture","digital agriculture",[26],{"code":59,"score":21,"supported":14,"title":60,"abstract":61,"tags":62},"J3",[],[],[29],[64,65],"数字农业 粮食安全 系统综述 非洲农业","数字农业 粮食安全","数字农业粮食安全系统综述非洲农业-1746",3,"10.70382\u002Fajbar.v13i1.032",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":81,"oa_url":6,"card":82,"direction":86,"ingested_from":88},"W7208759473",[72,75,77,79],{"name":73,"orcid":74},"Funso O. Alabuja","https:\u002F\u002Forcid.org\u002F0000-0002-7579-1041",{"name":76,"orcid":9},"Ejiofor D. Chukwubuike",{"name":78,"orcid":9},"Lateefah O. Lama-Abdul",{"name":80,"orcid":9},"Olabiyi Fatimah Adebisi",0,{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"系统综述撒哈拉以南非洲农业数字化对粮食安全的影响，发现其提升生产力但采用不均。","系统文献综述，分析2015-2025年同行评审文献及国际机构报告。","数字农业提高生产力、降低交易成本，但受基础设施、数字素养等制约。","数字乡村与农业信息化","可研究数字农业采用不均的驱动因素，如性别差异、制度障碍，探索包容性数字化路径。","openalex","2026-09-05T23:30:35.595189Z",{"total":91,"page":21,"page_size":91,"items":92},6,[93,169,240,307,387,487],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":108,"tags":110,"primary_topic":114,"secondary_topics":115,"classification":116,"search_phrases":146,"slug":149,"view_count":81,"doi":150,"paper":151,"created_at":168},5491,"Analysis of the Impact of Digital Technology on the Resilience of Anhui’s Agricultural Industry Chain: Threshold Effect of Relative Grain Yield","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010656","Improving the resilience of the agricultural industry chain to frequent external shocks and the intertwined agricultural production risks is critical for ensuring national food security and sustainable agricultural development. Taking the 16 prefecture-level cities in Anhui Province—a major grain-producing region in China—as the research sample, two composite proxy indices were constructed in this study; namely, an agricultural industry chain resilience index built upon aggregate official statistics (rather than direct observed shock-response outcomes) and an index capturing broad rural digital development instead of purely agriculture-specific digital adoption. Based on balanced panel data from 2015 to 2024, two-way fixed-effect, mediation-effect, and panel-threshold models were adopted in this study to examine the association, mediating mechanism, regional heterogeneity, and threshold characteristics conditioned on the relative grain yield. The empirical results show that the rural-oriented digital development index is significantly and robustly positively associated with our composite agricultural industry chain resilience proxy. Although agricultural TFP exhibits a statistically detectable partial mediating pattern in this correlational relationship, the economic magnitude of this indirect channel is extremely modest. As such, this finding indicates a potential theoretical transmission pathway rather than providing definitive empirical evidence for a causal mechanism. The promotion effect exhibits clear regional heterogeneity, with the strongest effect in Northern Anhui, followed by Central Anhui, while the coefficient is positive but insignificant in Southern Anhui. Furthermore, a single-threshold effect of the relative grain yield is identified; that is, the positive association between rural-oriented digital development and resilience proxy cannot be statistically detected when the grain yield remains below the threshold. Only after the agricultural grain yield increases can digital-related rural development release its resilience-related empowering dividends. We note that both composite indices are subject to measurement constraints linked to the local agricultural economic scale and broad rural digital conditions; these limitations are further discussed in the paper. This study provides empirical correlational evidence for the implementation of differentiated digital agriculture policies in major grain-producing provinces.","提升农业产业链对频繁外部冲击与交织叠加的农业生产风险的韧性，对保障国家粮食安全和农业可持续发展至关重要。本研究以中国粮食主产省安徽省的16个地级市为研究样本，构建了两个复合代理指标：一是基于官方统计数据总量（而非直接观测的冲击—响应结果）构建的农业产业链韧性指数，二是刻画农村数字化广泛发展（而非单纯农业领域数字化应用）的指数。基于2015—2024年平衡面板数据，本研究采用双向固定效应模型、中介效应模型和面板门槛模型，考察二者之间的关联、中介机制、区域异质性以及以相对粮食产量为条件变量的门槛特征。实证结果表明，农村导向的数字化发展指数与本研究的农业产业链韧性复合代理指标之间存在显著且稳健的正向关联。尽管农业全要素生产率在这一相关关系中呈现出统计上可检测的部分中介模式，但该间接渠道的经济量级极为有限。因此，这一发现指示的是一种潜在的理论传导路径，而非为因果机制提供确凿的实证证据。其促进效应表现出明显的区域异质性，皖北效应最强，皖中次之，皖南系数为正但不显著。此外，还识别出相对粮食产量的单一门槛效应，即当粮食产量低于门槛值时，农村导向的数字化发展与韧性代理指标之间的正向关联在统计上无法被检测到；只有在农业粮食产量提高之后，数字相关农村发展才能释放其韧性赋能红利。我们注意到，两个复合指数均受到与地方农业经济规模和农村数字化总体状况相关的测度约束；这些局限性在文中进一步讨论。本研究为粮食主产省实施差异化数字农业政策提供了实证相关性证据。","Sustainability","2026-10-20T00:00:00Z",74,{"impact":103,"substance":18,"depth":104,"authority":105,"freshness":106,"relevant":21,"comment":107},16,17,13,8,"基于安徽16市面板数据的实证研究，方法规范、结论有区域异质性价值，但属省级学术论文，公共影响有限，适合作为主题页聚合素材而非头条精选。",[109],{"name":99,"url":96},[26,111,28,112,113],"数字乡村","农业产业链韧性","安徽农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":117},{"candidates":118,"agricultural_evidence":45},[119,124,128,135,141],{"code":31,"score":120,"supported":45,"title":121,"abstract":122,"tags":123},7,[],[28,47],[28],{"code":51,"score":120,"supported":45,"title":125,"abstract":126,"tags":127},[],[26,56],[26],{"code":129,"score":91,"supported":45,"title":130,"abstract":131,"tags":134},"G",[],[132,133],"农村发展","rural development",[],{"code":136,"score":67,"supported":45,"title":137,"abstract":138,"tags":140},"J1",[],[139],"农业政策",[],{"code":142,"score":21,"supported":14,"title":143,"abstract":144,"tags":145},"I6",[],[],[111],[147,148],"安徽 农业产业链韧性 数字技术","安徽 粮食产量 数字乡村","安徽农业产业链韧性数字技术-5491","10.3390\u002Fsu182010656",{"doi":150,"openalex_id":152,"authors":153,"venue":99,"cited_by_count":81,"oa_url":96,"card":163,"direction":86,"ingested_from":88},"W7223005655",[154,157,159,161],{"name":155,"orcid":156},"Lan Ma","https:\u002F\u002Forcid.org\u002F0000-0003-2468-9917",{"name":158,"orcid":9},"Yuyan Li",{"name":160,"orcid":9},"张国亮",{"name":162,"orcid":9},"Yaoyao Li",{"tldr":164,"method":165,"finding":166,"direction":86,"opportunity":167},"以安徽16市面板数据检验农村数字发展对农业产业链韧性的影响及粮食产量门槛效应。","2015—2024年安徽16市平衡面板，双向固定效应、中介效应与面板门槛模型。","数字发展显著提升产业链韧性，存在粮食相对产量单门槛，且皖北效应最强、皖南不显著。","可细化数字技术指标、引入真实冲击响应韧性测度，并探究门槛形成机制与皖南失效原因。","2026-10-21T23:30:29.219265Z",{"id":170,"title":171,"url":172,"summary":173,"summary_zh":9,"content":9,"source_name":174,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":175,"is_selected":14,"score":176,"score_detail":177,"sources":182,"tags":187,"primary_topic":191,"secondary_topics":192,"classification":193,"search_phrases":223,"slug":226,"view_count":91,"doi":227,"paper":228,"created_at":239},4969,"Digital technology, agricultural resilience, and food security: A mini review","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.2002109\u002Ffull","Shen在《Frontiers in Sustainable Food Systems》(2026)发表的综述综合了国际数字农业文献与中国'数字经济—农业经济韧性'研究两条主线。提出三点发现:一是数字技术通过不同机制强化食物安全的四个维度——可获得性(生产力与投入效率)、可及性(市场信息与数字金融)、稳定性(溯源、预警与保险)、利用性(溯源、营养信息与移动支付);二是准自然实验证据显示数字基础设施、数字金融和人工智能主要通过社会化服务、土地流转、产业融合、人力资本而非仅'采纳'提升粮食生产与价值链韧性;三是收益分布不均,空间溢出方向取决于吸收能力、市场整合与要素流动性。","Frontiers in Sustainable Food Systems 2026",25,77,{"impact":178,"substance":179,"depth":17,"authority":180,"freshness":20,"relevant":21,"comment":181},22,21,14,"核心期刊综述，系统梳理数字技术强化粮食安全四维度的机制与准自然实验证据，兼具国际文献与中国经验，但发布日期不明、时效性偏弱，适合作为主题页深度聚合素材。",[183,184],{"name":174,"url":172},{"name":185,"url":186},"Frontiers in Sustainable Food Systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.2002109",[26,28,188,189,190],"农业韧性","空间溢出","数字金融",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":194},{"candidates":195,"agricultural_evidence":45},[196,201,208,213,218],{"code":31,"score":197,"supported":45,"title":198,"abstract":199,"tags":200},9,[47],[],[28],{"code":202,"score":91,"supported":45,"title":203,"abstract":204,"tags":207},"G2",[],[205,206],"土地流转","社会化服务",[],{"code":51,"score":209,"supported":45,"title":210,"abstract":211,"tags":212},4,[],[26],[26],{"code":214,"score":21,"supported":14,"title":215,"abstract":216,"tags":217},"G3",[],[],[190],{"code":219,"score":21,"supported":14,"title":220,"abstract":221,"tags":222},"I4",[],[],[189],[224,225],"Frontiers in Sustainable Food Systems 数字农业","数字技术 农业韧性 粮食安全","FrontiersinSustainableFoodSystems数字农业-4969","10.3389\u002Ffsufs.2026.2002109\u002Ffull",{"doi":227,"openalex_id":229,"authors":230,"venue":185,"cited_by_count":81,"oa_url":186,"card":234,"direction":86,"ingested_from":88},"W7223134273",[231],{"name":232,"orcid":233},"Yang Shen","https:\u002F\u002Forcid.org\u002F0000-0002-6781-6915",{"tldr":235,"method":236,"finding":237,"direction":86,"opportunity":238},"综述数字技术通过多机制强化食物安全四维度并提升农业韧性，但收益分布不均。","综合国际数字农业文献与中国数字经济—农业经济韧性研究两条主线。","数字技术经社会化服务、土地流转等提升韧性，收益不均且空间溢出取决于吸收能力。","可探究数字技术收益不均的成因及吸收能力、市场整合对空间溢出方向的调节机制。","2026-10-15T00:06:08.890950Z",{"id":241,"title":242,"url":243,"summary":244,"summary_zh":245,"content":9,"source_name":246,"source_url":243,"published_at":247,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":248,"score_detail":249,"sources":251,"tags":253,"primary_topic":257,"secondary_topics":258,"classification":259,"search_phrases":290,"slug":293,"view_count":21,"doi":294,"paper":295,"created_at":306},4600,"Optimization model of agricultural economic structure based on LSTM","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09594-x","With the development of artificial intelligence (AI) and agricultural big data, the adjustment of agricultural economic structure has shifted from empirical judgment to data-driven decision-making. This study uses the provincial panel data of agricultural economy in Hubei Province from 2010 to 2023 to construct a comprehensive framework of prediction and optimization. The forecasting layer uses the Long Short-Term Memory (LSTM) model to capture the dynamic relationship among agricultural GDP growth rate, main crop output, rural employment structure, agricultural product price index, financial subsidies and agricultural infrastructure investment. The optimization layer uses the prediction results as parameters. It defines three objectives. overall agricultural benefit, structural deviation and food security risk. It also includes restrictions on cultivated land area, budget, labor supply and food security. Based on these backgrounds, the adjustment scheme of agricultural economic structure was formulated. The results show that the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and r of LSTM model are 0.32, 0.45 and 0.91 respectively. Its prediction performance is better than autoregressive moving average (ARIMA), back propagation neural network (BPNN), support vector regression (SVR) and random forest (RF). These findings show that the combination of multivariate collaborative forecasting and structural optimization constraints can improve the efficiency of agricultural resource allocation. This method also provides quantitative support for rural revitalization, food security and digital agriculture policy design.","随着人工智能（AI）与农业大数据的发展，农业经济结构调整已从经验判断转向数据驱动决策。本研究利用2010—2023年湖北省农业经济省级面板数据，构建了预测与优化的综合框架。预测层采用长短期记忆（LSTM）模型，捕捉农业GDP增长率、主要作物产量、农村就业结构、农产品价格指数、财政补贴与农业基础设施投资之间的动态关系。优化层以预测结果作为参数，设定三个目标：农业整体效益、结构性偏差与粮食安全风险，并纳入耕地面积、预算、劳动力供给与粮食安全等约束条件。在此基础上，制定农业经济结构调整方案。结果表明，LSTM模型的平均绝对误差（MAE）、均方根误差（RMSE）与r分别为0.32、0.45和0.91，其预测性能优于自回归移动平均（ARIMA）、反向传播神经网络（BPNN）、支持向量回归（SVR）与随机森林（RF）。这些发现表明，多变量协同预测与结构优化约束的结合能够提高农业资源配置效率。该方法也为乡村振兴、粮食安全与数字农业政策设计提供了量化支持。","Discover Applied Sciences","2026-10-09T00:00:00Z",75,{"impact":103,"substance":18,"depth":104,"authority":105,"freshness":197,"relevant":21,"comment":250},"基于湖北省级面板数据构建LSTM预测与多目标优化框架，方法对比充分、结论有量化支撑，对数字农业与乡村振兴政策设计具参考价值。",[252],{"name":246,"url":243},[26,254,28,255,256],"农业人工智能","农业经济结构","LSTM预测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":260},{"candidates":261,"agricultural_evidence":45},[262,266,270,276,281,286],{"code":31,"score":120,"supported":45,"title":263,"abstract":264,"tags":265},[],[28,47],[28],{"code":51,"score":120,"supported":45,"title":267,"abstract":268,"tags":269},[],[26,56],[26],{"code":271,"score":209,"supported":45,"title":272,"abstract":273,"tags":275},"I2",[],[274],"artificial intelligence",[254],{"code":129,"score":67,"supported":45,"title":277,"abstract":278,"tags":280},[],[279],"乡村振兴",[],{"code":142,"score":67,"supported":45,"title":282,"abstract":283,"tags":285},[],[284],"农业大数据",[],{"code":136,"score":67,"supported":45,"title":287,"abstract":288,"tags":289},[],[139],[],[291,292],"湖北 农业经济结构 LSTM","农业经济结构 优化模型","湖北农业经济结构LSTM-4600","10.1007\u002Fs42452-026-09594-x",{"doi":294,"openalex_id":296,"authors":297,"venue":246,"cited_by_count":81,"oa_url":243,"card":300,"direction":86,"ingested_from":88},"W7221088951",[298],{"name":299,"orcid":9},"Xue Gong",{"tldr":301,"method":302,"finding":303,"direction":304,"opportunity":305},"用LSTM预测湖北农业经济指标，并构建多目标优化模型调整农业结构。","LSTM预测2010-2023年湖北面板数据，结合多目标优化与约束条件。","LSTM预测精度优于ARIMA、BPNN等，优化方案可提升农业资源配置效率。","农业人工智能与决策模型","可探索将LSTM与多目标优化耦合框架推广至其他省份或作物类型，并引入实时数据流。","2026-10-10T23:30:34.807708Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":312,"content":9,"source_name":313,"source_url":310,"published_at":314,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":315,"score_detail":316,"sources":318,"tags":320,"primary_topic":324,"secondary_topics":325,"classification":326,"search_phrases":359,"slug":362,"view_count":20,"doi":363,"paper":364,"created_at":386},4012,"African Pathways to the Three Zeros: Operationalizing Yunus Social Business Principles for Sustainable Development","https:\u002F\u002Fdoi.org\u002F10.71060\u002Fmpch5y56","Globally, persistent poverty, food insecurity, climate vulnerability, environmental degradation, and widening inequality demonstrate the limitations of fragmented approaches to sustainable development. Although the Sustainable Development Goals (SDGs) and Muhammad Yunus’s Three Zeros—Zero Poverty, Zero Hunger, and Zero Carbon Emissions—provide complementary visions, a significant research gap remains concerning how these aspirations can be operationalized through locally embedded economic systems. This study addresses this gap by examining how Yunus Social Business principles can be integrated with circular economy practices, waste valorization, sustainable livelihoods, and climate-smart agriculture. A qualitative conceptual research design was employed, combining systematic analysis of recent literature, institutional evidence, and African case illustrations from Kenya, Rwanda, Nigeria, Uganda, and Ethiopia. Systems Theory, Social Business Theory, and Sustainable Development Theory were used to guide the interpretation and construction of the proposed framework. Findings indicate that waste valorization can function as a strategic development mechanism linking employment generation, food production, resource recovery, and carbon mitigation. Evidence from African cases demonstrates that social enterprises and informal workers can contribute substantially to circular transitions when supported by appropriate institutional arrangements, finance, markets, and participatory governance. The study further finds that composting and other organic-resource recovery practices can strengthen climate-resilient agriculture and food security. The principal contribution is the development of an integrated African waste–food–climate social business framework that translates the Three Zeros from global aspirations into operational pathways. The study therefore positions social business as a potential institutional mechanism for connecting circularity, inclusion, climate resilience, and sustainable development.","在全球范围内，持续贫困、粮食不安全、气候脆弱性、环境退化以及日益加剧的不平等，凸显了碎片化可持续发展路径的局限性。尽管可持续发展目标（SDGs）与穆罕默德·尤努斯提出的“三零”愿景——零贫困、零饥饿、零碳排放——提供了互补性构想，但关于如何通过根植于本地的经济体系将这些愿景付诸实施，仍存在显著的研究空白。本研究通过探讨尤努斯社会企业原则如何与循环经济实践、废弃物价值化、可持续生计以及气候智慧型农业相融合，来填补这一空白。研究采用定性概念研究设计，结合对近期文献的系统分析、制度性证据以及来自肯尼亚、卢旺达、尼日利亚、乌干达和埃塞俄比亚的非洲案例说明。系统理论、社会企业理论和可持续发展理论被用于指导所提框架的阐释与构建。研究结果表明，废弃物价值化可以作为一种战略性发展机制，将就业创造、粮食生产、资源回收和碳减排联系起来。来自非洲案例的证据表明，在适当的制度安排、金融、市场和参与式治理支持下，社会企业和非正规劳动者能够为循环转型作出实质性贡献。研究进一步发现，堆肥及其他有机资源回收实践能够增强气候韧性农业和粮食安全。主要贡献在于构建了一个整合非洲废弃物—粮食—气候的社会企业框架，将“三零”从全球愿景转化为可操作的路径。因此，本研究将社会企业定位为一种潜在的制度机制，用以连接循环性、包容性、气候韧性和可持续发展。","Journal of Contemporary Issues in Open Distance and E-Learning","2026-10-01T00:00:00Z",62,{"impact":106,"substance":17,"depth":103,"authority":19,"freshness":106,"relevant":21,"comment":317},"概念性论文，提出非洲废弃物—食物—气候社会企业框架，与三农及可持续农业相关，但缺乏一手数据，公共影响有限。",[319],{"name":313,"url":310},[28,29,321,322,323],"气候智慧农业","循环农业","社会企业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":327},{"candidates":328,"agricultural_evidence":45},[329,333,339,345,350,355],{"code":31,"score":120,"supported":45,"title":330,"abstract":331,"tags":332},[],[28,47],[28],{"code":334,"score":209,"supported":45,"title":335,"abstract":336,"tags":338},"H7",[],[337],"climate resilience",[321],{"code":340,"score":67,"supported":45,"title":341,"abstract":342,"tags":344},"H4",[],[343],"碳排放",[],{"code":51,"score":67,"supported":45,"title":346,"abstract":347,"tags":349},[],[348],"smart agriculture",[],{"code":351,"score":21,"supported":14,"title":352,"abstract":353,"tags":354},"H8",[],[],[322],{"code":59,"score":21,"supported":14,"title":356,"abstract":357,"tags":358},[],[],[29],[360,361],"气候智慧农业 循环农业 社会企业 粮食安全","气候智慧农业 循环农业","气候智慧农业循环农业社会企业粮食安全-4012","10.71060\u002Fmpch5y56",{"doi":363,"openalex_id":365,"authors":366,"venue":313,"cited_by_count":81,"oa_url":310,"card":379,"direction":385,"ingested_from":88},"W7215122745",[367,369,371,373,376],{"name":368,"orcid":9},"Moses Kanyambindwa",{"name":370,"orcid":9},"Joyce Momanyi Nyangweso",{"name":372,"orcid":9},"Br.Dr. Jonas Yawovi Dzinekou",{"name":374,"orcid":375},"Olga Biosca","https:\u002F\u002Forcid.org\u002F0000-0003-0422-0003",{"name":377,"orcid":378},"Cam Donaldson","https:\u002F\u002Forcid.org\u002F0000-0003-4670-5340",{"tldr":380,"method":381,"finding":382,"direction":383,"opportunity":384},"构建非洲废弃物-食物-气候社会企业框架，将三零目标转化为可操作路径。","定性概念研究，系统分析文献、机构证据及肯尼亚等五国案例。","废弃物增值可联动就业、粮食、资源回收与减碳，社会企业助力循环转型。","农业绿色发展与碳","可实证检验该框架在非洲不同制度与市场条件下的适用性与绩效。","智慧农业 \u002F 农业物联网","2026-10-02T23:30:12.029509Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":9,"source_name":393,"source_url":390,"published_at":394,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":395,"score_detail":396,"sources":398,"tags":400,"primary_topic":404,"secondary_topics":405,"classification":415,"search_phrases":470,"slug":473,"view_count":474,"doi":475,"paper":476,"created_at":486},3156,"Climate Adaptation and Sustainable Agriculture in Africa: Strategies for Food Security","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijgem.vol.11.no2.2025.pg8.32","This study investigates the role of Climate-Smart Agriculture (CSA) and agroecology in enhancing agricultural resilience and food security in the face of climate change in Africa. Through a comprehensive review of existing literature, case studies, and policy frameworks, the research explores how these adaptive strategies are being implemented across the continent and assesses their effectiveness. The findings reveal that while CSA techniques—such as droughtresistant crop varieties, water-efficient irrigation, and soil management practices—offer tangible benefits to agricultural productivity and resilience, their widespread adoption remains constrained by limited access to finance, inadequate infrastructure, and insufficient extension services. Similarly, agroecological approaches, including crop diversification, agroforestry, and organic farming, have proven successful in fostering sustainable agricultural practices and enhancing biodiversity, yet face significant challenges from prevailing agricultural policies and institutional frameworks that favor large-scale industrial agriculture. The study recommends scaling up the adoption of CSA and agroecology by improving access to financing, technology, and farmer training programs. It also advocates for policy reforms that support agroecological practices and climate adaptation, as well as increased investment in research and development. Additionally, fostering multi-stakeholder collaborations, including public-private partnerships, is critical to overcoming institutional barriers and ensuring the effective implementation of climate adaptation strategies. The conclusion underscores the need for integrated policy frameworks that combine the strengths of CSA and agroecology to build a resilient, sustainable, and food-secure agricultural sector in Africa. By addressing both environmental and socioeconomic challenges, these strategies can significantly contribute to combating the impacts of climate change on African agriculture, ensuring long-term food security and sustainable development.","本研究探讨了气候智能型农业（Climate-Smart Agriculture, CSA）和农业生态学在增强非洲农业应对气候变化韧性和粮食安全方面的作用。通过系统梳理现有文献、案例研究和政策框架，本研究考察了这些适应性策略在非洲大陆的实施情况并评估其有效性。研究发现，尽管气候智能型农业技术——如抗旱作物品种、节水灌溉和土壤管理措施——对提高农业生产力和韧性具有切实效益，但其广泛采用仍受限于融资渠道有限、基础设施不足和推广服务欠缺。同样，农业生态学方法，包括作物多样化、农林业和有机农业，已被证明在促进可持续农业实践和增强生物多样性方面卓有成效，但仍面临来自有利于大规模工业化农业的现行农业政策和制度框架的重大挑战。本研究建议通过改善融资渠道、技术获取和农民培训项目来扩大气候智能型农业和农业生态学的采用。研究还倡导支持农业生态实践和气候适应的政策改革，以及增加研发投入。此外，促进多方利益相关者合作，包括公私合作伙伴关系，对于克服制度障碍和确保气候适应策略的有效实施至关重要。结论强调，需要建立综合性政策框架，将气候智能型农业和农业生态学的优势相结合，以在非洲构建具有韧性、可持续且粮食安全的农业部门。通过同时应对环境和社会经济挑战，这些策略能够为抵御气候变化对非洲农业的影响、确保长期粮食安全和可持续发展作出重大贡献。","IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT","2026-09-21T00:00:00Z",76,{"impact":17,"substance":18,"depth":104,"authority":105,"freshness":106,"relevant":21,"comment":397},"系统综述非洲气候智慧型农业与生态农业的适应策略及政策障碍，结论可靠但属文献综述，非突破性成果。",[399],{"name":393,"url":390},[28,29,401,402,403],"气候智慧型农业","农业适应","生态农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[406,411],{"code":351,"slug":407,"name":408,"path":409,"parent_code":410},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",{"code":334,"slug":412,"name":413,"path":414,"parent_code":410},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":38,"locked":14,"version":39,"method":40,"evidence":416},{"candidates":417,"agricultural_evidence":45},[418,422,428,435,444,451,458,462,466],{"code":31,"score":44,"supported":45,"title":419,"abstract":420,"tags":421},[47],[28,47],[28],{"code":351,"score":19,"supported":45,"title":423,"abstract":425,"tags":427},[424],"sustainable agriculture",[426],"可持续农业",[403],{"code":334,"score":13,"supported":45,"title":429,"abstract":430,"tags":434},[],[431,432,433],"气候变化","气候适应","climate change",[401],{"code":436,"score":197,"supported":45,"title":437,"abstract":438,"tags":443},"H6",[],[439,440,441,442],"生物多样性","农业生态","biodiversity","agroecology",[],{"code":445,"score":91,"supported":45,"title":446,"abstract":447,"tags":450},"H2",[],[448,449],"土壤","soil",[],{"code":452,"score":91,"supported":45,"title":453,"abstract":454,"tags":457},"H3",[],[455,456],"灌溉","irrigation",[],{"code":59,"score":209,"supported":45,"title":459,"abstract":460,"tags":461},[],[29],[29],{"code":51,"score":67,"supported":45,"title":463,"abstract":464,"tags":465},[],[348],[],{"code":136,"score":67,"supported":45,"title":467,"abstract":468,"tags":469},[],[139],[],[471,472],"气候智慧型农业 非洲","生态农业 粮食安全","气候智慧型农业非洲-3156",5,"10.56201\u002Fijgem.vol.11.no2.2025.pg8.32",{"doi":475,"openalex_id":477,"authors":478,"venue":393,"cited_by_count":21,"oa_url":9,"card":481,"direction":385,"ingested_from":88},"W7213982962",[479],{"name":480,"orcid":9},"Ayobami Samuel Owoyemi",{"tldr":482,"method":483,"finding":484,"direction":383,"opportunity":485},"综述气候智慧型农业与生态农业在非洲提升农业韧性与粮食安全的成效及障碍。","文献综述、案例研究与政策框架分析。","两类策略有效但受资金、基建、推广服务及政策偏向制约。","可量化评估CSA与生态农业在非洲不同区域的适配性与政策协同效果。","2026-09-22T23:30:10.505058Z",{"id":488,"title":489,"url":490,"summary":491,"summary_zh":492,"content":9,"source_name":493,"source_url":490,"published_at":494,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":395,"score_detail":495,"sources":497,"tags":499,"primary_topic":501,"secondary_topics":502,"classification":504,"search_phrases":527,"slug":530,"view_count":474,"doi":531,"paper":532,"created_at":543},2939,"Climate Change and Food Security in Africa: Harnessing Responsible AI for Sustainable Agricultural Transformation","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.11.no9.2025.pg79.102","Africa stands at the crossroads of two defining 21st-century challenges: climate change and food insecurity. The continents agricultural systems are increasingly disrupted by rising temperatures, erratic rainfall, and extreme weather events, exacerbating hunger, poverty, and rural vulnerability. At the same time, Artificial Intelligence (AI) presents transformative potential to support climate adaptation, improve agricultural resilience, and enhance food systems governance across Africa. This study undertakes a qualitative content analysis and critical synthesis of 25 peer-reviewed articles, institutional reports, and working papers to examine how AI is currently applied and could be more effectively leveraged for climate-resilient agriculture and food security on the continent. Findings reveal a growing body of innovations, from AIdriven weather forecasting and early warning systems to precision agriculture and supply chain optimization. Yet the study identifies persistent structural limitations, including unreliable data infrastructure, low digital capacity in rural areas, and governance gaps around ethical deployment, data ownership, and equitable access. Through African case studies, the paper explores how localized AI solutions when supported by inclusive policies, multi-stakeholder collaboration, and responsible innovation frameworks can mitigate climate-induced food shocks and drive sustainable development. The paper argues for a strategic alignment between AI deployment and national adaptation plans, emphasizing the need for stronger public-private partnerships, investments in AI-ready infrastructure, and the development of ethical and contextsensitive governance mechanisms. In doing so, it offers a roadmap for policymakers, development agencies, and researchers seeking to harness AI not just as a technological tool, but as a catalyst for systemic transformation in African food systems in the era of climate change.","非洲正处在21世纪两大决定性挑战的交汇点：气候变化与粮食不安全。随着气温上升、降雨异常和极端天气事件频发，非洲大陆的农业系统日益受到扰乱，饥饿、贫困和农村脆弱性问题不断加剧。与此同时，人工智能（AI）展现出变革性潜力，可支持非洲的气候适应、提升农业韧性并改善粮食系统治理。本研究对25篇同行评审论文、机构报告和工作论文进行了定性内容分析与批判性综合，考察AI当前在非洲大陆的应用方式，以及如何更有效地将其用于气候韧性农业和粮食安全。研究发现，相关创新不断涌现，涵盖AI驱动的天气预报和预警系统、精准农业以及供应链优化等领域。然而，研究也识别出持续存在的结构性制约，包括数据基础设施不可靠、农村地区数字能力薄弱，以及围绕伦理部署、数据所有权和公平获取的治理缺口。通过非洲案例研究，本文探讨了在包容性政策、多利益相关方协作和负责任创新框架支持下，本地化的AI解决方案如何能够缓解气候引发的粮食冲击并推动可持续发展。本文主张将AI部署与国家适应计划进行战略对接，强调需要加强公私伙伴关系、投资于AI就绪型基础设施，并建立合乎伦理且情境敏感的治理机制。由此，本文为政策制定者、发展机构和研究人员提供了一份路线图，旨在气候变化时代将AI不仅作为技术工具，更作为非洲粮食系统系统性转型的催化剂加以利用。","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-09-16T00:00:00Z",{"impact":17,"substance":18,"depth":104,"authority":105,"freshness":106,"relevant":21,"comment":496},"系统综述25篇文献，梳理AI在非洲气候韧性农业中的应用与治理缺口，对农业信息化有参考价值，但属境外区域研究，公共影响有限。",[498],{"name":493,"url":490},[500,254,28,29,432],"智慧农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[503],{"code":334,"slug":412,"name":413,"path":414,"parent_code":410},{"status":38,"locked":14,"version":39,"method":40,"evidence":505},{"candidates":506,"agricultural_evidence":45},[507,511,515,519,523],{"code":334,"score":17,"supported":45,"title":508,"abstract":509,"tags":510},[433],[431,432,433],[432],{"code":31,"score":44,"supported":45,"title":512,"abstract":513,"tags":514},[47],[28,47],[28],{"code":51,"score":120,"supported":45,"title":516,"abstract":517,"tags":518},[],[54,55],[500],{"code":271,"score":209,"supported":45,"title":520,"abstract":521,"tags":522},[],[274],[254],{"code":59,"score":21,"supported":14,"title":524,"abstract":525,"tags":526},[],[],[29],[528,529],"非洲 农业人工智能 粮食安全","AI 气候韧性农业 非洲","非洲农业人工智能粮食安全-2939","10.56201\u002Fijaes.vol.11.no9.2025.pg79.102",{"doi":531,"openalex_id":533,"authors":534,"venue":493,"cited_by_count":81,"oa_url":537,"card":538,"direction":385,"ingested_from":88},"W7213462904",[535],{"name":536,"orcid":9},"Izuchukwu Adamaagashi","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJAES\u002FVOL. 11 NO. 9 2025\u002FClimate Change and Food Security 79-102.pdf",{"tldr":539,"method":540,"finding":541,"direction":304,"opportunity":542},"通过定性分析25篇文献，探讨如何负责任地利用AI推动非洲气候适应型农业与粮食安全。","对25篇同行评议论文、机构报告和工作论文进行定性内容分析与批判性综合。","AI在天气预报、精准农业等方面有创新，但面临数据基础设施差、农村数字能力低和治理缺口等结构性限制。","可研究非洲本地化AI解决方案的伦理治理框架与公私合作模式，填补数据所有权和公平获取的研究空白。","2026-09-19T23:30:19.482449Z"]