[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4735":3,"related-4735":105},{"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,"primary_topic":30,"secondary_topics":36,"classification":49,"search_phrases":90,"slug":93,"view_count":94,"doi":8,"paper":95,"created_at":104},4735,"干旱区农技社会化服务试点政策对县域粮食生产的激励效应——基于双重机器学习因果推断","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F176878.html","基于2015至2023年200县面板,采用双重差分与双重机器学习方法评估2021年农业社会化服务试点政策。结果显示政策显著促进县域粮食产量,但呈短期脉冲效应,中等收入地区呈倒U型,主要依赖规模扩张而非效率扩张路径",null,"干旱区资源与环境 2026-10-08","2026-10-08T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,6,1,"基于200县面板与双重机器学习的因果推断，方法新颖、数据扎实，对评估社会化服务试点政策有实质参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"粮食安全","因果推断","县域农业","农业社会化服务","试点政策",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[37,43],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":50,"locked":13,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":57},[55,62,68,74,80,85],{"code":31,"score":56,"supported":57,"title":58,"abstract":60,"tags":61},12,true,[59],"社会化服务",[59],[28],{"code":44,"score":63,"supported":57,"title":64,"abstract":66,"tags":67},11,[65],"机器学习",[65],[],{"code":69,"score":70,"supported":57,"title":71,"abstract":72,"tags":73},"I7",9,[26],[],[26],{"code":38,"score":75,"supported":57,"title":76,"abstract":78,"tags":79},8,[77],"干旱",[],[],{"code":81,"score":20,"supported":13,"title":82,"abstract":83,"tags":84},"A8",[],[],[25],{"code":86,"score":20,"supported":13,"title":87,"abstract":88,"tags":89},"G4",[],[],[27],[91,92],"农业社会化服务试点 县域粮食产量","双重机器学习 粮食生产","农业社会化服务试点县域粮食产量-4735",0,{"doi":8,"openalex_id":8,"authors":96,"venue":8,"cited_by_count":94,"oa_url":8,"card":97,"direction":101,"ingested_from":103},[],{"tldr":98,"method":99,"finding":100,"direction":101,"opportunity":102},"评估干旱区农技社会化服务试点政策对县域粮食产量的因果效应。","2015-2023年200县面板，双重差分与双重机器学习因果推断。","政策显著增产但为短期脉冲效应，中等收入地区呈倒U型，靠规模扩张而非效率提升。","数字乡村与农业信息化","可探究政策长期效应衰减机制及如何转向效率提升型服务模式。","agent","2026-10-12T00:11:14.975039Z",{"total":19,"page":20,"page_size":19,"items":106},[107,180,267,376,443,538],{"id":108,"title":109,"url":110,"summary":111,"summary_zh":8,"content":8,"source_name":112,"source_url":110,"published_at":113,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":114,"score_detail":115,"sources":118,"tags":120,"primary_topic":124,"secondary_topics":129,"classification":132,"search_phrases":160,"slug":163,"view_count":164,"doi":165,"paper":166,"created_at":179},2299,"Causal Effects of Climate-Smart Agriculture Adoption on Smallholder Welfare and Food Security in Zambia: Evidence from Endogenous Switching Regression, Double Machine Learning, and Bayesian Estimation","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10994635\u002Fv1","Causal Effects of Climate-Smart Agriculture Adoption on Smallholder Welfare and Food Security in Zambia: Evidence from Endogenous Switching Regression, Double Machine Learning, and Bayesian Estimation。Research Square","Research Square","2026-09-11T00:00:00Z",67,{"impact":56,"substance":116,"depth":16,"authority":75,"freshness":70,"relevant":20,"comment":117},20,"方法组合新颖、结论有实证价值，但研究对象为赞比亚小农户，对国内三农实践的直接参考有限，适合作为国际经验类精选。",[119],{"name":112,"url":110},[25,26,121,122,123],"气候智慧农业","小农户生计","赞比亚",{"code":81,"slug":125,"name":126,"path":127,"parent_code":128},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[130,131],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":13,"version":51,"method":52,"evidence":133},{"candidates":134,"agricultural_evidence":57},[135,140,145,151,156],{"code":81,"score":56,"supported":57,"title":136,"abstract":138,"tags":139},[137],"food security",[137],[25],{"code":31,"score":63,"supported":57,"title":141,"abstract":143,"tags":144},[142],"smallholder",[142],[],{"code":146,"score":63,"supported":57,"title":147,"abstract":149,"tags":150},"I1",[148],"smart agriculture",[148],[],{"code":44,"score":63,"supported":57,"title":152,"abstract":154,"tags":155},[153],"machine learning",[153],[],{"code":38,"score":20,"supported":13,"title":157,"abstract":158,"tags":159},[],[],[121],[161,162],"气候智慧农业 小农户生计 因果推断 粮食安全","气候智慧农业 小农户生计","气候智慧农业小农户生计因果推断粮食安全-2299",3,"10.21203\u002Frs.3.rs-10994635\u002Fv1",{"doi":165,"openalex_id":167,"authors":168,"venue":112,"cited_by_count":94,"oa_url":110,"card":8,"direction":177,"ingested_from":178},"W7212246566",[169,172,174],{"name":170,"orcid":171},"Evans Kudzai Chinembiri","https:\u002F\u002Forcid.org\u002F0009-0003-7304-0214",{"name":173,"orcid":8},"Munyaradzi Mutenje",{"name":175,"orcid":176},"Nkele D. Malahlela","https:\u002F\u002Forcid.org\u002F0009-0001-1653-7553","智慧农业 \u002F 农业物联网","openalex","2026-09-13T23:30:09.568432Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":8,"source_name":186,"source_url":183,"published_at":187,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":188,"score_detail":189,"sources":192,"tags":194,"primary_topic":198,"secondary_topics":199,"classification":202,"search_phrases":245,"slug":248,"view_count":94,"doi":249,"paper":250,"created_at":266},5478,"Characterisation of Food Security Situation for Smallholder Farmers in Zvimba","https:\u002F\u002Fdoi.org\u002F10.71458\u002Frx0qpm30","This study provides a comprehensive analysis of the food security situation among smallholder farmers (SHFs) in Zvimba District, Zimbabwe. Smallholder agriculture is the bedrock of Zimbabwe's food system, yet it remains highly vulnerable to a complex array of shocks and stressors. Using a mixed-methods approach, this research investigates the prevalence, drivers and lived experiences of food insecurity within this community. A structured survey was administered to 412 randomly selected smallholder households across the district's wards. The quantitative data were supplemented by 12 Focus Group Discussions (FGDs) and 24 Key Informant Interviews (KIIs) with farmers, community leaders and agricultural extension officers to capture qualitative nuances. The study is grounded in the Food Security Theory (FST), which conceptualises food security through its four pillars: food availability, food access, food utilisation and food stability. Findings reveal a precarious food security situation. Quantitatively, 78.4% of households were found to be food insecure, with an average Hunger Months Index of 4.7 months per year. The study identifies a significant disconnect between maize production, the national staple and household consumption, with over 65% of households failing to produce enough maize to meet their annual requirements. Qualitative data illuminated the multi-faceted drivers of this insecurity. Key points to note include climate change whereby erratic rainfall and prolonged mid-season dry spells were consistently reported as the primary production challenges, limited access to farming inputs whereby high costs and unreliable supplies of certified seeds, fertilizers and agro[1]chemicals constrain productivity. Morelso, socio-economic factors such as poverty, lack of viable alternative livelihoods and limited access to formal credit perpetuate a cycle of vulnerability. In addition, institutional weaknesses, for example, challenges with agricultural extension services and market inefficiencies, exacerbate the situation. The study concludes that food insecurity in Zvimba is not merely a problem of food production, but is deeply rooted in structural issues affecting food access, food utilisation, as well as food stability. Recommendations are proposed for a multi-sectoral approach, including promoting Climate-Smart Agriculture (CSA) and drought-resistant crops, strengthening input support systems and rural finance, diversifying livelihood strategies and enhancing the capacity of extension services. This research contributes to a deeper understanding of the localised dynamics of food security and provides evidence-based guidance for policymakers, development practitioners and the farmers of Zvimba themselves.","本研究对津巴布韦兹维姆巴区小农户（smallholder farmers, SHFs）的粮食安全状况进行了全面分析。小农农业是津巴布韦粮食体系的基石，但仍极易受到一系列复杂冲击和压力的影响。本研究采用混合方法，考察了该社区粮食不安全的普遍程度、驱动因素及生活体验。研究对该区各选区随机抽取的412户小农户进行了结构化问卷调查。定量数据辅以12次焦点小组讨论（FGDs）和24次关键知情人访谈（KIIs），访谈对象包括农民、社区领袖和农业推广人员，以捕捉定性层面的细微差异。本研究以粮食安全理论（Food Security Theory, FST）为基础，该理论通过四大支柱界定粮食安全：粮食可供性、粮食获取、粮食利用和粮食稳定性。研究结果揭示了严峻的粮食安全形势。定量数据显示，78.4%的家庭存在粮食不安全，年均饥饿月份指数为4.7个月。研究发现，玉米产量——该国主粮——与家庭消费之间存在显著脱节，超过65%的家庭生产的玉米不足以满足其年度需求。定性数据揭示了这一不安全状况的多重驱动因素。值得注意的要点包括：气候变化，即降雨不稳定和持续的中期干旱被一致报告为主要生产挑战；农业投入品获取受限，即认证种子、化肥和农用化学品成本高昂且供应不可靠，制约了生产力。此外，贫困、缺乏可行的替代生计以及正规信贷获取受限等社会经济因素使脆弱性循环持续存在。此外，制度薄弱环节，例如农业推广服务面临的挑战和市场效率低下，加剧了这一状况。研究得出结论，兹维姆巴的粮食不安全不仅仅是粮食生产问题，而是深深植根于影响粮食获取、粮食利用以及粮食稳定性的结构性问题。研究提出了采取多部门方法的建议，包括推广气候智能型农业（Climate-Smart Agriculture, CSA）和抗旱作物、加强投入品支持体系和农村金融、多样化生计策略以及增强","Ngenani The Zimbabwe Ezekiel Guti Journal of Community Engagement and Societal Transformations","2026-10-20T00:00:00Z",55,{"impact":75,"substance":16,"depth":190,"authority":19,"freshness":75,"relevant":20,"comment":191},15,"基于412户调查与混合方法的扎实实证研究，但属津巴布韦地方性案例，公共影响与信源权威度有限，可作参考而非每日精选。",[193],{"name":186,"url":183},[25,195,121,196,197],"小农户","农业推广","津巴布韦",{"code":81,"slug":125,"name":126,"path":127,"parent_code":128},[200,201],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":50,"locked":13,"version":51,"method":52,"evidence":203},{"candidates":204,"agricultural_evidence":57},[205,209,213,220,227,234,241],{"code":81,"score":190,"supported":57,"title":206,"abstract":207,"tags":208},[137],[25,137],[25],{"code":31,"score":190,"supported":57,"title":210,"abstract":211,"tags":212},[142],[195,142],[195],{"code":38,"score":12,"supported":57,"title":214,"abstract":215,"tags":219},[],[216,77,217,218],"气候变化","climate change","drought",[121],{"code":221,"score":222,"supported":57,"title":223,"abstract":224,"tags":226},"G1",7,[],[196,225],"agricultural extension",[196],{"code":228,"score":19,"supported":57,"title":229,"abstract":230,"tags":233},"A3",[],[231,232],"玉米","maize",[],{"code":235,"score":19,"supported":57,"title":236,"abstract":237,"tags":240},"G3",[],[238,239],"农村金融","rural finance",[],{"code":146,"score":164,"supported":57,"title":242,"abstract":243,"tags":244},[],[148],[],[246,247],"Zvimba 小农户 粮食安全","津巴布韦 小农 玉米","Zvimba小农户粮食安全-5478","10.71458\u002Frx0qpm30",{"doi":249,"openalex_id":251,"authors":252,"venue":186,"cited_by_count":94,"oa_url":8,"card":261,"direction":177,"ingested_from":178},"W7223029383",[253,256,259],{"name":254,"orcid":255},"Casper Myambo","https:\u002F\u002Forcid.org\u002F0009-0007-8625-7526",{"name":257,"orcid":258},"Chipo Plaxedes Mubaya","https:\u002F\u002Forcid.org\u002F0000-0002-4123-0238",{"name":260,"orcid":8},"Patience Mutopo",{"tldr":262,"method":263,"finding":264,"direction":101,"opportunity":265},"调查津巴布韦Zvimba小农户粮食安全状况，分析其普遍性、成因与实际体验。","混合方法：412户问卷、12场焦点小组、24个关键人物访谈，基于粮食安全四支柱理","78.4%家庭粮食不安全，年均饥饿月4.7个月，主因是气候、投入、社会经济与制度等结构性问题。","可探索用移动端问卷与遥感产量监测构建小农户粮食安全动态预警与精准干预系统。","2026-10-21T23:30:08.937079Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":8,"source_name":273,"source_url":270,"published_at":274,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":275,"score_detail":276,"sources":280,"tags":282,"primary_topic":285,"secondary_topics":286,"classification":289,"search_phrases":357,"slug":360,"view_count":94,"doi":361,"paper":362,"created_at":375},5185,"When the rains are too much: El Niño, Smallholder Farmers and Food Security in Western Kenya","https:\u002F\u002Fdoi.org\u002F10.18697\u002Fajfand.155.ed182","When the rains are too much: El Niño, Smallholder Farmers and Food Security in Western Kenya For generations, farmers in Western Kenya have watched the skies carefully. The arrival, duration, and distribution of rainfall have traditionally provided important signals about when to prepare land, plant, weed, and harvest. But these signals are becoming increasingly difficult to read. Climate change is altering rainfall patterns, while increasingly frequent extreme weather events are making farming more uncertain. The current outlook for the October–December 2026 season brings this challenge sharply into focus. The IGAD Climate Prediction and Applications Centre (ICPAC) forecasts a wetter-than-normal season across much of the Greater Horn of Africa. The Lake Victoria Basin, which includes large parts of Western Kenya, is among the areas with a high likelihood of receiving more than 400 mm of seasonal rainfall. ICPAC reports that El Niño has developed and is expected to strengthen, while a positive Indian Ocean Dipole is also expected—climate conditions that are commonly associated with enhanced rainfall in the region, and is likely to run into February of 2027. But what does this mean for the smallholder farmer? The first point is that more rainfall does not necessarily mean more food. For a farmer whose livelihood depends almost entirely on rain-fed agriculture, abundant rainfall can be a blessing. It can replenish streams and water sources, improve soil moisture, restore pasture and create favourable conditions for crop production. If well distributed, the rains could contribute to improved harvests and household food supplies. The problem arises when rainfall is too intense, too concentrated or poorly distributed. A farmer may receive a great deal of rain in a few days and then experience a dry spell. Another farmer a few kilometres away may receive considerably less. Heavy downpours can cause flooding, waterlogging and soil erosion. Seeds may be washed away soon after planting. Young crops can be submerged or damaged. Valuable topsoil and nutrients can be carried away, reducing the productivity of land in subsequent seasons. This is particularly important in Western Kenya, where many households depend on small parcels of land for both food and income. A failed or damaged crop is not simply an agricultural loss. It can mean less food in the household, less money for school fees and health care, and fewer resources to purchase nutritious foods. The food-security paradox We must, therefore, look beyond agricultural production to food and nutrition security. A household may produce enough maize yet still have a poor diet if vegetables, legumes, fruits, milk, eggs or other foods are unavailable or unaffordable. Climate shocks can reduce this diversity. When crops are damaged, families may depend more heavily on whatever staple food remains available. This is why climate resilience must also be nutrition-sensitive. Western Kenya has a rich agricultural tradition and considerable potential for diversified food production. Farmers grow cereals, legumes, vegetables and root crops and keep livestock and poultry. Such diversity is not merely an agricultural advantage; it is a nutritional resource. It can provide different foods, spread risk and generate income at different times of the year. Climate adaptation should, therefore, not encourage farmers simply to produce more of one crop. It should help them build diverse and resilient farming systems. Women carry a large part of the burden The impact of erratic and excessive rainfall also has a gender dimension. Women in smallholder households are often deeply involved in food production, processing, preparation and household nutrition. When harvests fail, or food prices rise, women frequently have to find ways of stretching limited household resources to feed the family. When roads become impassable or markets are disrupted, women may have greater difficulty accessing food and agricultural inputs. When water sources are contaminated or damaged, household responsibilities can become even more demanding. Climate adaptation must therefore deliberately include women—not simply as beneficiaries, but as decision-makers, innovators, farmers and leaders. Young people must also be part of the solution There is another group that deserves greater attention: young people. Western Kenya cannot build climate-resilient agriculture while treating agriculture as an occupation of the past. Young people need to see opportunities in modern farming, agro-processing, food safety, climate information services, irrigation, digital agriculture and agricultural entrepreneurship. The increasing unpredictability of climate may actually provide an opportunity to make agriculture more innovative. Young people can help introduce new technologies, improve access to climate information, develop value-added products and create businesses around food production and processing. From forecasting to action The availability of a climate forecast is useful only if it reaches the people who need it in time. Farmers need information that answers practical questions: When should I plant? Which crops or varieties are appropriate? How can I protect my soil? What should I do if rainfall becomes excessive? How can I protect harvested food? Where can I obtain reliable seed and other inputs? Seasonal forecasts should, therefore, be translated into local, practical agricultural advice. ICPAC itself emphasizes that seasonal forecasts should be used together with weekly and monthly forecasts and national meteorological information. This requires stronger links between meteorological services, agricultural extension officers, researchers, county governments, farmer organizations and communities. We should also strengthen simple but important measures: water harvesting, drainage, terracing and soil conservation; planting crops and varieties suited to local conditions; maintaining vegetation cover; diversifying crops and livestock; improving storage facilities; and protecting food from moisture and contamination. Prepare before the rain comes The greatest lesson from the current El Niño outlook is perhaps that preparedness must begin before the first heavy rains arrive. Governments and development partners should not wait for floods, damaged roads, destroyed crops or food shortages before responding. Early warning should lead to early action. Farmers should be supported to prepare their farms, protect livestock, secure seed and inputs, strengthen food storage and identify alternative routes to markets. This is particularly important because the same season can produce both opportunities and losses. ICPAC notes that enhanced rainfall can support agricultural production and replenish water resources, but can simultaneously increase the risk of flooding, flash floods and landslides in vulnerable areas. A changing climate requires a different agricultural mindset Perhaps the most important change we need is in the way we think about farming. For too long, agricultural planning has often assumed that rainfall will behave according to a familiar calendar. That assumption is becoming increasingly unsafe. Farmers now have to manage not only too little rain, but also too much rain, rain at the wrong time and rain in the wrong place. The challenge is, therefore, not simply to produce more food. It is to build food systems that can continue providing adequate, diverse, safe and nutritious food under increasingly uncertain climatic conditions. Western Kenya's smallholder farmers should not be left alone to carry this burden. They need science that responds to their realities, extension services that reach them, climate information they can understand and use, financial services that help them manage risk, markets that function during difficult seasons, and policies that recognize the central role of smallholder agriculture in national food and nutrition security, and partners who are willing to appreciate and make use of indigenous knowledge. El Niño should, therefore, be viewed neither simply as a disaster nor automatically as a blessing. It is a climate event whose consequences will depend greatly on our preparedness. The question before us is not merely, “Will the rains come?” They probably will. The more important question is: Will our farmers, food systems and institutions be ready when they do? That is where climate resilience—and ultimately food and nutrition security—will be tested. Prof. Ruth Khasaya Oniang'o Founder and Editor-in-Chief, AJFAND ORCiD: https:\u002F\u002Forcid.org\u002F0009-0005-8344-9093","当雨水过多时：厄尔尼诺现象、小农户与肯尼亚西部的粮食安全\n\n世代以来，肯尼亚西部的农民一直密切关注着天空。降雨的到来、持续时间和分布，传统上为何时整地、播种、除草和收获提供了重要信号。但这些信号正变得越来越难以解读。气候变化正在改变降雨模式，而日益频繁的极端天气事件使农业变得更加不确定。当前对2026年10月至12月季节的展望使这一挑战凸显出来。政府间发展组织气候预测与应用中心（ICPAC）预测，大非洲之角大部分地区将迎来高于正常水平的湿润季节。维多利亚湖流域——包括肯尼亚西部的大部分地区——属于极有可能获得超过400毫米季节性降雨的区域之一。ICPAC报告称，厄尔尼诺现象已经形成并预计将增强，同时预计还将出现正印度洋偶极子——这些气候条件通常与该地区降雨增强相关，并可能持续到2027年2月。但这对小农户意味着什么？首先，更多降雨并不必然意味着更多粮食。对于生计几乎完全依赖雨养农业的农民来说，充沛的降雨可能是福。它可以补充溪流和水源、改善土壤湿度、恢复牧场，并为作物生产创造有利条件。如果分布良好，降雨可能有助于改善收成和家庭粮食供应。问题出现在降雨过于猛烈、过于集中或分布不均的时候。一个农民可能在几天内获得大量降雨，随后又经历一段干旱期。几公里外的另一个农民可能获得的降雨少得多。暴雨可能引发洪水、渍涝和土壤侵蚀。种子可能在播种后不久被冲走。幼苗可能被淹没或受损。宝贵的表土和养分可能被带走，降低土地在后续季节的生产力。这在肯尼亚西部尤为重要，因为许多家庭依赖小块土地获取食物和收入。作物歉收或受损不仅仅是农业损失。它可能意味着家庭食物减少、用于学费和医疗的钱减少，以及购买营养食品的资源减少。粮食安全……","African Journal of Food Agriculture Nutrition and Development","2026-10-16T00:00:00Z",75,{"impact":16,"substance":116,"depth":277,"authority":278,"freshness":75,"relevant":20,"comment":279},16,13,"核心期刊评论文章，结合El Niño预测讨论小农户气候适应与粮食营养安全，有实质分析与行动建议，但属区域案例、非信息化技术突破。",[281],{"name":273,"url":270},[283,25,195,121,284],"肯尼亚","气候预警",{"code":81,"slug":125,"name":126,"path":127,"parent_code":128},[287,288],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":50,"locked":13,"version":51,"method":52,"evidence":290},{"candidates":291,"agricultural_evidence":57},[292,296,300,306,313,317,323,329,335,341,347,351],{"code":81,"score":190,"supported":57,"title":293,"abstract":294,"tags":295},[137],[25,137],[25],{"code":31,"score":190,"supported":57,"title":297,"abstract":298,"tags":299},[142],[195,142],[195],{"code":38,"score":12,"supported":57,"title":301,"abstract":302,"tags":305},[],[216,77,217,303,304],"climate resilience","waterlogging",[121],{"code":307,"score":19,"supported":57,"title":308,"abstract":309,"tags":312},"H2",[],[310,311],"土壤","soil",[],{"code":228,"score":164,"supported":57,"title":314,"abstract":315,"tags":316},[],[232],[],{"code":318,"score":164,"supported":57,"title":319,"abstract":320,"tags":322},"B4",[],[321],"月季",[],{"code":324,"score":164,"supported":57,"title":325,"abstract":326,"tags":328},"C",[],[327],"livestock",[],{"code":330,"score":164,"supported":57,"title":331,"abstract":332,"tags":334},"C3",[],[333],"poultry",[],{"code":336,"score":164,"supported":57,"title":337,"abstract":338,"tags":340},"E4",[],[339],"food safety",[],{"code":342,"score":164,"supported":57,"title":343,"abstract":344,"tags":346},"F4",[],[345],"除草",[],{"code":221,"score":164,"supported":57,"title":348,"abstract":349,"tags":350},[],[225],[],{"code":352,"score":164,"supported":57,"title":353,"abstract":354,"tags":356},"H3",[],[355],"irrigation",[],[358,359],"El Niño 小农户 粮食安全","ICPAC 季节预报 肯尼亚","ElNiño小农户粮食安全-5185","10.18697\u002Fajfand.155.ed182",{"doi":361,"openalex_id":363,"authors":364,"venue":273,"cited_by_count":94,"oa_url":368,"card":369,"direction":101,"ingested_from":178},"W7222630373",[365],{"name":366,"orcid":367},"Ruth Khasaya Oniang'o","https:\u002F\u002Forcid.org\u002F0009-0005-8344-9093","https:\u002F\u002Fajfand.net\u002FVolume26\u002FNo8\u002FEditorialVol26No8.pdf",{"tldr":370,"method":371,"finding":372,"direction":373,"opportunity":374},"分析厄尔尼诺导致肯尼亚西部降雨过量对小农粮食安全的影响","基于ICPAC季节气候预测与厄尔尼诺、印度洋偶极子分析","降雨过多且分布不均会引发洪涝、土壤侵蚀，反而威胁小农粮食安全","农业绿色发展与碳","可结合遥感与农户调查，量化极端降雨时空分布对小农产量与粮食安全的非线性影响","2026-10-17T23:30:36.130973Z",{"id":377,"title":378,"url":379,"summary":380,"summary_zh":381,"content":8,"source_name":382,"source_url":379,"published_at":383,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":384,"score_detail":385,"sources":387,"tags":389,"primary_topic":392,"secondary_topics":393,"classification":395,"search_phrases":419,"slug":422,"view_count":20,"doi":423,"paper":424,"created_at":442},4983,"Climate Change and Livelihood Vulnerability among Smallholder Farming Households in the Diffa Region of Niger’s Lake Chad Basin","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104567","Climate change affects agricultural production, on which 80% of rural communities in sub-Saharan Africa depend for their livelihoods. In the Lake Chad Basin, recent fluctuations in hydro-climatic conditions have compromised crop yields and food security, severely affecting smallholder farmers’ well-being. Enhancing their adaptive capacity and resilience is therefore essential for sustainable development. This study assesses smallholder farmers’ vulnerability to climate change-induced extreme weather events in rural areas of the Lake Chad Basin using the Livelihood Vulnerability Index (LVI). Data were collected through questionnaire surveys of 200 farming households across four villages in the Diffa region of Niger. The LVI was calculated using nine components: sociodemographic profile, education and housing, livelihood strategy, social networks, food, water resources, health, land resources, and disasters and climate variability. Results showed that farmers in Kangarwa were the most vulnerable (LVI = 0.58), while those in Boulangou were the least vulnerable (0.51). Digargo and Mamari had similar vulnerability levels (0.55 and 0.56, respectively). Overall, farmers in all four villages were moderately vulnerable. Kangarwa’s higher vulnerability was mainly associated with social networks (0.81), livelihood strategy (0.75), and land resources (0.75). In Boulangou, low vulnerability was linked to water resources (0.12) and sociodemographic conditions (0.35), although land (0.69), livelihood strategy (0.67), and food (0.65) increased vulnerability. These findings can inform interventions to strengthen smallholder farmers’ resilience. Diversifying livelihoods and adopting climate-smart agriculture technologies could enhance adaptive capacity and reduce vulnerability in highly affected areas.","气候变化影响着农业生产，而撒哈拉以南非洲80%的农村社区依赖农业维持生计。在乍得湖流域，近期水文气候条件的波动已危及作物产量和粮食安全，严重影响小农户的福祉。因此，增强其适应能力和韧性对可持续发展至关重要。本研究采用生计脆弱性指数（LVI）评估乍得湖流域农村地区小农户对气候变化引发的极端天气事件的脆弱性。数据通过对尼日尔迪法地区四个村庄200个农户的问卷调查收集。LVI基于九个组成部分计算：社会人口特征、教育与住房、生计策略、社会网络、粮食、水资源、健康、土地资源以及灾害与气候变异性。结果表明，Kangarwa的农户最为脆弱（LVI = 0.58），而Boulangou的农户脆弱性最低（0.51）。Digargo和Mamari的脆弱性水平相近（分别为0.55和0.56）。总体而言，四个村庄的农户均处于中等脆弱水平。Kangarwa较高的脆弱性主要与社会网络（0.81）、生计策略（0.75）和土地资源（0.75）相关。在Boulangou，低脆弱性与水资源（0.12）和社会人口条件（0.35）相关，尽管土地（0.69）、生计策略（0.67）和粮食（0.65）增加了脆弱性。这些发现可为增强小农户韧性的干预措施提供依据。生计多样化和采用气候智能型农业技术可增强高受影响地区的适应能力并降低脆弱性。","Journal of Experimental Agriculture International","2026-10-14T00:00:00Z",65,{"impact":75,"substance":116,"depth":277,"authority":56,"freshness":70,"relevant":20,"comment":386},"基于200户问卷的LVI实证研究，数据与结论具体，但属区域性学术成果，公共影响有限。",[388],{"name":382,"url":379},[25,195,216,390,391],"气候适应","生计脆弱性",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[394],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":50,"locked":13,"version":51,"method":52,"evidence":396},{"candidates":397,"agricultural_evidence":57},[398,402,406,410,415],{"code":38,"score":277,"supported":57,"title":399,"abstract":400,"tags":401},[217],[216,217],[216,390],{"code":31,"score":190,"supported":57,"title":403,"abstract":404,"tags":405},[142],[195,142],[195],{"code":81,"score":222,"supported":57,"title":407,"abstract":408,"tags":409},[],[25,137],[25],{"code":352,"score":164,"supported":57,"title":411,"abstract":412,"tags":414},[],[413],"水资源",[],{"code":146,"score":164,"supported":57,"title":416,"abstract":417,"tags":418},[],[148],[],[420,421],"尼日尔 乍得湖流域 小农户","生计脆弱性指数 LVI","尼日尔乍得湖流域小农户-4983","10.9734\u002Fjeai\u002F2026\u002Fv48i104567",{"doi":423,"openalex_id":425,"authors":426,"venue":382,"cited_by_count":94,"oa_url":379,"card":437,"direction":177,"ingested_from":178},"W7221748179",[427,429,431,433,435],{"name":428,"orcid":8},"Issiaka Issaharou Matchi",{"name":430,"orcid":8},"I. Oumarou Abdou",{"name":432,"orcid":8},"H. Rabiou",{"name":434,"orcid":8},"A. Mahamane",{"name":436,"orcid":8},"M. Saadou",{"tldr":438,"method":439,"finding":440,"direction":373,"opportunity":441},"用生计脆弱性指数评估尼日尔迪法地区四个村庄小农户的气候变化脆弱性。","问卷调查200户农户，计算含9个成分的生计脆弱性指数（LVI）。","四村农户均呈中度脆弱，Kangarwa最脆弱（0.58），Boulangou最低（0.51）。","可结合气候智能农业技术与生计多样化，在脆弱性高发区开展干预效果评估研究。","2026-10-15T23:30:08.248266Z",{"id":444,"title":445,"url":446,"summary":447,"summary_zh":448,"content":8,"source_name":449,"source_url":446,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":450,"score_detail":451,"sources":454,"tags":456,"primary_topic":460,"secondary_topics":465,"classification":468,"search_phrases":512,"slug":515,"view_count":516,"doi":517,"paper":518,"created_at":537},4575,"A review of remote sensing for agricultural drought: technologies, indices, modelling advances and future prospects","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2738981","Agricultural drought is a complex hazard that jeopardises food security and progress toward the Sustainable Development Goals. We systematically reviewed peer-reviewed English-language literature published between 2015 and 2025, focusing on Remote Sensing (RS)-driven agricultural drought monitoring and prediction. Eligible studies were identified from the Web of Science and Scopus databases (last searched in August 2025); review articles, duplicates, and irrelevant studies were excluded, resulting in 181 articles. This review critically examined the evolution of sensors, drought indices, modelling approaches, and emerging technologies used to assess agricultural drought across diverse agro-ecological regions. Specifically, we (1) systematically evaluated the use of optical, thermal, and microwave RS platforms, along with satellite-based, model-based, and reanalysis products for agricultural drought monitoring; (2) reviewed widely used RS-based drought indices, including single indices grouped into nine categories, as well as composite indices; (3) reviewed software or programming languages applied; (4) assessed the progression of modelling approaches from traditional statistical and time-series methods to machine learning (ML) and deep learning (DL) frameworks; and (5) examined validation strategies applied to agricultural drought assessment. The analysis reveals a growing research interest in this topic, with a strong geographic concentration in Asia, particularly China. Optical and thermal platforms (e.g. MODIS and Landsat) dominate the literature (~54%) due to their accessibility, followed by satellite-derived precipitation products (17%), while microwave soil-moisture missions remain underutilized (~7%). Regarding RS-based indices, most studies employed more than one index: 44% analysed multiple individual indices, 51% used composite indices (either alone or in combination with individual indices), and only 5% relied on a single index. Vegetation indices, such as NDVI, remain workhorses; however, their sensitivity to soil background effects and canopy saturation has driven increased adoption of thermal composites (e.g. VHI), as well as adaptive weighting schemes and principal component analysis-based approaches. Moreover, computational tools used in drought research can be broadly grouped into geospatial and RS platforms for large-scale data processing and analysis (e.g. QGIS, GEE), and general-purpose programming languages (e.g. R, Python) for model development and advanced analytics. Regarding the methodology, statistical and time-series approaches account for 64% of the reviewed studies, followed by ML models (20%) and DL methods (15%), while explainable artificial intelligence (XAI) remains nascent. Techniques, such as random forests, support vector machines, and CNN and LSTM architectures, generally outperform traditional models but require large training datasets and raise concerns related to interpretability and ethics. In terms of validation, hydro-climatic indices such as SPI and SPEI are most commonly used (32%), followed by crop yield and yield anomaly data (19%). This review further identifies key research gaps across sensors, indices, and modelling approaches and proposes a roadmap for developing equitable, interpretable, and resilient agricultural drought monitoring systems. Emerging opportunities include context-aware and interpretable AI, multi-sensor and multi-scale data fusion across optical, thermal, and microwave domains, integration of physiologically meaningful indicators such as sun-induced chlorophyll fluorescence (SIF), robust uncertainty quantification, and the development of hybrid and physics-informed modelling frameworks.","农业干旱是一种复杂的灾害，威胁粮食安全和可持续发展目标的实现。我们系统综述了2015年至2025年间发表的同行评议英文文献，聚焦于遥感（RS）驱动的农业干旱监测与预测。符合条件的研究通过Web of Science和Scopus数据库检索确定（最后检索时间为2025年8月）；综述文章、重复文献及不相关研究被排除，最终纳入181篇文献。本综述批判性地审视了用于评估不同农业生态区农业干旱的传感器、干旱指数、建模方法及新兴技术的演变。具体而言，我们（1）系统评估了光学、热红外和微波遥感平台，以及基于卫星、基于模型和再分析产品在农业干旱监测中的应用；（2）综述了广泛使用的基于遥感的干旱指数，包括归为九类的单一指数以及复合指数；（3）综述了所应用的软件或编程语言；（4）评估了建模方法从传统统计和时间序列方法向机器学习（ML）和深度学习（DL）框架的演进；（5）考察了应用于农业干旱评估的验证策略。分析显示，该主题的研究兴趣日益增长，地理上高度集中于亚洲，尤其是中国。光学和热红外平台（如MODIS和Landsat）因其可获取性在文献中占主导地位（约54%），其次是卫星降水产品（17%），而微波土壤水分任务仍未得到充分利用（约7%）。在基于遥感的指数方面，大多数研究采用了不止一种指数：44%分析了多个单一指数，51%使用了复合指数（单独使用或与单一指数结合使用），仅5%依赖单一指数。植被指数（如NDVI）仍是主力工具；然而，其对土壤背景效应和冠层饱和的敏感性推动了热红外复合指数（如VHI）以及自适应权重方案和基于主成分分析的方法得到更多采用。此外，干旱研究中使用的计算工具可大致分为用于大规模数据处理和分析的地理空间与遥感平台（如QGIS、GEE），以及用于模型开发的通用编程语言（如R、Python）。","GIScience & Remote Sensing",81,{"impact":16,"substance":17,"depth":452,"authority":18,"freshness":75,"relevant":20,"comment":453},19,"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[455],{"name":449,"url":446},[457,25,65,458,459],"智慧农业","遥感监测","农业干旱",{"code":461,"slug":462,"name":463,"path":464,"parent_code":48},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[466,467],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":13,"version":51,"method":52,"evidence":469},{"candidates":470,"agricultural_evidence":57},[471,477,481,488,492,496,502,508],{"code":461,"score":190,"supported":57,"title":472,"abstract":474,"tags":476},[473],"remote sensing",[475,473],"遥感",[458],{"code":38,"score":18,"supported":57,"title":478,"abstract":479,"tags":480},[218],[77,218],[],{"code":44,"score":12,"supported":57,"title":482,"abstract":483,"tags":487},[],[484,65,153,485,486],"深度学习","deep learning","artificial intelligence",[65],{"code":81,"score":222,"supported":57,"title":489,"abstract":490,"tags":491},[],[25,137],[25],{"code":307,"score":19,"supported":57,"title":493,"abstract":494,"tags":495},[],[310,311],[],{"code":497,"score":164,"supported":57,"title":498,"abstract":499,"tags":501},"D",[],[500],"水产",[],{"code":503,"score":164,"supported":57,"title":504,"abstract":505,"tags":507},"H6",[],[506],"农业生态",[],{"code":146,"score":20,"supported":13,"title":509,"abstract":510,"tags":511},[],[],[457],[513,514],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575",2,"10.1080\u002F15481603.2026.2738981",{"doi":517,"openalex_id":519,"authors":520,"venue":449,"cited_by_count":94,"oa_url":530,"card":531,"direction":177,"ingested_from":178},"W7221081979",[521,524,527],{"name":522,"orcid":523},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":525,"orcid":526},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":528,"orcid":529},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":532,"method":533,"finding":534,"direction":535,"opportunity":536},"系统综述2015-2025年遥感农业干旱监测与预测的传感器、指数、建模及验证进展。","基于Web of Science与Scopus的181篇文献系统综述，涵盖光学\u002F","光学热红外平台占主导，微波土壤水分利用不足；复合指数与机器学习建模成为趋势。","农业遥感与作物表型","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","2026-10-10T23:30:14.933078Z",{"id":539,"title":540,"url":541,"summary":542,"summary_zh":543,"content":8,"source_name":544,"source_url":541,"published_at":545,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":546,"score_detail":547,"sources":549,"tags":551,"primary_topic":554,"secondary_topics":555,"classification":558,"search_phrases":618,"slug":621,"view_count":20,"doi":622,"paper":623,"created_at":651},1380,"Harnessing Digital Twin (DT) Technology for Food Security and Climate Resilience in Sub-Saharan Africa (SSA)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102532","Sub-Saharan Africa (SSA) faces chronic food insecurity despite possessing approximately 60% of the world’s uncultivated arable land. Field trials generate evidence but are costly and insufficiently scaled to address accelerating climate and demographic pressures. Digital twin (DT) technology, defined as the continuous, bidirectional virtual replication of physical systems using real-time data, supports monitoring, modelling, and optimisation of agrifood systems. To our knowledge, however, no published synthesis has examined DT agriculture research through the lens of SSA food systems or smallholder farming realities. A PRISMA-compliant systematic review was conducted across bibliographic databases using a pre-defined Boolean search and adapted PICOS eligibility criteria. Forty peer-reviewed studies published between 2019 and 2025 were included. Structured extraction captured bibliometric, geographic, methodological, technological, and technology readiness level (TRL) data, analysed using frequency tabulation and thematic clustering. DT agriculture publications roughly tripled between 2019 and 2023 (from three to ten studies), yet 57.5% of studies remain at TRL 2–3. Europe and East Asia dominate the evidence base; no study was led by an SSA institution, and no study reported SSA-based deployment on priority crops such as maize, cassava, sorghum, or cowpea. Internet of Things (IoT) sensor infrastructure (92.5%) and artificial intelligence\u002Fmachine learning (AI\u002FML) algorithms (52.5%) constitute the dominant technology stack. Five thematic clusters were identified: smart farming infrastructure, precision water and nutrient management, controlled environment agriculture, agrifood supply chains, and livestock and aquaculture systems. Government-funded studies tend to achieve higher TRL scores than academically funded counterparts. Synthesising these findings into a system-level, deployment-oriented analysis, we derive a conceptual SSA-DT framework. It combines offline-first edge-computing architecture, hybrid crop modelling, and multi-stakeholder interfaces, with dietary diversity and climate resilience as the two key performance indicators (KPIs), aligned with IITA’s 2024–2030 Strategy. North–South research partnerships and targeted CGIAR investment in DT technology are identified as priority actions for closing the SSA evidence gap.","撒哈拉以南非洲（SSA）尽管拥有全球约60%的未开垦耕地，却长期面临粮食不安全问题。田间试验虽能提供实证，但成本高昂且规模不足以应对日益加剧的气候与人口压力。数字孪生（DT）技术，定义为利用实时数据对物理系统进行持续、双向的虚拟复制，可支持农业食品系统的监测、建模与优化。然而，据我们所知，尚无已发表的综述从SSA粮食系统或小农生产现实的角度审视DT农业研究。本研究遵循PRISMA规范，在书目数据库中采用预定义的布尔检索和适应性PICOS资格标准进行系统综述，纳入2019至2025年间发表的40篇同行评审研究。结构化提取涵盖文献计量、地理、方法、技术及技术就绪水平（TRL）数据，并采用频次制表和主题聚类进行分析。DT农业出版物在2019至2023年间约增长两倍（从三篇增至十篇），但57.5%的研究仍处于TRL 2–3级。欧洲和东亚主导了证据基础；没有一项研究由SSA机构牵头，也没有研究报告在SSA地区针对玉米、木薯、高粱或豇豆等优先作物进行部署。物联网（IoT）传感器基础设施（92.5%）和人工智能\u002F机器学习（AI\u002FML）算法（52.5%）构成主导技术栈。研究识别出五个主题聚类：智慧农业基础设施、精准水肥管理、受控环境农业、农业食品供应链以及畜牧和水产养殖系统。政府资助的研究往往比学术资助的研究达到更高的TRL评分。将这些发现综合为系统层面、面向部署的分析，我们提出了一个概念性的SSA-DT框架。该框架结合离线优先的边缘计算架构、混合作物建模和多利益相关方接口，以膳食多样性和气候韧性作为两项关键绩效指标（KPI），与国际热带农业研究所（IITA）2024–2030年战略保持一致。南北研究伙伴关系和CGIAR对DT技术的定向投资被确定为缩小SSA证据差距的优先行动。","Smart Agricultural Technology","2026-09-01T00:00:00Z",79,{"impact":16,"substance":17,"depth":452,"authority":278,"freshness":222,"relevant":20,"comment":548},"系统综述数字孪生技术在撒哈拉以南非洲的应用，揭示研究空白并提出框架，对全球农业信息化有参考价值。",[550],{"name":544,"url":541},[457,552,25,553],"非洲","数字孪生",{"code":81,"slug":125,"name":126,"path":127,"parent_code":128},[556,557],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":13,"version":51,"method":52,"evidence":559},{"candidates":560,"agricultural_evidence":57},[561,566,570,574,578,587,591,598,603,608,614],{"code":146,"score":452,"supported":57,"title":562,"abstract":564,"tags":565},[563],"digital twin",[457,553,563],[553,457],{"code":38,"score":63,"supported":57,"title":567,"abstract":568,"tags":569},[303],[303],[],{"code":81,"score":70,"supported":57,"title":571,"abstract":572,"tags":573},[137],[],[25],{"code":44,"score":70,"supported":57,"title":575,"abstract":576,"tags":577},[],[65,153,486],[],{"code":579,"score":70,"supported":57,"title":580,"abstract":581,"tags":586},"I5",[],[582,583,584,585],"物联网","边缘计算","internet of things","IoT",[],{"code":228,"score":19,"supported":57,"title":588,"abstract":589,"tags":590},[],[231,232],[],{"code":592,"score":19,"supported":57,"title":593,"abstract":594,"tags":597},"A5",[],[595,596],"高粱","sorghum",[],{"code":324,"score":19,"supported":57,"title":599,"abstract":600,"tags":602},[],[601,327],"畜牧",[],{"code":497,"score":19,"supported":57,"title":604,"abstract":605,"tags":607},[],[500,606],"aquaculture",[],{"code":69,"score":19,"supported":57,"title":609,"abstract":610,"tags":613},[],[611,612],"文献计量","bibliometric",[],{"code":31,"score":164,"supported":57,"title":615,"abstract":616,"tags":617},[],[142],[],[619,620],"数字孪生 智慧农业 粮食安全 非洲","数字孪生 智慧农业","数字孪生智慧农业粮食安全非洲-1380","10.1016\u002Fj.atech.2026.102532",{"doi":622,"openalex_id":624,"authors":625,"venue":544,"cited_by_count":94,"oa_url":645,"card":646,"direction":101,"ingested_from":178},"W7204898011",[626,629,631,634,636,639,641,643],{"name":627,"orcid":628},"Henri E. Z. Tonnang","https:\u002F\u002Forcid.org\u002F0000-0002-9424-9186",{"name":630,"orcid":8},"Francis Chianu",{"name":632,"orcid":633},"Siyabusa Mkuhlani","https:\u002F\u002Forcid.org\u002F0000-0001-7872-2036",{"name":635,"orcid":8},"Francis Muthoni",{"name":637,"orcid":638},"John Choptiany","https:\u002F\u002Forcid.org\u002F0009-0008-9961-4606",{"name":640,"orcid":8},"Franck B.N. Tonle",{"name":642,"orcid":8},"Bonoukpoe M. Sokame",{"name":644,"orcid":8},"Mercy Lung’aho","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007574\u002Fpdf",{"tldr":647,"method":648,"finding":649,"direction":101,"opportunity":650},"系统综述数字孪生技术在撒哈拉以南非洲农业的应用，提出SSA-DT框架。","PRISMA系统综述，检索2019-2025年40篇研究，频率统计和主题聚类。","DT农业研究增长但TRL低，SSA无主导研究，提出离线优先边缘计算框架。","SSA地区数字孪生农业研究空白，可探索离线优先架构与本地作物模型结合。","2026-09-02T23:30:04.414055Z"]