[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3612":3,"related-3612":53},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":52},3612,"Longitudinal analysis of input use farm management practices and climate adaptation on Ethiopian oilseed crop yields","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44279-026-00781-3","This study examines the dynamic drivers of oilseed crop yields in Ethiopia, a subsector that plays a vital role in Ethiopia’s agricultural economy. It aims to identify the key determinants of oilseed productivity, focusing on agricultural inputs, farm management practices, climate change adaptation strategies, and institutional factors, using a dynamic System Generalised Method of Moments (GMM) model. This model is applied to pseudo-panel data from the Ethiopian Annual Agricultural Survey (2003–2021). The results indicate that yields from the previous season significantly affect current yields. This is confirmed by an elasticity of lagged yields of 0.992. Land used for cultivation is found to be the main input with an elasticity of 0.812, while urea fertiliser has a negative elasticity (-0.162), indicating inefficiency in its use. Productivity growth is also found to be driven by specific oilseed crop management practices such as soil conservation (32.91%) and crop protection (47.79%). Climate change adaptation practices also contribute to oilseed productivity growth, with specific emphasis on practices such as terracing (18.88%) and water catchments (21.89%). Access to credit also increases productivity by 14.67%. These results are useful in developing an integrated policy aimed at improving oilseed productivity in Ethiopia.","本研究考察了埃塞俄比亚油料作物产量的动态驱动因素，该子行业在埃塞俄比亚农业经济中发挥着至关重要的作用。研究旨在识别油料作物生产力的关键决定因素，重点关注农业投入品、农场管理实践、气候变化适应策略以及制度因素，采用动态系统广义矩估计（GMM）模型。该模型应用于埃塞俄比亚年度农业调查（2003—2021年）的伪面板数据。结果表明，上一季度的产量对当前产量具有显著影响，滞后产量的弹性为0.992，证实了这一点。种植用地是主要投入要素，弹性为0.812，而尿素化肥的弹性为负（-0.162），表明其使用效率低下。研究还发现，生产力增长受到特定油料作物管理实践的驱动，如土壤保持（32.91%）和作物保护（47.79%）。气候变化适应措施也对油料作物生产力增长有所贡献，尤其体现在梯田建设（18.88%）和集水措施（21.89%）等实践上。获得信贷同样使生产力提高14.67%。这些结果有助于制定旨在提高埃塞俄比亚油料作物生产力的综合政策。",null,"Discover Agriculture","2026-09-25T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,21,17,8,1,"基于埃塞俄比亚21年农业调查数据的实证研究，方法规范、结论具体，对油料作物投入与气候适应政策有参考价值，但属他国经验，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"气候适应","化肥减量","水土保持","油料作物","农业信贷",[32,33],"埃塞俄比亚 油料作物 产量","埃塞俄比亚 农业调查 面板数据","埃塞俄比亚油料作物产量-3612",0,"10.1007\u002Fs44279-026-00781-3",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":43,"card":44,"direction":50,"ingested_from":51},"W7214287899",[40],{"name":41,"orcid":42},"Daregot Berihun Tenessa","https:\u002F\u002Forcid.org\u002F0009-0005-5480-7445","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44279-026-00781-3.pdf",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"基于埃塞俄比亚2003-2021年面板数据，分析投入、管理、气候适应与制度因素对油料作物产量的动态影","动态系统GMM模型，使用埃塞俄比亚年度农业调查伪面板数据。","上期产量、土地和尿素显著影响产量，土壤保护、作物保护、梯田、集水及信贷均促进生产力。","农业人工智能与决策模型","可结合机器学习与动态面板模型，在类似小农体系中识别气候适应策略的异质性效应与最优组合。","智慧农业 \u002F 农业物联网","openalex","2026-09-27T23:30:14.450427Z",{"total":54,"page":21,"page_size":54,"items":55},6,[56,100,141,187,238,282],{"id":57,"title":58,"url":59,"summary":60,"summary_zh":9,"content":9,"source_name":61,"source_url":59,"published_at":11,"category":12,"cover_url":9,"hotness":62,"is_selected":14,"score":63,"score_detail":64,"sources":67,"tags":72,"search_phrases":77,"slug":80,"view_count":35,"doi":81,"paper":82,"created_at":99},3565,"An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability","https:\u002F\u002Fdoi.org\u002F10.7759\u002Fs44389-026-00295-5","An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability。Cureus Journal of Computer Science.","Cureus Journal of Computer Science.",25,39,{"impact":20,"substance":54,"depth":13,"authority":54,"freshness":65,"relevant":21,"comment":66},9,"主题契合智慧农业与农业AI，但摘要仅重复标题、无方法与数据细节，信息增量有限，暂不建议进入每日精选。",[68,69],{"name":61,"url":59},{"name":70,"url":71},"Cureus Journal of Computer Science 2026-09-25","https:\u002F\u002Fwww.cureusjournals.com\u002Farticles\u002F20543",[73,74,75,76,26],"智慧农业","农业人工智能","可解释AI","精准农业",[78,79],"气候适应 精准农业 可解释AI","农业人工智能 智慧农业 气候适应 精准农业","气候适应精准农业可解释AI-3565","10.7759\u002Fs44389-026-00295-5",{"doi":81,"openalex_id":83,"authors":84,"venue":61,"cited_by_count":35,"oa_url":59,"card":9,"direction":48,"ingested_from":51},"W7214363342",[85,88,91,93,95,97],{"name":86,"orcid":87},"Mritunjay Kr. Ranjan","https:\u002F\u002Forcid.org\u002F0000-0003-0240-4909",{"name":89,"orcid":90},"Rohit Gupta","https:\u002F\u002Forcid.org\u002F0000-0002-4436-8275",{"name":92,"orcid":9},"Nitin  D Mali",{"name":94,"orcid":9},"Ansh  A Rajore",{"name":96,"orcid":9},"Gaurav Narendra Patil",{"name":98,"orcid":9},"Ankita  N Patil","2026-09-26T23:30:47.893666Z",{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":115,"tags":117,"search_phrases":120,"slug":123,"view_count":35,"doi":124,"paper":125,"created_at":140},3514,"Artificial Intelligence for Climate Adaptation Decision Support in Data-Poor Developing Regions","https:\u002F\u002Fdoi.org\u002F10.22541\u002Fessoar.15009304\u002Fv1","Climate adaptation is a sequence of decisions taken under uncertainty, and the regions where climate risk is rising fastest are those with the least information to guide them. Only about 10 per cent of deaths are registered in the WHO African Region; nearly 90 per cent of required surface weather observations are missing across least developed countries and small island states; and only 40 per cent of African countries have multi-hazard early warning systems. This report examines whether artificial intelligence — machine learning, remote sensing and predictive analytics — can close these information gaps and improve adaptation decisions in data-poor developing regions. The report organises the problem as a decision chain with three information gaps — observation, prediction and decision — followed by an action gap that AI cannot close. It finds that AI has advanced fastest on prediction: AI weather models became operational at ECMWF in 2025, AI flood forecasts now cover 100 countries and about 700 million people, satellite nowcasts reach a continent with little radar, and AI monsoon-onset forecasts reached 38 million Indian farmers in 2025. On observation, satellite machine learning explains around 70 per cent of the variation in village wealth but only up to about half of the variation in changes over time. On decision, evidence from Togo, Bangladesh and Kenya shows that AI-assisted targeting, forecast-based triggers and satellite index insurance can deliver assistance faster and better, within clear limits. The report's central argument is the ground-truth paradox: AI stretches scarce observations further, but every AI product must be trained and verified against ground truth, so reliance on AI raises the value of each remaining station, survey and label. The 2025 interruption of FEWS NET and termination of the DHS Program show how fragile that foundation is. Because the value of information is the product of skill, lead time, reach, trust and the means to act, the highest returns usually lie not in more skilful models but in dissemination, institutions and prearranged finance. The report sets out a risk register, a six-principle policy framework, actions by actor and a roadmap to 2030.","气候适应是在不确定性下做出的一系列决策，而气候风险上升最快的地区恰恰是指导信息最匮乏的地区。世卫组织非洲区域仅登记了约10%的死亡病例；最不发达国家和小岛屿国家缺失了近90%所需的地面天气观测数据；仅有40%的非洲国家拥有多灾种早期预警系统。本报告考察人工智能——机器学习、遥感和预测分析——能否弥合这些信息缺口，改善数据匮乏的发展中地区的适应决策。报告将这一问题组织为一条决策链，包含三个信息缺口——观测、预测和决策——以及一个人工智能无法弥合的行动缺口。报告发现，人工智能在预测方面进展最快：人工智能天气模型于2025年在欧洲中期天气预报中心（ECMWF）投入业务运行，人工智能洪水预报现已覆盖100个国家和约7亿人口，卫星临近预报覆盖了一个几乎没有雷达的大陆，人工智能季风爆发预报于2025年惠及3800万印度农民。在观测方面，卫星机器学习可解释村庄财富约70%的变异，但对时间变化的解释力仅约一半。在决策方面，来自多哥、孟加拉国和肯尼亚的证据表明，人工智能辅助的目标定位、基于预报的触发机制和卫星指数保险能够在明确限度内更快、更好地提供援助。报告的核心论点是地面真值悖论：人工智能能够将稀缺的观测数据发挥更大效用，但每个人工智能产品都必须依据地面真值进行训练和验证，因此对人工智能的依赖提升了每一个剩余站点、调查和标注数据的价值。2025年FEWS NET的中断和DHS项目的终止表明这一基础何等脆弱。由于信息的价值是技能、提前期、覆盖面、信任和行动手段的乘积，最高回报通常不在于更精密的模型，而在于传播、制度和预先安排的融资。报告提出了风险登记册、六项原则的政策框架、各行为主体的行动以及到2030年的路线图。","OpenAlex","2026-09-22T00:00:00Z",86,{"impact":110,"substance":111,"depth":112,"authority":113,"freshness":20,"relevant":21,"comment":114},22,24,19,13,"系统梳理AI在数据匮乏地区气候适应决策中的观测、预测与决策三类信息缺口，提出“地面真值悖论”，数据与结论扎实，对农业信息化与智慧农业有较强参考价值。",[116],{"name":106,"url":103},[73,74,26,118,119],"遥感监测","早期预警",[121,122],"AI 气候适应 决策支持","数据匮乏地区 农业预警","AI气候适应决策支持-3514","10.22541\u002Fessoar.15009304\u002Fv1",{"doi":124,"openalex_id":126,"authors":127,"venue":9,"cited_by_count":35,"oa_url":133,"card":134,"direction":139,"ingested_from":51},"W7214097088",[128,130],{"name":129,"orcid":9},"H Heuristics",{"name":131,"orcid":132},"Hunter Hughes","https:\u002F\u002Forcid.org\u002F0009-0002-6161-9387","https:\u002F\u002Fessopenarchive.org\u002Fdoi\u002Fpdf\u002F10.22541\u002Fessoar.15009304\u002Fv1",{"tldr":135,"method":136,"finding":137,"direction":48,"opportunity":138},"评估AI能否弥补数据匮乏地区气候适应决策的信息缺口，并提出地面真值悖论。","梳理观测、预测、决策三环节，结合AI天气模型、卫星ML与多国案例证据。","AI预测进展最快，但依赖地面真值；最高回报常在传播、制度与预置资金而非模型。","可研究AI辅助农业气候适应中地面真值稀缺下的验证与信任机制，及预置资金触发设计。","数字乡村与农业信息化","2026-09-25T23:30:46.008325Z",{"id":142,"title":143,"url":144,"summary":145,"summary_zh":146,"content":9,"source_name":147,"source_url":144,"published_at":148,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":149,"score_detail":150,"sources":154,"tags":156,"search_phrases":159,"slug":162,"view_count":35,"doi":163,"paper":164,"created_at":186},3363,"Integrated Assessment of Soil Erosion Drivers Using RUSLE, Remote Sensing, and Scenario-Based Machine Learning in a Data-Scarce Watershed of the Chota Nagpur Plateau, Eastern India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02576-x","Integrated Assessment of Soil Erosion Drivers Using RUSLE, Remote Sensing, and Scenario-Based Machine Learning in a Data-Scarce Watershed of the Chota Nagpur Plateau, Eastern India。Journal of the Indian Society of Remote Sensing","基于RUSLE、遥感和情景机器学习的土壤侵蚀驱动因素综合评估——以印度东部乔塔纳格普尔高原数据稀缺流域为例。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing","2026-09-23T00:00:00Z",63,{"impact":20,"substance":151,"depth":152,"authority":113,"freshness":65,"relevant":21,"comment":153},18,15,"方法组合有新意但属区域案例研究，影响范围有限，可作为遥感与水土保持主题的补充素材。",[155],{"name":147,"url":144},[157,118,158,28],"机器学习","土壤侵蚀",[160,161],"Chota Nagpur Plateau 土壤侵蚀","RUSLE 遥感 机器学习","ChotaNagpurPlateau土壤侵蚀-3363","10.1007\u002Fs12524-026-02576-x",{"doi":163,"openalex_id":165,"authors":166,"venue":147,"cited_by_count":35,"oa_url":9,"card":180,"direction":184,"ingested_from":51},"W7214122637",[167,170,173,175,177],{"name":168,"orcid":169},"Mukesh Kumar Tiwari","https:\u002F\u002Forcid.org\u002F0000-0003-0385-4426",{"name":171,"orcid":172},"Prabhat Kumar Guru","https:\u002F\u002Forcid.org\u002F0000-0002-9294-2091",{"name":174,"orcid":9},"Sanjeet Kumar",{"name":176,"orcid":9},"Yogesh A. Rajwade",{"name":178,"orcid":179},"Narendra Singh Chandel","https:\u002F\u002Forcid.org\u002F0000-0003-4850-4702",{"tldr":181,"method":182,"finding":183,"direction":184,"opportunity":185},"结合RUSLE、遥感与情景机器学习，评估印度Chota Nagpur高原缺数据流域的土壤侵蚀驱动因素","RUSLE模型、遥感数据与情景机器学习集成分析。","在数据稀缺流域识别出土壤侵蚀关键驱动因子并预测不同情景下的侵蚀风险。","农业遥感与作物表型","可探索缺数据区多源遥感与机器学习融合的土壤侵蚀动态监测与情景预警方法。","2026-09-24T23:30:21.065678Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":9,"source_name":193,"source_url":190,"published_at":148,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":194,"score_detail":195,"sources":197,"tags":199,"search_phrases":204,"slug":207,"view_count":35,"doi":208,"paper":209,"created_at":237},3340,"Characterization of cattle manure-derived biochar and its synergistic effects with inorganic and organic fertilizers on soil properties, microbial community, and forage maize yield","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1936002","The impact of biochar on soil fertility and plant growth depends on its feedstock, physiochemical properties, and porous structure. However, cattle manure-derived biochar has been less used in practice, and its effectiveness on improving crop yield and the underlying mechanism are still unclear. This study characterized the properties and structures of biochar pyrolyzed from cattle manure from 200 °C to 600 °C. Subsequently, a field experiment was conducted to investigate the influence of biochar and its mixture with inorganic or organic fertilizer on soil properties, microbial community structure, and maize growth and yield. Based on a combined consideration of biochar yield, pore characteristics, specific surface area, and functional-group retention, 300 °C was selected as a suitable target pyrolysis temperature, with a relatively high biochar yield of 75.93%. The results revealed that reducing the inorganic fertilizer application rate by 50% and supplementing with biochar (FB50) resulted in a maize yield of 63.33 t·hm −2 , which was 9.83% higher than that under full inorganic fertilizer application (F; 57.67 t·hm −2 ), although the difference was not statistically significant, suggesting a sustainable approach to reducing chemical fertilizer use. Biochar likely improved maize growth by supporting keystone microbial taxa such as Chloroflexi , Proteobacteria , and Firmicutes , which are involved in nutrient cycling and enzymatic activities. Furthermore, co-applying biochar with organic fertilizer improved soil properties and microbial diversity, but did not improve maize production. Specifically, adding full-rate biochar (MB100) increased soil pH from 4.78 to 5.31 and soil organic carbon by 75.22% relative to manure alone (B). Overall, cattle manure-derived biochar offers a practical waste management solution, improving plant yields and soil health, while reducing chemical fertilizer reliance.","生物炭对土壤肥力和植物生长的影响取决于其原料、理化性质和多孔结构。然而，牛粪源生物炭在实际生产中应用较少，其提高作物产量的效果及潜在机制尚不清楚。本研究对200 °C至600 °C热解牛粪制备的生物炭的性质和结构进行了表征。随后，通过田间试验研究了生物炭及其与无机或有机肥料配施对土壤性质、微生物群落结构以及玉米生长和产量的影响。综合考虑生物炭产率、孔隙特征、比表面积和官能团保留情况，选择300 °C作为适宜的目标热解温度，此时生物炭产率较高，为75.93%。结果表明，将无机肥施用量减少50%并配施生物炭（FB50）可使玉米产量达到63.33 t·hm⁻²，比全量无机肥处理（F；57.67 t·hm⁻²）高9.83%，尽管差异未达统计显著水平，但这表明了一种减少化肥使用的可持续途径。生物炭可能通过支持关键微生物类群如绿弯菌门（Chloroflexi）、变形菌门（Proteobacteria）和厚壁菌门（Firmicutes）来促进玉米生长，这些微生物参与养分循环和酶活性。此外，生物炭与有机肥配施改善了土壤性质和微生物多样性，但未提高玉米产量。具体而言，与单施粪肥（B）相比，添加全量生物炭（MB100）使土壤pH从4.78提高到5.31，土壤有机碳增加了75.22%。总体而言，牛粪源生物炭提供了一种实用的废弃物管理方案，既能提高植物产量和土壤健康，又能减少对化肥的依赖。","Frontiers in Sustainable Food Systems",72,{"impact":17,"substance":18,"depth":19,"authority":113,"freshness":65,"relevant":21,"comment":196},"牛粪生物炭热解参数与田间试验数据扎实，为粪污资源化与化肥减施提供实证，但属细分领域研究，公共影响有限。",[198],{"name":193,"url":190},[200,27,201,202,203],"生物炭","青贮玉米","土壤微生物","畜禽粪污资源化",[205,206],"牛粪生物炭 化肥减量 玉米","畜禽粪污资源化 土壤微生物 化肥减量 青贮玉米","牛粪生物炭化肥减量玉米-3340","10.3389\u002Ffsufs.2026.1936002",{"doi":208,"openalex_id":210,"authors":211,"venue":193,"cited_by_count":35,"oa_url":190,"card":231,"direction":235,"ingested_from":51},"W7214105860",[212,215,217,220,222,224,226,229],{"name":213,"orcid":214},"Yuanfeng Zhao","https:\u002F\u002Forcid.org\u002F0000-0001-6762-7906",{"name":216,"orcid":9},"Ruofei Guan",{"name":218,"orcid":219},"Jiang Ran","https:\u002F\u002Forcid.org\u002F0009-0001-4635-2402",{"name":221,"orcid":9},"Q. Wang",{"name":223,"orcid":9},"Hao Deng",{"name":225,"orcid":9},"Jingrui Zhou",{"name":227,"orcid":228},"Kaikai Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-6380-4928",{"name":230,"orcid":9},"Nan Jiang",{"tldr":232,"method":233,"finding":234,"direction":235,"opportunity":236},"研究牛粪生物炭特性及其与无机\u002F有机肥配施对土壤、微生物和青贮玉米产量的影响。","200-600°C热解牛粪生物炭表征，结合田间试验分析土壤、微生物与玉米产量。","300°C热解生物炭配合减半无机肥使玉米增产9.83%，并改善土壤与微生物群落。","农业绿色发展与碳","可探究牛粪生物炭替代化肥的长期固碳效应及不同土壤类型下的微生物调控机制。","2026-09-24T23:30:07.724953Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":9,"source_name":244,"source_url":241,"published_at":245,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":246,"score_detail":247,"sources":250,"tags":252,"search_phrases":255,"slug":258,"view_count":35,"doi":259,"paper":260,"created_at":281},3073,"Evolution of soil erosion and sediment delivery modelling over six decades: paradigms, limitations and new solutions","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15715124.2026.2717245","Modelling sediment delivery, namely the linkage between hillslope erosion and yield in river systems, is still a challenge. In this study, 1,075 publications from Elsevier's Scopus were analysed to trace the evolution from lumped to spatially distributed approaches. Results confirm that no single model type dominates, while several studies rely on simplified approaches, and data- and model-related errors remain unresolved. Artificial intelligence (AI) and remote sensing (RS) can address such limitations. However, although AI has emerged as either a complementary tool for generating new climate or land-use scenarios or a standalone framework, its adoption has not been transformative so far. Similarly, RS is increasingly used to characterise topography and land cover, but is poorly leveraged to determine other factors influencing sediment dynamics. This overview of existing and evolving methods provides useful insights to support river basin management and model selection. Overall, this study underscores the need for further integration of diverse data sources and processing methods, while future research should prioritise the trade-offs among model complexity, accuracy, and scalability.","模拟泥沙输移，即坡面侵蚀与河流系统产沙之间的联系，仍然是一项挑战。本研究分析了来自Elsevier Scopus数据库的1，075篇文献，以追溯从集总式方法到空间分布式方法的演变历程。结果证实，尚无单一模型类型占据主导地位，而若干研究依赖于简化方法，且与数据和模型相关的误差仍未得到解决。人工智能（AI）和遥感（RS）能够应对这些局限性。然而，尽管AI已作为生成新气候或土地利用情景的补充工具或独立框架出现，但其应用迄今尚未带来变革性影响。同样，RS越来越多地用于表征地形和土地覆盖，但在确定影响泥沙动力学的其他因素方面利用不足。本综述对现有及不断演变的方法进行了概述，为支持流域管理和模型选择提供了有益见解。总体而言，本研究强调了进一步整合多样化数据来源和处理方法的必要性，同时未来研究应优先考虑模型复杂性、精度和可扩展性之间的权衡。","International Journal of River Basin Management","2026-09-19T00:00:00Z",66,{"impact":20,"substance":248,"depth":19,"authority":113,"freshness":20,"relevant":21,"comment":249},20,"基于1075篇文献的六十年土壤侵蚀与泥沙输移建模综述，指出AI与遥感应用尚未形成变革性突破，对农业水土保持与流域管理有参考价值，但属学术综述、影响面有限。",[251],{"name":244,"url":241},[74,253,158,28,254],"农业遥感","流域管理",[256,257],"土壤侵蚀 泥沙输移 模型","遥感 AI 流域管理","土壤侵蚀泥沙输移模型-3073","10.1080\u002F15715124.2026.2717245",{"doi":259,"openalex_id":261,"authors":262,"venue":244,"cited_by_count":35,"oa_url":275,"card":276,"direction":184,"ingested_from":51},"W7213758675",[263,266,269,272],{"name":264,"orcid":265},"Melissa Latella","https:\u002F\u002Forcid.org\u002F0000-0003-3678-6992",{"name":267,"orcid":268},"Monia Santini","https:\u002F\u002Forcid.org\u002F0000-0002-8041-8241",{"name":270,"orcid":271},"Pierfranco Costabile","https:\u002F\u002Forcid.org\u002F0000-0003-1147-9929",{"name":273,"orcid":274},"Roberta Padulano","https:\u002F\u002Forcid.org\u002F0000-0003-4881-4495","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15715124.2026.2717245?needAccess=true",{"tldr":277,"method":278,"finding":279,"direction":184,"opportunity":280},"分析1075篇文献，梳理六十年土壤侵蚀与泥沙输移模型从集总到分布式的演变。","Scopus文献计量分析，综述AI与遥感在泥沙输移建模中的应用。","无单一模型占主导，AI与遥感应用尚未变革性，需整合多源数据并权衡复杂度、精度与可扩展性。","可探索AI与多源遥感深度融合的分布式泥沙输移模型，兼顾精度与可扩展性。","2026-09-21T23:30:25.637249Z",{"id":283,"title":284,"url":285,"summary":286,"summary_zh":287,"content":9,"source_name":288,"source_url":285,"published_at":289,"category":12,"cover_url":9,"hotness":62,"is_selected":14,"score":290,"score_detail":291,"sources":293,"tags":297,"search_phrases":302,"slug":305,"view_count":21,"doi":9,"paper":306,"created_at":316},2953,"Seasonal agricultural vulnerability in semi-arid Morocco: combining remote sensing and farmer knowledge to inform climate adaptation","https:\u002F\u002Fmel.cgiar.org\u002Freporting\u002Fdownloadmelspace\u002Fhash\u002F5daf70ceb0d05067d3d316f855cfa4f0","Seasonal agricultural vulnerability in semi-arid Morocco: combining remote sensing and farmer knowledge to inform climate adaptation。MELSpace (ICARDA (The International Center for Agricultural Research in Dry Areas))","半干旱摩洛哥的季节性农业脆弱性：结合遥感与农民知识为气候适应提供依据。MELSpace（ICARDA（国际干旱地区农业研究中心））","MELSpace (ICARDA (The International Center for Agricultural Research in Dry Areas))","2026-09-17T00:00:00Z",71,{"impact":17,"substance":248,"depth":151,"authority":113,"freshness":20,"relevant":21,"comment":292},"国际干旱农业研究机构成果，遥感与农户知识结合评估季节性脆弱性，方法有参考价值但属区域案例，影响力有限。",[294,295],{"name":288,"url":285},{"name":288,"url":296},"https:\u002F\u002Fhdl.handle.net\u002F20.500.11766\u002F70850",[298,26,299,300,301],"遥感","农户知识","干旱农业","摩洛哥",[303,304],"ICARDA 摩洛哥 遥感 气候适应","农户知识 干旱农业 气候适应 摩洛哥","ICARDA摩洛哥遥感气候适应-2953",{"doi":9,"openalex_id":307,"authors":308,"venue":288,"cited_by_count":35,"oa_url":285,"card":311,"direction":184,"ingested_from":51},"W7213576262",[309],{"name":310,"orcid":9},"Cesar Ivan Alvarez",{"tldr":312,"method":313,"finding":314,"direction":184,"opportunity":315},"结合遥感与农户知识评估摩洛哥半干旱区季节性农业脆弱性，以支持气候适应。","遥感植被指数与农户访谈\u002F地方知识结合，分析季节性脆弱性。","遥感与农户知识互补，可更全面识别半干旱区季节性农业脆弱性。","可探索遥感指标与农户感知的定量耦合模型，用于气候适应决策支持。","2026-09-19T23:30:34.812798Z"]