[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4159":3,"related-4159":98},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":36,"classification":48,"search_phrases":83,"slug":86,"view_count":73,"doi":8,"paper":87,"created_at":97},4159,"Dynamic regulation of grid-scale high-efficiency precision irrigation for rice under climate variability（基于气候变化的网格尺度水稻精准灌溉动态调控）","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs44264-026-00172-7","Dong Wenhao、Xu YaoWen、Zhangzhong Lili、Xu Xianghui、Wei Shiyu、Sun Ziqiang、Chen Yingshan、Zhang Pingan、Li Mo（npj Sustainable Agriculture 2026, 4(1): 61）。针对气候变化与区域水量供需失衡，文章提出「模拟-优化」协同的网格尺度水稻精准灌溉调度框架：使用WOFOST模型结合Sobol敏感性分析与粒子群优化，与多目标优化模型耦合生成可动态调整的灌溉策略。在黑龙江省三江平原与松嫩平原（332,500 km²）应用：模型精度提高，区域产量提升1.5%、灌溉用水减少8.0%；动态调度策略相比传统灌溉节水10.1%。",null,"npj Sustainable Agriculture 2026, 4(1): 61","2026-09-28T00:00:00Z","论文",10,false,83,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,23,18,14,6,1,"核心期刊论文，提出模拟-优化协同的网格尺度水稻精准灌溉框架，在三江平原等区域验证节水增产效果，方法新颖、数据规模大，对智慧农业与气候适应有参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","水稻","气候适应","精准灌溉","三江平原",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[37,43],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":49,"locked":13,"version":50,"method":51,"evidence":52},"classified","2026-10-22.2","rules",{"candidates":53,"agricultural_evidence":56},[54,60,65,71,78],{"code":32,"score":55,"supported":56,"title":57,"abstract":58,"tags":59},20,true,[27,33],[27],[27],{"code":38,"score":55,"supported":56,"title":61,"abstract":63,"tags":64},[62,39],"灌溉",[62],[29],{"code":44,"score":66,"supported":56,"title":67,"abstract":69,"tags":70},12,[68],"气候变化",[68],[28],{"code":72,"score":73,"supported":56,"title":74,"abstract":75,"tags":77},"H8",3,[],[76],"sustainable agriculture",[],{"code":79,"score":21,"supported":13,"title":80,"abstract":81,"tags":82},"I1",[],[],[26],[84,85],"三江平原 水稻 精准灌溉","WOFOST 水稻 灌溉调度","三江平原水稻精准灌溉-4159",{"doi":8,"openalex_id":8,"authors":88,"venue":8,"cited_by_count":89,"oa_url":8,"card":90,"direction":94,"ingested_from":96},[],0,{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"提出模拟-优化协同的网格尺度水稻精准灌溉调度框架，实现动态调控。","WOFOST作物模型、Sobol敏感性分析、粒子群优化与多目标优化耦合。","区域产量提升1.5%、灌溉用水减少8.0%，动态调度较传统灌溉节水10.1%。","智慧农业 \u002F 农业物联网","可探索将网格尺度灌溉调度与实时物联网墒情监测、遥感数据同化结合，实现气候变率下的在线滚动优化。","agent","2026-10-04T00:09:10.154416Z",{"total":20,"page":21,"page_size":20,"items":99},[100,183,246,362,450,539],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":8,"source_name":106,"source_url":103,"published_at":107,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":108,"score_detail":109,"sources":113,"tags":115,"primary_topic":119,"secondary_topics":125,"classification":128,"search_phrases":157,"slug":160,"view_count":161,"doi":162,"paper":163,"created_at":182},3607,"Analysis of drought carry-over effect on apple trees using the Leafiness-LiDAR index","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104603","Apple trees ( Malus × domestica Borkh.) are widely grown in Mediterranean regions, where drought-induced irrigation restrictions can impair canopy development and productivity. This study assessed the Leafiness-LiDAR Index (LLI), derived from terrestrial Light Detection and Ranging (LiDAR) point clouds, as a proxy for Leaf Area Index (LAI) and as a non-destructive indicator of canopy response and recovery following water deficit. An ‘Opal®’ apple orchard was monitored over three growing seasons (2023–2025), encompassing one drought year and two recovery years. In 2023, full irrigation (FI; 480 mm) was compared with deficit irrigation (DI; 300 mm; 37.5% less water) under two planting densities (0.5 and 1.0 m tree spacing). Mixed-effects models revealed significant Year × Irrigation interactions for LAI (p = 0.006) and LLI (p \u003C 0.001), but not for yield (p = 0.654), suggesting a partial decoupling between structural and productive recovery. During the drought year, DI reduced LLI by 37% and yield by 47%. Following irrigation restoration, LLI differences persisted, particularly in 2024, indicating lasting structural effects of water deficit. Higher planting density promoted canopy recovery and increased yield. LLI proved to be a valuable, non-destructive estimator of LAI for detecting drought legacy effects and tracking canopy recovery, supporting precision orchard management under Mediterranean water scarcity.","苹果树（Malus × domestica Borkh.）广泛种植于地中海地区，该地区因干旱导致的灌溉限制会损害树冠发育和生产力。本研究评估了基于地面激光雷达（LiDAR）点云衍生的叶量-激光雷达指数（Leafiness-LiDAR Index, LLI）作为叶面积指数（Leaf Area Index, LAI）的替代指标，以及作为水分亏缺后树冠响应与恢复的非破坏性指标的可行性。对一个‘Opal®’苹果园进行了三个生长季（2023—2025年）的监测，涵盖一个干旱年和两个恢复年。2023年，在两种种植密度（株距0.5 m和1.0 m）下，比较了充分灌溉（FI；480 mm）与亏缺灌溉（DI；300 mm；减少37.5%水量）。混合效应模型显示，LAI（p = 0.006）和LLI（p \u003C 0.001）存在显著的年×灌溉交互作用，而产量（p = 0.654）则无此交互作用，表明结构与产量恢复之间存在部分脱耦。在干旱年，DI使LLI降低37%，产量降低47%。灌溉恢复后，LLI差异持续存在，尤其在2024年，表明水分亏缺具有持久的结构性影响。较高的种植密度促进了树冠恢复并提高了产量。LLI被证明是一种有价值的、非破坏性的LAI估算指标，可用于检测干旱遗留效应并追踪树冠恢复，为地中海水资源短缺条件下的精准果园管理提供支持。","Biosystems Engineering","2026-09-26T00:00:00Z",77,{"impact":110,"substance":16,"depth":18,"authority":19,"freshness":111,"relevant":21,"comment":112},15,8,"地中海苹果园三年试验证实LiDAR叶量指数可无损监测干旱遗留效应与冠层恢复，方法新颖、数据扎实，对果园精准灌溉有参考价值。",[114],{"name":106,"url":103},[26,116,117,118,29],"苹果","遥感","果园管理",{"code":120,"slug":121,"name":122,"path":123,"parent_code":124},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[126,127],{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":49,"locked":13,"version":50,"method":51,"evidence":129},{"candidates":130,"agricultural_evidence":56},[131,137,143,148,152],{"code":120,"score":18,"supported":56,"title":132,"abstract":134,"tags":136},[133],"apple",[122,116,135,133],"orchard",[116],{"code":44,"score":19,"supported":56,"title":138,"abstract":140,"tags":142},[139],"drought",[141,139],"干旱",[],{"code":38,"score":12,"supported":56,"title":144,"abstract":145,"tags":147},[],[62,146,39],"水资源",[29],{"code":79,"score":21,"supported":13,"title":149,"abstract":150,"tags":151},[],[],[26],{"code":153,"score":21,"supported":13,"title":154,"abstract":155,"tags":156},"I3",[],[],[117],[158,159],"LiDAR 苹果园 干旱","Leafiness-LiDAR Index 苹果","LiDAR苹果园干旱-3607",4,"10.1016\u002Fj.biosystemseng.2026.104603",{"doi":162,"openalex_id":164,"authors":165,"venue":106,"cited_by_count":89,"oa_url":103,"card":175,"direction":179,"ingested_from":181},"W7214458453",[166,169,172],{"name":167,"orcid":168},"Leire Sandonís-Pozo","https:\u002F\u002Forcid.org\u002F0000-0003-2472-0259",{"name":170,"orcid":171},"José Antonio Martínez-Casasnovas","https:\u002F\u002Forcid.org\u002F0000-0003-1480-3632",{"name":173,"orcid":174},"Miquel Pascual","https:\u002F\u002Forcid.org\u002F0000-0002-3329-4207",{"tldr":176,"method":177,"finding":178,"direction":179,"opportunity":180},"用LiDAR叶量指数追踪苹果园干旱遗留效应与冠层恢复。","三年果园试验，地面LiDAR点云提取LLI，混合效应模型分析。","亏缺灌溉使LLI降37%、产量降47%，复水后结构差异仍持续。","农业遥感与作物表型","可探索LLI与多源遥感融合，构建果园干旱遗留效应早期预警与精准补灌决策模型。","openalex","2026-09-27T23:30:09.907348Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":8,"source_name":189,"source_url":186,"published_at":190,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":191,"score_detail":192,"sources":197,"tags":199,"primary_topic":202,"secondary_topics":203,"classification":205,"search_phrases":227,"slug":230,"view_count":73,"doi":231,"paper":232,"created_at":245},2920,"Breeding rice for optimal maturity across diverse sowing windows under future climate change scenarios in Chongqing area","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1899019","Introduction It is of great significance to optimize rice cultivars for different sowing dates under future climate change for rice sustainable production in Chongqing. Methods In this study, using the APSIM-Rice model and Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (SSP) scenarios, we investigated the changes of yield, water consumption and water use efficiency (WUE) across six sowing dates and three cultivars (early, normal and late-maturing cultivars) under baseline period (1981 – 2010) and future climate 2scenarios (2031-2100, SSP2-4.5 and SSP5-8.5). In this study, the climate model BCC-CSM2-MR was selected due to its reliable simulation of China’s climate and compatibility with the APSIM-Rice model. Results Results showed that rice yield with normal cultivar in the baseline period peaked on March 30, with the value of 6,310 kg ha− −1 , and reached its minimum on March 1, with the value of 4,580 kg ha− −1 . Water consumption during the rice growing period increased with the delayed sowing dates (212 mm on March 1 to 276 mm on April 20). The response trend of water use efficiency (WUE) to different sowing dates was identical to that of yield, with the maximum WUE of 23.30 kg ha− −1 mm −1 achieved when sown on March 30. Under future SSP2-4.5 and SSP5-8.5 scenarios, early sowing (March 1–20) consistently enhanced yield and WUE for normal cultivars (maximum increments were 25.40% and 29.00% for yield and WUE), while late sowing (March 30–April 20) caused severe losses (up to 26.90% and 34.60% for yield and WUE for April 10 under SSP5-8.5 in the 2060s), with water consumption rising across all sowing dates. Early-maturing cultivars reduced yield (10.60%–44.60%) and WUE (7.70%–43.90%) across all sowing dates under future scenarios, whereas late-maturing cultivars synergized with early sowing to boost yield (up to 25.62%) and WUE (up to 32.85%) but exacerbated losses for late sowing, with water consumption increasing significantly (up to 56.30% under SSP5-8.5 in the 2060s). Discussion These findings provide critical scientific support for optimizing sowing date and cultivar combinations, thereby enhancing the climate resilience and sustainability of rice production in Chongqing and similar subtropical monsoon regions. However, in the future, more climate models, extreme climate impacts, and agronomic factors should be considered","引言 优化不同播期下的水稻品种对重庆未来气候变化背景下的水稻可持续生产具有重要意义。方法 本研究利用APSIM-Rice模型和耦合模式比较计划第六阶段（CMIP6）共享社会经济路径（SSP）情景，研究了基准期（1981—2010年）和未来气候情景（2031—2100年，SSP2-4.5和SSP5-8.5）下6个播期和3个品种（早熟、中熟和晚熟品种）的产量、耗水量和水分利用效率（WUE）变化。本研究选择气候模式BCC-CSM2-MR，因其对中国气候的模拟可靠且与APSIM-Rice模型兼容。结果 结果表明，基准期中熟品种水稻产量在3月30日达到峰值，为6 310 kg ha⁻¹，在3月1日降至最低，为4 580 kg ha⁻¹。水稻生育期耗水量随播期推迟而增加（3月1日的212 mm增至4月20日的276 mm）。水分利用效率（WUE）对不同播期的响应趋势与产量一致，3月30日播种时WUE最高，为23.30 kg ha⁻¹ mm⁻¹。在未来SSP2-4.5和SSP5-8.5情景下，早播（3月1—20日）持续提高中熟品种的产量和WUE（产量和WUE的最大增幅分别为25.40%和29.00%），而晚播（3月30日—4月20日）造成严重损失（在2060年代SSP5-8.5情景下，4月10日播种的产量和WUE损失分别高达26.90%和34.60%），且所有播期的耗水量均增加。在未来情景下，早熟品种在所有播期均降低产量（10.60%—44.60%）和WUE（7.70%—43.90%），而晚熟品种与早播协同提高产量（最高25.62%）和WUE（最高32.85%），但加剧了晚播的损失，耗水量显著增加（在2060年代SSP5-8.5情景下最高达56.30%）。讨论 这些发现为优化播期和品种组合提供了关键科学支撑，从而增强重庆及类似亚热带季风区水稻生产的气候韧性和可持续性。然而，未来应考虑更多气候模式、极端气候影响和农艺因素。","Frontiers in Sustainable Food Systems","2026-09-18T00:00:00Z",78,{"impact":193,"substance":16,"depth":18,"authority":194,"freshness":195,"relevant":21,"comment":196},16,13,9,"基于APSIM-Rice与CMIP6情景模拟重庆水稻播期与品种组合，数据扎实、结论对区域气候适应性育种有参考价值，但属细分领域研究，影响力有限。",[198],{"name":189,"url":186},[26,27,68,200,201],"品种选育","播期优化",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[204],{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},{"status":49,"locked":13,"version":50,"method":51,"evidence":206},{"candidates":207,"agricultural_evidence":56},[208,214,218,223],{"code":44,"score":18,"supported":56,"title":209,"abstract":211,"tags":213},[210],"climate change",[68,210,212],"climate resilience",[68],{"code":32,"score":110,"supported":56,"title":215,"abstract":216,"tags":217},[33],[27,33],[27],{"code":219,"score":21,"supported":13,"title":220,"abstract":221,"tags":222},"F1",[],[],[200],{"code":79,"score":21,"supported":13,"title":224,"abstract":225,"tags":226},[],[],[26],[228,229],"APSIM-Rice 水稻 播期","重庆 水稻 品种 气候","APSIM-Rice水稻播期-2920","10.3389\u002Ffsufs.2026.1899019",{"doi":231,"openalex_id":233,"authors":234,"venue":189,"cited_by_count":89,"oa_url":186,"card":239,"direction":243,"ingested_from":181},"W7213555399",[235,237],{"name":236,"orcid":8},"Jianzhao Tang",{"name":238,"orcid":8},"Jianping Zhang",{"tldr":240,"method":241,"finding":242,"direction":243,"opportunity":244},"用APSIM-Rice与CMIP6情景模拟重庆不同播期和品种水稻产量、耗水与水分利用效率。","APSIM-Rice模型结合CMIP6 SSP2-4.5\u002FSSP5-8.5情景及","未来早播配晚熟品种可增产提效，晚播则大幅减产，各播期耗水均上升。","农业人工智能与决策模型","可引入多模型集合、极端气候与氮肥管理等农艺因素，优化播期-品种组合的适应策略。","2026-09-19T23:30:08.567885Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":251,"content":8,"source_name":252,"source_url":249,"published_at":190,"category":11,"cover_url":8,"hotness":12,"is_selected":56,"score":253,"score_detail":254,"sources":258,"tags":260,"primary_topic":264,"secondary_topics":265,"classification":272,"search_phrases":313,"slug":316,"view_count":161,"doi":317,"paper":318,"created_at":361},2912,"Spatial optimization of water-saving irrigation in Chinese rice paddies: Balancing yield, greenhouse gases, and cost using NSGA-II","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104976","CONTEXT Optimizing the spatial allocation of water-saving irrigation (WSI) promotion is critical for balancing rice production, greenhouse gas (GHG) mitigation, and economic costs, yet remains challenging at a national scale due to computational intractability. OBJECTIVE This study aims to develop a spatially explicit optimization framework to identify WSI promotion pathways across China's rice paddies that simultaneously maximize yield gain, maximize GHG reduction, and minimize implementation cost. METHODS We coupled K-means clustering with a multi-objective evolutionary algorithm (NSGA-II). Based on machine-learning predicted yield and GHG emissions for 157,417 flooded irrigation grids, we first clustered these grids into 500 environmentally and agronomically homogeneous groups. We then formulated a continuous optimization problem with cluster-level conversion rates as decision variables, simultaneously maximizing national yield gain, maximizing GHG reduction, and minimizing implementation cost. RESULTS AND CONCLUSIONS The Pareto front comprised 100 non-dominated solutions spanning promotion rates from 61.8% to 80.3%. The optimal solution, selected by normalized scoring, achieved 6.47 Mt. yield gain and 63.5 Mt. CO 2 e GHG reduction at a cost of 10.14 billion CNY, corresponding to a national promotion rate of 80.3%. Cluster-scale conversion rates exhibited significant spatial heterogeneity and positive correlation with cluster size ( r = 0.23), revealing economies of scale in WSI promotion. Compared with a random promotion strategy at 90% adoption, our optimized solution delivered 74% higher yield gain with 9.7 percentage points lower promotion effort while achieving comparable GHG reduction. SIGNIFICANCE Our framework provides a spatially explicit decision-support tool for precision agricultural policy, demonstrating that smart spatial allocation can substantially enhance the efficiency of limited resources in scaling climate-smart agricultural practices.","背景 优化节水灌溉（WSI）推广的空间配置对于平衡水稻生产、温室气体（GHG）减排和经济成本至关重要，但由于计算上的不可处理性，在全国尺度上仍具挑战性。目的 本研究旨在开发一个空间显式优化框架，以识别中国稻田的WSI推广路径，同时最大化产量增益、最大化GHG减排并最小化实施成本。方法 我们将K-means聚类与多目标进化算法（NSGA-II）相结合。基于机器学习预测的157,417个淹水灌溉网格的产量和GHG排放，我们首先将这些网格聚类为500个在环境和农艺上同质的组。然后，我们构建了一个以组级转换率为决策变量的连续优化问题，同时最大化全国产量增益、最大化GHG减排并最小化实施成本。结果与结论 Pareto前沿包含100个非支配解，推广率从61.8%到80.3%不等。通过归一化评分选出的最优解实现了6.47 Mt的产量增益和63.5 Mt CO₂e的GHG减排，成本为101.4亿元人民币，对应全国推广率为80.3%。组尺度转换率表现出显著的空间异质性，并与组规模呈正相关（r = 0.23），揭示了WSI推广中的规模经济。与90%采纳率的随机推广策略相比，我们的优化方案在推广力度低9.7个百分点的情况下实现了高出74%的产量增益，同时实现了相当的GHG减排。意义 我们的框架为精准农业政策提供了一个空间显式的决策支持工具，表明智能空间配置可以显著提高有限资源在推广气候智慧型农业实践中的效率。","Agricultural Systems",89,{"impact":255,"substance":17,"depth":256,"authority":19,"freshness":195,"relevant":21,"comment":257},24,19,"基于NSGA-II的全国稻田节水灌溉空间优化框架，数据规模大、结论具体，对气候智慧型农业政策有决策参考价值。",[259],{"name":252,"url":249},[26,27,261,262,263],"空间优化","节水灌溉","农业减排",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[266,267],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":268,"slug":269,"name":270,"path":271,"parent_code":124},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",{"status":49,"locked":13,"version":50,"method":51,"evidence":273},{"candidates":274,"agricultural_evidence":56},[275,280,284,290,297,302,308],{"code":32,"score":18,"supported":56,"title":276,"abstract":277,"tags":279},[33],[27,278,33],"稻田",[27],{"code":38,"score":110,"supported":56,"title":281,"abstract":282,"tags":283},[39],[62,39],[262],{"code":268,"score":19,"supported":56,"title":285,"abstract":287,"tags":289},[286],"greenhouse",[288,286],"温室",[],{"code":291,"score":20,"supported":56,"title":292,"abstract":293,"tags":296},"J1",[],[294,295],"农业政策","agricultural policy",[],{"code":79,"score":161,"supported":56,"title":298,"abstract":299,"tags":301},[],[300],"精准农业",[26],{"code":303,"score":73,"supported":56,"title":304,"abstract":305,"tags":307},"I2",[],[306],"机器学习",[],{"code":309,"score":21,"supported":13,"title":310,"abstract":311,"tags":312},"H4",[],[],[263],[314,315],"中国稻田 节水灌溉 空间优化","NSGA-II 水稻 温室气体 减排","中国稻田节水灌溉空间优化-2912","10.1016\u002Fj.agsy.2026.104976",{"doi":317,"openalex_id":319,"authors":320,"venue":252,"cited_by_count":89,"oa_url":249,"card":355,"direction":359,"ingested_from":181},"W7213551595",[321,323,326,329,331,333,335,337,340,342,345,348,351,353],{"name":322,"orcid":8},"Qiang Xu",{"name":324,"orcid":325},"Fan Yao","https:\u002F\u002Forcid.org\u002F0000-0002-4393-7296",{"name":327,"orcid":328},"Dan Wei","https:\u002F\u002Forcid.org\u002F0000-0003-4401-567X",{"name":330,"orcid":8},"Hui Gao",{"name":332,"orcid":8},"Min Jiang",{"name":334,"orcid":8},"Wenya Chen",{"name":336,"orcid":8},"Yourui Cao",{"name":338,"orcid":339},"Peng Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-3036-1507",{"name":341,"orcid":8},"A. I. Abdo",{"name":343,"orcid":344},"Liujun Xiao","https:\u002F\u002Forcid.org\u002F0000-0002-1900-1586",{"name":346,"orcid":347},"Hao Liang","https:\u002F\u002Forcid.org\u002F0000-0002-9955-6492",{"name":349,"orcid":350},"Xiaoqing Cui","https:\u002F\u002Forcid.org\u002F0000-0002-1970-5145",{"name":352,"orcid":8},"Xia Liang",{"name":354,"orcid":8},"Huiqing Bai",{"tldr":356,"method":357,"finding":358,"direction":359,"opportunity":360},"构建空间优化框架，为中国稻田节水灌溉推广寻找兼顾产量、温室气体与成本的路径。","耦合K-means聚类与NSGA-II多目标进化算法，基于15.7万网格的机器学","最优方案增产6.47 Mt、减排63.5 Mt CO2e，成本101.4亿元，推广率80.3%，存在","农业绿色发展与碳","可将该空间优化框架扩展到其他气候智慧型农业技术，并耦合农户采纳行为与政策激励。","2026-09-19T23:30:05.508592Z",{"id":363,"title":364,"url":365,"summary":366,"summary_zh":367,"content":8,"source_name":368,"source_url":365,"published_at":369,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":370,"score_detail":371,"sources":374,"tags":376,"primary_topic":380,"secondary_topics":381,"classification":383,"search_phrases":416,"slug":419,"view_count":20,"doi":420,"paper":421,"created_at":449},1898,"Connecting earth observation anomalies to farmer surveys for monitoring impacts of agricultural drought on rainfed rice yields in Nigeria","https:\u002F\u002Fdoi.org\u002F10.5194\u002Feo-1-77-2026","Abstract. Agricultural drought threatens rainfed rice production in Nigeria, where smallholder farmers depend on rainfall and have limited capacity to buffer climate shocks. While meteorological drought indices such as the SPI and the SPEI are widely used in national early warning systems, their ability to capture the impacts of droughts on rainfed rice yields at the smallholder field-level remains uncertain. This study evaluates the added value of earth observation (EO)-derived vegetation and soil moisture anomalies for monitoring and predicting drought impacts on rainfed rice yields in Nigeria. Satellite-based Normalized Difference Vegetation Index anomalies (NDVIA) and Soil Water Index anomalies (SWIA) were derived using a zonal clustering and thresholding approach and combined with farmer survey and yield data collected from 146 rainfed rice farmers across four major rice-growing states between 2019 and 2024. Multivariate regression models were used to assess the relationships between EO indicator anomalies and annual yield changes, and the effects of different zonal clustering and anomaly thresholds on anomaly calculation were evaluated. Results show that SPI and SPEI explain a substantial share of yield variability in some years, particularly when droughts coincide with sensitive phenological stages. However, EO-based anomaly indicators, especially SWIA (maximum improved R2 = 0.25), provide complementary information and significantly improve yield predictions in years when meteorological indices alone perform poorly. The timing of anomalies relative to rice phenology was critical, with droughts during panicle initiation having the largest yield impacts. Integrating EO-based vegetation and soil moisture anomaly indicators with existing meteorological indices can contribute to the monitoring of agricultural droughts and improve the operational relevance of early warning systems for rainfed rice farmers in Nigeria.","摘要：农业干旱威胁着尼日利亚依赖雨养的水稻生产，当地小农户依靠降雨为生，缓冲气候冲击的能力有限。尽管诸如标准化降水指数（SPI）和标准化降水蒸散指数（SPEI）等气象干旱指数被广泛应用于国家早期预警系统，但它们在捕捉小农田间尺度上干旱对雨养水稻产量影响的能力仍不确定。本研究评估了基于地球观测（EO）的植被和土壤湿度异常在监测和预测尼日利亚干旱对雨养水稻产量影响方面的附加价值。采用分区聚类和阈值方法，推导出基于卫星的归一化差异植被指数异常（NDVIA）和土壤水分指数异常（SWIA），并结合2019年至2024年间从四个主要水稻种植州的146位雨养水稻农户收集的农户调查和产量数据。使用多元回归模型评估EO指标异常与年度产量变化之间的关系，并评估不同分区聚类和异常阈值对异常计算的影响。结果表明，在某些年份，SPI和SPEI解释了产量变异的很大一部分，特别是当干旱与敏感物候阶段重合时。然而，基于EO的异常指标，尤其是SWIA（最大改进R²=0.25），提供了补充信息，并在气象指数单独表现不佳的年份显著改善了产量预测。异常相对于水稻物候的时机至关重要，穗分化期发生的干旱对产量影响最大。将基于EO的植被和土壤湿度异常指标与现有气象指数相结合，有助于监测农业干旱，并提高尼日利亚雨养水稻农户早期预警系统的业务相关性。","Earth Observation","2026-09-07T00:00:00Z",74,{"impact":110,"substance":16,"depth":18,"authority":66,"freshness":372,"relevant":21,"comment":373},7,"研究结合遥感与农户调查，提升雨养水稻干旱监测精度，对非洲小农预警有参考价值。",[375],{"name":368,"url":365},[26,377,27,378,379],"农业遥感","干旱监测","尼日利亚",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[382],{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},{"status":49,"locked":13,"version":50,"method":51,"evidence":384},{"candidates":385,"agricultural_evidence":56},[386,390,394,401,408,412],{"code":32,"score":110,"supported":56,"title":387,"abstract":388,"tags":389},[33],[27,33],[27],{"code":44,"score":110,"supported":56,"title":391,"abstract":392,"tags":393},[139],[141,139],[378],{"code":395,"score":20,"supported":56,"title":396,"abstract":397,"tags":400},"G2",[],[398,399],"小农户","smallholder",[],{"code":402,"score":20,"supported":56,"title":403,"abstract":404,"tags":407},"H2",[],[405,406],"土壤","soil",[],{"code":79,"score":21,"supported":13,"title":409,"abstract":410,"tags":411},[],[],[26],{"code":153,"score":21,"supported":13,"title":413,"abstract":414,"tags":415},[],[],[377],[417,418],"农业遥感 尼日利亚 干旱监测 智慧农业","农业遥感 尼日利亚","农业遥感尼日利亚干旱监测智慧农业-1898","10.5194\u002Feo-1-77-2026",{"doi":420,"openalex_id":422,"authors":423,"venue":368,"cited_by_count":89,"oa_url":365,"card":444,"direction":179,"ingested_from":181},"W7162756810",[424,427,429,432,434,437,440,442],{"name":425,"orcid":426},"Nick Gutkin","https:\u002F\u002Forcid.org\u002F0000-0003-4708-3483",{"name":428,"orcid":8},"Chiamaka I. Ehiemere",{"name":430,"orcid":431},"Koen De Vos","https:\u002F\u002Forcid.org\u002F0000-0002-4607-7877",{"name":433,"orcid":8},"Nnamdi Ehiemere",{"name":435,"orcid":436},"Jeroen Degerickx","https:\u002F\u002Forcid.org\u002F0000-0001-9022-9623",{"name":438,"orcid":439},"Sarah Gebruers","https:\u002F\u002Forcid.org\u002F0000-0001-8426-4158",{"name":441,"orcid":8},"Uchechukwu Nwafor",{"name":443,"orcid":8},"Anne Gobin",{"tldr":445,"method":446,"finding":447,"direction":179,"opportunity":448},"评估EO异常指标监测尼日利亚雨养水稻干旱影响，补充气象指数，提高产量预测。","结合NDVI和土壤水分指数异常、聚类阈值法、农户调查与回归模型。","EO指标（尤其SWIA）补充气象指数，显著提高干旱年份产量预测，关键物候期干旱影响最大。","可探索将EO异常与物候模型结合，开发针对小农的干旱预警系统，并验证其他作物和区域。","2026-09-08T23:30:18.761711Z",{"id":451,"title":452,"url":453,"summary":454,"summary_zh":455,"content":8,"source_name":456,"source_url":453,"published_at":457,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":458,"score_detail":459,"sources":462,"tags":464,"primary_topic":468,"secondary_topics":469,"classification":471,"search_phrases":520,"slug":523,"view_count":89,"doi":8,"paper":524,"created_at":538},5312,"Agricultural Production under Climate Change in Afghanistan: Impacts, Challenges and Adaptation Options (A Review)","https:\u002F\u002Fesrj.edu.af\u002Fesrj\u002Farticle\u002Fview\u002F123","Climate change poses serious challenges to agriculture in Afghanistan, a country largely dependent on rainfed farming and highly vulnerable to water scarcity, drought, and temperature extremes. Understanding the impacts of climate variability and identifying effective adaptation strategies are essential to sustain agricultural productivity and ensure food security. This systematic review synthesizes empirical, modeling, and review studies on climate change effects on Afghan agriculture, assesses adaptation measures, and integrates regional experiences from Pakistan, Iran, Tajikistan, and Uzbekistan. A structured literature search was conducted using peer-reviewed articles, institutional reports, and regional studies published between 2000 and 2025. Studies were selected based on relevance to Afghan agro-ecological systems, climate stressors, and adaptation strategies. We extracted data using a structured table format following PRISMA guidelines, combining national and regional evidence. Findings indicate that water scarcity, prolonged drought, rising temperatures, and erratic precipitation are the main stressors affecting major crops, including wheat, barley, chickpea, maize, and cotton. Yield reductions ranged from 12% to 25% across provinces. Adaptation strategies such as Climate-Smart Agriculture (CSA), efficient irrigation (drip and sprinkler), rainwater harvesting, mulching, and drought- or heat-tolerant crop varieties were most effective. Regional evidence confirms the transferability of these measures under similar agro-climatic conditions. In conclusion, climate change significantly threatens agricultural productivity in Afghanistan. Evidence-based adaptation strategies, supported by coordinated policy and capacity-building initiatives, can enhance resilience, protect rural livelihoods, and ensure sustainable food security.","气候变化对阿富汗农业构成严峻挑战，该国农业在很大程度上依赖雨养耕作，极易受到水资源短缺、干旱和极端温度的影响。理解气候变异的影响并确定有效的适应策略，对于维持农业生产力和保障粮食安全至关重要。本系统综述综合了关于气候变化对阿富汗农业影响的实证研究、模拟研究和综述研究，评估了适应措施，并整合了巴基斯坦、伊朗、塔吉克斯坦和乌兹别克斯坦的区域经验。采用结构化文献检索方法，检索了2000年至2025年间发表的同行评审论文、机构报告和区域研究。研究依据其与阿富汗农业生态系统、气候胁迫因子和适应策略的相关性进行筛选。我们遵循PRISMA指南，采用结构化表格格式提取数据，综合了国家层面和区域层面的证据。研究结果表明，水资源短缺、持续干旱、气温上升和降水不稳定是影响主要作物（包括小麦、大麦、鹰嘴豆、玉米和棉花）的主要胁迫因子。各省产量降幅在12%至25%之间。气候智慧型农业（CSA）、高效灌溉（滴灌和喷灌）、雨水收集、覆盖栽培以及耐旱或耐热作物品种等适应策略最为有效。区域证据证实了这些措施在相似农业气候条件下的可推广性。总之，气候变化显著威胁阿富汗的农业生产力。以协调政策和能力建设举措为支撑的循证适应策略能够增强韧性、保护农村生计并确保可持续的粮食安全。","ESRJ","2026-10-16T00:00:00Z",73,{"impact":18,"substance":55,"depth":460,"authority":66,"freshness":20,"relevant":21,"comment":461},17,"系统综述整合2000-2025年证据，量化减产12%-25%并提出可迁移的适应策略，对区域农业气候韧性研究有参考价值，但非国内三农政策或产业事件，时效性一般。",[463],{"name":456,"url":453},[26,465,68,466,467],"粮食安全","适应性策略","水资源短缺",{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},[470],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":49,"locked":13,"version":50,"method":51,"evidence":472},{"candidates":473,"agricultural_evidence":56},[474,478,482,488,495,502,509,514],{"code":44,"score":18,"supported":56,"title":475,"abstract":476,"tags":477},[210],[68,141,210,139],[68],{"code":38,"score":195,"supported":56,"title":479,"abstract":480,"tags":481},[],[62,146,39],[],{"code":483,"score":372,"supported":56,"title":484,"abstract":485,"tags":487},"A8",[],[465,486],"food security",[465],{"code":489,"score":20,"supported":56,"title":490,"abstract":491,"tags":494},"A2",[],[492,493],"小麦","wheat",[],{"code":496,"score":20,"supported":56,"title":497,"abstract":498,"tags":501},"A3",[],[499,500],"玉米","maize",[],{"code":503,"score":20,"supported":56,"title":504,"abstract":505,"tags":508},"A6",[],[506,507],"棉花","cotton",[],{"code":79,"score":161,"supported":56,"title":510,"abstract":511,"tags":513},[],[512],"smart agriculture",[26],{"code":515,"score":73,"supported":56,"title":516,"abstract":517,"tags":519},"H6",[],[518],"农业生态",[],[521,522],"阿富汗 气候变化","气候智慧型农业 灌溉","阿富汗气候变化-5312",{"doi":8,"openalex_id":525,"authors":526,"venue":456,"cited_by_count":89,"oa_url":453,"card":533,"direction":94,"ingested_from":181},"W7222907396",[527,529,531],{"name":528,"orcid":8},"Mohammad Shafiq Faieq",{"name":530,"orcid":8},"Mustafa Abid",{"name":532,"orcid":8},"Abdul khalil Afghani",{"tldr":534,"method":535,"finding":536,"direction":359,"opportunity":537},"系统综述气候变化对阿富汗农业的影响、挑战与适应策略。","按PRISMA检索2000-2025年文献，整合阿富汗及周边四国区域证据。","水资源短缺与干旱致主要作物减产12%-25%，气候智慧型农业与节水灌溉最有效。","可研究气候智慧型农业措施在阿富汗及周边干旱区的本地化适配与推广机制。","2026-10-19T23:30:11.285828Z",{"id":540,"title":541,"url":542,"summary":543,"summary_zh":544,"content":8,"source_name":545,"source_url":542,"published_at":546,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":547,"score_detail":548,"sources":550,"tags":552,"primary_topic":556,"secondary_topics":557,"classification":565,"search_phrases":609,"slug":612,"view_count":89,"doi":613,"paper":614,"created_at":638},4989,"Advances in micro-irrigation and supplementary irrigation in millets of dryland ecosystems","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16312","Dryland ecosystems cover approximately 41 % of the Earth's terrestrial surface and support nearly two billion people. This review synthesises recent advances in micro-irrigation and supplementary irrigation strategies for enhancing millet productivity and resilience in dryland ecosystems. Drylands are characterised by low and erratic rainfall, high evapotranspiration, poor soil fertility and frequent drought stress, making agricultural production vulnerable to climate variability and water scarcity. Millets are drought-tolerant, climate-resilient cereals cultivated in dryland agroecosystems. Despite their resilience, millet productivity remains constrained by increasing rainfall variability, prolonged intra-seasonal dry spells and rising atmospheric aridity associated with climate change. Evidence from semi-arid and arid regions indicates that micro-irrigation systems, including drip, subsurface drip and micro-sprinkler irrigation, significantly improve yield and biomass accumulation compared to conventional surface irrigation methods. These irrigation systems also enhance water use efficiency (WUE) and nutrient use efficiency (NUE), making them highly suitable for millet production in dryland ecosystems. The integration of fertigation further improves nutrient synchronisation with crop demand, reduces leaching losses and supports soil fertility. The review also highlights optimised irrigation scheduling based on evapotranspiration, soil moisture thresholds, deficit irrigation principles and sensor-based automation. Smart irrigation technologies incorporating soil moisture sensors and Internet of Things (IoT)-based control systems show strong potential to enhance precision water management and reduce water consumption by up to 30–35 %. Their adoption, supported by technological innovation, policy frameworks and location-specific research, will promote sustainable water management and resource-use efficiency in millet-based dryland ecosystems. Future research should focus on optimising irrigation scheduling, smart technologies and cost-effective solutions for water-limited environments.","旱地生态系统约占地球陆地表面的41%，支撑着近20亿人口。本文综述了微灌与补充灌溉策略在提升旱地生态系统中小米生产力与恢复力方面的最新进展。旱地降水少且不稳定、蒸散量大、土壤肥力低、干旱胁迫频繁，使农业生产易受气候变率和缺水的影响。小米是种植于旱地农业生态系统中的耐旱、气候适应型谷物。尽管具有恢复力，小米生产力仍受降雨变率增大、季节性持续干旱期延长以及气候变化相关的大气干燥度上升的制约。来自半干旱和干旱地区的证据表明，与传统地面灌溉方式相比，微灌系统（包括滴灌、地下滴灌和微喷灌）显著提高了产量和生物量积累。这些灌溉系统还提高了水分利用效率（WUE）和养分利用效率（NUE），使其非常适合旱地生态系统中的小米生产。水肥一体化（fertigation）的整合进一步改善了养分与作物需求的同步性，减少了淋失损失，并有助于维持土壤肥力。本文还重点介绍了基于蒸散量、土壤水分阈值、亏缺灌溉原则和基于传感器的自动化的优化灌溉制度。结合土壤水分传感器和基于物联网（IoT）控制系统的智能灌溉技术，在提升精准水分管理和减少高达30%–35%的用水量方面展现出巨大潜力。在技术创新、政策框架和因地制宜研究的支持下，这些技术的采用将促进以小米为基础的旱地生态系统中的可持续水分管理和资源利用效率。未来研究应聚焦于优化灌溉制度、智能技术以及面向水资源受限环境的成本效益解决方案。","Plant Science Today","2026-10-14T00:00:00Z",76,{"impact":18,"substance":55,"depth":460,"authority":194,"freshness":111,"relevant":21,"comment":549},"系统综述微灌与补充灌溉提升旱地谷子产量与水肥效率，含物联网智能灌溉节水30-35%等实质结论，对旱作智慧农业有参考价值。",[551],{"name":545,"url":542},[26,553,554,262,555],"农业物联网","旱作农业","谷子高粱",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[558,564],{"code":559,"slug":560,"name":561,"path":562,"parent_code":563},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":44,"slug":45,"name":46,"path":47,"parent_code":42},{"status":49,"locked":13,"version":50,"method":51,"evidence":566},{"candidates":567,"agricultural_evidence":56},[568,573,580,584,591,595,601,605],{"code":38,"score":256,"supported":56,"title":569,"abstract":570,"tags":572},[39],[62,146,39,571],"evapotranspiration",[554,262],{"code":559,"score":12,"supported":56,"title":574,"abstract":575,"tags":579},[],[576,577,578],"物联网","internet of things","IoT",[553],{"code":44,"score":195,"supported":56,"title":581,"abstract":582,"tags":583},[],[68,28,141,210,139],[],{"code":585,"score":20,"supported":56,"title":586,"abstract":587,"tags":590},"F3",[],[588,589],"水肥一体化","fertigation",[],{"code":402,"score":20,"supported":56,"title":592,"abstract":593,"tags":594},[],[405,406],[],{"code":596,"score":73,"supported":56,"title":597,"abstract":598,"tags":600},"A5",[],[599],"millet",[],{"code":515,"score":73,"supported":56,"title":602,"abstract":603,"tags":604},[],[518],[],{"code":79,"score":21,"supported":13,"title":606,"abstract":607,"tags":608},[],[],[26],[610,611],"微灌 补充灌溉 旱地 谷子","智慧灌溉 水分利用效率 谷子","微灌补充灌溉旱地谷子-4989","10.14719\u002Fpst.16312",{"doi":613,"openalex_id":615,"authors":616,"venue":545,"cited_by_count":89,"oa_url":632,"card":633,"direction":94,"ingested_from":181},"W7221796668",[617,620,623,626,629],{"name":618,"orcid":619},"M Megha","https:\u002F\u002Forcid.org\u002F0009-0006-7303-6946",{"name":621,"orcid":622},"N Vadivel","https:\u002F\u002Forcid.org\u002F0000-0002-5301-5324",{"name":624,"orcid":625},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":627,"orcid":628},"P. Ayyadurai","https:\u002F\u002Forcid.org\u002F0000-0003-1889-6047",{"name":630,"orcid":631},"PD Rathinasamy","https:\u002F\u002Forcid.org\u002F0000-0001-8793-3513","https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F16312\u002F16748",{"tldr":634,"method":635,"finding":636,"direction":94,"opportunity":637},"综述旱地微灌与补充灌溉提升小米产量与水分利用效率的研究进展。","综述滴灌、地下滴灌、微喷及水肥一体化、传感器与IoT智能灌溉技术。","微灌显著提高小米产量、WUE和NUE，智能灌溉可节水30–35%。","可研究低成本IoT灌溉决策模型在小米旱地的本地化适配与长期效应。","2026-10-15T23:30:08.971713Z"]