[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2527":3,"related-2527":126},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":90,"slug":93,"view_count":94,"doi":95,"paper":96,"created_at":125},2527,"Extracting Summer-Harvested Crops in the Baojixia Irrigation District Using CycleGAN and Transfer Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183155","Remote sensing-based mapping of crop planting structures in irrigation districts plays a vital role in forecasting regional production and optimizing water resource allocation. However, optical satellite imagery is limited by insufficient spatial resolution when applied to fragmented farmland landscapes, while unmanned aerial vehicle (UAV) imagery is limited by spatial coverage and high data processing costs. To address these bottlenecks, this study proposed a cross-scale collaborative extraction framework utilizing the CycleGAN network and transfer learning to map summer harvest crops (winter wheat and rapeseed) in the Baojixia Irrigation District for the year 2023. First, multiple semantic segmentation models—including U-Net, DeepLabv3+, SegFormer, and HRNet—were evaluated on a joint satellite–UAV dataset, with U-Net selected as the optimal backbone. Next, CycleGAN was introduced to perform style translation from the UAV domain to the satellite domain. This step generated high-fidelity, satellite-like images that preserve UAV-derived high-resolution spatial details, which were subsequently used to pre-train the U-Net backbone, significantly reducing the labor of manual annotation. Finally, the model was fine-tuned with real satellite images to achieve precise crop extraction. Results indicated that this framework accelerates model convergence and improves segmentation accuracy. The proposed method achieved an mIoU of 85.09%, an mPA (Recall) of 91.63%, a Precision of 91.97%, an Accuracy of 93.57%, and an F1-Score of 91.80%, outperforming the baseline U-Net model by 2.98%, 2.00%, 1.66%, 1.52%, and 1.83%, respectively. By successfully transferring high-resolution prior knowledge into the satellite feature space, this study provides a cost-effective and highly accurate solution for crop identification in complex agricultural landscapes, breaking the spatial limitations of UAV remote sensing.","基于遥感的灌区作物种植结构制图对于区域产量预测与水资源优化配置具有重要作用。然而，光学卫星影像在应用于破碎化农田景观时受限于空间分辨率不足，而无人机（UAV）影像则受限于空间覆盖范围和数据获取成本高。为解决这些瓶颈问题，本研究提出了一种利用CycleGAN网络和迁移学习的跨尺度协同提取框架，用于绘制2023年宝鸡峡灌区夏收作物（冬小麦和油菜）分布图。首先，在卫星—无人机联合数据集上评估了多种语义分割模型——包括U-Net、DeepLabv3+、SegFormer和HRNet——并选择U-Net作为最优主干网络。其次，引入CycleGAN进行从无人机域到卫星域的风格转换。该步骤生成了高保真、类卫星影像，同时保留了无人机来源的高分辨率空间细节，随后用于预训练U-Net主干网络，显著减少了人工标注的工作量。最后，利用真实卫星影像对模型进行微调，以实现精确的作物提取。结果表明，该框架加速了模型收敛并提高了分割精度。所提方法达到了85.09%的mIoU、91.63%的mPA（召回率）、91.97%的精确率、93.57%的总体精度和91.80%的F1分数，分别优于基线U-Net模型2.98%、2.00%、1.66%、1.52%和1.83%。通过成功将高分辨率先验知识迁移至卫星特征空间，本研究为复杂农业景观中的作物识别提供了一种经济高效且高精度的解决方案，突破了无人机遥感的空间局限性。",null,"Remote Sensing","2026-09-14T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,8,1,"提出CycleGAN跨尺度协同框架，将无人机高分辨率先验迁移至卫星影像，实现灌溉区夏收作物高精度提取，方法新颖、指标扎实，对农业遥感监测有实用价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","遥感","作物识别","迁移学习","灌溉区",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,59,65,72,80,85],{"code":33,"score":52,"supported":53,"title":54,"abstract":55,"tags":58},17,true,[34],[56,34,57],"水资源","water resource",[],{"code":40,"score":60,"supported":53,"title":61,"abstract":62,"tags":64},9,[],[42,63,41],"winter wheat",[],{"code":66,"score":67,"supported":53,"title":68,"abstract":69,"tags":71},"I3",7,[],[28,70],"remote sensing",[28],{"code":73,"score":74,"supported":53,"title":75,"abstract":76,"tags":79},"A4",6,[],[77,78],"油菜","rapeseed",[],{"code":81,"score":22,"supported":14,"title":82,"abstract":83,"tags":84},"I1",[],[],[27],{"code":86,"score":22,"supported":14,"title":87,"abstract":88,"tags":89},"I2",[],[],[30],[91,92],"作物识别 智慧农业 迁移学习 灌溉区","作物识别 智慧农业","作物识别智慧农业迁移学习灌溉区-2527",4,"10.3390\u002Frs18183155",{"doi":95,"openalex_id":97,"authors":98,"venue":10,"cited_by_count":117,"oa_url":6,"card":118,"direction":122,"ingested_from":124},"W7212518427",[99,101,103,105,107,109,112,114],{"name":100,"orcid":9},"Zili Chen",{"name":102,"orcid":9},"Zhilong Gao",{"name":104,"orcid":9},"Zefeng Jia",{"name":106,"orcid":9},"Pengjie Pan",{"name":108,"orcid":9},"Wen Gao",{"name":110,"orcid":111},"Jun Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-6972-6828",{"name":113,"orcid":9},"Zijie Niu",{"name":115,"orcid":116},"Dongyan Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3509-7482",0,{"tldr":119,"method":120,"finding":121,"direction":122,"opportunity":123},"用CycleGAN和迁移学习融合无人机与卫星影像，提取宝鸡峡灌区夏收作物。","CycleGAN风格迁移+U-Net迁移学习，联合卫星-无人机数据集。","框架提升分割精度，mIoU达85.09%，优于基线U-Net。","农业遥感与作物表型","可探索跨尺度迁移学习在更多作物和区域的应用，降低标注成本。","openalex","2026-09-15T23:30:20.433643Z",{"total":74,"page":22,"page_size":74,"items":127},[128,227,316,370,449,515],{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":9,"source_name":134,"source_url":131,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":141,"tags":143,"primary_topic":147,"secondary_topics":148,"classification":150,"search_phrases":201,"slug":204,"view_count":117,"doi":205,"paper":206,"created_at":226},4242,"Integrated Irrigation and Disease Management Strategies for Sustainable Wheat Production and Improved Plant Health and Grain Yield","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.774","Wheat production was expected to provide reliable grain quality and farmer income while using less water and facing climatic variability, soil degradation, disease development, and rising energy costs. This literature-based paper critically synthesized verified research on irrigation, plant disease management, irrigation–disease interactions, sustainability, digital agriculture, and the Pakistan\u002Fdeveloping-economy context. It considered irrigation and plant health as related management areas rather than completely separate input decisions. The available evidence did not identify a universal irrigation technology or deficit percentage. Instead, it supported a context-specific approach that considered stored soil water and rainfall, protected important growth stages, managed canopy humidity and nitrogen, used resistant cultivars and clean seed, and applied surveillance, forecasting, and fungicides when justified. Global and China-focused studies reported that moderate deficit or optimized water-saving irrigation could improve water productivity, whereas severe water deficit commonly reduced yield. Jointing\u002Fstem elongation was repeatedly identified as an important stage for avoiding water stress, while anthesis and early grain filling were also important when water availability and disease risk required attention. Disease-management evidence emphasized genetic resistance, sanitation, crop rotation, scouting, and forecasting before chemical control. Fusarium head blight required particular attention to weather conditions around anthesis, residue inoculum, cultivar resistance, fungicide timing, and deoxynivalenol risk. Irrigation increased yield without increasing disease in some environments, although dense nitrogen-rich canopies, overhead wetting, and saturated soils could favor foliar diseases and waterlogging. Digital tools were considered decision-support infrastructure that linked observations, analysis, and management actions. Wheat-specific evidence was relatively stronger for soil-moisture scheduling and weather-based stripe-rust forecasting, whereas many claims involving artificial intelligence, UAVs, and variable-rate irrigation remained predictive or transferable rather than directly supported by causal field evidence. Evidence from Pakistan indicated dependence on groundwater, salinity and waterlogging problems, uneven disease surveillance, and barriers to technology adoption. Differences among official statistics also indicated the need for careful reconciliation rather than simple averaging. The paper proposed an integrated framework involving diagnosis, planning, stage-aware irrigation, plant-health protection, evaluation, and seasonal feedback. It also identified the need for multi-location and multi-season studies that evaluated water use, disease, grain quality, energy use, greenhouse-gas emissions, profitability, and equity together.","小麦生产被期望在提供可靠籽粒品质和农民收入的同时，减少用水并应对气候变异、土壤退化、病害发展和能源成本上升。这篇基于文献的论文批判性地综合了关于灌溉、植物病害管理、灌溉–病害相互作用、可持续性、数字农业以及巴基斯坦\u002F发展中经济体背景的已验证研究。它将灌溉和植物健康视为相关的管理领域，而非完全独立的投入决策。现有证据并未确定一种通用的灌溉技术或亏缺百分比。相反，它支持一种因地制宜的方法，即考虑土壤储水和降雨、保护重要生育阶段、管理冠层湿度和氮素、使用抗性品种和清洁种子，并在有依据时应用监测、预报和杀菌剂。全球和中国的研究报告称，适度亏缺或优化节水灌溉可提高水分生产力，而严重水分亏缺通常降低产量。拔节\u002F茎伸长阶段被反复确定为避免水分胁迫的重要阶段，而在水分可用性和病害风险需要关注时，开花期和灌浆早期也很重要。病害管理证据强调在化学防治之前采用遗传抗性、卫生措施、轮作、田间巡查和预报。赤霉病需要特别关注开花期前后的天气条件、残体接种体、品种抗性、杀菌剂施用时机和脱氧雪腐镰刀菌烯醇风险。在某些环境中，灌溉提高了产量而未增加病害，尽管密集的富氮冠层、 overhead wetting 和饱和土壤可能有利于叶部病害和渍害。数字工具被视为连接观测、分析和管理行动的决策支持基础设施。小麦-specific 证据在土壤水分调度和基于天气的条锈病预报方面相对更强，而许多涉及人工智能、无人机和变量灌溉的说法仍属于预测性或可迁移性，而非直接得到因果田间证据的支持。来自巴基斯坦的证据表明对地下水的依赖、盐渍化和渍害问题、病害监测不均衡以及技术采用的障碍。官方统计数据之间的差异也表明需要谨慎。","Kashmir Journal of Academic Research and Development","2026-10-04T00:00:00Z",66,{"impact":138,"substance":139,"depth":52,"authority":21,"freshness":60,"relevant":22,"comment":140},12,20,"综述性论文，系统整合灌溉与病害协同管理及数字农业证据，对小麦节水抗病生产有参考价值，但非原创数据、层级偏学术。",[142],{"name":134,"url":131},[144,27,42,145,146],"数字农业","病害防控","灌溉管理",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[149],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":151},{"candidates":152,"agricultural_evidence":53},[153,158,163,168,175,182,189,196],{"code":40,"score":154,"supported":53,"title":155,"abstract":156,"tags":157},15,[41],[42,41],[42],{"code":33,"score":154,"supported":53,"title":159,"abstract":160,"tags":162},[34],[161,34],"灌溉",[146],{"code":81,"score":21,"supported":53,"title":164,"abstract":165,"tags":167},[],[144,166],"digital agriculture",[144,27],{"code":169,"score":74,"supported":53,"title":170,"abstract":171,"tags":174},"A7",[],[172,173],"轮作","crop rotation",[],{"code":176,"score":74,"supported":53,"title":177,"abstract":178,"tags":181},"H2",[],[179,180],"土壤","soil",[],{"code":183,"score":74,"supported":53,"title":184,"abstract":185,"tags":188},"H7",[],[186,187],"渍害","waterlogging",[],{"code":190,"score":191,"supported":53,"title":192,"abstract":193,"tags":195},"B7",3,[],[194],"greenhouse",[],{"code":86,"score":191,"supported":53,"title":197,"abstract":198,"tags":200},[],[199],"artificial intelligence",[],[202,203],"小麦 灌溉 病害 综合管理","小麦 节水灌溉 赤霉病","小麦灌溉病害综合管理-4242","10.71317\u002Fkjard.2.10.2026.774",{"doi":205,"openalex_id":207,"authors":208,"venue":134,"cited_by_count":117,"oa_url":131,"card":219,"direction":225,"ingested_from":124},"W7219840451",[209,211,213,215,217],{"name":210,"orcid":9},"Amir Khan Jamali",{"name":212,"orcid":9},"Zahir Muhammad Larik",{"name":214,"orcid":9},"Videsh Kumar",{"name":216,"orcid":9},"Um E Habiba",{"name":218,"orcid":9},"Amanullah Maree",{"tldr":220,"method":221,"finding":222,"direction":223,"opportunity":224},"综述小麦灌溉与病害管理整合策略，强调因地制宜而非通用方案。","文献综述，综合灌溉、病害、数字农业及巴基斯坦等发展经济体证据。","适度亏缺灌溉可提高水分生产力，但严重亏缺减产；病害管理需结合抗性、预测与适时用药。","智慧农业 \u002F 农业物联网","AI、无人机和变量灌溉在小麦中的因果田间证据不足，可开展整合灌溉-病害的验证研究。","数字乡村与农业信息化","2026-10-05T23:30:30.962770Z",{"id":228,"title":229,"url":230,"summary":231,"summary_zh":232,"content":9,"source_name":233,"source_url":230,"published_at":234,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":235,"score_detail":236,"sources":239,"tags":241,"primary_topic":244,"secondary_topics":245,"classification":246,"search_phrases":279,"slug":282,"view_count":271,"doi":283,"paper":284,"created_at":315},4602,"Phenology-driven multisensor unsupervised crop mapping in data-scarce irrigated agriculture: a case of Adea District in Ethiopia","https:\u002F\u002Fdoi.org\u002F10.2166\u002Fwpt.2026.492","ABSTRACT Accurate irrigation planning, water allocation, and agricultural productivity assessment are frequently constrained in data-scare regions where reliable ground-based crop information and spatial coverage data are limited. This study addresses these gaps using a phenology-driven, multisensor unsupervised crop-mapping framework implemented within Google Earth Engine (GEE). The approach integrates monthly normalized difference vegetation index (NDVI) profiles from Sentinel-2, Sentinel-1VV backscatter, and digital elevation model–derived terrain slope into unsupervised K-means clustering algorithm. Adea District, Ethiopia, served as a demonstration site to evaluate and interpret the phenological signatures of key irrigated crops. The identified crop types were mapped to estimate spatial coverage and compared with the ground truth observations of irrigated wheat and tomato fields. The approach successfully distinguished irrigated wheat and tomato fields with an overall classification accuracy of 91%, a Kappa coefficient of 0.82, and estimated cultivated areas of 666.93 and 812.35 ha, respectively. Combining phenological profiles with multisensor satellite observations significantly enhance unsupervised classification performance in heterogeneous, smallholder-dominated agricultural landscapes. The cloud-based framework offers a computationally scalable and cost-effective solution for crop mapping, and water productivity monitoring in data-scarce agricultural regions.","在数据稀缺地区，可靠的基于地面作物信息和空间覆盖数据往往有限，精准灌溉规划、水资源分配和农业生产力评估因此常受制约。本研究针对这些不足，采用了一种由物候驱动、多传感器协同的无监督作物制图框架，并在Google Earth Engine（GEE）平台上实现。该方法将Sentinel-2的月度归一化植被指数（NDVI）时序、Sentinel-1 VV后向散射以及数字高程模型衍生的地形坡度整合到无监督K均值聚类算法中。以埃塞俄比亚Adea区为示范区，评估和解读主要灌溉作物的物候特征。对识别出的作物类型进行制图以估算空间覆盖范围，并与灌溉小麦和番茄田的地面实测数据进行对比。该方法成功区分了灌溉小麦和番茄田，总体分类精度达91%，Kappa系数为0.82，估算种植面积分别为666.93公顷和812.35公顷。将物候特征与多传感器卫星观测相结合，可显著提升异质性小农主导农业景观中的无监督分类性能。该基于云的框架为数据稀缺农业地区的作物制图和水分生产力监测提供了一种计算可扩展且成本效益高的解决方案。","Water Practice & Technology","2026-10-09T00:00:00Z",67,{"impact":21,"substance":139,"depth":52,"authority":237,"freshness":60,"relevant":22,"comment":238},13,"方法新颖、精度可靠，但属区域案例研究，产业影响有限，可作为技术参考入选。",[240],{"name":233,"url":230},[27,28,29,242,243],"Google Earth Engine","灌溉农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":247},{"candidates":248,"agricultural_evidence":53},[249,253,257,264,270,275],{"code":33,"score":13,"supported":53,"title":250,"abstract":251,"tags":252},[],[161,56,34],[243],{"code":40,"score":74,"supported":53,"title":254,"abstract":255,"tags":256},[],[42,41],[],{"code":258,"score":74,"supported":53,"title":259,"abstract":260,"tags":263},"B1",[],[261,262],"番茄","tomato",[],{"code":265,"score":191,"supported":53,"title":266,"abstract":267,"tags":269},"G2",[],[268],"smallholder",[],{"code":66,"score":271,"supported":14,"title":272,"abstract":273,"tags":274},2,[],[],[242,28],{"code":81,"score":22,"supported":14,"title":276,"abstract":277,"tags":278},[],[],[27],[280,281],"埃塞俄比亚 Adea 灌溉作物 遥感","Sentinel-2 NDVI 作物分类","埃塞俄比亚Adea灌溉作物遥感-4602","10.2166\u002Fwpt.2026.492",{"doi":283,"openalex_id":285,"authors":286,"venue":233,"cited_by_count":117,"oa_url":230,"card":310,"direction":225,"ingested_from":124},"W7221136167",[287,290,293,296,299,302,305,307],{"name":288,"orcid":289},"Belete Berhanu Kidanewold","https:\u002F\u002Forcid.org\u002F0000-0002-9876-8549",{"name":291,"orcid":292},"Jemal Mohammed Hassen","https:\u002F\u002Forcid.org\u002F0000-0002-7417-173X",{"name":294,"orcid":295},"Rahel Sintayehu Tessema","https:\u002F\u002Forcid.org\u002F0009-0006-2798-1752",{"name":297,"orcid":298},"Saba Kidane","https:\u002F\u002Forcid.org\u002F0009-0002-7403-9939",{"name":300,"orcid":301},"Mulugeta Melese","https:\u002F\u002Forcid.org\u002F0009-0008-3539-910X",{"name":303,"orcid":304},"Tadesse Shimels","https:\u002F\u002Forcid.org\u002F0009-0009-3351-5952",{"name":306,"orcid":9},"Mussie Alemayehu",{"name":308,"orcid":309},"Daneal Fekersillassie","https:\u002F\u002Forcid.org\u002F0009-0006-6194-0903",{"tldr":311,"method":312,"finding":313,"direction":122,"opportunity":314},"基于物候与多传感器数据，在埃塞俄比亚缺数据灌区实现无监督作物制图。","GEE平台融合Sentinel-2 NDVI、Sentinel-1 VV及DEM","成功区分灌溉小麦和番茄，总体精度91%，Kappa 0.82，面积分别为666.93和812.35公","可探索物候特征与多源遥感在缺数据小农灌区的迁移学习及作物水生产力监测。","2026-10-10T23:30:36.165502Z",{"id":317,"title":318,"url":319,"summary":320,"summary_zh":9,"content":9,"source_name":321,"source_url":9,"published_at":322,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":323,"score_detail":324,"sources":326,"tags":328,"primary_topic":331,"secondary_topics":332,"classification":338,"search_phrases":357,"slug":360,"view_count":191,"doi":9,"paper":361,"created_at":369},2996,"基于高光谱遥感和跨区域迁移学习的冬小麦叶绿素含量评估","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7686128266574430720","甘肃农业大学李怡悦等与中国农业科学院作物科学研究所肖永贵、孟亚雄研究员团队合作，针对高光谱遥感跨区域叶绿素评估中因\"域偏移\"导致的模型泛化性能下降问题，提出一种稳健自适应迁移学习框架（RATL），通过自适应特征选择、特征权重调整与域适应训练3模块协同实现跨区域知识有效迁移。基于2023—2024年冬小麦灌浆后期新乡与周口两地共1491份冠层高光谱与叶片SPAD数据，设置4种场景比较RATL与直接迁移、迁移成分分析和相关对齐之间的精度。","智慧农业(中英文)2026,8(4):47-59","2026-09-16T13:43:00Z",76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":74,"relevant":22,"comment":325},"方法新颖、数据扎实的跨区域叶绿素遥感评估研究，对智慧农业遥感应用有参考价值。",[327],{"name":321,"url":319},[27,28,329,30,330],"冬小麦","叶绿素监测",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[333],{"code":66,"slug":334,"name":335,"path":336,"parent_code":337},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":339},{"candidates":340,"agricultural_evidence":53},[341,345,349,353],{"code":40,"score":138,"supported":53,"title":342,"abstract":343,"tags":344},[42],[42],[329],{"code":66,"score":138,"supported":53,"title":346,"abstract":347,"tags":348},[28],[28],[28],{"code":81,"score":22,"supported":14,"title":350,"abstract":351,"tags":352},[],[],[27],{"code":86,"score":22,"supported":14,"title":354,"abstract":355,"tags":356},[],[],[30],[358,359],"冬小麦 叶绿素 高光谱遥感","跨区域迁移学习 冬小麦","冬小麦叶绿素高光谱遥感-2996",{"doi":9,"openalex_id":9,"authors":362,"venue":9,"cited_by_count":117,"oa_url":9,"card":363,"direction":122,"ingested_from":368},[],{"tldr":364,"method":365,"finding":366,"direction":122,"opportunity":367},"提出RATL迁移学习框架，解决高光谱遥感跨区域冬小麦叶绿素评估的域偏移问题。","基于新乡与周口1491份冠层高光谱与SPAD数据，对比RATL与直接迁移、TCA","RATL通过自适应特征选择、权重调整与域适应训练，有效提升跨区域叶绿素评估泛化精度。","可探索多作物、多生育期及不同传感器间的迁移学习，构建通用跨域叶绿素评估模型。","agent","2026-09-20T00:03:07.685193Z",{"id":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":9,"source_name":376,"source_url":373,"published_at":377,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":378,"score_detail":379,"sources":381,"tags":383,"primary_topic":388,"secondary_topics":389,"classification":391,"search_phrases":431,"slug":434,"view_count":191,"doi":435,"paper":436,"created_at":448},2034,"Multi-objective water–energy–food–environment Nexus optimization for wheat production in the North China plain","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1897603","Intensive wheat production in the North China Plain is central to regional food security, but it also places strong pressure on groundwater resources, fertilizer demand, energy use, and greenhouse gas emissions. Integrated water-energy-food-environment (WEFE) planning is therefore needed to identify strategies that can reduce environmental burdens for a fixed wheat-production functional unit. This study develops a farm-scale WEFE nexus optimization model for 1 ha of wheat production and compares five scenarios: baseline, circular economy, decarbonization, water scarcity, and integrated sustainability. The model evaluates alternative water-supply, nutrient-recovery, and electricity-generation pathways using cost, life-cycle greenhouse gas emissions, circularity, and nexus performance indicators. Under the baseline compromise solution, total cost and greenhouse gas emissions were 549.67 USD ha −1 and 1537.72 kg CO₂-eq ha −1 , respectively, with a renewable-energy share of 60.46% and irrigation circularity of 12.73%. The integrated scenario achieved the strongest overall performance, reducing greenhouse gas emissions by about 49% to 784.79 kg CO₂-eq ha −1 , increasing fertilizer recovery to 40%, and raising the renewable-energy share to 92.70% under the fixed wheat-production requirement. These results show that coordinated water reuse, nutrient recovery, and electricity decarbonization provide greater sustainability gains than single-sector interventions. The study contributes a nexus-based optimization framework for evaluating circular and low-carbon agricultural transitions in water-stressed cereal production systems.","华北平原的集约化小麦生产对区域粮食安全至关重要，但也对地下水资源、化肥需求、能源利用和温室气体排放构成巨大压力。因此，需要开展水-能-粮-环境（WEFE）综合规划，以确定在固定小麦生产功能单位下能够降低环境负担的策略。本研究构建了一个针对1公顷小麦生产的农场尺度WEFE关联优化模型，并比较了五种情景：基准情景、循环经济情景、脱碳情景、水资源短缺情景和综合可持续情景。该模型采用成本、生命周期温室气体排放、循环性和关联性能指标来评估替代性供水、养分回收和发电路径。在基准折中方案下，总成本和温室气体排放分别为549.67美元\u002F公顷和1537.72千克CO₂当量\u002F公顷，可再生能源占比为60.46%，灌溉循环率为12.73%。综合情景实现了最佳整体表现，在固定小麦生产需求下，温室气体排放减少约49%至784.79千克CO₂当量\u002F公顷，肥料回收率提高至40%，可再生能源占比提升至92.70%。这些结果表明，协同推进水的回用、养分回收和电力脱碳比单一部门干预能带来更大的可持续性收益。本研究为评估缺水谷物生产系统中的循环和低碳农业转型提供了一个基于关联的优化框架。","Frontiers in Sustainable Food Systems","2026-09-09T00:00:00Z",79,{"impact":19,"substance":18,"depth":19,"authority":237,"freshness":21,"relevant":22,"comment":380},"华北平原小麦水-能-粮-环境耦合优化研究，量化了减碳49%、肥料回收40%等结论，方法新颖数据扎实，对缺水区小麦绿色生产有参考价值。",[382],{"name":376,"url":373},[42,384,385,386,387],"水肥一体化","循环农业","绿色低碳","华北平原",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[390],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":392},{"candidates":393,"agricultural_evidence":53},[394,398,402,409,416,421,426],{"code":40,"score":154,"supported":53,"title":395,"abstract":396,"tags":397},[41],[42,41],[42],{"code":33,"score":60,"supported":53,"title":399,"abstract":400,"tags":401},[],[161,56,34],[],{"code":403,"score":67,"supported":53,"title":404,"abstract":405,"tags":408},"F3",[],[406,407],"肥料","fertilizer",[384],{"code":410,"score":74,"supported":53,"title":411,"abstract":412,"tags":415},"A8",[],[413,414],"粮食安全","food security",[],{"code":190,"score":74,"supported":53,"title":417,"abstract":418,"tags":420},[],[419,194],"温室",[],{"code":422,"score":22,"supported":14,"title":423,"abstract":424,"tags":425},"H4",[],[],[386],{"code":427,"score":22,"supported":14,"title":428,"abstract":429,"tags":430},"H8",[],[],[385],[432,433],"水肥一体化 华北平原 循环农业 绿色低碳","水肥一体化 华北平原","水肥一体化华北平原循环农业绿色低碳-2034","10.3389\u002Ffsufs.2026.1897603",{"doi":435,"openalex_id":437,"authors":438,"venue":376,"cited_by_count":117,"oa_url":441,"card":442,"direction":446,"ingested_from":124},"W7212082572",[439],{"name":440,"orcid":9},"Gaodi Yang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1897603\u002Fpdf",{"tldr":443,"method":444,"finding":445,"direction":446,"opportunity":447},"构建农场尺度水-能-粮-环境耦合优化模型，比较五种情景以优化华北平原小麦生产的可持续性。","农场尺度WEFE耦合优化模型，评估水、养分、电力路径，用成本、生命周期温室气体、","综合情景温室气体减排约49%，肥料回收率升至40%，可再生能源占比达92.7%，优于单部门干预。","农业绿色发展与碳","可将该WEFE耦合优化框架扩展到其他缺水作物系统，并耦合农户行为与政策激励进行情景分析。","2026-09-10T23:30:07.730098Z",{"id":450,"title":451,"url":452,"summary":453,"summary_zh":454,"content":9,"source_name":376,"source_url":452,"published_at":377,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":323,"score_detail":455,"sources":457,"tags":459,"primary_topic":464,"secondary_topics":465,"classification":470,"search_phrases":497,"slug":500,"view_count":22,"doi":501,"paper":502,"created_at":514},2033,"Regenerative benefits of legume–wheat intercropping systems on soil quality and resource-use efficiency under deficit irrigation in Egypt","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1923296","Background Soil degradation and water scarcity are major constraints to sustainable cereal production in arid and semi-arid regions. Legume–wheat intercropping may simultaneously improve soil quality and resource-use efficiency, but its regenerative performance under deficit irrigation remains insufficiently quantified. Methods A two-year field experiment was conducted during 2021\u002F22 and 2022\u002F23 in El-Minia Governorate, Middle Egypt, using a split-plot design with three replications. Three irrigation regimes—100%, 85%, and 70% of crop evapotranspiration (ETc)—were evaluated in combination with sole wheat and wheat intercropped with faba bean, pea, or fahl clover. Soil organic matter, available N, P, and K, soil pH, Soil Quality Index (SQI), crop yield, land equivalent ratio (LER), water equivalent ratio (WER), and change in water use (DWU) were assessed. Results All legume–wheat intercropping systems significantly improved soil organic matter and available macronutrients relative to sole wheat. Faba bean–wheat generally produced the greatest improvements in soil properties and SQI. The beneficial effects of intercropping persisted under severe deficit irrigation (70% ETc). Pea–wheat achieved the highest total LER (1.39) and WER (1.26), together with the highest wheat grain yield and a DWU of -20.48%. Conclusion Legume–wheat intercropping enhanced soil quality, land-use efficiency, and water-use efficiency under deficit irrigation. The results demonstrate that integrating legumes with wheat can provide a practical regenerative strategy for improving productivity and resilience of wheat-based systems in water-limited environments.","背景 土壤退化与水资源短缺是干旱和半干旱地区可持续谷物生产的主要限制因素。豆科作物-小麦间作可能同时改善土壤质量和资源利用效率，但其在亏缺灌溉条件下的再生性能仍缺乏充分的定量评估。方法 于2021\u002F22和2022\u002F23年度在埃及中部明亚省开展了一项为期两年的田间试验，采用裂区设计，设三次重复。评估了三种灌溉制度——分别为作物蒸散量（ETc）的100%、85%和70%——与单作小麦及小麦分别与蚕豆、豌豆或埃及三叶草间作的组合。测定了土壤有机质、速效氮、磷、钾、土壤pH、土壤质量指数（SQI）、作物产量、土地当量比（LER）、水当量比（WER）以及水分利用变化量（DWU）。结果 与单作小麦相比，所有豆科作物-小麦间作系统均显著提高了土壤有机质和速效大量元素含量。蚕豆-小麦间作通常在改善土壤性质和SQI方面效果最佳。间作的有益效应在严重亏缺灌溉（70% ETc）条件下依然持续。豌豆-小麦间作实现了最高的总LER（1.39）和WER（1.26），同时小麦籽粒产量最高，DWU为-20.48%。结论 豆科作物-小麦间作在亏缺灌溉条件下提高了土壤质量、土地利用效率和水分利用效率。结果表明，将豆科作物与小麦整合可为水资源受限环境中提高小麦种植系统的生产力和韧性提供一种实用的再生策略。",{"impact":17,"substance":18,"depth":52,"authority":237,"freshness":21,"relevant":22,"comment":456},"两年田间试验量化了亏缺灌溉下豆科-小麦间作的土壤改良与资源利用效率提升，数据扎实、结论可靠，对干旱半干旱区小麦可持续生产有参考价值。",[458],{"name":376,"url":452},[42,460,461,462,463],"土壤健康","节水灌溉","间作套种","豆科绿肥",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[466,467],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":176,"slug":180,"name":468,"path":469,"parent_code":37},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":46,"locked":14,"version":47,"method":48,"evidence":471},{"candidates":472,"agricultural_evidence":53},[473,478,482,486,492],{"code":33,"score":19,"supported":53,"title":474,"abstract":475,"tags":477},[34],[161,56,34,476],"evapotranspiration",[461],{"code":40,"score":154,"supported":53,"title":479,"abstract":480,"tags":481},[41],[42,41],[42],{"code":176,"score":154,"supported":53,"title":483,"abstract":484,"tags":485},[180],[179,180],[460],{"code":169,"score":20,"supported":53,"title":487,"abstract":489,"tags":491},[488],"intercropping",[490,488],"间作",[],{"code":183,"score":191,"supported":53,"title":493,"abstract":494,"tags":496},[],[495],"干旱",[],[498,499],"土壤健康 节水灌溉 豆科绿肥 间作套种","土壤健康 节水灌溉","土壤健康节水灌溉豆科绿肥间作套种-2033","10.3389\u002Ffsufs.2026.1923296",{"doi":501,"openalex_id":503,"authors":504,"venue":376,"cited_by_count":117,"oa_url":452,"card":509,"direction":446,"ingested_from":124},"W7212074132",[505,507],{"name":506,"orcid":9},"S.A.H. Ouda",{"name":508,"orcid":9},"Abd El-Hafeez Zohry",{"tldr":510,"method":511,"finding":512,"direction":446,"opportunity":513},"埃及两年试验表明，豆科-小麦间作在亏缺灌溉下仍能提升土壤质量与资源利用效率。","裂区试验，3种灌溉水平与4种种植模式，测SQI、LER、WER等指标。","蚕豆-小麦改善土壤最佳，豌豆-小麦LER和WER最高，间作效益在70%ETc下仍存在。","可探究间作系统在不同气候与土壤类型下的长期固碳减排潜力及微生物机制。","2026-09-10T23:30:07.672063Z",{"id":516,"title":517,"url":518,"summary":519,"summary_zh":9,"content":9,"source_name":520,"source_url":9,"published_at":521,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":522,"score_detail":523,"sources":525,"tags":527,"primary_topic":529,"secondary_topics":530,"classification":537,"search_phrases":565,"slug":566,"view_count":60,"doi":567,"paper":9,"created_at":568},150,"Residual-corrected decision-level fusion model for collaborative irrigation-fertilization decision support in winter wheat","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.111908","中国农业科学院农田灌溉研究所节水高效灌溉技术与装备团队融合冬小麦无人机多光谱遥感数据与田间水肥管理数据,研发出残差校正决策层融合模型(RC-DLF),为小麦关键生育期水肥协同高效利用提供智慧决策支撑。该模型依托田间定位试验采集小麦关键生育期无人机多光谱冠层信息,整合水肥管理指标构建多源数据集,创新构建RC-DLF,内嵌权重优化、二阶校准、残差校正模块有效消解单一模型预测偏差。田间试验结果显示,相较传统模型,小麦开花期、灌浆期产量预测决定系数R²分别提升至0.62、0.67,整体预测误差明显降低。","Computers and Electronics in Agriculture 2026 (中国农科院农田灌溉研究所)","2026-07-29T00:00:00Z",80,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":524},"国家级科研机构在智慧农业水肥协同领域的创新模型，数据详实，具有行业参考价值。",[526],{"name":520,"url":518},[528],"中国农科院",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[531,532],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":403,"slug":533,"name":534,"path":535,"parent_code":536},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":46,"locked":14,"version":47,"method":48,"evidence":538},{"candidates":539,"agricultural_evidence":53},[540,545,550,555,560],{"code":40,"score":541,"supported":53,"title":542,"abstract":543,"tags":544},19,[63,41],[42],[],{"code":33,"score":546,"supported":53,"title":547,"abstract":548,"tags":549},11,[34],[161],[],{"code":403,"score":21,"supported":53,"title":551,"abstract":553,"tags":554},[552],"fertilization",[],[],{"code":86,"score":21,"supported":53,"title":556,"abstract":558,"tags":559},[557],"decision support",[],[],{"code":66,"score":74,"supported":53,"title":561,"abstract":562,"tags":564},[],[28,563],"无人机多光谱",[],[],"Residual-correcteddecision-levelfusionmo-150","10.1016\u002Fj.compag.2026.111908","2026-08-01T06:01:55.489617Z"]