[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-150":3,"related-150":88},{"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":26,"secondary_topics":32,"classification":45,"search_phrases":83,"slug":84,"view_count":85,"doi":86,"paper":8,"created_at":87},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。",null,"Computers and Electronics in Agriculture 2026 (中国农科院农田灌溉研究所)","2026-07-29T00:00:00Z","论文",10,false,80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,8,1,"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。",[23],{"name":9,"url":6},[25],"中国农科院",{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[33,39],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":46,"locked":13,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,58,64,69,75],{"code":27,"score":52,"supported":53,"title":54,"abstract":56,"tags":57},19,true,[55,28],"winter wheat",[29],[],{"code":40,"score":59,"supported":53,"title":60,"abstract":61,"tags":63},11,[41],[62],"灌溉",[],{"code":34,"score":19,"supported":53,"title":65,"abstract":67,"tags":68},[66],"fertilization",[],[],{"code":70,"score":19,"supported":53,"title":71,"abstract":73,"tags":74},"I2",[72],"decision support",[],[],{"code":76,"score":77,"supported":53,"title":78,"abstract":79,"tags":82},"I3",6,[],[80,81],"遥感","无人机多光谱",[],[],"Residual-correcteddecision-levelfusionmo-150",9,"10.1016\u002Fj.compag.2026.111908","2026-08-01T06:01:55.489617Z",{"total":77,"page":20,"page_size":77,"items":89},[90,193,339,384,466,551],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":8,"source_name":96,"source_url":93,"published_at":97,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":98,"score_detail":99,"sources":104,"tags":106,"primary_topic":111,"secondary_topics":112,"classification":114,"search_phrases":165,"slug":168,"view_count":169,"doi":170,"paper":171,"created_at":192},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":100,"substance":101,"depth":102,"authority":19,"freshness":85,"relevant":20,"comment":103},12,20,17,"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。",[105],{"name":96,"url":93},[107,108,29,109,110],"数字农业","智慧农业","病害防控","灌溉管理",{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},[113],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":13,"version":47,"method":48,"evidence":115},{"candidates":116,"agricultural_evidence":53},[117,122,126,132,139,146,153,160],{"code":27,"score":118,"supported":53,"title":119,"abstract":120,"tags":121},15,[28],[29,28],[29],{"code":40,"score":118,"supported":53,"title":123,"abstract":124,"tags":125},[41],[62,41],[110],{"code":127,"score":19,"supported":53,"title":128,"abstract":129,"tags":131},"I1",[],[107,130],"digital agriculture",[107,108],{"code":133,"score":77,"supported":53,"title":134,"abstract":135,"tags":138},"A7",[],[136,137],"轮作","crop rotation",[],{"code":140,"score":77,"supported":53,"title":141,"abstract":142,"tags":145},"H2",[],[143,144],"土壤","soil",[],{"code":147,"score":77,"supported":53,"title":148,"abstract":149,"tags":152},"H7",[],[150,151],"渍害","waterlogging",[],{"code":154,"score":155,"supported":53,"title":156,"abstract":157,"tags":159},"B7",3,[],[158],"greenhouse",[],{"code":70,"score":155,"supported":53,"title":161,"abstract":162,"tags":164},[],[163],"artificial intelligence",[],[166,167],"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",0,"10.71317\u002Fkjard.2.10.2026.774",{"doi":170,"openalex_id":172,"authors":173,"venue":96,"cited_by_count":169,"oa_url":93,"card":184,"direction":190,"ingested_from":191},"W7219840451",[174,176,178,180,182],{"name":175,"orcid":8},"Amir Khan Jamali",{"name":177,"orcid":8},"Zahir Muhammad Larik",{"name":179,"orcid":8},"Videsh Kumar",{"name":181,"orcid":8},"Um E Habiba",{"name":183,"orcid":8},"Amanullah Maree",{"tldr":185,"method":186,"finding":187,"direction":188,"opportunity":189},"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","openalex","2026-10-05T23:30:30.962770Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":8,"source_name":199,"source_url":196,"published_at":200,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":201,"sources":204,"tags":206,"primary_topic":210,"secondary_topics":211,"classification":218,"search_phrases":278,"slug":281,"view_count":155,"doi":282,"paper":283,"created_at":338},3759,"Synergistic interaction effects of precision phosphorus fertigation and residue management on productivity, profitability, and resource-use efficiency of wheat under conservation agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1944629","Phosphorus (P) management under conventional surface-broadcast application in calcareous soils of the Indo-Gangetic Plains (IGP) suffers from severe fixation losses, limiting phosphorus-use efficiency (PUE) to 10–25% and contributing to yield gaps in wheat, a globally important food security crops. A two-year (2023–24 and 2024–25) split-plot field experiment was conducted within a mid-term conservation agriculture (CA)-based maize-wheat-greengram system to evaluate nine phosphorus fertigation levels and split application schedules through subsurface drip fertigation (SSDF), combined with two residue management practices (retention: R+, no-retention: R-), on growth, yield, water productivity, economics, and resource-use efficiency and post-harvest available phosphorus of wheat (cv. HD 2967). Treatments included no-P (control), SSDF of P as DAP at 50, 75, and 100% of the recommended dose (RDP; 60 kg P 2 O 5 ha −1 ) applied either fully as basal (10 DAS) or split (10 and 25 DAS), along with a nano-DAP foliar spray, compared against conventional CA with flood irrigation and basal broadcast P (CA-FI-100% RDP-B). SSDF treatments consumed only 178–275 mm of total water input against 437–452 mm under flood irrigation. Among SSDF treatments, 100% RDP applied in two splits (100% RDP-2S) consistently recorded the highest growth attributes, grain yield (6.23 and 6.31 t ha −1 ), and economic returns (BCR: 1.8–2) across both years, improving grain yield by 10.6–11.5% and net returns by 13.7% over CA-FI-100% RDP-B. Notably, 75% RDP-2S was statistically comparable to 100% RDP-2S for yield and profitability, demonstrating that a 25% reduction in P dose under precision SSDF does not compromise productivity. Total and irrigation water productivity improved by 127.7 and 156.7%, respectively, under 100% RDP-2S over CA-FI-100% RDP-B. Residue retention further enhanced productivity, increasing grain yield by 14.0–15.0% and net returns by 26.4–27.5%. Post-harvest soil available P was 18.0 and 43.3% higher under SSDF than under CA-FI-100% RDP-B at 0–15 and 15–30 cm, respectively. The interaction of split P fertigation and residue retention was consistently superior for grain yield and water productivity. These findings establish 75–100% RDP delivered through split SSDF, combined with residue retention, as an agronomically superior, water-efficient, economically viable strategy for sustainable wheat intensification in the semi-arid IGPs.","在印度河-恒河平原（IGP）石灰性土壤上，常规地表撒施条件下的磷（P）管理面临严重的固定损失，导致磷利用效率（PUE）仅为10–25%，并造成小麦——一种全球重要的粮食安全作物——的产量差距。在中期保护性农业（CA）为基础的玉米-小麦-绿豆轮作体系中，开展了一项为期两年（2023–24和2024–25）的裂区田间试验，评估九种磷肥滴灌水平和分次施用方案通过地下滴灌施肥（SSDF）对小麦（品种HD 2967）生长、产量、水分生产力、经济效益、资源利用效率及收获后土壤有效磷的影响，并结合两种秸秆管理方式（还田：R+，不还田：R-）。处理包括：无磷（对照）、以DAP形式通过SSDF施用推荐施磷量（RDP；60 kg P₂O₅ ha⁻¹）的50%、75%和100%，分别以全部基施（播种后10天）或分次施用（播种后10天和25天），同时配合纳米DAP叶面喷施，并与常规保护性农业配合漫灌和基施撒施磷（CA-FI-100% RDP-B）进行比较。SSDF处理的总水分投入仅为178–275 mm，而漫灌条件下为437–452 mm。在SSDF处理中，100% RDP分两次施用（100% RDP-2S）在两年中均持续表现出最高的生长指标、籽粒产量（6.23和6.31 t ha⁻¹）和经济效益（BCR：1.8–2），较CA-FI-100% RDP-B分别提高籽粒产量10.6–11.5%和净收益13.7%。值得注意的是，75% RDP-2S在产量和盈利能力上与100% RDP-2S无统计学差异，表明在精准SSDF下减少25%的磷用量不会影响生产力。与CA-FI-100% RDP-B相比，100% RDP-2S处理下总水分生产力和灌溉水分生产力分别提高了127.7%和156.7%。秸秆还田进一步提高了生产力，使籽粒产量增加14.0–15.0%，净收益增加26.4–27.5%。收获后土壤有效磷在0–15 cm和15–30 cm土层中，SSDF较CA-FI-100% RDP-B分别高出18.0%和43.3%。分次磷肥滴灌与秸秆还田的交互作用在籽粒产量和水分生产力方面始终表现最优。这些研究结果表明，通过分次SSDF施用75–100% RDP并结合秸秆还田，是一种农艺上优越、水分高效、经济可行的策略，可用于半干旱印度河-恒河平原小麦的可持续集约化生产。","Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z",{"impact":16,"substance":17,"depth":16,"authority":202,"freshness":85,"relevant":20,"comment":203},13,"两年田间试验证实分次地下滴灌施磷配合秸秆还田可减磷25%并显著提升小麦产量与水肥利用效率，数据扎实、结论可靠，对半干旱地区精准水肥管理有直接参考价值。",[205],{"name":199,"url":196},[108,207,29,208,209],"精准施肥","保护性耕作","水肥一体化",{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},[212,213],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":214,"slug":215,"name":216,"path":217,"parent_code":44},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":46,"locked":13,"version":47,"method":48,"evidence":219},{"candidates":220,"agricultural_evidence":53},[221,228,232,237,244,251,255,259,263,269,274],{"code":34,"score":222,"supported":53,"title":223,"abstract":225,"tags":227},16,[224],"fertigation",[226,224],"施肥",[209,207],{"code":27,"score":118,"supported":53,"title":229,"abstract":230,"tags":231},[28],[29,28],[29],{"code":214,"score":100,"supported":53,"title":233,"abstract":235,"tags":236},[234],"conservation agriculture",[234],[208],{"code":238,"score":77,"supported":53,"title":239,"abstract":240,"tags":243},"A3",[],[241,242],"玉米","maize",[],{"code":245,"score":77,"supported":53,"title":246,"abstract":247,"tags":250},"A8",[],[248,249],"粮食安全","food security",[],{"code":140,"score":77,"supported":53,"title":252,"abstract":253,"tags":254},[],[143,144],[],{"code":40,"score":77,"supported":53,"title":256,"abstract":257,"tags":258},[],[62,41],[],{"code":133,"score":155,"supported":53,"title":260,"abstract":261,"tags":262},[],[136],[],{"code":264,"score":155,"supported":53,"title":265,"abstract":266,"tags":268},"H5",[],[267],"秸秆还田",[],{"code":147,"score":155,"supported":53,"title":270,"abstract":271,"tags":273},[],[272],"干旱",[],{"code":127,"score":20,"supported":13,"title":275,"abstract":276,"tags":277},[],[],[108],[279,280],"地下滴灌 磷肥 小麦","保护性耕作 秸秆还田 小麦","地下滴灌磷肥小麦-3759","10.3389\u002Ffsufs.2026.1944629",{"doi":282,"openalex_id":284,"authors":285,"venue":199,"cited_by_count":169,"oa_url":196,"card":333,"direction":188,"ingested_from":191},"W7214579192",[286,288,291,293,295,297,299,302,304,306,308,310,312,315,317,319,322,325,328,331],{"name":287,"orcid":8},"Kadagonda Nithinkumar",{"name":289,"orcid":290},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":292,"orcid":8},"Devika A. R.",{"name":294,"orcid":8},"Arjun Singh",{"name":296,"orcid":8},"Susama Sudhishri",{"name":298,"orcid":8},"Vinod Kumar Singh",{"name":300,"orcid":301},"Sanjay Singh Rathore","https:\u002F\u002Forcid.org\u002F0000-0001-6725-1478",{"name":303,"orcid":8},"Sarvendra Kumar",{"name":305,"orcid":8},"Teekam Singh",{"name":307,"orcid":8},"Jitendra Rajput",{"name":309,"orcid":8},"Pravin Kumar Upadhyaya",{"name":311,"orcid":8},"R. N. Sahoo",{"name":313,"orcid":314},"ARPULA SAIRAM","https:\u002F\u002Forcid.org\u002F0009-0009-9034-6032",{"name":316,"orcid":8},"Aye Aye San",{"name":318,"orcid":8},"K. S. Sachin",{"name":320,"orcid":321},"Mummasani Asritha","https:\u002F\u002Forcid.org\u002F0009-0002-4619-9943",{"name":323,"orcid":324},"Ramanjit Kaur","https:\u002F\u002Forcid.org\u002F0000-0001-5117-050X",{"name":326,"orcid":327},"Mona Nagargade","https:\u002F\u002Forcid.org\u002F0000-0003-1062-512X",{"name":329,"orcid":330},"Vishal Tyagi","https:\u002F\u002Forcid.org\u002F0000-0001-5593-3076",{"name":332,"orcid":8},"Subhash Babu",{"tldr":334,"method":335,"finding":336,"direction":188,"opportunity":337},"研究保护性农业下精准磷肥滴灌与秸秆还田对小麦产量、收益和资源效率的协同效应。","两年裂区田间试验，地下滴灌分次施磷与秸秆还田处理，测产量、水生产力与经济指标。","75–100%推荐磷量分次滴灌配秸秆还田可增产10%以上、节水超一倍且不减产。","可探索基于土壤磷素实时监测的变量施磷决策模型，并与秸秆还田互作机制结合优化。","2026-09-29T23:30:06.859116Z",{"id":340,"title":341,"url":342,"summary":343,"summary_zh":8,"content":8,"source_name":344,"source_url":8,"published_at":345,"category":346,"cover_url":8,"hotness":12,"is_selected":13,"score":347,"score_detail":348,"sources":350,"tags":352,"primary_topic":358,"secondary_topics":359,"classification":361,"search_phrases":379,"slug":382,"view_count":155,"doi":8,"paper":8,"created_at":383},3108,"安徽省农科院作物所\"一种小麦抗倒伏肥料及其制备方法\"等 2 项专利赋权转化公示——转让总金额 5.3 万元","https:\u002F\u002Fwww.aaas.org.cn\u002F4303171\u002F71129984.html","9-16 安徽省农科院作物所发布小麦栽培与耕作团队张向前、杜世州、乔玉强、曹承富、李玮、陈欢、赵竹等 7 位完成人 2 项职务科技成果赋权转化公示：专利\"一种小麦抗倒伏肥料及其制备方法\"（ZL202211176053X）、\"一种大豆高产肥料及其制备方法\"（ZL202410297486.3），赋权方式均为职务科技成果长期使用权（10 年），完成人占 70%、转化方占 20%、院占 10%；分别以 5 万元、0.3 万元转让给垦浩农业科技（广州）有限公司。公示时间 9-16 至 10-2 共 15 个工作日。","安徽省农业科学院","2026-09-16T00:00:00Z","报道",53,{"impact":19,"substance":222,"depth":12,"authority":202,"freshness":77,"relevant":20,"comment":349},"省级科研机构职务成果赋权转化公示，含具体专利号、金额与权益分配，信息实在但影响限于行业层面。",[351],{"name":344,"url":342},[353,354,355,356,357],"安徽省农科院","专利转化","小麦抗倒伏","大豆高产","成果赋权",{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},[360],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":46,"locked":13,"version":47,"method":48,"evidence":362},{"candidates":363,"agricultural_evidence":53},[364,368,373],{"code":27,"score":59,"supported":53,"title":365,"abstract":366,"tags":367},[29],[29],[],{"code":34,"score":59,"supported":53,"title":369,"abstract":371,"tags":372},[370],"肥料",[370],[],{"code":374,"score":155,"supported":53,"title":375,"abstract":376,"tags":378},"A4",[],[377],"大豆",[],[380,381],"安徽省农科院 小麦抗倒伏肥料 专利","小麦抗倒伏肥料 大豆高产肥料 赋权转化","安徽省农科院小麦抗倒伏肥料专利-3108","2026-09-22T00:05:35.824526Z",{"id":385,"title":386,"url":387,"summary":388,"summary_zh":389,"content":8,"source_name":96,"source_url":387,"published_at":390,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":391,"score_detail":392,"sources":394,"tags":396,"primary_topic":400,"secondary_topics":401,"classification":403,"search_phrases":435,"slug":438,"view_count":155,"doi":439,"paper":440,"created_at":465},2655,"Response of Wheat Crop to The Application of Different Micronutrients and Their Influence on Growth and Development","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.9.2026.605","Micronutrients are required in small quantities but play essential roles in wheat growth, physiological processes, yield formation, and grain nutritional quality. Deficiencies of zinc (Zn), iron (Fe), manganese (Mn), copper (Cu), and boron (B) can restrict plant development and reduce productivity, particularly in soils with limited nutrient availability. This study summarizes the roles of major micronutrients in wheat growth and development, with emphasis on their effects on germination, root development, tillering, biomass production, photosynthesis, nutrient uptake, enzyme activity, yield components, and grain quality. The study also evaluates major approaches for micronutrient management, including soil application, seed treatment, foliar application, and integrated fertilization. Particular attention is given to the influence of soil properties, environmental conditions, wheat genotype, fertilizer dose, application timing, and application method on micronutrient availability and utilization efficiency. Recent evidence indicates that balanced micronutrient management can improve wheat physiological performance, yield components, grain yield, and nutritional value, while agronomic biofortification offers an important strategy for increasing grain Zn and Fe concentrations. However, the effectiveness of micronutrient application varies considerably with soil conditions, climatic stresses, genotype, and management practices. Therefore, site-specific nutrient management, soil testing, genotype selection, and stage-specific application are essential for improving micronutrient-use efficiency. Integrating micronutrient management with wheat breeding and precision agriculture can contribute to sustainable productivity and improved grain nutritional quality. Future research should focus on genotype-specific micronutrient requirements, nutrient interactions, precision application technologies, and climate-resilient strategies for efficient wheat production and enhanced food and nutritional security.","微量营养元素虽然需求量小，但在小麦生长、生理过程、产量形成和籽粒营养品质中发挥着重要作用。锌（Zn）、铁（Fe）、锰（Mn）、铜（Cu）和硼（B）的缺乏会限制作物发育并降低生产力，尤其是在养分有效性有限的土壤中。本研究综述了主要微量营养元素在小麦生长发育中的作用，重点阐述了其对萌发、根系发育、分蘖、生物量积累、光合作用、养分吸收、酶活性、产量构成和籽粒品质的影响。研究还评估了微量营养元素管理的主要途径，包括土壤施用、种子处理、叶面喷施和综合施肥。特别关注了土壤性质、环境条件、小麦基因型、肥料用量、施用时期和施用方法对微量营养元素有效性和利用效率的影响。近期证据表明，平衡的微量营养元素管理可以改善小麦生理性能、产量构成、籽粒产量和营养价值，而农艺生物强化是提高籽粒锌和铁浓度的重要策略。然而，微量营养元素施用的效果因土壤条件、气候胁迫、基因型和管理措施的不同而有很大差异。因此，针对特定地点的养分管理、土壤测试、基因型选择和分期施用对于提高微量营养元素利用效率至关重要。将微量营养元素管理与小麦育种和精准农业相结合，有助于实现可持续生产力并改善籽粒营养品质。未来研究应聚焦于基因型特异性微量营养元素需求、养分互作、精准施用技术以及气候韧性策略，以实现高效小麦生产并增强粮食和营养安全。","2026-09-14T00:00:00Z",68,{"impact":18,"substance":16,"depth":222,"authority":100,"freshness":19,"relevant":20,"comment":393},"综述性论文，系统梳理微量元素对小麦生长与品质的作用及管理策略，对科学施肥与营养强化有参考价值，但非原创试验数据，影响力偏细分领域。",[395],{"name":96,"url":387},[248,29,397,398,399],"生物强化","微量元素","科学施肥",{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},[402],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":46,"locked":13,"version":47,"method":48,"evidence":404},{"candidates":405,"agricultural_evidence":53},[406,410,416,420,426,430],{"code":27,"score":118,"supported":53,"title":407,"abstract":408,"tags":409},[28],[29,28],[29],{"code":34,"score":12,"supported":53,"title":411,"abstract":412,"tags":415},[],[226,370,413,414,66],"养分管理","fertilizer",[399],{"code":140,"score":77,"supported":53,"title":417,"abstract":418,"tags":419},[],[143,144],[],{"code":127,"score":77,"supported":53,"title":421,"abstract":422,"tags":425},[],[423,424],"精准农业","precision agriculture",[],{"code":245,"score":20,"supported":13,"title":427,"abstract":428,"tags":429},[],[],[248],{"code":431,"score":20,"supported":13,"title":432,"abstract":433,"tags":434},"F2",[],[],[397],[436,437],"微量元素 生物强化 科学施肥 粮食安全","微量元素 生物强化","微量元素生物强化科学施肥粮食安全-2655","10.71317\u002Fkjard.2.9.2026.605",{"doi":439,"openalex_id":441,"authors":442,"venue":96,"cited_by_count":169,"oa_url":457,"card":458,"direction":464,"ingested_from":191},"W7213313809",[443,445,447,449,451,453,455],{"name":444,"orcid":8},"Muhammad Adnan Rafique",{"name":446,"orcid":8},"Aftab Ahmad Sheikh",{"name":448,"orcid":8},"Syed Asghar Hussain Shah",{"name":450,"orcid":8},"Saima Nazar",{"name":452,"orcid":8},"Asia Shaheen",{"name":454,"orcid":8},"Muhammad Imran",{"name":456,"orcid":8},"Hafiz Abu Bakar Amin","https:\u002F\u002Frjsaonline.org\u002Findex.php\u002FKJARD\u002Farticle\u002Fdownload\u002F605\u002F852",{"tldr":459,"method":460,"finding":461,"direction":462,"opportunity":463},"综述小麦对锌铁锰铜硼等微量元素的响应及其对生长、产量和品质的影响。","文献综述，分析土壤施用、种子处理、叶面喷施及综合施肥等管理方式。","平衡微量元素管理可提升小麦生理、产量和营养品质，农艺强化可增加籽粒锌铁。","农业绿色发展与碳","可结合基因型与精准农业，研究特定基因型微量元素需求及气候适应型精准施用技术。","农业遥感与作物表型","2026-09-16T23:30:21.993931Z",{"id":467,"title":468,"url":469,"summary":470,"summary_zh":471,"content":8,"source_name":472,"source_url":469,"published_at":390,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":473,"score_detail":474,"sources":476,"tags":478,"primary_topic":482,"secondary_topics":483,"classification":485,"search_phrases":518,"slug":521,"view_count":522,"doi":523,"paper":524,"created_at":550},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。","Remote Sensing",78,{"impact":222,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":475},"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。",[477],{"name":472,"url":469},[108,80,479,480,481],"作物识别","迁移学习","灌溉区",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[484],{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},{"status":46,"locked":13,"version":47,"method":48,"evidence":486},{"candidates":487,"agricultural_evidence":53},[488,494,498,504,510,514],{"code":40,"score":102,"supported":53,"title":489,"abstract":490,"tags":493},[41],[491,41,492],"水资源","water resource",[],{"code":27,"score":85,"supported":53,"title":495,"abstract":496,"tags":497},[],[29,55,28],[],{"code":76,"score":499,"supported":53,"title":500,"abstract":501,"tags":503},7,[],[80,502],"remote sensing",[80],{"code":374,"score":77,"supported":53,"title":505,"abstract":506,"tags":509},[],[507,508],"油菜","rapeseed",[],{"code":127,"score":20,"supported":13,"title":511,"abstract":512,"tags":513},[],[],[108],{"code":70,"score":20,"supported":13,"title":515,"abstract":516,"tags":517},[],[],[480],[519,520],"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":523,"openalex_id":525,"authors":526,"venue":472,"cited_by_count":169,"oa_url":469,"card":545,"direction":464,"ingested_from":191},"W7212518427",[527,529,531,533,535,537,540,542],{"name":528,"orcid":8},"Zili Chen",{"name":530,"orcid":8},"Zhilong Gao",{"name":532,"orcid":8},"Zefeng Jia",{"name":534,"orcid":8},"Pengjie Pan",{"name":536,"orcid":8},"Wen Gao",{"name":538,"orcid":539},"Jun Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-6972-6828",{"name":541,"orcid":8},"Zijie Niu",{"name":543,"orcid":544},"Dongyan Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3509-7482",{"tldr":546,"method":547,"finding":548,"direction":464,"opportunity":549},"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。","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。","2026-09-15T23:30:20.433643Z",{"id":552,"title":553,"url":554,"summary":555,"summary_zh":556,"content":8,"source_name":557,"source_url":554,"published_at":558,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":559,"score_detail":560,"sources":562,"tags":564,"primary_topic":566,"secondary_topics":567,"classification":574,"search_phrases":593,"slug":596,"view_count":597,"doi":598,"paper":599,"created_at":609},2332,"Wheat Nitrogen Fertilizer Management Using GreenSeeker Handheld Crop Canopy Sensor","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.11.no3.2025.pg12.22","One of the essential factors in increasing agricultural yields of cereal crops is increasing the grain yield without increasing production costs. Wheat (Triticum aestivum L.) is the main crop in the food basket of the entire world. Therefore, it is necessary to determine the appropriate nitrogen (N) requirements to obtain the optimal production of wheat crops by investigating the impact of different N levels on the wheat crop yield in the Al-Muthanna region, as well as determining the possibility of predicting grain yield using the GreenSeeker handheld proximal crop canopy sensorbased differences vegetative difference index (NDVI). Thus, there is a need to re-evaluate the previous recommendations using remote sensing techniques. The experimental treatments were five levels of N fertilizer including (0 Kg N ha-1 , 50 kg N ha-1 , 100 kg N ha-1 ,150 kg N ha-1, and 200 kg N ha-1, and each N level was divided into 100%, 70%, 50% percentages, respectively. This study was conducted at the Experiment Station and Agriculture Research of the College of AgricultureAl-Muthanna University. The NDVI measurements were obtained at FK5, FK7, and FK9 according to the Feekes scale growth stage. The results indicated significant differences in grain yield between different levels of N fertilizer and a 70% percentage outperformed on the 100% and 50% treatments for each of the two N levels of 200 and 150 kg ha-1 . The results also show significant differences between NDVI values for different N fertilizer levels. The NDVI readings and wheat yield values increased and followed a similar pattern with increasing N fertilizer levels. This suggests that NDVI can predict wheat grain yield when the NDVI is not saturated. This study showed the potential of using GreenSeeker proximal crop canopy sensor-based NDVI readings as a useful tool to predict wheat grain yield.","提高谷类作物农业产量的关键因素之一是在不增加生产成本的前提下提高谷物产量。小麦（Triticum aestivum L.）是全球粮食结构中的主要作物。因此，有必要通过研究不同氮水平对Al-Muthanna地区小麦作物产量的影响，确定获得小麦作物最佳产量所需的适宜氮（N）需求量，并确定利用基于GreenSeeker手持式近地作物冠层传感器的归一化植被差异指数（NDVI）预测谷物产量的可能性。因此，需要利用遥感技术重新评估以往的推荐方案。试验处理包括五个氮肥水平（0 kg N ha⁻¹、50 kg N ha⁻¹、100 kg N ha⁻¹、150 kg N ha⁻¹和200 kg N ha⁻¹），每个氮水平分别按100%、70%、50%的比例施用。本研究在Al-Muthanna大学农学院实验站与农业研究中心进行。NDVI测定根据Feekes尺度生育阶段在FK5、FK7和FK9时期进行。结果表明，不同氮肥水平间谷物产量存在显著差异，在200和150 kg ha⁻¹两个氮水平下，70%施用量处理均优于100%和50%处理。结果还显示，不同氮肥水平间NDVI值存在显著差异。随着氮肥水平的提高，NDVI读数和小麦产量值均增加并呈现相似的变化趋势。这表明在NDVI未饱和时，NDVI可以预测小麦谷物产量。本研究表明，利用基于GreenSeeker近地作物冠层传感器的NDVI读数作为预测小麦谷物产量的有用工具具有潜力。","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-09-10T00:00:00Z",61,{"impact":100,"substance":16,"depth":118,"authority":12,"freshness":77,"relevant":20,"comment":561},"基于GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥用量，方法实用但属区域性田间试验，产业影响有限。",[563],{"name":557,"url":554},[108,207,29,80,565],"NDVI",{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},[568,569],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":76,"slug":570,"name":571,"path":572,"parent_code":573},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":46,"locked":13,"version":47,"method":48,"evidence":575},{"candidates":576,"agricultural_evidence":53},[577,581,585,589],{"code":27,"score":118,"supported":53,"title":578,"abstract":579,"tags":580},[28],[29,28],[29],{"code":34,"score":100,"supported":53,"title":582,"abstract":583,"tags":584},[414],[414],[207],{"code":76,"score":19,"supported":53,"title":586,"abstract":587,"tags":588},[],[80,502],[565,80],{"code":127,"score":20,"supported":13,"title":590,"abstract":591,"tags":592},[],[],[108],[594,595],"智慧农业 精准施肥 小麦 遥感","智慧农业 精准施肥","智慧农业精准施肥小麦遥感-2332",2,"10.56201\u002Fijaes.vol.11.no3.2025.pg12.22",{"doi":598,"openalex_id":600,"authors":601,"venue":557,"cited_by_count":169,"oa_url":8,"card":604,"direction":464,"ingested_from":191},"W7212289620",[602],{"name":603,"orcid":8},"Mohammed A. Naser",{"tldr":605,"method":606,"finding":607,"direction":464,"opportunity":608},"用GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥管理。","伊拉克田间试验，5个氮水平，Feekes 5\u002F7\u002F9期测NDVI。","NDVI与产量随氮肥增加同步上升，未饱和时可预测产量，70%氮量表现更优。","可探索NDVI饱和条件下的替代植被指数及不同品种\u002F气候区的氮肥推荐模型迁移。","2026-09-13T23:30:26.260534Z"]