[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4694":3,"related-4694":112},{"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":33,"secondary_topics":38,"classification":45,"search_phrases":91,"slug":94,"view_count":22,"doi":95,"paper":96,"created_at":111},4694,"Precision Agriculture Approaches for Nutrient Management in Maize: A Comprehensive Review of Productivity, Efficiency and Economic Benefits","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104559","Maize nutrient management is an unusually demanding target for precision agriculture because crop demand, soil nutrient supply, weather, rooting conditions and management history vary simultaneously across space and time. Uniform fertiliser programmes can therefore be efficient in one part of a field and inefficient in another, while a recommendation that is appropriate before planting may become suboptimal after rainfall, mineralisation and crop establishment have altered nutrient availability. This critical narrative review evaluates precision approaches for nutrient management in maize, with emphasis on productivity, nutrient-use efficiency and farm-level economic performance. Literature published from 1 January 1990 to 2 August 2026 was examined using agricultural and multidisciplinary scholarly sources, supplemented by citation searching and bibliographic verification. The evidence was organised around the decision chain linking measurement, diagnosis, prescription and nutrient application. Strongest empirical support exists for site-specific and in-season nitrogen management because nitrogen is mobile, strongly coupled to weather and readily expressed in canopy signals. Chlorophyll meters, active optical sensors, aerial and unmanned aerial vehicle imagery, management zones, crop models and machine-learning approaches can improve the timing or spatial targeting of nitrogen, but diagnostic accuracy does not automatically translate into an economically superior fertiliser prescription. Multi-nutrient decision-support systems broaden precision management beyond nitrogen and can improve the balance of nitrogen, phosphorus and potassium, particularly where farmer practice is poorly aligned with crop demand. Across the evidence base, yield gains are often modest when conventional fertilisation is already adequate; benefits more consistently arise through reduced excess nutrient use, improved nutrient-use efficiency, better risk management or correction of under-fertilisation. Profitability is highly conditional on field heterogeneity, fertiliser and grain prices, baseline management, technology cost, scale and weather. Future progress requires multi-year on-farm validation of complete decision chains, stronger treatment of uncertainty and multi-stress confounding, improved multi-nutrient diagnostics, and economic evaluations that include fixed, transaction and information costs. Precision nutrient management is best understood as an adaptive decision framework rather than a single sensor or variable-rate technology.","玉米养分管理对精准农业而言是一个异常苛刻的目标，因为作物需求、土壤养分供应、天气、根系条件和管理历史在空间和时间上同时变化。因此，均匀施肥方案在田块某一部分可能高效，而在另一部分则可能低效；而播种前适宜的推荐方案，在降雨、矿化和作物建成改变养分有效性之后，也可能变得次优。这篇批判性叙述综述评估了玉米养分管理的精准方法，重点关注生产力、养分利用效率和农场层面经济表现。利用农业与多学科学术来源，检索了1990年1月1日至2026年8月2日发表的文献，并以引文检索和书目核验作为补充。证据围绕连接测量、诊断、处方和养分施用的决策链进行组织。最强的实证支持存在于位点特异性和季内氮管理，因为氮具有移动性、与天气强耦合，并易于在冠层信号中表达。叶绿素仪、主动光学传感器、航空和无人机影像、管理分区、作物模型和机器学习方法可以改善氮的施用时机或空间靶向，但诊断准确性并不会自动转化为经济上更优的施肥处方。多养分决策支持系统将精准管理扩展到氮之外，并能改善氮、磷、钾的平衡，尤其是在农户实践与作物需求严重不匹配的情况下。在整个证据基础中，当常规施肥已经充足时，产量增益往往有限；效益更一致地来自减少过量养分使用、提高养分利用效率、改善风险管理或纠正施肥不足。盈利能力高度取决于田块异质性、肥料和谷物价格、基线管理、技术成本、规模和天气。未来进展需要对完整决策链进行多年农场验证，更有力地处理不确定性和多胁迫混杂，改进多养分诊断，以及纳入固定成本、交易成本和信息成本的经济评价。精准养分管理最好被理解为一个适应性决策框架",null,"Journal of Experimental Agriculture International","2026-10-10T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,9,1,"系统综述精准养分管理在玉米上的产量、效率与经济性证据，方法学扎实但属学术综述，产业落地价值需长期验证。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智慧农业","变量施肥","玉米","精准农业","遥感监测","养分管理",{"code":34,"slug":35,"name":29,"path":36,"parent_code":37},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":54},[51,59,64,71,79,86],{"code":52,"score":53,"supported":54,"title":55,"abstract":57,"tags":58},"I1",16,true,[56],"precision 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Chattopadhyay",0,{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"综述玉米精准养分管理在生产力、效率与经济效益上的证据与局限。","系统检索1990-2026年文献，按测量-诊断-处方-施用决策链组织证据。","氮素精准管理证据最强，但诊断精度不必然带来经济更优的施肥处方。","农业人工智能与决策模型","亟需多年多点田间验证完整决策链，并纳入不确定性、多养分诊断与信息成本的经济评价。","openalex","2026-10-11T23:30:51.450082Z",{"total":73,"page":22,"page_size":73,"items":113},[114,187,249,308,394,445],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":9,"content":9,"source_name":119,"source_url":117,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":127,"tags":129,"primary_topic":132,"secondary_topics":133,"classification":139,"search_phrases":165,"slug":168,"view_count":73,"doi":169,"paper":170,"created_at":186},3523,"Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10458-y","Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system。Precision Agriculture","Precision Agriculture","2026-09-25T00:00:00Z",69,{"impact":17,"substance":123,"depth":19,"authority":124,"freshness":125,"relevant":22,"comment":126},18,14,8,"精准农业核心期刊论文，探讨土壤空间变异对变量播种与施肥效果的影响，方法新颖、结论可靠，对智慧农业田间管理有参考价值，但属细分领域研究，影响范围有限。",[128],{"name":119,"url":117},[27,28,29,30,130,131],"向日葵","变量播种",{"code":34,"slug":35,"name":29,"path":36,"parent_code":37},[134,135],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":72,"slug":77,"name":136,"path":137,"parent_code":138},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":140},{"candidates":141,"agricultural_evidence":54},[142,146,151,155,160],{"code":34,"score":17,"supported":54,"title":143,"abstract":144,"tags":145},[35],[35],[29],{"code":40,"score":17,"supported":54,"title":147,"abstract":149,"tags":150},[148],"fertilization",[148],[28],{"code":72,"score":65,"supported":54,"title":152,"abstract":153,"tags":154},[77],[77],[],{"code":52,"score":156,"supported":54,"title":157,"abstract":158,"tags":159},5,[],[56],[27,30],{"code":161,"score":22,"supported":14,"title":162,"abstract":163,"tags":164},"A4",[],[],[130],[166,167],"Precision Agriculture 变量播种 变量施肥","灌溉玉米 向日葵 土壤变异","PrecisionAgriculture变量播种变量施肥-3523","10.1007\u002Fs11119-026-10458-y",{"doi":169,"openalex_id":171,"authors":172,"venue":119,"cited_by_count":103,"oa_url":9,"card":9,"direction":9,"ingested_from":110},"W7214357812",[173,176,178,180,182,184],{"name":174,"orcid":175},"María Videgain","https:\u002F\u002Forcid.org\u002F0000-0002-3630-7931",{"name":177,"orcid":9},"J. A. Martínez-Casasnovas",{"name":179,"orcid":9},"S. Artero",{"name":181,"orcid":9},"A. Vigo",{"name":183,"orcid":9},"M. Vidal",{"name":185,"orcid":9},"F. J. García-Ramos","2026-09-26T23:30:03.127069Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":9,"source_name":193,"source_url":190,"published_at":194,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":199,"tags":201,"primary_topic":203,"secondary_topics":204,"classification":206,"search_phrases":226,"slug":229,"view_count":230,"doi":231,"paper":232,"created_at":248},1754,"Low-cost autonomous unmanned ground vehicle for in-situ phosphorus screening to support zone-based fertilization in maize crops","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112349","Low-cost autonomous unmanned ground vehicle for in-situ phosphorus screening to support zone-based fertilization in maize crops。Computers and Electronics in Agriculture","低成本自主无人地面车辆用于玉米作物原位磷筛查以支持分区施肥。农业计算机与电子","Computers and Electronics in Agriculture","2026-09-05T00:00:00Z",62,{"impact":17,"substance":123,"depth":53,"authority":124,"freshness":197,"relevant":22,"comment":198},2,"低成本无人车用于磷素原位检测，支持玉米分区施肥，技术可行但时效性低。",[200],{"name":193,"url":190},[27,202,28,29],"农业机器人",{"code":34,"slug":35,"name":29,"path":36,"parent_code":37},[205],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":207},{"candidates":208,"agricultural_evidence":54},[209,213,217,222],{"code":34,"score":60,"supported":54,"title":210,"abstract":211,"tags":212},[35],[29,35],[29],{"code":40,"score":60,"supported":54,"title":214,"abstract":215,"tags":216},[148],[68,148],[28],{"code":218,"score":22,"supported":14,"title":219,"abstract":220,"tags":221},"F6",[],[],[202],{"code":52,"score":22,"supported":14,"title":223,"abstract":224,"tags":225},[],[],[27],[227,228],"农业机器人 变量施肥 智慧农业 玉米","农业机器人 变量施肥","农业机器人变量施肥智慧农业玉米-1754",4,"10.1016\u002Fj.compag.2026.112349",{"doi":231,"openalex_id":233,"authors":234,"venue":193,"cited_by_count":103,"oa_url":9,"card":242,"direction":246,"ingested_from":110},"W7208817219",[235,237,240],{"name":236,"orcid":9},"Lalo Villacorta",{"name":238,"orcid":239},"Adrian Medrano","https:\u002F\u002Forcid.org\u002F0009-0009-2010-3828",{"name":241,"orcid":9},"Miguel Lara",{"tldr":243,"method":244,"finding":245,"direction":246,"opportunity":247},"研发低成本自主地面机器人，原位筛查玉米磷含量，支持分区施肥。","低成本UGV搭载原位传感器，结合自主导航与磷筛查技术。","实现低成本、自主的田间磷检测，为分区施肥提供依据。","智慧农业 \u002F 农业物联网","可延伸研究多养分原位传感与自主决策，或结合无人机与地面机器人协同，提升精准施肥效率。","2026-09-06T23:30:01.426286Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":9,"content":254,"source_name":255,"source_url":9,"published_at":256,"category":257,"cover_url":9,"hotness":13,"is_selected":14,"score":258,"score_detail":259,"sources":261,"tags":263,"primary_topic":269,"secondary_topics":270,"classification":278,"search_phrases":303,"slug":306,"view_count":81,"doi":9,"paper":9,"created_at":307},3208,"就在明天！丰收节主场活动有哪些亮点？攻略提前看！——百亩高蛋白玉米水肥一体化标准化种植+智能收割\u002F精量播种三大场景看科技赋能良田","https:\u002F\u002Fwww.ntv.cn\u002Fcontent\u002F1\u002F749\u002F991749881.html","9-22 农视网提前探访襄阳丰收节主场活动：湖北省襄阳市作为湖北首个产能过百亿斤的粮食大市，本届丰收节主场活动落地襄阳东郊村，三大场景看科技赋能良田——百亩高蛋白玉米田示范区集中展示高蛋白玉米水肥一体化标准化种植技术（\"三高协同\"）；田间智能收割、土地深翻、精量播种等现代化农事作业场景实景呈现；农业科普研学基地涵盖长江稻作文明、科技农业、绿色农业、质量农业、品牌农业、宜居宜业和美乡村六大主题板块，配套智慧农业水肥一体化沙盘和\"动物乐园\"等互动体验。丰收市集重拾襄阳传统赶集烟火气，汇聚农产品展销、沉浸式趣味体验、特色品牌推介等丰富活动。","9月的荆楚大地，\n\n稻田飘香，丰收在望！\n\n2026年9月23日，\n\n第九个“中国农民丰收节”即将如约而至！\n\n湖北省襄阳市地处祖国的南北过渡带，\n\n素有“鄂北粮仓”的美誉，\n\n是湖北首个产能过百亿斤的粮食大市。\n\n本届丰收节主场活动落地襄阳东郊村，\n\n这里准备了哪些精彩纷呈的活动？\n\n农视网记者提前探访踩点，\n\n带你抢先解锁这场热气腾腾的丰收盛宴！\n\n**三大场景看科技赋能良田**\n\n**夯实丰收底气**\n\n走进东郊村“丰景营地”，一派丰收欢腾景象。\n\n百亩高蛋白玉米田成为现场一道亮眼景致，示范区集中展示高蛋白玉米水肥一体化标准化种植技术，茁壮的植株诉说着高产、高蛋白、高收益“三高协同”的丰收故事。\n\n![Image 1: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084793820006.jpg)\n\n百亩高蛋白玉米种植示范地。\n\n田间，智能农机蓄势待发，智能收割、土地深翻、精量播种等现代化农事作业场景将在这里实景呈现。\n\n![Image 2: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084793243191.jpg)\n\n智能农机作业展示现场。\n\n从“丰景营地”往村内走，最引人瞩目的，是铺展向远方的金色稻田，稻穗饱满低垂，在微风里轻轻起伏。绿色高标准种植技术落地田间，把沃土、良种与科学管护融为一体，绘就一幅稻浪翻滚、粮丰物阜的乡村丰收画卷。\n\n![Image 3: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084794207170.jpg)\n\n游客正在稻田边拍照。\n\n**农业科普研学基地**\n\n**展示现代农业建设成果**\n\n丰收节主场活动另一大亮点，就是农业科普研学基地，基地试运营以来收获满满好评，人气持续高涨。\n\n![Image 4: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084794700388.jpg)\n\n农业科普研学基地内吸引众多游客拍照。\n\n走进研学基地，能一次性打卡六大主题板块：长江稻作文明、科技农业、绿色农业、质量农业、品牌农业、宜居宜业和美乡村。展区搭配图文、实物展品，生动讲述湖北在粮食稳产、科技兴农、绿色发展、长江大保护等方面的现代化农业建设成果，沉浸式传播荆楚大地的农业故事。\n\n基地内丰富的互动体验装置人气十足：曲辕犁、龙骨水车等传统农具重现古代农耕智慧；智慧农业水肥一体化沙盘直观演示现代农业管控技术；“动物乐园”集中展示枣北牛、楚宝黑头羊、鄂通两头乌等湖北本土特色畜禽品种；还有“为自己点餐”趣味互动，让参观者在动手体验中学习膳食营养均衡知识。多样的互动项目，让农业知识看得见、摸得着、学得会。\n\n![Image 5: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084794277066.jpg)\n\n研学的小学生正在智慧农业水肥一体化灌溉系统模型沙盘前认真参观。\n\n**丰收市集聚好物**\n\n**农产品展销营造丰收氛围**\n\n今年丰收节，人气顶流非丰收市集莫属！\n\n![Image 6: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084794830353.jpg)\n\n丰收市集吸引不少村民驻足。\n\n市集重拾襄阳传统赶集的浓浓烟火气，汇聚农产品展销、沉浸式趣味体验、特色品牌推介等丰富活动。江汉大米、潜江龙虾、仙桃黄鳝、随州香菇、秭归脐橙等湖北地标农品集中亮相，现场还有各类试吃品鉴活动，让大家近距离感受地道农味。\n\n![Image 7: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084794774587.jpg)\n\n![Image 8: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084794482198.jpg)\n\n![Image 9: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084795321450.jpg)\n\n来自湖北本地的特色农产品品牌吸引游客品尝。\n\n市集不止湖北好物，全国农业精品品牌、欠发达地区特色好物也同台展示展销。琳琅满目的特产、热闹的人群，处处洋溢着喜庆丰盈的丰收氛围感，一站式领略来自五湖四海的丰收喜悦！\n\n![Image 10: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084795454862.jpg)\n\n![Image 11: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084795558546.jpg)\n\n![Image 12: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084795461593.jpg)\n\n全国农业精品品牌、欠发达地区特色好物在丰收节进行展示展销。\n\n除此之外，丰收节主场活动利用全国优质农产品资源，推出“乡村厨神展示活动”，莲藕全席宴、宜昌鮰鱼宴、襄阳八大碗、钟祥蟠龙宴等特色宴席轮番亮相，一道道乡土美味尽显荆楚大地丰收滋味。\n\n![Image 13: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084795666643.jpg)\n\n特色菜品在“乡村厨神展示活动”亮相。\n\n**农业非遗技艺展演**\n\n**近距离感受老手艺魅力**\n\n以农为媒，以文润丰。丰收节主场精心打造非遗文化展演专区，多项特色农业非遗技艺轮番亮相，让传统老手艺在丰收时节焕发新生。\n\n竹艺编织、木版年画、手工米酒酿造、传统泥塑、民俗剪纸、枣阳粗布制作等非遗项目现场展演，手艺人巧手翻飞、匠心演绎，全方位展示传统手艺的独特魅力，让大众近距离触摸乡土文化底蕴、感受传统民俗韵味。\n\n![Image 14: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084795113339.jpg)\n\n枣阳粗布制作工艺展示。\n\n同时，现场开设沉浸式农耕研学体验项目，晒秋摆盘、传统碾米、手工磨面等经典农事体验，让游客亲身参与传统农耕劳作，沉浸式感受秋收乐趣，为丰收佳节增添满满烟火趣味。\n\n“村戏连台”好戏不断，楚剧等本土特色戏曲将轮番上演，乡音袅袅，尽显乡土文化活力。\n\n![Image 15: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084796625227.jpg)\n\n非遗技艺轮番上演。\n\n**带货达人齐聚东郊**\n\n**电商直播大赛打造消费“大卖场”**\n\n本次丰收节创新线上传播与销售模式，精心举办“云播丰年”村播电商直播大赛，众多带货达人齐聚东郊村，助力农品出圈、赋能乡村振兴。\n\n![Image 16: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084796167570.jpg)\n\n![Image 17: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084796230704.jpg)\n\n“云播丰年”村播电商直播大赛正在紧锣密鼓筹备中。\n\n活动集中推介展销襄阳优质粮油、地道襄阳牛肉面、枣阳黄桃等本土特色农产品，以“线上直播带货+线下产销对接”双向发力，打通农产品产销链路、拓宽销售渠道。持续擦亮“襄飘天下”区域公用品牌，以节兴商、以节促销，全力打造城乡联动、供需互通的乡村消费“大卖场”，让襄阳优质农品走出乡土、走向全国。\n\n**村晚舞台农民做主角**\n\n**唱响东郊村丰收故事**\n\n丰收的舞台，属于每一位耕耘的农民。\n\n本次丰收节聚焦农民主体、彰显乡土本色，专属“村晚”舞台将在9月23日晚上19:00精彩上线，让广大农民成为节日的主角。\n\n![Image 18: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084796305126.jpg)\n\n丰收村晚舞台。\n\n硬核“村装”登上村T台：东郊村村民扛着农具、绣娘带着手艺自信登台，把传统手工艺和乡村美学穿在身上，秀出新时代农民的精气神。\n\n兴农人亮绝活：“铁锹哥”祁春新、山西唐尧锣鼓艺术团同台，兴农人代表现场过招，还有正大集团、云天化等企业嘉宾助阵。\n\n歌声里的田野：水木年华缪杰唱响《未来归来》《智在田野间》，刘劲深情讲述《那些年 我们这样唱》，乌兰图雅压轴《美丽中国》。\n\n![Image 19: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084796121108.jpg)\n\n![Image 20: 图片](https:\u002F\u002Frmtsource.ntv.cn\u002Ffiles\u002Fapp\u002Fnsw\u002Fimages\u002F2609\u002F22\u002F1790084796932199.jpg)\n\n参加丰收村晚的演员正在进行彩排。\n\n田间地头变舞台，13个节目层层递进，从五谷归仓讲到美丽中国！村民们登台献艺、载歌载舞，用最质朴的表演、最真挚的情感，讲述东郊村的振兴故事、唱响属于全国农民朋友的丰收赞歌！\n\n中国农民丰收节，\n\n不止是属于亿万农民的丰收盛典，\n\n更是各地集中展示乡村振兴丰硕成果，\n\n田间为幕，大地为台\n\n9 月 23 日\n\n相约湖北枣阳东郊村\n\n精彩纷呈的丰收盛宴已备好\n\n满满烟火、万般丰收静待相逢","农视网","2026-09-22T09:00:00Z","报道",64,{"impact":123,"substance":124,"depth":17,"authority":13,"freshness":13,"relevant":22,"comment":260},"全国性丰收节主场活动的现场探访报道，科技赋能良田与农产品展销内容具体，但属活动预告性质，信息增量有限。",[262],{"name":255,"url":252},[27,264,265,266,267,268],"智能农机","水肥一体化","农民丰收节","高蛋白玉米","电商直播助农",{"code":34,"slug":35,"name":29,"path":36,"parent_code":37},[271,272],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":273,"slug":274,"name":275,"path":276,"parent_code":277},"J4","seasonal-agriculture","农时与节庆活动","\u002Ftopic\u002Fpolicy\u002Fseasonal-agriculture","J",{"status":46,"locked":14,"version":47,"method":48,"evidence":279},{"candidates":280,"agricultural_evidence":54},[281,285,289,294,298],{"code":34,"score":17,"supported":54,"title":282,"abstract":283,"tags":284},[29],[29],[267],{"code":40,"score":17,"supported":54,"title":286,"abstract":287,"tags":288},[265],[265],[265],{"code":273,"score":17,"supported":54,"title":290,"abstract":292,"tags":293},[291],"丰收节",[291],[266],{"code":52,"score":230,"supported":54,"title":295,"abstract":296,"tags":297},[],[27],[27],{"code":299,"score":22,"supported":14,"title":300,"abstract":301,"tags":302},"F5",[],[],[264],[304,305],"襄阳 东郊村 丰收节","高蛋白玉米 水肥一体化","襄阳东郊村丰收节-3208","2026-09-23T00:04:26.921701Z",{"id":309,"title":310,"url":311,"summary":312,"summary_zh":313,"content":9,"source_name":314,"source_url":311,"published_at":315,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":316,"score_detail":317,"sources":320,"tags":322,"primary_topic":326,"secondary_topics":327,"classification":328,"search_phrases":376,"slug":379,"view_count":197,"doi":380,"paper":381,"created_at":393},5235,"Role of Smart Fertilization in Modern Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijpss\u002F2026\u002Fv38i106378","Global food demand is increasing rapidly owing to population growth, placing considerable pressure on agricultural systems to enhance productivity under constraints on natural resources. Although conventional fertiliser use has contributed significantly to improved yields, its excessive and imbalanced application has resulted in soil degradation, nutrient leaching, groundwater contamination, and increased emissions of nitrous oxide (N₂O), thereby reducing nutrient use efficiency (NUE). In this context, smart fertilisation has emerged as a science-driven nutrient management strategy that emphasises the application of the right nutrient source at the right rate, time and place (4R principle), supported by advanced tools such as soil and plant sensors, remote sensing, geographic information systems, and decision-support platforms. Key components include controlled-release fertilisers, nano-fertilisers, bio-formulations, fertigation systems, variable rate technology (VRT), and unmanned aerial vehicle (UAV)-based crop monitoring. Evidence from multi-location field studies across diverse agro-climatic regions of India and other countries indicates that smart fertilisation can enhance nutrient use efficiency by 50–80%, reduce fertiliser input by 19–30%, and improve crop yields by 22–30% in major crops such as rice, wheat, maize and pulses. With the global fertiliser market exceeding USD 200 billion, the adoption of precision nutrient management technologies is gaining momentum. Overall, smart fertilisation offers a sustainable intensification pathway by improving productivity while simultaneously minimising environmental externalities. However, challenges related to technology accessibility, cost, and farmer adoption remain critical constraints that require policy and institutional support.","由于人口增长，全球粮食需求迅速上升，给农业系统带来了巨大压力，要求在自然资源约束下提高生产力。尽管常规施肥对提高产量作出了显著贡献，但过量和不平衡施肥已导致土壤退化、养分淋失、地下水污染以及氧化亚氮（N₂O）排放增加，从而降低了养分利用效率（NUE）。在此背景下，智能施肥作为一种以科学为驱动的养分管理策略应运而生，强调在正确的时间、正确的地点以正确的用量施用正确的养分来源（4R原则），并借助土壤与植物传感器、遥感、地理信息系统和决策支持平台等先进工具加以支撑。其关键组成部分包括控释肥料、纳米肥料、生物制剂、水肥一体化系统、变量施肥技术（VRT）以及基于无人机（UAV）的作物监测。来自印度及其他国家不同农业气候区多点田间研究的数据表明，智能施肥可使养分利用效率提高50%–80%，减少肥料投入19%–30%，并使水稻、小麦、玉米和豆类等主要作物增产22%–30%。随着全球肥料市场规模超过2000亿美元，精准养分管理技术的采用正在加速推进。总体而言，智能施肥通过提高生产力同时最大限度减少环境负外部性，提供了一条可持续集约化路径。然而，技术可及性、成本和农民采用等方面的挑战仍是关键制约因素，需要政策和制度支持。","International Journal of Plant & Soil Science","2026-10-17T00:00:00Z",83,{"impact":318,"substance":318,"depth":123,"authority":20,"freshness":125,"relevant":22,"comment":319},22,"系统综述智能施肥技术体系与多国田间数据，量化增产节肥效果，对智慧农业与绿色投入品方向有参考价值，但属综述性论文且非国内案例，时效与权威度中等。",[321],{"name":314,"url":311},[27,323,324,28,31,325],"无人机","精准施肥","养分利用效率",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":329},{"candidates":330,"agricultural_evidence":54},[331,336,343,350,357,361,365,372],{"code":40,"score":18,"supported":54,"title":332,"abstract":333,"tags":335},[148],[68,69,265,32,334],"fertigation",[325,28,324],{"code":87,"score":337,"supported":54,"title":338,"abstract":339,"tags":342},7,[],[340,341],"遥感","remote sensing",[31],{"code":344,"score":73,"supported":54,"title":345,"abstract":346,"tags":349},"A1",[],[347,348],"水稻","rice",[],{"code":351,"score":73,"supported":54,"title":352,"abstract":353,"tags":356},"A2",[],[354,355],"小麦","wheat",[],{"code":34,"score":73,"supported":54,"title":358,"abstract":359,"tags":360},[],[29,35],[],{"code":72,"score":73,"supported":54,"title":362,"abstract":363,"tags":364},[],[76,77],[],{"code":366,"score":73,"supported":54,"title":367,"abstract":368,"tags":371},"I4",[],[369,370],"地理信息","geographic information",[],{"code":52,"score":22,"supported":14,"title":373,"abstract":374,"tags":375},[],[],[27],[377,378],"智能施肥 4R原则","变量施肥技术 无人机","智能施肥4R原则-5235","10.9734\u002Fijpss\u002F2026\u002Fv38i106378",{"doi":380,"openalex_id":382,"authors":383,"venue":314,"cited_by_count":103,"oa_url":311,"card":388,"direction":246,"ingested_from":110},"W7222838957",[384,386],{"name":385,"orcid":9},"S. T. Panusha",{"name":387,"orcid":9},"T. M. Soumya",{"tldr":389,"method":390,"finding":391,"direction":246,"opportunity":392},"综述智能施肥（4R原则+传感器\u002F遥感\u002FVRT）提升养分利用效率与产量的机制与证据。","整合多地点田间试验证据，梳理传感器、遥感、GIS、控释\u002F纳米肥与无人机监测等技术","智能施肥可提高养分利用效率50–80%、减施化肥19–30%、增产22–30%，但受成本与农户采纳制","可研究低成本传感器+无人机变量施肥在小农户场景的适配与采纳机制，及减氮减排的量化评估。","2026-10-18T23:30:07.850055Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":9,"content":9,"source_name":399,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":400,"score_detail":401,"sources":403,"tags":405,"primary_topic":407,"secondary_topics":408,"classification":409,"search_phrases":432,"slug":435,"view_count":197,"doi":9,"paper":436,"created_at":444},4477,"玉米生产精准农业系统综述：技术、应用与产量优化（2020—2026）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37500368","依据PRISMA 2020指南，系统检索Scopus与Web of Science共464篇文献，最终纳入129项研究；样本构成为田间试验（28.6%）、遥感与精准农业整合研究（25.5%）、机器学习应用（19.4%）、气候知情精准农业策略（23.4%）和土壤退化研究（3.1%）；研究表明精准农业技术通过定点管理和数据驱动决策支持，提升了玉米生产力、养分利用效率、水利用效率和产量预测准确性；指出采用精准农业仍受制于实施成本高、技术复杂、数据有限等因素。","Precision Agriculture in Maize Production Systematic Review 2026",74,{"impact":123,"substance":318,"depth":123,"authority":20,"freshness":81,"relevant":22,"comment":402},"基于PRISMA 2020的系统综述，纳入129项研究，数据规模与方法规范性强，对玉米精准农业技术路径与瓶颈有实质参考价值，但发布日期不明、时效性偏弱。",[404],{"name":399,"url":397},[27,406,84,29,30,340],"产量预测",{"code":34,"slug":35,"name":29,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":410},{"candidates":411,"agricultural_evidence":54},[412,416,420,424,428],{"code":52,"score":20,"supported":54,"title":413,"abstract":414,"tags":415},[30],[30],[27,30],{"code":34,"score":17,"supported":54,"title":417,"abstract":418,"tags":419},[29],[29],[29],{"code":87,"score":156,"supported":54,"title":421,"abstract":422,"tags":423},[],[340],[406,340],{"code":80,"score":230,"supported":54,"title":425,"abstract":426,"tags":427},[],[84],[84],{"code":72,"score":81,"supported":54,"title":429,"abstract":430,"tags":431},[],[76],[],[433,434],"玉米 精准农业 系统综述","玉米 遥感 机器学习 产量","玉米精准农业系统综述-4477",{"doi":9,"openalex_id":9,"authors":437,"venue":9,"cited_by_count":103,"oa_url":9,"card":438,"direction":246,"ingested_from":443},[],{"tldr":439,"method":440,"finding":441,"direction":246,"opportunity":442},"系统综述2020—2026年玉米精准农业技术、应用与产量优化研究。","按PRISMA 2020检索Scopus与WoS，纳入129项研究做分类分析。","精准农业通过定点管理与数据决策提升玉米产量、养分和水利用效率。","成本高、技术复杂与数据不足制约推广，低成本轻量化方案与本地化数据共享是研究空白。","agent","2026-10-09T00:05:02.451957Z",{"id":446,"title":447,"url":448,"summary":449,"summary_zh":450,"content":9,"source_name":119,"source_url":448,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":54,"score":451,"score_detail":452,"sources":455,"tags":457,"primary_topic":460,"secondary_topics":461,"classification":463,"search_phrases":498,"slug":501,"view_count":73,"doi":502,"paper":503,"created_at":535},3525,"Multi-source data assimilation of Sentinel-2 reflectance and SMAP soil moisture into APSIM for maize biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10442-6","Crop growth models (CGMs) are valuable tools for agricultural monitoring. However, the need for many input parameters, the uncertainties related to model parametrization and structure, and the lack of spatial information motivate the application of techniques such as data assimilation (DA). This paper proposes a DA framework to improve maize biomass estimation. A particle filter (PF) was used to assimilate remotely sensed reflectance and soil moisture (SM) data, both independently and simultaneously, into the Agricultural Production Systems sIMulator (APSIM) model. Reflectance observations from Sentinel-2 were assimilated through coupling APSIM with the radiative transfer model (RTM) PROSAIL, while SMAP L-band SM products were directly assimilated into APSIM. The synthetic experiment, designed to evaluate the reliability of the proposed procedure, highlighted the strength of assimilating reflectance to constrain crop traits and of SM to provide complementary information on crop water status and to contribute to more robust ensemble trajectories. Real-case results confirmed these findings. DA assimilation of SM proved valuable particularly under data gaps and drought conditions. Although it did not consistently surpass single-source assimilation strategies, the joint assimilation yielded consistent results, especially in 2023, where RMSE, nRMSE and bias were 1258.83 kg\u002Fha, 16.62%, and - 220.52 kg\u002Fha, respectively. The proposed framework demonstrates the potential of multi-source DA to enhance biomass estimation and support robust, spatially explicit crop monitoring.","作物生长模型（CGM）是农业监测的重要工具。然而，其对大量输入参数的需求、模型参数化与结构相关的不确定性以及空间信息的缺乏，推动了数据同化（DA）等技术的应用。本文提出了一种数据同化框架，以改进玉米生物量估算。采用粒子滤波（PF）将遥感反射率和土壤水分（SM）数据分别独立及同时同化进入农业生产系统模拟器（APSIM）模型。Sentinel-2反射率观测数据通过将APSIM与辐射传输模型（RTM）PROSAIL耦合进行同化，而SMAP L波段土壤水分产品则直接同化进入APSIM。为评估所提方法可靠性而设计的合成实验，凸显了同化反射率在约束作物性状方面的优势，以及同化土壤水分在提供作物水分状况补充信息和促进更稳健集合轨迹方面的作用。实际案例结果证实了这些发现。土壤水分数据同化在数据缺失和干旱条件下尤为有价值。尽管联合同化未能持续超越单源同化策略，但其结果具有一致性，尤其在2023年，RMSE、nRMSE和偏差分别为1258.83 kg\u002Fha、16.62%和-220.52 kg\u002Fha。所提出的框架展示了多源数据同化在增强生物量估算和支持稳健、空间显式作物监测方面的潜力。",82,{"impact":123,"substance":318,"depth":453,"authority":124,"freshness":21,"relevant":22,"comment":454},19,"将Sentinel-2反射率与SMAP土壤水分同化进APSIM模型估算玉米生物量，方法新颖、结论可靠，对作物遥感监测有参考价值。",[456],{"name":119,"url":448},[27,29,458,31,459],"作物模型","数据同化",{"code":34,"slug":35,"name":29,"path":36,"parent_code":37},[462],{"code":72,"slug":77,"name":136,"path":137,"parent_code":138},{"status":46,"locked":14,"version":47,"method":48,"evidence":464},{"candidates":465,"agricultural_evidence":54},[466,470,474,481,485,489,493],{"code":34,"score":60,"supported":54,"title":467,"abstract":468,"tags":469},[35],[29,35],[29],{"code":72,"score":124,"supported":54,"title":471,"abstract":472,"tags":473},[77],[76,77],[],{"code":475,"score":73,"supported":54,"title":476,"abstract":477,"tags":480},"H7",[],[478,479],"干旱","drought",[],{"code":87,"score":230,"supported":54,"title":482,"abstract":483,"tags":484},[],[340],[31],{"code":52,"score":22,"supported":14,"title":486,"abstract":487,"tags":488},[],[],[27],{"code":80,"score":22,"supported":14,"title":490,"abstract":491,"tags":492},[],[],[458],{"code":494,"score":22,"supported":14,"title":495,"abstract":496,"tags":497},"I5",[],[],[459],[499,500],"Sentinel-2 SMAP APSIM 玉米生物量","多源数据同化 玉米 遥感","Sentinel-2SMAPAPSIM玉米生物量-3525","10.1007\u002Fs11119-026-10442-6",{"doi":502,"openalex_id":504,"authors":505,"venue":119,"cited_by_count":103,"oa_url":448,"card":529,"direction":533,"ingested_from":110},"W7214405444",[506,508,511,514,517,519,521,524,526],{"name":507,"orcid":9},"Manuela Montella",{"name":509,"orcid":510},"Christian Bossung","https:\u002F\u002Forcid.org\u002F0000-0003-4651-2645",{"name":512,"orcid":513},"Thanh Huy Nguyen","https:\u002F\u002Forcid.org\u002F0000-0003-2471-350X",{"name":515,"orcid":516},"Marco Chini","https:\u002F\u002Forcid.org\u002F0000-0002-9094-0367",{"name":518,"orcid":9},"Jean François Iffly",{"name":520,"orcid":9},"Thomas Udelhoven",{"name":522,"orcid":523},"Julia Kubanek","https:\u002F\u002Forcid.org\u002F0000-0001-9597-2029",{"name":525,"orcid":9},"Zoltan Szantoi",{"name":527,"orcid":528},"Miriam Machwitz","https:\u002F\u002Forcid.org\u002F0000-0002-4999-673X",{"tldr":530,"method":531,"finding":532,"direction":533,"opportunity":534},"将Sentinel-2反射率与SMAP土壤水分同化进APSIM模型，提升玉米生物量估算精度。","粒子滤波同化Sentinel-2反射率（经PROSAIL耦合）与SMAP土壤水分","联合同化结果稳健，2023年RMSE为1258.83 kg\u002Fha，nRMSE 16.62%，偏差-2","农业遥感与作物表型","可探索多源数据同化在数据缺失与干旱条件下的自适应权重策略，并推广至其他作物与区域。","2026-09-26T23:30:03.247019Z"]