[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3136":3,"related-3136":105},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":21,"tags":23,"primary_topic":29,"secondary_topics":34,"classification":41,"search_phrases":64,"slug":67,"view_count":19,"doi":68,"paper":69,"created_at":104},3136,"Integrated water–carbon management reshapes trade-offs among greenhouse gas emissions, productivity, and economic returns in rice systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104991","Integrated water–carbon management reshapes trade-offs among greenhouse gas emissions, productivity, and economic returns in rice systems。Agricultural Systems",null,"Agricultural Systems","2026-09-22T00:00:00Z","论文",10,false,82,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":12,"relevant":19,"comment":20},18,22,14,1,"核心期刊新研究，提出水碳协同管理以平衡稻田减排、产量与收益，方法新颖、结论有实质增量，对农业绿色低碳与智慧灌溉有参考价值。",[22],{"name":9,"url":6},[24,25,26,27,28],"水稻","农业温室气体","水碳协同","稻田减排","农业经济评估",{"code":30,"slug":31,"name":24,"path":32,"parent_code":33},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[35],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",{"status":42,"locked":13,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,53,59],{"code":30,"score":48,"supported":49,"title":50,"abstract":51,"tags":52},12,true,[31],[31],[24],{"code":36,"score":54,"supported":49,"title":55,"abstract":57,"tags":58},11,[56],"greenhouse",[56],[],{"code":60,"score":19,"supported":13,"title":61,"abstract":62,"tags":63},"H4",[],[],[25],[65,66],"水稻 水碳管理 温室气体","稻田 减排 经济收益","水稻水碳管理温室气体-3136","10.1016\u002Fj.agsy.2026.104991",{"doi":68,"openalex_id":70,"authors":71,"venue":9,"cited_by_count":102,"oa_url":6,"card":8,"direction":8,"ingested_from":103},"W7213974400",[72,74,76,78,80,82,85,88,90,92,94,96,99,100],{"name":73,"orcid":8},"Zewei Jiang",{"name":75,"orcid":8},"Shihong Yang",{"name":77,"orcid":8},"Qingqing Pang",{"name":79,"orcid":8},"Pete Smith",{"name":81,"orcid":8},"Mohamed Abdalla",{"name":83,"orcid":84},"Matthias Kuhnert","https:\u002F\u002Forcid.org\u002F0000-0003-3284-2133",{"name":86,"orcid":87},"Saeed Karbin","https:\u002F\u002Forcid.org\u002F0000-0002-8327-8037",{"name":89,"orcid":8},"Yi Xu",{"name":91,"orcid":8},"Jie Zhang",{"name":93,"orcid":8},"Haonan Qiu",{"name":95,"orcid":8},"Xishan Song",{"name":97,"orcid":98},"Qinbo Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5327-8837",{"name":89,"orcid":8},{"name":101,"orcid":8},"Yakov Kuzyakov",0,"openalex","2026-09-22T23:30:05.426576Z",{"total":106,"page":19,"page_size":106,"items":107},6,[108,231,284,348,389,441],{"id":109,"title":110,"url":111,"summary":112,"summary_zh":113,"content":8,"source_name":9,"source_url":111,"published_at":114,"category":11,"cover_url":8,"hotness":12,"is_selected":49,"score":115,"score_detail":116,"sources":122,"tags":124,"primary_topic":129,"secondary_topics":130,"classification":138,"search_phrases":182,"slug":185,"view_count":166,"doi":186,"paper":187,"created_at":230},2912,"Spatial optimization of water-saving irrigation in Chinese rice paddies: Balancing yield, greenhouse gases, and cost using NSGA-II","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104976","CONTEXT Optimizing the spatial allocation of water-saving irrigation (WSI) promotion is critical for balancing rice production, greenhouse gas (GHG) mitigation, and economic costs, yet remains challenging at a national scale due to computational intractability. OBJECTIVE This study aims to develop a spatially explicit optimization framework to identify WSI promotion pathways across China's rice paddies that simultaneously maximize yield gain, maximize GHG reduction, and minimize implementation cost. METHODS We coupled K-means clustering with a multi-objective evolutionary algorithm (NSGA-II). Based on machine-learning predicted yield and GHG emissions for 157,417 flooded irrigation grids, we first clustered these grids into 500 environmentally and agronomically homogeneous groups. We then formulated a continuous optimization problem with cluster-level conversion rates as decision variables, simultaneously maximizing national yield gain, maximizing GHG reduction, and minimizing implementation cost. RESULTS AND CONCLUSIONS The Pareto front comprised 100 non-dominated solutions spanning promotion rates from 61.8% to 80.3%. The optimal solution, selected by normalized scoring, achieved 6.47 Mt. yield gain and 63.5 Mt. CO 2 e GHG reduction at a cost of 10.14 billion CNY, corresponding to a national promotion rate of 80.3%. Cluster-scale conversion rates exhibited significant spatial heterogeneity and positive correlation with cluster size ( r = 0.23), revealing economies of scale in WSI promotion. Compared with a random promotion strategy at 90% adoption, our optimized solution delivered 74% higher yield gain with 9.7 percentage points lower promotion effort while achieving comparable GHG reduction. SIGNIFICANCE Our framework provides a spatially explicit decision-support tool for precision agricultural policy, demonstrating that smart spatial allocation can substantially enhance the efficiency of limited resources in scaling climate-smart agricultural practices.","背景 优化节水灌溉（WSI）推广的空间配置对于平衡水稻生产、温室气体（GHG）减排和经济成本至关重要，但由于计算上的不可处理性，在全国尺度上仍具挑战性。目的 本研究旨在开发一个空间显式优化框架，以识别中国稻田的WSI推广路径，同时最大化产量增益、最大化GHG减排并最小化实施成本。方法 我们将K-means聚类与多目标进化算法（NSGA-II）相结合。基于机器学习预测的157,417个淹水灌溉网格的产量和GHG排放，我们首先将这些网格聚类为500个在环境和农艺上同质的组。然后，我们构建了一个以组级转换率为决策变量的连续优化问题，同时最大化全国产量增益、最大化GHG减排并最小化实施成本。结果与结论 Pareto前沿包含100个非支配解，推广率从61.8%到80.3%不等。通过归一化评分选出的最优解实现了6.47 Mt的产量增益和63.5 Mt CO₂e的GHG减排，成本为101.4亿元人民币，对应全国推广率为80.3%。组尺度转换率表现出显著的空间异质性，并与组规模呈正相关（r = 0.23），揭示了WSI推广中的规模经济。与90%采纳率的随机推广策略相比，我们的优化方案在推广力度低9.7个百分点的情况下实现了高出74%的产量增益，同时实现了相当的GHG减排。意义 我们的框架为精准农业政策提供了一个空间显式的决策支持工具，表明智能空间配置可以显著提高有限资源在推广气候智慧型农业实践中的效率。","2026-09-18T00:00:00Z",89,{"impact":117,"substance":118,"depth":119,"authority":18,"freshness":120,"relevant":19,"comment":121},24,23,19,9,"基于NSGA-II的全国稻田节水灌溉空间优化框架，数据规模大、结论具体，对气候智慧型农业政策有决策参考价值。",[123],{"name":9,"url":111},[125,24,126,127,128],"智慧农业","空间优化","节水灌溉","农业减排",{"code":30,"slug":31,"name":24,"path":32,"parent_code":33},[131,137],{"code":132,"slug":133,"name":134,"path":135,"parent_code":136},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":42,"locked":13,"version":43,"method":44,"evidence":139},{"candidates":140,"agricultural_evidence":49},[141,146,152,157,164,171,178],{"code":30,"score":16,"supported":49,"title":142,"abstract":143,"tags":145},[31],[24,144,31],"稻田",[24],{"code":132,"score":147,"supported":49,"title":148,"abstract":149,"tags":151},15,[133],[150,133],"灌溉",[127],{"code":36,"score":18,"supported":49,"title":153,"abstract":154,"tags":156},[56],[155,56],"温室",[],{"code":158,"score":106,"supported":49,"title":159,"abstract":160,"tags":163},"J1",[],[161,162],"农业政策","agricultural policy",[],{"code":165,"score":166,"supported":49,"title":167,"abstract":168,"tags":170},"I1",4,[],[169],"精准农业",[125],{"code":172,"score":173,"supported":49,"title":174,"abstract":175,"tags":177},"I2",3,[],[176],"机器学习",[],{"code":60,"score":19,"supported":13,"title":179,"abstract":180,"tags":181},[],[],[128],[183,184],"中国稻田 节水灌溉 空间优化","NSGA-II 水稻 温室气体 减排","中国稻田节水灌溉空间优化-2912","10.1016\u002Fj.agsy.2026.104976",{"doi":186,"openalex_id":188,"authors":189,"venue":9,"cited_by_count":102,"oa_url":111,"card":224,"direction":228,"ingested_from":103},"W7213551595",[190,192,195,198,200,202,204,206,209,211,214,217,220,222],{"name":191,"orcid":8},"Qiang Xu",{"name":193,"orcid":194},"Fan Yao","https:\u002F\u002Forcid.org\u002F0000-0002-4393-7296",{"name":196,"orcid":197},"Dan Wei","https:\u002F\u002Forcid.org\u002F0000-0003-4401-567X",{"name":199,"orcid":8},"Hui Gao",{"name":201,"orcid":8},"Min Jiang",{"name":203,"orcid":8},"Wenya Chen",{"name":205,"orcid":8},"Yourui Cao",{"name":207,"orcid":208},"Peng Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-3036-1507",{"name":210,"orcid":8},"A. I. Abdo",{"name":212,"orcid":213},"Liujun Xiao","https:\u002F\u002Forcid.org\u002F0000-0002-1900-1586",{"name":215,"orcid":216},"Hao Liang","https:\u002F\u002Forcid.org\u002F0000-0002-9955-6492",{"name":218,"orcid":219},"Xiaoqing Cui","https:\u002F\u002Forcid.org\u002F0000-0002-1970-5145",{"name":221,"orcid":8},"Xia Liang",{"name":223,"orcid":8},"Huiqing Bai",{"tldr":225,"method":226,"finding":227,"direction":228,"opportunity":229},"构建空间优化框架，为中国稻田节水灌溉推广寻找兼顾产量、温室气体与成本的路径。","耦合K-means聚类与NSGA-II多目标进化算法，基于15.7万网格的机器学","最优方案增产6.47 Mt、减排63.5 Mt CO2e，成本101.4亿元，推广率80.3%，存在","农业绿色发展与碳","可将该空间优化框架扩展到其他气候智慧型农业技术，并耦合农户采纳行为与政策激励。","2026-09-19T23:30:05.508592Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":8,"content":236,"source_name":237,"source_url":8,"published_at":238,"category":239,"cover_url":8,"hotness":12,"is_selected":13,"score":240,"score_detail":241,"sources":245,"tags":247,"primary_topic":252,"secondary_topics":253,"classification":254,"search_phrases":279,"slug":282,"view_count":102,"doi":8,"paper":8,"created_at":283},5449,"安徽省农科院专家在淮南潘集「绿色吨半粮」基地开展水稻良种与高效肥测产观摩","https:\u002F\u002Fwww.panji.gov.cn\u002Fpjzx\u002Fjcgz\u002F11259072035.html","10 月 14 日上午，水稻良种和高效肥展示观摩会在淮南市潘集区贺疃镇「绿色吨半粮」示范基地举行，来自中国农业大学、南京农业大学、安徽省农业科学院、安徽农业大学的专家教授以及种粮大户、基层农技人员参加。示范田内多个水稻新品种分区种植，同步设置高效肥与常规施肥对照试验田块，通过机收和人员测产直观展示良种搭配高效肥料的增产效果，为做好新一年店集贡米品种选育与宣传推广提供技术依据，推动水稻单产提升行动。","为加快优良水稻品种、高效新型肥料示范推广，推动良种良法配套落地，助力粮食稳产增产，10月14日上午，水稻良种和高效肥展示观摩会在贺疃镇“绿色吨半粮”示范基地举行。来自中国农业大学、南京农业大学、安徽省农业科学院、安徽农业大学的专家教授以及种粮大户、基层农技人员齐聚基地，现场观摩对比，开展实收测产。\n\n在示范田内，多个水稻新品种分区种植，同步设置高效肥与常规施肥对照试验田块。观摩会现场，通过查看不同品种的长势、抗倒伏、抗病性，对比施用高效肥和常规肥料水稻的株型、穗粒、结实情况，现场讲解品种特性、科学施肥、田间管护等关键技术要点，为种粮大户答疑解惑，做好新一年的店集贡米的品种选育、宣传推广。现场通过机收和人员测产，直观展示了优良品种搭配高效肥料的增产效果，用真实数据验证良种与高效肥相结合的增产提质、节肥增效优势，对推进水稻单产提升行动具有积极作用。\n\n潘集区农技推广中心主任杨中保表示：“近年来，我们立足本地农业生产实际，聚焦粮食安全底线、农业增效目标、农民增收核心，扎实推进农作物良种筛选推广、配套栽培良法落地、新型肥料示范应用等一系列重点工作，通过建示范、抓培训、强指导、树样板，良种良法配套、科学施肥增效的农业生产新模式全面落地，让农作物抗灾能力、单产水平、农产品品质稳步提升，农业生产综合效益持续提高，农业生产取得了阶段性成效。”\n\n通过现场观摩不同品种的水稻生长特性，选用适配本地气候土壤的水稻良种，配套应用高效肥料，能够提升肥料利用率，对于减少化肥投入，改善稻米品质，推动水稻绿色高产，壮大潘集水稻产业的有效路径。此次观摩测产活动，搭建起科研院所、农技部门与种植主体交流对接的平台，打通农业科技成果转化 “最后一公里”。\n\n潘集区农技推广中心主任杨中保表示：“下一步，我们农技部门要锚定农业高质量发展总目标，紧扣稳粮、提质、增效、绿色核心任务，持续深耕良种良法推广、科学施肥示范工作，推动农业生产提质增效、提档升级，为稳定粮食生产、壮大特色农业、助力乡村产业振兴提供有力支撑。”","淮南市潘集区人民政府 2026-10-16","2026-10-16T00:00:00Z","报道",50,{"impact":48,"substance":18,"depth":54,"authority":242,"freshness":243,"relevant":19,"comment":244},8,5,"地方示范基地的良种良法观摩测产活动，有现场数据和多方信源，但属区域性农技推广动态，公共价值有限。",[246],{"name":237,"url":234},[24,248,249,250,251],"单产提升","科学施肥","良种良法","绿色吨半粮",{"code":30,"slug":31,"name":24,"path":32,"parent_code":33},[],{"status":42,"locked":13,"version":43,"method":44,"evidence":255},{"candidates":256,"agricultural_evidence":49},[257,261,269,274],{"code":30,"score":48,"supported":49,"title":258,"abstract":259,"tags":260},[24],[24],[24],{"code":262,"score":263,"supported":49,"title":264,"abstract":265,"tags":268},"F3",7,[],[266,267],"施肥","肥料",[249],{"code":270,"score":166,"supported":49,"title":271,"abstract":272,"tags":273},"A8",[],[248],[248],{"code":275,"score":19,"supported":13,"title":276,"abstract":277,"tags":278},"F1",[],[],[250],[280,281],"潘集 绿色吨半粮 水稻","安徽省农科院 水稻 高效肥 测产","潘集绿色吨半粮水稻-5449","2026-10-21T00:10:02.459897Z",{"id":285,"title":286,"url":287,"summary":288,"summary_zh":8,"content":289,"source_name":290,"source_url":8,"published_at":291,"category":239,"cover_url":8,"hotness":12,"is_selected":13,"score":292,"score_detail":293,"sources":297,"tags":299,"primary_topic":304,"secondary_topics":305,"classification":306,"search_phrases":343,"slug":346,"view_count":102,"doi":8,"paper":8,"created_at":347},5447,"安徽省农科院「银月丝苗」331 亩示范片测产 亩产 605.2 公斤 增产 31.6%","https:\u002F\u002Fwww.aaas.org.cn\u002F4303179\u002F71505657.html","安徽省农业科学院农业机械装备与工程所牵头、联合水稻研究所实施的省财政农业科技成果转化项目「粮油农文旅融合集成技术转化应用」，10 月 12 日在黄山市休宁县商山镇荪田村对水稻新品种「银月丝苗」百亩示范片开展现场测产验收。示范片面积 331 亩，配套水稻机直播、机插同步侧深施肥、稻田杂草与病虫害绿色防控等技术，实收折算干谷亩产 605.2 公斤，比去年黄山市水稻平均亩产 460.0 公斤高出 145.2 公斤、增产幅度 31.6%，远超项目亩增产 50 公斤的预期目标。","发布时间：2026-10-15 14:46   来源：安徽省农业科学院机械与工程所    阅读： 次   字体：[大] [中] [小]   保护视力色：\n\n2026年10月12日，我院的安徽省财政农业科技成果转化项目“粮油农文旅融合集成技术转化应用”，在黄山市休宁县商山镇荪田村实施的水稻新品种“银月丝苗”百亩示范片进行现场测产验收。该项目由农业机械装备与工程所牵头、联合水稻研究所共同实施。\n\n专家组听取了实施情况汇报，实地考察了项目示范片并机收测产。示范片面积331亩，种植了水稻新品种“银月丝苗”，配套应用了水稻机直播、机插同步侧深施肥、稻田杂草绿色防控、优质稻病虫害绿色防控等绿色高产优质栽培技术。示范片处于黄熟期，专家组随机选取代表性田块，实收折算干谷亩产达605.2公斤。示范片单产比去年黄山市统计的水稻平均亩产460.0公斤高出145.2公斤，增产幅度达31.6%，远超项目增产50公斤的预期目标。\n\n示范片田间生长整齐平衡，熟相好，未发现明显病虫草害，充分展现了该品种高产、优质、抗倒伏能力较强的综合优势，项目区生态环境优良，稻米产量高、品质优，同时展现了稻丰田园的大地景观，为游客初秋乡村旅游和美食体验提供了良好去处。","安徽省农业科学院 2026-10-15","2026-10-15T00:00:00Z",69,{"impact":16,"substance":294,"depth":48,"authority":295,"freshness":106,"relevant":19,"comment":296},20,13,"省级科研机构主导的新品种示范测产，数据具体、增产显著，具备行业参考价值，但属区域性进展，时效略滞后。",[298],{"name":290,"url":287},[24,300,301,302,303],"成果转化","测产验收","绿色栽培","银月丝苗",{"code":30,"slug":31,"name":24,"path":32,"parent_code":33},[],{"status":42,"locked":13,"version":43,"method":44,"evidence":307},{"candidates":308,"agricultural_evidence":49},[309,313,318,322,328,334,338],{"code":30,"score":263,"supported":49,"title":310,"abstract":311,"tags":312},[],[24,144],[24],{"code":314,"score":166,"supported":49,"title":315,"abstract":316,"tags":317},"G1",[],[300],[300],{"code":262,"score":173,"supported":49,"title":319,"abstract":320,"tags":321},[],[266],[],{"code":323,"score":173,"supported":49,"title":324,"abstract":325,"tags":327},"F4",[],[326],"绿色防控",[],{"code":329,"score":173,"supported":49,"title":330,"abstract":331,"tags":333},"F5",[],[332],"农业机械",[],{"code":270,"score":19,"supported":13,"title":335,"abstract":336,"tags":337},[],[],[301],{"code":339,"score":19,"supported":13,"title":340,"abstract":341,"tags":342},"H8",[],[],[302],[344,345],"安徽省农科院 银月丝苗 水稻","休宁 水稻 测产 605.2公斤","安徽省农科院银月丝苗水稻-5447","2026-10-21T00:10:02.221618Z",{"id":349,"title":350,"url":351,"summary":352,"summary_zh":8,"content":353,"source_name":354,"source_url":8,"published_at":238,"category":239,"cover_url":8,"hotness":12,"is_selected":13,"score":355,"score_detail":356,"sources":359,"tags":361,"primary_topic":365,"secondary_topics":366,"classification":367,"search_phrases":384,"slug":387,"view_count":102,"doi":8,"paper":8,"created_at":388},5444,"四川省农科院生核所「花香优872」百亩连片实收亩产 635 公斤","https:\u002F\u002Fwww.chinawestagr.com\u002Fzwgk\u002Fshowcontent.asp?id=67505","四川省农业科学院生物核与农业工程研究所在成都郫都区举办郫都粮经复合综合试验站杂交水稻新品种百亩连片现场观摩与验收会，重点展示该所选育的优质一级香型杂交稻「花香优872」百亩连片示范方。副所长王平研究员就该品种特征特性、配套栽培技术要点及粮经复合茬口衔接模式作现场讲解。种植大户反馈该品种在迟播条件下百亩片现场实收平均亩产 635 公斤，机械化效率提升 2.1%，抗病性与机收适应性表现良好。","λԺ                ʱ䣺2026\u002F10\u002F16171\n\nΪӿӽˮƷƹӦãƶ֡أ1011գԺڳɶۯٰۯۺվӽˮƷְĶƬֳĦջᡣʡũԺйרҡҵҵֲ󻧲μӡ\n\nĿصչʾԺѡһӽ“872”ĶƬʾԺƽоԱ͸Ʒԡ༼Ҫ㼰ϲνģʽֳ⡣λ﹡ʵز쿴ƷֵҶ̬ṹͿ֣ΧɹӦúͻеֲЧʵȷ˽ֲ󻧽ֲ飬϶ƷڿԺͻӦԷ֣ڳٲ“872”ĶƬֳʵƽĶ635еЧ2.1%ҵҵֲ󻧾͸ƷֵĽһƹ˶ԽǢ̸\n\n˴λͨ“—ҵ—ֲ”ƽ̨ѡֱָۡѧһԺΧˮֲЧӿ߲ʡ࿹˻Ʒֵɸѡչʾ׼ɣֱչʾ“”ںϵЧDZӶƹӦá","四川省农业科学院 2026-10-16",64,{"impact":16,"substance":357,"depth":48,"authority":295,"freshness":243,"relevant":19,"comment":358},16,"省级科研机构水稻新品种百亩连片实收测产，数据具体、有示范推广价值，但属区域性进展，时效稍旧。",[360],{"name":354,"url":351},[24,362,363,364],"种业振兴","粮食单产","新品种示范",{"code":30,"slug":31,"name":24,"path":32,"parent_code":33},[],{"status":42,"locked":13,"version":43,"method":44,"evidence":368},{"candidates":369,"agricultural_evidence":49},[370,375,380],{"code":30,"score":263,"supported":49,"title":371,"abstract":372,"tags":374},[],[24,373],"杂交稻",[24],{"code":329,"score":173,"supported":49,"title":376,"abstract":377,"tags":379},[],[378],"机械化",[],{"code":275,"score":19,"supported":13,"title":381,"abstract":382,"tags":383},[],[],[362],[385,386],"花香优872 水稻","四川省农科院 花香优872 测产","花香优872水稻-5444","2026-10-21T00:10:01.936262Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":8,"content":394,"source_name":395,"source_url":8,"published_at":396,"category":239,"cover_url":8,"hotness":12,"is_selected":13,"score":397,"score_detail":398,"sources":400,"tags":402,"primary_topic":407,"secondary_topics":408,"classification":414,"search_phrases":436,"slug":439,"view_count":102,"doi":8,"paper":8,"created_at":440},5432,"广东农科院水稻所主办 2026 年再生稻技术培训和现场观摩会在始兴举行","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F573077.shtm","广东省农业科学院水稻研究所主办的 2026 年再生稻技术培训和现场观摩会在韶关市始兴县举行，面向粤北「一季有余、两季不足」光温受限区域挖掘周年粮食产能。始兴县澄江镇善亨村 750 亩再生稻示范基地在省农科院水稻所、省农技推广中心和省水稻产业技术体系技术支撑下实行标准化种植，集中展示多个优质稻、杂交稻品种并开展品种对比试验。近年来始兴再生稻已连续 3 年实现头季、再生季两季亩产突破「吨粮」。","广东省农业技术推广中心种植业技术与种业推广部部长罗学梅在总结中充分肯定再生稻项目取得的技术突破和示范成效，并就下一阶段强化技术指导、完善示范推广体系、推动再生稻扩大应用等工作作出部署。他指出，要进一步加强科研、推广、种业和种植主体协同联动，加快适宜品种、先进技术和标准模式落地，为稳定粮食生产提供有力支撑。\n\n随后，与会人员前往始兴县澄江镇善亨村750亩再生稻示范基地开展现场观摩。该基地在广东省农业科学院水稻研究所、广东省农业技术推广中心以及广东省水稻产业技术体系的技术支撑下实行标准化种植，集中展示多个优质稻、杂交稻品种，并开展品种对比试验。技术人员走进田间，结合不同品种长势，围绕品种筛选、栽培管理、促芽肥施用、病虫害防控和机械化收割等关键环节进行讲解，并面对面解答参会人员提出的生产问题。\n\n参训人员沿着田埂仔细察看苗情和穗型，认真听取田间讲解，踊跃交流技术要点，现场学习氛围浓厚。此次活动将室内授课与田间教学有机结合，进一步提升了基层农技人员和种植主体对再生稻技术的认识，增强了推广应用信心。与会人员表示，将结合本地生产实际，把学习成果转化为田间管理措施，积极提升再生稻种植水平。\n\n版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。","科学网 2026-10-17","2026-10-17T00:00:00Z",58,{"impact":357,"substance":295,"depth":54,"authority":48,"freshness":106,"relevant":19,"comment":399},"省级科研机构主办的再生稻技术培训与田间观摩，有示范基地和品种对比等实质内容，但属常规推广活动，影响力限于区域。",[401],{"name":395,"url":392},[403,24,404,405,406],"广东","再生稻","农技推广","粮食生产",{"code":30,"slug":31,"name":24,"path":32,"parent_code":33},[409],{"code":314,"slug":410,"name":411,"path":412,"parent_code":413},"extension-transfer","农技推广与成果转化","\u002Ftopic\u002Frural-services\u002Fextension-transfer","G",{"status":42,"locked":13,"version":43,"method":44,"evidence":415},{"candidates":416,"agricultural_evidence":49},[417,422,427,432],{"code":30,"score":418,"supported":49,"title":419,"abstract":420,"tags":421},27,[24,404],[24,373,404],[404,24],{"code":314,"score":147,"supported":49,"title":423,"abstract":425,"tags":426},[424],"技术培训",[405,424],[405],{"code":270,"score":166,"supported":49,"title":428,"abstract":429,"tags":431},[],[430],"粮食产能",[406],{"code":33,"score":19,"supported":13,"title":433,"abstract":434,"tags":435},[],[],[406],[437,438],"广东农科院 再生稻 始兴","再生稻 技术培训 观摩会","广东农科院再生稻始兴-5432","2026-10-21T00:10:01.007510Z",{"id":442,"title":443,"url":444,"summary":445,"summary_zh":446,"content":8,"source_name":447,"source_url":444,"published_at":448,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":449,"score_detail":450,"sources":452,"tags":454,"primary_topic":458,"secondary_topics":459,"classification":466,"search_phrases":497,"slug":500,"view_count":102,"doi":501,"paper":502,"created_at":516},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,{"impact":16,"substance":17,"depth":16,"authority":48,"freshness":242,"relevant":19,"comment":451},"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[453],{"name":447,"url":444},[125,455,24,456,457],"农业人工智能","产量预测","遥感",{"code":30,"slug":31,"name":24,"path":32,"parent_code":33},[460],{"code":461,"slug":462,"name":463,"path":464,"parent_code":465},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":42,"locked":13,"version":43,"method":44,"evidence":467},{"candidates":468,"agricultural_evidence":49},[469,473,478,484,489,493],{"code":30,"score":147,"supported":49,"title":470,"abstract":471,"tags":472},[31],[24,31],[24],{"code":461,"score":242,"supported":49,"title":474,"abstract":475,"tags":477},[],[457,476],"remote sensing",[456,457],{"code":270,"score":106,"supported":49,"title":479,"abstract":480,"tags":483},[],[481,482],"粮食安全","food security",[],{"code":158,"score":173,"supported":49,"title":485,"abstract":486,"tags":488},[],[487],"农业规划",[],{"code":165,"score":19,"supported":13,"title":490,"abstract":491,"tags":492},[],[],[125],{"code":172,"score":19,"supported":13,"title":494,"abstract":495,"tags":496},[],[],[455],[498,499],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":501,"openalex_id":503,"authors":504,"venue":447,"cited_by_count":102,"oa_url":444,"card":510,"direction":514,"ingested_from":103},"W7222942064",[505,507],{"name":506,"orcid":8},"Nayana Venkatesh",{"name":508,"orcid":509},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":511,"method":512,"finding":513,"direction":514,"opportunity":515},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","农业遥感与作物表型","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z"]