[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-68":3,"related-68":92},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":87,"slug":90,"view_count":52,"doi":8,"paper":8,"created_at":91},68,"江苏泰州高港90后新农人陈桂平：AI多光谱无人机+处方图实现稻田\"变量精准施肥\"","https:\u002F\u002Fk.sina.com.cn\u002Farticle_5952915720_162d2490806704ji4g.html","2026-07-27 江苏泰州高港区许庄街道希韶家庭农场主陈桂平投入5万多元，率先引进大疆农用多光谱无人机，对300多亩稻田开展\"处方施肥\"：施肥前多光谱无人机升空做\"CT\"，依托多光谱影像实时监测水稻长势、生物量、叶面积指数等信息生成精准施肥\"处方图\"；通过系统数据互通导入植保无人机\"大脑\"，自动变量精确施肥。作业效率提升10%以上，预计每亩节约成本20-30元，同时减少20%肥料用量，\"减肥\"与\"增效\"同步实现。\"十五五\"期间高港区计划新增农用无人机60台以上。",null,"去年，希韶家庭农场投入5万多元，在全市率先引进大疆农用多光谱无人机。施肥前，无人机先升空巡查给稻田做“CT”，依托多光谱影像等技术，实时监测水稻长势、生物量、叶面积指数等信息，形成精准判断并生成不同田块施肥“处方图”。\n\n通过无人机系统数据互通平台，将多光谱无人机生成的“处方”导入植保无人机“大脑”，即可实现自动变量精确施肥，让稻田分别获取生长所需的定制“营养餐”。\n\n“有了多光谱无人机这套设备，种田更省心、更划算了！”陈桂平算了一笔账：依托“处方图”，植保无人机变量施肥用药更加精准，作业效率提升10%以上，预计每亩可节约成本20至30元，同时减少20%肥料用量，“减肥”与“增效”同步实现。","泰州高新发布 · 2026-07-27","2026-07-27T00:00:00Z","报道",10,false,58,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":20,"relevant":21,"comment":22},12,18,14,7,1,"地市级农业科技应用案例，有具体数据支撑，但信源为地方媒体，时效性一般。",[24],{"name":10,"url":6},[12,26,27,28,29,30,31],"大疆","智慧农场","江苏泰州高港","多光谱无人机","处方图","变量施肥",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","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":53},[51,59,64,71,77,82],{"code":40,"score":52,"supported":53,"title":54,"abstract":56,"tags":58},15,true,[55],"施肥",[55,57],"肥料",[31],{"code":33,"score":19,"supported":53,"title":60,"abstract":62,"tags":63},[61],"稻田",[35,61],[],{"code":65,"score":66,"supported":53,"title":67,"abstract":68,"tags":70},"F4",3,[],[69],"植保",[],{"code":72,"score":66,"supported":53,"title":73,"abstract":74,"tags":76},"G2",[],[75],"家庭农场",[],{"code":78,"score":21,"supported":14,"title":79,"abstract":80,"tags":81},"F6",[],[],[27],{"code":83,"score":21,"supported":14,"title":84,"abstract":85,"tags":86},"I3",[],[],[29],[88,89],"多光谱无人机 江苏泰州高港 变量施肥 智慧农场","多光谱无人机 江苏泰州高港","多光谱无人机江苏泰州高港变量施肥智慧农场-68","2026-07-29T23:54:12.440119Z",{"total":93,"page":21,"page_size":93,"items":94},6,[95,147,230,332,401,450],{"id":96,"title":97,"url":98,"summary":99,"summary_zh":8,"content":100,"source_name":101,"source_url":8,"published_at":102,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":103,"score_detail":104,"sources":109,"tags":111,"primary_topic":116,"secondary_topics":117,"classification":119,"search_phrases":141,"slug":144,"view_count":145,"doi":8,"paper":8,"created_at":146},5288,"赤壁水稻智慧施肥千亩示范片区亩产突破700公斤 化肥减量、耕地地力持续提升","https:\u002F\u002Fnews.cjn.cn\u002Fzjjjdpd\u002Fxn_20064\u002F202610\u002Ft5348946.htm","10月9日湖北省农科院、省农业农村厅测产专家组赴赤壁市赤壁镇九毫村,经严格实测水稻智慧施肥千亩示范片区水稻亩产突破700公斤,片区总面积1154亩;该片区集成应用智慧科学施肥方案,推广绿色肥料、机械侧深施肥、无人机追肥、秸秆还田等先进农业技术,实现粮食稳产增产与农业绿色发展双赢;赤壁市今年水稻总产量预计可达23万吨以上。","10月9日，省农科院、省农业农村厅测产专家组赴赤壁市赤壁镇九毫村，对水稻智慧施肥千亩示范片区开展现场实收测产，经严格实测，片区水稻亩产突破700公斤。\n\n本次实收测产片区总面积1154亩。专家组现场随机选定田块，依次完成水稻脱粒、装袋、称重、籽粒含水率检测等一系列规范流程。\n\n该示范片区集成应用智慧科学施肥方案，推广绿色肥料、机械侧深施肥、无人机追肥、秸秆还田等先进农业技术。技术落地后，农田化肥投入有效减少，农业面源污染得到控制，耕地地力持续提升，实现粮食稳产增产与农业绿色发展双赢，为赤壁市乃至咸宁市秋粮稳产增产打造可复制、可推广的实践样板。\n\n近年来，赤壁市深耕科学施肥推广，以新技术、新产品、新机具“三新”集成推广为抓手，对接省农业科技服务支点建设“五大提质行动”，推动良田、良种、良机、良法深度融合，加快农业关键技术落地。全市水稻常年种植面积稳定在43万亩左右，今年水稻总产量预计可达23万吨以上。（ 见博、王智雨）","长江网(2026-10-14)","2026-10-14T00:00:00Z",57,{"impact":105,"substance":18,"depth":106,"authority":93,"freshness":107,"relevant":21,"comment":108},16,13,4,"省级农科院测产验证的智慧施肥千亩示范实收数据，具可复制推广价值，但为地方性报道且已过一周时效。",[110],{"name":101,"url":98},[112,35,113,114,115],"绿色农业","耕地地力","化肥减量","智慧施肥",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[118],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":120},{"candidates":121,"agricultural_evidence":53},[122,126,130,136],{"code":40,"score":52,"supported":53,"title":123,"abstract":124,"tags":125},[55],[55,57],[114],{"code":33,"score":17,"supported":53,"title":127,"abstract":128,"tags":129},[35],[35],[35],{"code":131,"score":66,"supported":53,"title":132,"abstract":133,"tags":135},"H5",[],[134],"秸秆还田",[],{"code":137,"score":21,"supported":14,"title":138,"abstract":139,"tags":140},"H8",[],[],[112],[142,143],"赤壁 水稻 智慧施肥","九毫村 水稻 测产","赤壁水稻智慧施肥-5288",0,"2026-10-19T00:07:41.890918Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":152,"content":8,"source_name":153,"source_url":150,"published_at":154,"category":155,"cover_url":8,"hotness":13,"is_selected":14,"score":156,"score_detail":157,"sources":162,"tags":164,"primary_topic":169,"secondary_topics":170,"classification":172,"search_phrases":208,"slug":211,"view_count":212,"doi":213,"paper":214,"created_at":229},3920,"Automated Leaf Color Chart (LCC)-BasedNitrogen Fertilizer Recommendation UsingPaddy Leaf Image Color Classification","https:\u002F\u002Fdoi.org\u002F10.70818\u002Fijbass.2026.v07i02.0075","The demand for accurate and timely assessment of crop health is high in precision agriculture to support proper nutrient management. In this work, we present an intelligent deep learning-based solution that evaluates the colour of paddy rice leaves to provide real-time, context-sensitive recommendations regarding the addition of nitrogen fertilizers to grow rice sustainably. For optimum nitrogen management and sustainability in the production of rice crops, an accurate assessment of the color of paddy leaves is required. Herein, we present a deep learning-based framework for paddy leaf color classification with the capability of recommending an appropriate amount of nitrogen fertilizer as needed in real-time. In this research, the Paddy Net dataset containing 12,000 augmented rice leaf images will be used for training the neural network used for classifying the rice leaf's color based on the Leaf Color Chart (LCC) system, which consists of LCC levels from 2 to 5. Additionally, several deep learning architectures were trainedand tested, specifically Custom CNNs, VGG16, ResNet50 and EfficientNetB7, to identify which architecture provided the best classification performance of rice leaf color. In terms of the experimental results, it can be concluded that the Custom CNN with a classification accuracy of 97.55% significantly outperforms the transfer learning models, VGG16 (78.89%) and EfficientNetB7 (78.67%). To also improve practicality, multimodal vision-language model LLa MA 3.2-11B Vision is integrated into the system, allowing it to provide real-time, context-based nitrogen fertilizer recommendations based on visual input images. Additionally, the proposed framework takes into account other agronomic factors such as the growth stage of the crop, type of soil, and fertilizer application history. This allows for more accurate and relevant recommendations. The proposed framework allows for precise and objective nitrogen management without the subjective use of visual assessment methods typically used by farmers. Overall, the proposed system for fertilizer application is significant for the development of precision agriculture because it increases the efficiency of the decision-making process, optimizes the use of fertilizer, and increases the practice of sustainable rice cultivation.","在精准农业中，对作物健康状况进行准确且及时评估的需求很高，以支持合理的养分管理。在这项工作中，我们提出了一种基于深度学习的智能解决方案，通过评估水稻叶片的颜色，实时提供与情境相关的氮肥添加建议，以实现水稻的可持续种植。为了实现水稻作物生产中的优化氮管理和可持续性，需要对水稻叶片颜色进行准确评估。在此，我们提出了一个基于深度学习的稻田叶片颜色分类框架，能够根据实时需求推荐适量的氮肥。在本研究中，包含12,000张增强水稻叶片图像的Paddy Net数据集将用于训练神经网络，该网络基于叶色卡（Leaf Color Chart, LCC）系统对水稻叶片颜色进行分类，LCC等级从2到5。此外，我们训练并测试了多种深度学习架构，特别是自定义CNN、VGG16、ResNet50和EfficientNetB7，以确定哪种架构在水稻叶片颜色分类中表现最佳。从实验结果可以得出结论，自定义CNN以97.55%的分类准确率显著优于迁移学习模型VGG16（78.89%）和EfficientNetB7（78.67%）。为提高实用性，系统还集成了多模态视觉语言模型LLaMA 3.2-11B Vision，使其能够基于视觉输入图像提供实时、基于情境的氮肥推荐。此外，所提出的框架还考虑了其他农艺因素，如作物生长阶段、土壤类型和施肥历史，从而提供更准确和相关的建议。该框架实现了精确、客观的氮管理，避免了农民通常采用的主观视觉评估方法。总体而言，所提出的施肥系统对精准农业的发展具有重要意义，因为它提高了决策过程的效率，优化了肥料使用，并促进了可持续水稻种植实践。","International Journal of Business Arts and Scientific Study","2026-09-30T00:00:00Z","论文",63,{"impact":17,"substance":158,"depth":159,"authority":93,"freshness":160,"relevant":21,"comment":161},20,17,8,"用深度学习与视觉语言模型实现水稻叶色分级与氮肥实时推荐，方法新颖、数据规模可观，但期刊影响力有限，属细分领域技术进展。",[163],{"name":153,"url":150},[165,166,167,35,168],"智慧农业","农业人工智能","精准施肥","氮肥管理",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[171],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":173},{"candidates":174,"agricultural_evidence":53},[175,182,187,194,201],{"code":40,"score":176,"supported":53,"title":177,"abstract":179,"tags":181},19,[178],"fertilizer",[55,57,180,178],"养分管理",[168,167],{"code":33,"score":13,"supported":53,"title":183,"abstract":184,"tags":186},[],[35,61,34,185],"paddy",[35],{"code":188,"score":20,"supported":53,"title":189,"abstract":190,"tags":193},"I1",[],[191,192],"精准农业","precision agriculture",[165],{"code":195,"score":20,"supported":53,"title":196,"abstract":197,"tags":200},"I2",[],[198,199],"深度学习","deep learning",[166],{"code":202,"score":93,"supported":53,"title":203,"abstract":204,"tags":207},"H2",[],[205,206],"土壤","soil",[],[209,210],"水稻 叶片颜色 氮肥推荐","Paddy Net 深度学习 叶色卡","水稻叶片颜色氮肥推荐-3920",2,"10.70818\u002Fijbass.2026.v07i02.0075",{"doi":213,"openalex_id":215,"authors":216,"venue":153,"cited_by_count":145,"oa_url":150,"card":222,"direction":226,"ingested_from":228},"W7214974109",[217,219],{"name":218,"orcid":8},"Rusanara Akhter",{"name":220,"orcid":221},"Md Zahidul Islam","https:\u002F\u002Forcid.org\u002F0000-0002-4868-4945",{"tldr":223,"method":224,"finding":225,"direction":226,"opportunity":227},"用深度学习对水稻叶片颜色分级，并结合多模态大模型实时推荐氮肥用量。","Paddy Net 1.2万张增强叶片图像，训练Custom CNN、VGG16","Custom CNN分类准确率达97.55%，显著优于VGG16（78.89%）和Efficient","农业人工智能与决策模型","可探索轻量化模型部署到手机端，并结合多源农情数据提升跨品种、跨区域推荐泛化能力。","openalex","2026-10-01T23:30:59.367113Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":8,"source_name":236,"source_url":233,"published_at":237,"category":155,"cover_url":8,"hotness":13,"is_selected":14,"score":238,"score_detail":239,"sources":242,"tags":244,"primary_topic":249,"secondary_topics":250,"classification":258,"search_phrases":303,"slug":306,"view_count":21,"doi":307,"paper":308,"created_at":331},2295,"Biofilm biofertilizer mitigates water stress in rice: impact on growth, yield, and grain quality","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1819208","Introduction Continuous flooding in paddy ( Oryza sativa L.) cultivation challenges sustainable agriculture and food security amid climate change. Biofilm biofertilizer (BFBF) can improve soil organic matter and nutrient content without compromising yield, but its effect on plant water use and grain quality under restricted irrigation remains untested. This study evaluated BFBF's effect on rice water consumption under progressively limited irrigation. Methods A 3-month pot experiment used wetland (At 314) and dryland (Bg 251) rice varieties under two fertilizer practices, chemical fertilizer (CF, 340 kg NPK ha −1 ) and BFBF (225 kg NPK ha −1 + 2.5 L BFBF ha −1 ), and three water treatments (flooded, 100% field capacity, 65% field capacity), in a three-factor factorial completely randomized design ( n = 3). Total water usage, water use efficiency (WUE), net assimilation rate (NAR), relative growth rate (RGR), yield components, macronutrients (carbohydrates, proteins, lipids), and heavy metal content were recorded; significance was assessed via three-factor ANOVA (variety × fertilizer × water) with Tukey’s HSD test ( p \u003C 0.05). Results Total water use was significantly reduced under BFBF only in the wetland variety (ca. 12% under flooded, 10% at 100% field capacity, and no reduction at 65% field capacity). Grain Cd and Pb fell by 34 and 81%, respectively, under BFBF. Carbohydrate, protein, and lipid contents, Fe and Zn concentrations, and yield components were unaffected by fertilizer practice, varying only by variety and\u002For water treatment. BFBF increased NAR and RGR by ca. 33 and 7%, respectively, across all water treatments. Overall, BFBF matched full-rate CF for yield and nutritional composition while reducing water use and heavy-metal accumulation under dryland, water-limited conditions. Conclusion Collectively, these results position BFBF as a candidate drought-resilience strategy for rice cultivation, reducing water demand and heavy-metal accumulation without compromising grain yield or quality, particularly for dryland, water-limited conditions. Multi-season field trials are needed to confirm these responses at scale.","引言 水稻（Oryza sativa L.）栽培中的持续淹水在气候变化背景下对可持续农业和粮食安全构成挑战。生物膜生物肥料（biofilm biofertilizer，BFBF）能够在不影响产量的前提下提高土壤有机质和养分含量，但其在限制灌溉条件下对植物水分利用和稻米品质的影响尚未得到验证。本研究评估了BFBF在逐步限制灌溉条件下对水稻耗水量的影响。方法 采用为期3个月的盆栽试验，选用湿地品种（At 314）和旱地品种（Bg 251），设置两种施肥方式——化肥（chemical fertilizer，CF，340 kg NPK ha⁻¹）和BFBF（225 kg NPK ha⁻¹ + 2.5 L BFBF ha⁻¹），以及三种水分处理（淹水、100%田间持水量、65%田间持水量），采用三因素析因完全随机设计（n = 3）。记录总用水量、水分利用效率（water use efficiency，WUE）、净同化速率（net assimilation rate，NAR）、相对生长速率（relative growth rate，RGR）、产量构成因素、大量营养素（碳水化合物、蛋白质、脂质）及重金属含量；通过三因素方差分析（品种 × 肥料 × 水分）结合Tukey's HSD检验评估显著性（p \u003C 0.05）。结果 仅在湿地品种中，BFBF显著降低了总用水量（淹水条件下约降低12%，100%田间持水量下约降低10%，65%田间持水量下无降低）。BFBF处理下稻米Cd和Pb分别降低34%和81%。碳水化合物、蛋白质和脂质含量、Fe和Zn浓度及产量构成因素不受施肥方式影响，仅随品种和\u002F或水分处理变化。在所有水分处理下，BFBF使NAR和RGR分别提高约33%和7%。总体而言，BFBF在产量和营养组成方面与全量化肥相当，同时在旱地、水分受限条件下减少了用水量和重金属积累。结论 综合来看，这些结果表明BFBF可作为水稻栽培中应对干旱的候选策略，在不影响籽粒产量或品质的前提下降低水分需求和重金属积累，尤其适用于旱地、水分受限条件。仍需多季田间试验以在规模化条件下验证这些响应。","Frontiers in Sustainable Food Systems","2026-09-11T00:00:00Z",75,{"impact":52,"substance":240,"depth":18,"authority":106,"freshness":160,"relevant":21,"comment":241},21,"盆栽试验证实生物膜菌肥在限水条件下可减水降镉铅且不减产，属有数据支撑的节水抗逆新结论，但仅为单季盆栽研究，产业级影响有限。",[243],{"name":236,"url":233},[35,245,246,247,248],"节水灌溉","抗旱育种","生物膜菌肥","重金属阻控",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[251,252],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":253,"slug":254,"name":255,"path":256,"parent_code":257},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":259},{"candidates":260,"agricultural_evidence":53},[261,266,271,279,286,293,297],{"code":33,"score":18,"supported":53,"title":262,"abstract":263,"tags":265},[34],[35,264,34,185],"稻米",[35],{"code":40,"score":267,"supported":53,"title":268,"abstract":269,"tags":270},9,[],[55,57,178],[],{"code":253,"score":267,"supported":53,"title":272,"abstract":273,"tags":278},[],[274,275,276,277],"气候变化","干旱","climate change","drought",[],{"code":280,"score":20,"supported":53,"title":281,"abstract":282,"tags":285},"H3",[],[283,284],"灌溉","irrigation",[245],{"code":287,"score":93,"supported":53,"title":288,"abstract":289,"tags":292},"A8",[],[290,291],"粮食安全","food security",[],{"code":202,"score":93,"supported":53,"title":294,"abstract":295,"tags":296},[],[205,206],[],{"code":137,"score":93,"supported":53,"title":298,"abstract":299,"tags":302},[],[300,301],"可持续农业","sustainable agriculture",[],[304,305],"生物膜菌肥 重金属阻控 抗旱育种 节水灌溉","生物膜菌肥 重金属阻控","生物膜菌肥重金属阻控抗旱育种节水灌溉-2295","10.3389\u002Ffsufs.2026.1819208",{"doi":307,"openalex_id":309,"authors":310,"venue":236,"cited_by_count":145,"oa_url":233,"card":325,"direction":329,"ingested_from":228},"W7212303356",[311,313,315,317,320,323],{"name":312,"orcid":8},"Chamudika Gunawardhana",{"name":314,"orcid":8},"Aravindi Epa",{"name":316,"orcid":8},"Sidath Ekanayake",{"name":318,"orcid":319},"Mahesh Premarathna","https:\u002F\u002Forcid.org\u002F0000-0002-6127-3956",{"name":321,"orcid":322},"Gamini Seneviratne","https:\u002F\u002Forcid.org\u002F0000-0003-1562-4097",{"name":324,"orcid":8},"Saman Seneweera",{"tldr":326,"method":327,"finding":328,"direction":329,"opportunity":330},"盆栽试验表明生物膜菌肥可降低水稻耗水与重金属积累，且不减产。","3个月盆栽三因素试验，2品种×2施肥×3水分，测耗水、生长、产量与品质。","菌肥使湿地稻耗水降约12%，籽粒镉铅降34%和81%，产量品质与全量化肥相当。","农业绿色发展与碳","可开展多季田间验证，并探究菌肥节水降镉的微生物机制与区域适配性。","2026-09-13T23:30:07.401817Z",{"id":333,"title":334,"url":335,"summary":336,"summary_zh":337,"content":8,"source_name":236,"source_url":335,"published_at":237,"category":155,"cover_url":8,"hotness":13,"is_selected":14,"score":338,"score_detail":339,"sources":341,"tags":343,"primary_topic":348,"secondary_topics":349,"classification":351,"search_phrases":371,"slug":374,"view_count":212,"doi":375,"paper":376,"created_at":400},2126,"Reduced potassium fertilization combined with magnesium application improves rice population quality and regulates grain yield and quality","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1921807","Inroduction Sichuan is a major hybrid rice-producing region, where paddy soils commonly suffer from acidification and severe magnesium leaching, resulting in low available-Mg status. Excessive potassium application intensifies K-Mg absorption antagonism and exacerbates magnesium deficiency, while blind potassium reduction accelerates plant senescence and impairs rice yield and quality. Although magnesium participates in photosynthesis and assimilate translocation, little is known about the combined effects of reduced potassium fertilization and magnesium supplementation on rice growth and agronomic performance. Methods A two-year field experiment was conducted to examine rice yield, grain quality, and nutrient uptake under varying potassium-magnesium combinations, aiming to provide fertilization references for green rice production in Sichuan hilly regions. A two-factor split-plot design was employed, with three potassium rates (0, 120, and 180 kg\u002F ha, denoted K0, K1, and K2) as main plots and three magnesium rates (0, 30, and 60 kg\u002Fha, denoted M0, M1, and M2) as sub-plots. Leaf area index, dry matter accumulation, chlorophyll content, yield components, and grain quality traits were assessed in this study. Results The results showed that the K1 treatment significantly improved grain yield, with relatively better comprehensive performance observed under the K1M2 combination. Averaged across 2 years, K1 increased grain yield by 26.55% relative to K0 and exhibited comparable yield to K2. Under K1, the M2 treatment further increased yield by 6.21% relative to M1. Moreover, K1M2 significantly enhanced leaf area index and chlorophyll content, while improving milling and appearance quality and reducing grain chalkiness and amylose content. Discussion These findings offer a theoretical and practical basis for improving rice yield and grain quality and promoting green, high-quality and efficient rice cultivation in the hilly regions of Sichuan.","引言 四川是杂交水稻主产区，稻田土壤普遍存在酸化与镁素淋溶严重的问题，导致土壤有效镁含量偏低。过量施钾会加剧钾镁吸收拮抗，加重镁素缺乏，而盲目减钾则会加速植株衰老，损害水稻产量与品质。尽管镁参与光合作用与同化物转运，但关于减钾配施镁对水稻生长及农艺性状的联合效应尚不清楚。方法 通过两年田间试验，研究不同钾镁配施组合下水稻产量、稻米品质及养分吸收特征，旨在为四川丘陵区水稻绿色生产提供施肥参考。采用两因素裂区设计，以3个钾水平（0、120、180 kg\u002Fha，记为K0、K1、K2）为主区，3个镁水平（0、30、60 kg\u002Fha，记为M0、M1、M2）为裂区。本研究测定了叶面积指数、干物质积累量、叶绿素含量、产量构成因素及稻米品质性状。结果 结果表明，K1处理显著提高籽粒产量，其中K1M2组合综合表现相对较优。两年平均而言，K1较K0使籽粒产量提高26.55%，且产量与K2相当。在K1条件下，M2处理较M1进一步增产6.21%。此外，K1M2显著提高叶面积指数与叶绿素含量，同时改善碾磨品质与外观品质，降低籽粒垩白度与直链淀粉含量。讨论 这些研究结果为提高水稻产量与稻米品质、促进四川丘陵区水稻绿色优质高效栽培提供了理论与实践依据。",76,{"impact":52,"substance":240,"depth":159,"authority":106,"freshness":13,"relevant":21,"comment":340},"两年田间试验数据扎实，为四川丘陵区水稻减钾配镁施肥提供实证参考，但属区域性栽培技术研究，产业影响有限。",[342],{"name":236,"url":335},[35,344,345,346,347],"科学施肥","绿色种植","钾镁配施","四川丘陵",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[350],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":352},{"candidates":353,"agricultural_evidence":53},[354,358,363,367],{"code":33,"score":18,"supported":53,"title":355,"abstract":356,"tags":357},[34],[35,264,61,34,185],[35],{"code":40,"score":52,"supported":53,"title":359,"abstract":361,"tags":362},[360],"fertilization",[55,360],[344],{"code":202,"score":66,"supported":53,"title":364,"abstract":365,"tags":366},[],[205],[],{"code":137,"score":21,"supported":14,"title":368,"abstract":369,"tags":370},[],[],[345],[372,373],"四川丘陵 科学施肥 绿色种植 钾镁配施","四川丘陵 科学施肥","四川丘陵科学施肥绿色种植钾镁配施-2126","10.3389\u002Ffsufs.2026.1921807",{"doi":375,"openalex_id":377,"authors":378,"venue":236,"cited_by_count":145,"oa_url":335,"card":395,"direction":329,"ingested_from":228},"W7212147499",[379,381,383,385,387,389,391,393],{"name":380,"orcid":8},"Jianming Ding",{"name":382,"orcid":8},"Xiaotian Jiang",{"name":384,"orcid":8},"Ran Wang",{"name":386,"orcid":8},"Keyuan Zhang",{"name":388,"orcid":8},"Lubing Jia",{"name":390,"orcid":8},"Yuetong Mo",{"name":392,"orcid":8},"Rongping Zhang",{"name":394,"orcid":8},"Peng Ma",{"tldr":396,"method":397,"finding":398,"direction":329,"opportunity":399},"研究减钾配施镁对四川丘陵水稻产量和品质的影响。","两年田间裂区试验，设3个钾水平和3个镁水平。","减钾至120 kg\u002Fha配施60 kg\u002Fha镁可增产并改善稻米品质。","可探究减钾配镁下土壤-植株镁钾互作机制及适宜区域阈值。","2026-09-11T23:30:07.632874Z",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":8,"content":8,"source_name":406,"source_url":8,"published_at":407,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":408,"score_detail":409,"sources":411,"tags":413,"primary_topic":418,"secondary_topics":419,"classification":424,"search_phrases":445,"slug":448,"view_count":66,"doi":8,"paper":8,"created_at":449},1113,"江西省农业科学院土壤肥料与资源环境研究所志愿服务队赴上高开展再生稻生产技术指导","https:\u002F\u002Fwww.jxaas.com\u002F","江西省农业科学院土壤肥料与资源环境研究所志愿服务队近日赴上高县开展再生稻生产技术指导,围绕头季收获、留茬高度、肥水管理等关键技术开展实地培训,助力江西再生稻产业发展。","江西省农业科学院","2026-08-27T01:00:00Z",54,{"impact":52,"substance":17,"depth":160,"authority":17,"freshness":20,"relevant":21,"comment":410},"省级科研机构开展再生稻技术指导，对地方产业有实际推动，但内容为常规报道，深度一般。",[412],{"name":406,"url":404},[414,415,416,417],"再生稻","科技服务","江西省农科院","土肥与资环所",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[420,421],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":202,"slug":206,"name":422,"path":423,"parent_code":257},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":46,"locked":14,"version":47,"method":48,"evidence":425},{"candidates":426,"agricultural_evidence":53},[427,431,436,440],{"code":33,"score":17,"supported":53,"title":428,"abstract":429,"tags":430},[414],[414],[414],{"code":40,"score":432,"supported":53,"title":433,"abstract":434,"tags":435},11,[57],[57],[],{"code":202,"score":432,"supported":53,"title":437,"abstract":438,"tags":439},[205],[205],[],{"code":441,"score":21,"supported":14,"title":442,"abstract":443,"tags":444},"G1",[],[],[415],[446,447],"土肥与资环所 江西省农科院 科技服务 再生稻","土肥与资环所 江西省农科院","土肥与资环所江西省农科院科技服务再生稻-1113","2026-08-30T00:11:10.512237Z",{"id":451,"title":452,"url":453,"summary":454,"summary_zh":455,"content":8,"source_name":456,"source_url":453,"published_at":457,"category":155,"cover_url":8,"hotness":13,"is_selected":14,"score":458,"score_detail":459,"sources":462,"tags":464,"primary_topic":468,"secondary_topics":469,"classification":470,"search_phrases":518,"slug":521,"view_count":212,"doi":522,"paper":523,"created_at":536},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":460,"substance":460,"depth":18,"authority":106,"freshness":160,"relevant":21,"comment":461},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。",[463],{"name":456,"url":453},[165,465,167,31,466,467],"无人机","遥感监测","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":471},{"candidates":472,"agricultural_evidence":53},[473,479,485,489,496,503,507,514],{"code":40,"score":158,"supported":53,"title":474,"abstract":475,"tags":478},[360],[55,57,476,180,477],"水肥一体化","fertigation",[467,31,167],{"code":83,"score":20,"supported":53,"title":480,"abstract":481,"tags":484},[],[482,483],"遥感","remote sensing",[466],{"code":33,"score":93,"supported":53,"title":486,"abstract":487,"tags":488},[],[35,34],[],{"code":490,"score":93,"supported":53,"title":491,"abstract":492,"tags":495},"A2",[],[493,494],"小麦","wheat",[],{"code":497,"score":93,"supported":53,"title":498,"abstract":499,"tags":502},"A3",[],[500,501],"玉米","maize",[],{"code":202,"score":93,"supported":53,"title":504,"abstract":505,"tags":506},[],[205,206],[],{"code":508,"score":93,"supported":53,"title":509,"abstract":510,"tags":513},"I4",[],[511,512],"地理信息","geographic information",[],{"code":188,"score":21,"supported":14,"title":515,"abstract":516,"tags":517},[],[],[165],[519,520],"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":522,"openalex_id":524,"authors":525,"venue":456,"cited_by_count":145,"oa_url":453,"card":530,"direction":534,"ingested_from":228},"W7222838957",[526,528],{"name":527,"orcid":8},"S. T. Panusha",{"name":529,"orcid":8},"T. M. Soumya",{"tldr":531,"method":532,"finding":533,"direction":534,"opportunity":535},"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","智慧农业 \u002F 农业物联网","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"]