[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-331":3,"related-331":86},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":49,"search_phrases":80,"slug":83,"view_count":84,"doi":8,"paper":8,"created_at":85},331,"第十七届全国土壤肥料研究所管理与发展研讨会在内蒙古呼和浩特举行","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fnkyw\u002F4ed11f077aa44976891b204c29dfc1c1.htm","中国农业科学院8月4日发布,第十七届全国土壤肥料研究所管理与发展研讨会在内蒙古呼和浩特召开,会议围绕高标准农田建设、黑土地保护、盐碱地综合利用、第三次全国土壤普查成果应用、肥料减量增效与新型肥料研发等核心议题,系统交流各省土肥所\"十四五\"建设成效与\"十五五\"重点方向,推动全国土肥科技协作和成果转化落地。",null,"中国农业科学院 2026-08-04","2026-08-04T00:00:00Z","报道",10,false,71,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,18,12,14,5,1,"全国性土肥科研协作会议，聚焦高标准农田与黑土地保护等核心议题，信息有实质内容，但为会议报道，深度一般，时效稍旧。",[24],{"name":9,"url":6},[26,27,28,29,30],"内蒙古","高标准农田","中国农科院","黑土地保护","土壤肥料",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},"H1","farmland","耕地保护与高标准农田","\u002Ftopic\u002Fenvironment\u002Ffarmland",{"status":50,"locked":13,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":56},[55,62,68,73],{"code":32,"score":19,"supported":56,"title":57,"abstract":59,"tags":61},true,[58],"土壤",[58,60],"盐碱地",[],{"code":39,"score":63,"supported":56,"title":64,"abstract":66,"tags":67},11,[65],"肥料",[65],[],{"code":45,"score":63,"supported":56,"title":69,"abstract":70,"tags":72},[],[27,29,71],"农田建设",[27,29],{"code":74,"score":75,"supported":56,"title":76,"abstract":77,"tags":79},"G1",3,[],[78],"成果转化",[],[81,82],"中国农科院 高标准农田 黑土地保护 土壤肥料","中国农科院 高标准农田","中国农科院高标准农田黑土地保护土壤肥料-331",0,"2026-08-09T23:54:48.464469Z",{"total":87,"page":21,"page_size":87,"items":88},6,[89,163,279,351,423,501],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":8,"content":8,"source_name":94,"source_url":8,"published_at":95,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":96,"score_detail":97,"sources":102,"tags":104,"primary_topic":107,"secondary_topics":108,"classification":110,"search_phrases":158,"slug":161,"view_count":87,"doi":8,"paper":8,"created_at":162},180,"土壤“增肥” 良田焕新——辽宁铁岭市做好黑土地保护“加减乘除”法","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260803\u002F20260803_1\u002Fnmrb_20260803_13367_1_2084029265637117957.html","铁岭以保护性耕作、秸秆还田和粪肥还田改善黑土地质量。昌图县“五级百站”体系累计处理畜禽粪污超50万吨、还田面积达20万亩，项目区化肥施用量降低10%至15%。文章同时披露补贴兑付、基层力量和耕作层厚度等短板及下一步治理安排。","农民日报","2026-08-03T00:00:00Z",68,{"impact":17,"substance":98,"depth":99,"authority":18,"freshness":100,"relevant":21,"comment":101},20,16,2,"省级黑土地保护实践，数据详实，央媒报道，但时效性差。",[103],{"name":94,"url":92},[105,29,106],"粮食安全","农业绿色发展",{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},[109],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":50,"locked":13,"version":51,"method":52,"evidence":111},{"candidates":112,"agricultural_evidence":56},[113,118,123,130,136,142,148,153],{"code":45,"score":114,"supported":56,"title":115,"abstract":116,"tags":117},9,[29],[],[29],{"code":32,"score":119,"supported":56,"title":120,"abstract":121,"tags":122},8,[58],[],[],{"code":124,"score":125,"supported":56,"title":126,"abstract":127,"tags":129},"H8",4,[],[128],"保护性耕作",[106],{"code":131,"score":75,"supported":56,"title":132,"abstract":133,"tags":135},"C",[],[134],"畜禽",[],{"code":137,"score":75,"supported":56,"title":138,"abstract":139,"tags":141},"C6",[],[140],"畜禽粪污",[],{"code":143,"score":75,"supported":56,"title":144,"abstract":145,"tags":147},"H5",[],[146],"秸秆还田",[],{"code":149,"score":21,"supported":13,"title":150,"abstract":151,"tags":152},"A8",[],[],[105],{"code":154,"score":21,"supported":13,"title":155,"abstract":156,"tags":157},"H4",[],[],[106],[159,160],"农业绿色发展 黑土地保护 粮食安全","农业绿色发展 黑土地保护","农业绿色发展黑土地保护粮食安全-180","2026-08-02T23:54:39.405157Z",{"id":164,"title":165,"url":166,"summary":167,"summary_zh":168,"content":8,"source_name":169,"source_url":166,"published_at":170,"category":171,"cover_url":8,"hotness":12,"is_selected":13,"score":172,"score_detail":173,"sources":176,"tags":178,"primary_topic":184,"secondary_topics":185,"classification":187,"search_phrases":222,"slug":225,"view_count":75,"doi":226,"paper":227,"created_at":278},4292,"Agronomic performance and soil chemical properties following the application of pelletized compost and vermicompost derived from vineyard pruning residues and sewage sludge in lettuce production","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1948663","Introduction Compost and vermicompost are recognized as effective organic amendments for supporting plant growth, promoting nutrient recycling and enhancing soil quality within circular economy strategies. In this context, the present study evaluated the short-term performance of pelletized compost (CC) and vermicompost (VC) produced from vineyard prunings and sewage sludge on lettuce ( Lactuca sativa L.) growth, leaf biochemical traits, mineral composition, and soil chemical properties. The performance of both organic products was compared with a commercial organomineral fertilizer (CF) and an unfertilized control (CT). Methods A completely randomized greenhouse pot experiment was conducted for 55 days comprising four treatments with eight replicates each. . Data were analyzed by one-way ANOVA followed by Tukey´s HSD test at p \u003C 0.05, with non-parametric analysis used when ANOVA assumptions were not met. Results and discussion At the end of the experiment, organic amendments performed comparably to the commercial fertilizer CF for most morphological parameters, despite differences in absolute nutrient inputs among treatments, although shoot fresh weight remained higher under CF. Leaf biochemical responses varied among treatments: CC and CF exhibited high chlorophyll and carotenoid contents, whereas VC and CT showed lower pigment concentrations. Total soluble sugars were highest in CT and lowest in CF, while total protein and phenolic contents were not significantly affected by treatment. Leaf macronutrient concentrations under CC and VC were generally comparable to CF, while for micronutrients Fe and Mn concentrations were higher in CF. Final soil analyses showed higher organic matter, extractable P and K, and cation exchange capacity under organic fertilization, whereas CF showed the lowest soil pH. Trace metal concentration remained low in all treatments. Conclusions These findings support the potential use of compost and vermicompost derived from vineyard pruning residues and sewage sludge as recycled organic amendments for horticultural production. However, further research is recommended to validate these results under long-term field conditions and diverse soil types.","引言 堆肥和蚯蚓粪被认为是有效的有机改良剂，可在循环经济策略中支持植物生长、促进养分循环并提升土壤质量。在此背景下，本研究评估了由葡萄园修剪枝条和污水污泥制成的颗粒化堆肥（CC）和蚯蚓粪（VC）对生菜（Lactuca sativa L.）生长、叶片生化性状、矿质组成及土壤化学性质的短期效果。并将这两种有机产品与一种商品化有机无机复混肥（CF）及未施肥对照（CT）进行了比较。方法 采用完全随机温室盆栽试验，持续55天，包括4个处理，每处理8次重复。数据采用单因素方差分析，随后进行Tukey HSD检验，显著性水平为p \u003C 0.05；当不满足方差分析假设时，采用非参数分析。结果与讨论 试验结束时，尽管各处理间绝对养分投入量存在差异，但有机改良剂在大多数形态指标上与商品化肥料CF表现相当，不过CF下地上部鲜重仍较高。叶片生化响应因处理而异：CC和CF表现出较高的叶绿素和类胡萝卜素含量，而VC和CT的色素浓度较低。可溶性总糖在CT中最高，在CF中最低，而总蛋白和酚类物质含量未受处理的显著影响。CC和VC下叶片大量元素浓度总体与CF相当，而微量元素Fe和Mn浓度在CF中较高。最终土壤分析显示，有机施肥下有机质、可提取P和K以及阳离子交换量较高，而CF下土壤pH最低。所有处理中痕量金属浓度均保持较低水平。结论 这些发现支持将源自葡萄园修剪残体和污水污泥的堆肥和蚯蚓粪作为再生有机改良剂用于园艺生产的潜力。然而，建议进一步研究以在长期田间条件和多种土壤类型下验证这些结果。","Frontiers in Sustainable Food Systems","2026-10-05T00:00:00Z","论文",66,{"impact":119,"substance":98,"depth":99,"authority":174,"freshness":114,"relevant":21,"comment":175},13,"温室盆栽短期试验，验证葡萄修剪废弃物与污泥制成颗粒堆肥和蚯蚓粪的农艺效果，结论有参考价值但需长期田间验证。",[177],{"name":169,"url":166},[179,180,181,182,183],"生菜","土壤改良","有机肥","蚯蚓粪","堆肥",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[186],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":13,"version":51,"method":52,"evidence":188},{"candidates":189,"agricultural_evidence":56},[190,195,202,209,216],{"code":32,"score":191,"supported":56,"title":192,"abstract":193,"tags":194},15,[33],[58,33],[180],{"code":39,"score":12,"supported":56,"title":196,"abstract":197,"tags":201},[],[198,65,199,200],"施肥","fertilizer","fertilization",[181],{"code":203,"score":204,"supported":56,"title":205,"abstract":206,"tags":208},"B1",7,[],[179,207],"萝卜",[179],{"code":210,"score":87,"supported":56,"title":211,"abstract":212,"tags":215},"B7",[],[213,214],"温室","greenhouse",[],{"code":217,"score":75,"supported":56,"title":218,"abstract":219,"tags":221},"B2",[],[220],"葡萄",[],[223,224],"葡萄修剪废弃物 污泥 堆肥 生菜","土壤改良 有机肥 蚯蚓粪 堆肥","葡萄修剪废弃物污泥堆肥生菜-4292","10.3389\u002Ffsufs.2026.1948663",{"doi":226,"openalex_id":228,"authors":229,"venue":169,"cited_by_count":84,"oa_url":166,"card":271,"direction":275,"ingested_from":277},"W7220437154",[230,233,236,239,242,244,247,250,253,256,259,262,265,268],{"name":231,"orcid":232},"Maria Cristina Morais","https:\u002F\u002Forcid.org\u002F0000-0002-6598-7454",{"name":234,"orcid":235},"Elisabete Nascimento-Gonçalves","https:\u002F\u002Forcid.org\u002F0000-0001-6430-1513",{"name":237,"orcid":238},"Lia‐Tânia Dinis","https:\u002F\u002Forcid.org\u002F0000-0002-9612-0909",{"name":240,"orcid":241},"Sandra Pereira","https:\u002F\u002Forcid.org\u002F0000-0003-2998-814X",{"name":243,"orcid":8},"Zélia Branco",{"name":245,"orcid":246},"Henda Lopes","https:\u002F\u002Forcid.org\u002F0000-0002-2830-1783",{"name":248,"orcid":249},"Tiago Azevedo","https:\u002F\u002Forcid.org\u002F0000-0002-3030-4510",{"name":251,"orcid":252},"Catarina Medeiros","https:\u002F\u002Forcid.org\u002F0009-0005-2706-7023",{"name":254,"orcid":255},"Ana Maria Coimbra","https:\u002F\u002Forcid.org\u002F0000-0003-3110-7153",{"name":257,"orcid":258},"João Claro","https:\u002F\u002Forcid.org\u002F0000-0001-5936-1036",{"name":260,"orcid":261},"Guilhermina Marques","https:\u002F\u002Forcid.org\u002F0000-0003-0963-5785",{"name":263,"orcid":264},"João Ricardo Sousa","https:\u002F\u002Forcid.org\u002F0000-0003-4365-9086",{"name":266,"orcid":267},"Paula A. Oliveira","https:\u002F\u002Forcid.org\u002F0000-0001-9519-4044",{"name":269,"orcid":270},"Marta Roboredo","https:\u002F\u002Forcid.org\u002F0000-0002-6177-4988",{"tldr":272,"method":273,"finding":274,"direction":275,"opportunity":276},"评估葡萄修剪物与污泥制成的颗粒堆肥和蚯蚓粪对生菜生长及土壤化学性质的影响。","温室盆栽试验，4处理8重复，55天，方差分析与Tukey检验。","两种有机肥多数形态指标媲美商品肥，且提升土壤有机质、磷钾和阳离子交换量。","农业绿色发展与碳","可开展长期田间试验，验证不同土壤类型下有机肥的稳定效果与重金属累积风险。","openalex","2026-10-06T23:30:06.098411Z",{"id":280,"title":281,"url":282,"summary":283,"summary_zh":284,"content":8,"source_name":285,"source_url":282,"published_at":286,"category":171,"cover_url":8,"hotness":12,"is_selected":13,"score":287,"score_detail":288,"sources":290,"tags":292,"primary_topic":298,"secondary_topics":299,"classification":301,"search_phrases":332,"slug":335,"view_count":100,"doi":336,"paper":337,"created_at":350},3680,"AI-Driven Nutrient Optimization Systems for Precision Agriculture: A Data-Integrated Approach to Reducing Nutrient Runoff and Enhancing Soil Health in Agricultural Systems","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.85286","Nutrients play a fundamental role in plant growth, soil fertility, and overall agricultural productivity. However, inefficient fertilizer application in conventional farming systems often leads to nutrient imbalance, reduced soil quality, and increased environmental degradation. The emergence of precision agriculture and artificial intelligence (AI) has introduced new opportunities for improving nutrient management through data-driven decision-making. This study investigates an AI-driven nutrient optimization system designed to enhance fertilizer efficiency while reducing nutrient runoff and improving soil health. Findings indicate that AI-based systems significantly improve nutrient use efficiency by enabling site-specific fertilizer recommendations and adaptive management strategies. The integration of multi-source agricultural data enhances prediction accuracy for soil nutrient status and crop demand, while also supporting early identification of nutrient loss risks such as runoff and leaching. The study concludes that AI-driven nutrient optimization provides a sustainable and scalable approach for modern agriculture, supporting both productivity enhancement and environmental protection through intelligent, data-integrated farming systems.","养分在植物生长、土壤肥力和农业整体生产力中发挥着基础性作用。然而，传统农业系统中低效的施肥方式往往导致养分失衡、土壤质量下降以及环境退化加剧。精准农业与人工智能（AI）的出现，为通过数据驱动决策改善养分管理带来了新的机遇。本研究探讨了一种AI驱动的养分优化系统，旨在提高肥料利用效率，同时减少养分径流并改善土壤健康。研究结果表明，基于AI的系统通过实现特定地块的施肥建议和适应性管理策略，显著提高了养分利用效率。多源农业数据的整合提升了土壤养分状况和作物需求的预测精度，同时也有助于早期识别径流和淋溶等养分流失风险。研究得出结论，AI驱动的养分优化为现代农业提供了一种可持续且可扩展的方法，通过智能化、数据整合的农业系统，同时支持生产力提升和环境保护。","International Journal For Multidisciplinary Research","2026-09-26T00:00:00Z",61,{"impact":18,"substance":17,"depth":191,"authority":119,"freshness":119,"relevant":21,"comment":289},"论文提出AI驱动的养分优化系统，方法有新意但属学术探讨，产业落地影响有限，可作为智慧农业技术动态收录。",[291],{"name":285,"url":282},[293,294,295,296,297],"智慧农业","农业人工智能","精准施肥","土壤健康","养分流失",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[300],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":13,"version":51,"method":52,"evidence":302},{"candidates":303,"agricultural_evidence":56},[304,308,315,320,326],{"code":32,"score":191,"supported":56,"title":305,"abstract":306,"tags":307},[33],[58,33],[296],{"code":309,"score":191,"supported":56,"title":310,"abstract":312,"tags":314},"I1",[311],"precision agriculture",[313,311],"精准农业",[293],{"code":39,"score":12,"supported":56,"title":316,"abstract":317,"tags":319},[],[198,65,318,199],"养分管理",[295],{"code":321,"score":125,"supported":56,"title":322,"abstract":323,"tags":325},"I2",[],[324],"artificial intelligence",[294],{"code":327,"score":75,"supported":56,"title":328,"abstract":329,"tags":331},"I6",[],[330],"agricultural data",[],[333,334],"AI 养分优化 精准农业","养分流失 土壤健康","AI养分优化精准农业-3680","10.36948\u002Fijfmr.2026.v08i05.85286",{"doi":336,"openalex_id":338,"authors":339,"venue":285,"cited_by_count":84,"oa_url":282,"card":344,"direction":348,"ingested_from":277},"W7214553198",[340,342],{"name":341,"orcid":8},"Olufisayo Obebe",{"name":343,"orcid":8},"Ebenezer Akinola",{"tldr":345,"method":346,"finding":347,"direction":348,"opportunity":349},"研究AI驱动的养分优化系统，通过数据集成实现精准施肥，减少养分流失并改善土壤健康。","集成多源农业数据与AI算法，进行站点特异性施肥推荐和自适应管理。","AI系统显著提高养分利用效率，增强土壤养分与作物需求预测精度，并早期识别流失风险。","农业人工智能与决策模型","可探索多源数据融合的实时决策模型，以及在不同土壤气候条件下AI系统的可扩展性与长期环境效应评估。","2026-09-28T23:30:49.535963Z",{"id":352,"title":353,"url":354,"summary":355,"summary_zh":8,"content":8,"source_name":356,"source_url":354,"published_at":357,"category":171,"cover_url":8,"hotness":12,"is_selected":13,"score":358,"score_detail":359,"sources":362,"tags":364,"primary_topic":369,"secondary_topics":374,"classification":377,"search_phrases":401,"slug":404,"view_count":87,"doi":405,"paper":406,"created_at":422},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":18,"substance":17,"depth":360,"authority":19,"freshness":119,"relevant":21,"comment":361},17,"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。",[363],{"name":356,"url":354},[293,365,366,313,367,368],"变量施肥","玉米","向日葵","变量播种",{"code":370,"slug":371,"name":366,"path":372,"parent_code":373},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[375,376],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":50,"locked":13,"version":51,"method":52,"evidence":378},{"candidates":379,"agricultural_evidence":56},[380,384,388,392,396],{"code":370,"score":18,"supported":56,"title":381,"abstract":382,"tags":383},[371],[371],[366],{"code":39,"score":18,"supported":56,"title":385,"abstract":386,"tags":387},[200],[200],[365],{"code":32,"score":63,"supported":56,"title":389,"abstract":390,"tags":391},[33],[33],[],{"code":309,"score":20,"supported":56,"title":393,"abstract":394,"tags":395},[],[311],[293,313],{"code":397,"score":21,"supported":13,"title":398,"abstract":399,"tags":400},"A4",[],[],[367],[402,403],"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":405,"openalex_id":407,"authors":408,"venue":356,"cited_by_count":84,"oa_url":8,"card":8,"direction":8,"ingested_from":277},"W7214357812",[409,412,414,416,418,420],{"name":410,"orcid":411},"María Videgain","https:\u002F\u002Forcid.org\u002F0000-0002-3630-7931",{"name":413,"orcid":8},"J. A. Martínez-Casasnovas",{"name":415,"orcid":8},"S. Artero",{"name":417,"orcid":8},"A. Vigo",{"name":419,"orcid":8},"M. Vidal",{"name":421,"orcid":8},"F. J. García-Ramos","2026-09-26T23:30:03.127069Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":8,"source_name":429,"source_url":426,"published_at":430,"category":171,"cover_url":8,"hotness":12,"is_selected":13,"score":431,"score_detail":432,"sources":434,"tags":436,"primary_topic":442,"secondary_topics":443,"classification":445,"search_phrases":479,"slug":482,"view_count":100,"doi":483,"paper":484,"created_at":500},2807,"Integrating drip fertigation and nitrification inhibitors for rhizosphere-scale control of nitrogen transformations","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1894587","Reactive nitrogen (Nr) losses from fertilized agricultural soils remain one of the dominant drivers of groundwater nitrate contamination and nitrous oxide (N 2 O) emissions, yet mitigation strategies are largely designed around input optimization rather than control of in-soil nitrogen transformations. Drip fertigation and nitrification inhibitors (NIs) have independently shown potential to improve nitrogen efficiency, however their integrated effects remain fragmented across irrigation and inhibitor-focused literature. Consequently, an integrated mechanistic framework explaining how drip irrigation-induced microenvironments regulate NI performance and nitrogen transformation is lacking. Here, we argue that the integration of drip fertigation with NIs, hereafter referred to as drip-NIs integration, is best understood as rhizosphere process control rather than input management: drip defines a bounded reaction-transport domain, and NIs slow down the conversion of ammonium (NH 4 + ) to nitrate (NO 3 - ). This review aims to synthesize evidence across NI types, crops, and drip configurations to examine how localized wetting patterns govern ammonium-nitrate partitioning, microbial processes, and inhibitor fate within the root zone. The available evidence suggests that NI performance may depend on spatial and temporal overlap among oxygen recovery, NH 4 + availability, and active ammonia oxidizers within the wetted bulb, however direct spatial validation remains limited. Based on these insights, we propose the Designer Rhizosphere Model (DRM) as a conceptual, hypothesis-generating framework linking controllable design variables (emitter placement, irrigation waveform, fertigation chemistry, NI formulation) with the measurable state variables (oxygen availability, NH 4 + \u002FNO 3 - fields, inhibitor exposure), microbial process rates, and yield-scaled environmental outcomes. The DRM requires validation through spatially field experiments before practical application. The resulting framework suggests that drip-NIs integration may reduce nitrate leaching and greenhouse gas emissions while sustaining productivity, but outcomes are context-dependent and may be constrained by salinity accumulation, acidification, oxygen limitation, ammonium toxicity, and off-target effects. Future research should prioritize spatially resolved measurements, sensor-guided fertigation, drip-compatible inhibitor formulations, and process-based decision support models. Collectively, this review reframes nitrogen management from simple fertilizer placement to rhizosphere process control for improving agricultural sustainability.","施肥农业土壤中的活性氮（Nr）损失仍是地下水硝酸盐污染和一氧化二氮（N₂O）排放的主要驱动因素之一，然而减排策略大多围绕投入优化而非土壤内氮转化的调控来设计。滴灌施肥和硝化抑制剂（NIs）各自已显示出提高氮效率的潜力，但二者的集成效应在灌溉和抑制剂相关文献中仍呈碎片化状态。因此，目前缺乏一个整合性的机理框架来解释滴灌诱导的微环境如何调控硝化抑制剂效能和氮转化。本文认为，将滴灌施肥与硝化抑制剂集成（以下称为滴灌-硝化抑制剂集成）最好理解为根际过程调控而非投入管理：滴灌界定了一个有边界的反应-传输域，硝化抑制剂则减缓铵态氮（NH₄⁺）向硝态氮（NO₃⁻）的转化。本综述旨在综合不同硝化抑制剂类型、作物和滴灌配置下的证据，考察局部湿润模式如何调控根区内铵态氮-硝态氮分配、微生物过程及抑制剂归趋。现有证据表明，硝化抑制剂效能可能取决于湿润体内氧气恢复、NH₄⁺有效性与活跃氨氧化菌之间的时空重叠，然而直接的空间验证仍然有限。基于这些认识，我们提出“设计型根际模型”（Designer Rhizosphere Model, DRM）作为一个概念性的、假设生成框架，将可控设计变量（滴头位置、灌溉波形、施肥化学、硝化抑制剂配方）与可测量状态变量（氧气有效性、NH₄⁺\u002FNO₃⁻场、抑制剂暴露）、微生物过程速率及产量标度的环境结果联系起来。DRM在实际应用前需要通过空间田间试验进行验证。由此形成的框架表明，滴灌-硝化抑制剂集成可能在维持生产力的同时减少硝酸盐淋失和温室气体排放，但结果依赖于具体情境，并可能受到盐分积累、酸化、氧气限制、铵毒害和脱靶效应的制约。未来研究应优先开展空间分辨测量、传感器引导施肥、滴灌兼容型抑制剂配方以及基于过程的决策支持模型。总体而言，本综述","Frontiers in Plant Science","2026-09-17T00:00:00Z",82,{"impact":17,"substance":16,"depth":17,"authority":19,"freshness":12,"relevant":21,"comment":433},"该综述提出滴灌与水肥一体化结合硝化抑制剂的根际过程调控框架，为农业绿色减排提供新思路，但尚属概念模型，需田间验证。",[435],{"name":429,"url":426},[437,438,439,440,441],"水肥一体化","智慧灌溉","氮素管理","农业减排","根际调控",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[444],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":50,"locked":13,"version":51,"method":52,"evidence":446},{"candidates":447,"agricultural_evidence":56},[448,453,459,466,470,475],{"code":39,"score":17,"supported":56,"title":449,"abstract":451,"tags":452},[450],"fertigation",[198,199,450],[437],{"code":32,"score":360,"supported":56,"title":454,"abstract":456,"tags":458},[455],"rhizosphere",[58,457,33,455],"根际",[],{"code":460,"score":204,"supported":56,"title":461,"abstract":462,"tags":465},"H3",[],[463,464],"灌溉","irrigation",[438],{"code":210,"score":87,"supported":56,"title":467,"abstract":468,"tags":469},[],[213,214],[],{"code":321,"score":75,"supported":56,"title":471,"abstract":472,"tags":474},[],[473],"decision support",[],{"code":154,"score":21,"supported":13,"title":476,"abstract":477,"tags":478},[],[],[440],[480,481],"水肥一体化 农业减排 智慧灌溉 根际调控","水肥一体化 农业减排","水肥一体化农业减排智慧灌溉根际调控-2807","10.3389\u002Ffpls.2026.1894587",{"doi":483,"openalex_id":485,"authors":486,"venue":429,"cited_by_count":84,"oa_url":426,"card":495,"direction":348,"ingested_from":277},"W7213456426",[487,489,491,493],{"name":488,"orcid":8},"Muhammad Zain",{"name":490,"orcid":8},"Sheheryar Khan",{"name":492,"orcid":8},"Hongjun Lei",{"name":494,"orcid":8},"Abdul Ghafoor",{"tldr":496,"method":497,"finding":498,"direction":275,"opportunity":499},"综述提出滴灌施肥与硝化抑制剂整合应视为根际过程调控，并构建设计根际模型框架。","综述整合硝化抑制剂类型、作物与滴灌配置证据，提出概念性设计根际模型。","抑制剂效果取决于湿润体内氧气恢复、铵态氮与氨氧化菌的时空重叠，但缺乏空间验证。","可开展空间分辨的田间试验验证设计根际模型，并开发传感器引导的滴灌施肥决策模型。","2026-09-17T23:31:04.332274Z",{"id":502,"title":503,"url":504,"summary":505,"summary_zh":506,"content":8,"source_name":169,"source_url":504,"published_at":507,"category":171,"cover_url":8,"hotness":12,"is_selected":13,"score":508,"score_detail":509,"sources":511,"tags":513,"primary_topic":518,"secondary_topics":519,"classification":521,"search_phrases":550,"slug":553,"view_count":100,"doi":554,"paper":555,"created_at":586},1521,"Organic and inorganic fertilization differently affect the agronomic performance of two okra (Abelmoschus esculentus L. Moench) varieties and soil fertility","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1818681","Okra is a key vegetable in West Africa, but poor fertilization practices and low soil fertility constrain its productivity. This study evaluated the responses of two okra varieties (AVOK1504 and Konni) to twelve fertilization treatments, including poultry manure (PM), foliar biostimulant (ABC grower), NPK, Urea, and K 2 SO 4 , applied alone or in combination. A split-plot design with three replications was used, with “Variety” as the main factor and “Fertilizers” as the sub-factor. The field trial was implemented over two seasons (years 2021 and 2022) at Abomey-Calavi (Southern Benin) with Sudano-Guinean climatic conditions. Growth parameters, yield components, soil properties, and climatic data were collected. Analysis of variance with the least significant difference at p \u003C 0.05, was performed to assess statistical differences in growth parameters, yield, and yield components. Fertilization regimes significantly influenced plant height, stem diameter, branching, and leaf development ( p \u003C 0.001). T2 induced early flowering and fruiting for the Konni variety, whatever the season: respectively 38.3 and 39.3 days after sowing (season1) 47 and 48 days after sowing (season2). Konni yielded significantly higher than AVOK1504 regardless the season ( p \u003C 0.001). Sole organic fertilizer and its combinations with mineral fertilizers enhanced growth and yield, with 10 t\u002Fha PM (T2), 10 t\u002Fha PM + 200 kg NPK + 50 kg Urea + 50 kg K₂SO₄ (T6) and T7 (T6 + 250L\u002Fha ABC grower) achieving the highest yields: 4.3 t\u002Fha, 5.3 t\u002Fha and 5.8 t\u002Fha, respectively, for Konni against 5.1 t\u002Fha, 4.8 t\u002Fha and 5.3 t\u002Fha, respectively, for AVOK1504. Sole PM at 10 t\u002Fha (T2) showed similar statistical yield performance as T6 and T7, despite its low inputs composition, and additionally most effectively enhanced soil pH, total N, Ca, and Mg. Notably, none of the treatments increased soil organic carbon or available phosphorus, an unexpected result given the high carbon content of the PM applied. Findings highlight the agronomic benefits of integrated nutrient management for optimizing okra productivity and soil health. These results inform fertilizer recommendations and sustainable soil fertility management strategies for okra-producing regions in Benin and similar agroecological zones.","秋葵是西非的一种重要蔬菜，但不良的施肥实践和低土壤肥力制约了其生产力。本研究评估了两个秋葵品种（AVOK1504和Konni）对十二种施肥处理的响应，这些处理包括禽粪（PM）、叶面生物刺激素（ABC grower）、NPK、尿素和K₂SO₄，单独施用或组合施用。采用三次重复的裂区设计，以“品种”为主因素，“肥料”为副因素。田间试验于2021年和2022年两个生长季在贝宁南部阿博梅-卡拉维（Abomey-Calavi）进行，该地区属苏丹-几内亚气候条件。收集了生长参数、产量构成要素、土壤性质和气候数据。采用最小显著差数法（p \u003C 0.05）进行方差分析，以评估生长参数、产量及产量构成要素的统计差异。施肥制度显著影响株高、茎粗、分枝和叶片发育（p \u003C 0.001）。无论季节如何，T2处理均诱导Konni品种提早开花和结果：播种后分别第38.3天和39.3天（第一季）、第47天和48天（第二季）。无论季节如何，Konni的产量均显著高于AVOK1504（p \u003C 0.001）。单独施用有机肥及其与无机肥的组合处理促进了生长和产量，其中10 t\u002Fha PM（T2）、10 t\u002Fha PM + 200 kg NPK + 50 kg尿素 + 50 kg K₂SO₄（T6）和T7（T6 + 250 L\u002Fha ABC grower）获得了最高产量：Konni分别为4.3 t\u002Fha、5.3 t\u002Fha和5.8 t\u002Fha，而AVOK1504分别为5.1 t\u002Fha、4.8 t\u002Fha和5.3 t\u002Fha。尽管单独施用10 t\u002Fha PM（T2）的养分投入组成较低，但其产量表现与T6和T7在统计上相当，且最有效地提高了土壤pH、全氮、钙和镁含量。值得注意的是，所有处理均未增加土壤有机碳或有效磷含量，鉴于所施禽粪的碳含量较高，这一结果出乎意料。研究结果凸显了综合养分管理在优化秋葵生产力和土壤健康方面的农艺效益。这些结果为贝宁秋葵产区及类似农业生态区的肥料推荐和可持续土壤肥力管理策略提供了依据。","2026-09-02T00:00:00Z",65,{"impact":18,"substance":17,"depth":99,"authority":18,"freshness":204,"relevant":21,"comment":510},"研究有机无机肥配施对秋葵产量和土壤肥力的影响，为西非可持续施肥提供数据支持。",[512],{"name":169,"url":504},[181,514,515,516,517],"无机肥","秋葵","土壤肥力","产量",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[520],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":50,"locked":13,"version":51,"method":52,"evidence":522},{"candidates":523,"agricultural_evidence":56},[524,528,532,538,544],{"code":39,"score":17,"supported":56,"title":525,"abstract":526,"tags":527},[200],[198,65,318,199,200],[181],{"code":32,"score":191,"supported":56,"title":529,"abstract":530,"tags":531},[33],[58,33],[516],{"code":203,"score":87,"supported":56,"title":533,"abstract":534,"tags":537},[],[535,536],"蔬菜","vegetable",[],{"code":539,"score":75,"supported":56,"title":540,"abstract":541,"tags":543},"C3",[],[542],"poultry",[],{"code":545,"score":75,"supported":56,"title":546,"abstract":547,"tags":549},"H6",[],[548],"农业生态",[],[551,552],"土壤肥力 无机肥 有机肥 产量","土壤肥力 无机肥","土壤肥力无机肥有机肥产量-1521","10.3389\u002Ffsufs.2026.1818681",{"doi":554,"openalex_id":556,"authors":557,"venue":169,"cited_by_count":84,"oa_url":504,"card":580,"direction":584,"ingested_from":277},"W7206149898",[558,560,563,565,567,569,571,573,576,578],{"name":559,"orcid":8},"Judith Y. F. A. Honfoga",{"name":561,"orcid":562},"Sognigbé N’Danikou","https:\u002F\u002Forcid.org\u002F0000-0002-4163-8567",{"name":564,"orcid":8},"Eric C. Legba",{"name":566,"orcid":8},"Armand Agbannoussou",{"name":568,"orcid":8},"Jordan S. Houegban",{"name":570,"orcid":8},"Morel Avlessi",{"name":572,"orcid":8},"Ramasamy Srinivasan",{"name":574,"orcid":575},"Lukas Pawera","https:\u002F\u002Forcid.org\u002F0000-0001-8548-3491",{"name":577,"orcid":8},"Enoch G. Achigan-Dako",{"name":579,"orcid":8},"Christophe Gandonou",{"tldr":581,"method":582,"finding":583,"direction":584,"opportunity":585},"研究有机与无机肥对两种秋葵品种产量和土壤肥力的影响，发现有机肥及组合效果最佳。","田间试验，裂区设计，两种秋葵品种，十二种施肥处理，两年重复，方差分析。","有机肥（10t\u002Fha禽粪）及其与化肥组合显著提高产量和土壤肥力，但未增加土壤有机碳和有效磷。","智慧农业 \u002F 农业物联网","可探索有机肥对土壤碳库和磷有效性的长期影响机制，以及基于作物模型的精准施肥推荐系统。","2026-09-03T23:30:07.902045Z"]