[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2807":3},{"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":22,"tags":24,"view_count":30,"doi":31,"paper":32,"created_at":51},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",null,"Frontiers in Plant Science","2026-09-17T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":21},18,22,14,1,"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。",[23],{"name":10,"url":6},[25,26,27,28,29],"水肥一体化","智慧灌溉","氮素管理","农业减排","根际调控",0,"10.3389\u002Ffpls.2026.1894587",{"doi":31,"openalex_id":33,"authors":34,"venue":10,"cited_by_count":30,"oa_url":6,"card":43,"direction":49,"ingested_from":50},"W7213456426",[35,37,39,41],{"name":36,"orcid":9},"Muhammad Zain",{"name":38,"orcid":9},"Sheheryar Khan",{"name":40,"orcid":9},"Hongjun Lei",{"name":42,"orcid":9},"Abdul Ghafoor",{"tldr":44,"method":45,"finding":46,"direction":47,"opportunity":48},"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。","农业绿色发展与碳","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。","农业人工智能与决策模型","openalex","2026-09-17T23:31:04.332274Z"]