[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3269":3,"related-3269":117},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":88,"slug":91,"view_count":92,"doi":93,"paper":94,"created_at":116},3269,"Heterogeneous allocation of organic fertilizer under spatial constraints: evidence from Northern Ghana","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1938147","Organic fertilizer use is a key component of sustainable soil fertility management in smallholder systems though its adoption remains limited across sub-Saharan Africa. Although previous studies have primarily focused on household-level adoption, less attention has been paid as to how farmers allocate organic inputs across heterogeneous plots under resource constraints. This study addresses this research gap by examining the plot-level determinants of organic fertilizer use in a heterogeneous smallholder system in Northern Ghana. Using household survey data covering the 2023 and 2024 cropping seasons, the analysis explicitly distinguishes among three decision stages: plot-level organic fertilizer use, intra-farm allocation, and application intensity. Econometric models were used to evaluate the associations of plot accessibility, crop choice, soil characteristics, and regional conditions. The results indicated that the allocation of organic inputs was associated with plot characteristics and varied across plots. Maize plots were more likely to receive organic fertilizer, whereas plots perceived as fertile were less likely to do so. Longer travel time from the homestead to the plot was negatively associated with plot-level organic fertilizer use and with within-household allocation, indicating that less accessible plots were less likely to receive organic fertilizer, whereas the association with application intensity differed by input type. Importantly, allocation patterns differed by input type; that is, poultry manure was concentrated in nearby plots, whereas compost was applied more widely across space. These findings suggest that organic fertilizer use reflects a context-specific allocation strategy associated with spatial constraints and input characteristics. As such, this study provides a more nuanced understanding of smallholder decision-making by going beyond a mere binary focus on adoption. Instead of assuming uniform application, policy and extension efforts should emphasize targeted site-specific interventions that may enhance fertilizer effectiveness and promote sustainable soil fertility management.","有机肥使用是小农系统中可持续土壤肥力管理的关键组成部分，但在撒哈拉以南非洲地区，其采用率仍然有限。尽管以往研究主要关注农户层面的采用情况，但对资源约束下农户如何在异质性地块之间分配有机投入品的关注较少。本研究通过考察加纳北部异质性小农系统中农户层面有机肥使用的影响因素，填补了这一研究空白。利用涵盖2023年和2024年种植季的农户调查数据，分析明确区分了三个决策阶段：地块层面的有机肥使用、农场内分配以及施用强度。研究采用计量经济模型评估地块可达性、作物选择、土壤特征和区域条件之间的关联。结果表明，有机投入品的分配与地块特征相关，且在不同地块之间存在差异。玉米地块更有可能获得有机肥，而被认为肥沃的地块则较不可能获得有机肥。从宅基地到地块的通行时间越长，与地块层面有机肥使用和农户内部分配均呈负相关，表明可达性较差的地块较不可能获得有机肥，而施用强度与通行时间的关联则因投入品类型而异。重要的是，分配模式因投入品类型而不同，即家禽粪便集中在附近地块，而堆肥则在空间上施用更为广泛。这些发现表明，有机肥使用反映了一种与空间约束和投入品特征相关的、情境特定的分配策略。因此，本研究超越了对采用与否的简单二元关注，提供了对小农决策更为细致的理解。政策和推广工作不应假设均匀施用，而应强调有针对性的、因地制宜的干预措施，以提高肥料有效性并促进可持续土壤肥力管理。",null,"Frontiers in Sustainable Food Systems","2026-09-22T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,21,17,13,9,1,"基于加纳北部两年农户调查的论文，揭示有机肥在异质地块间的配置策略，方法扎实但属区域性研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"精准施肥","撒哈拉以南非洲","小农户","有机肥","土壤肥力",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,61,69,76,83],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":52},16,true,[49],"fertilizer",[51,49],"肥料",[30,27],{"code":54,"score":55,"supported":47,"title":56,"abstract":57,"tags":60},"H2",7,[],[58,59],"土壤","soil",[31],{"code":62,"score":63,"supported":47,"title":64,"abstract":65,"tags":68},"A3",6,[],[66,67],"玉米","maize",[],{"code":70,"score":63,"supported":47,"title":71,"abstract":72,"tags":75},"C3",[],[73,74],"家禽","poultry",[],{"code":77,"score":78,"supported":47,"title":79,"abstract":80,"tags":82},"G2",4,[],[81],"smallholder",[29],{"code":84,"score":22,"supported":14,"title":85,"abstract":86,"tags":87},"J3",[],[],[28],[89,90],"小农户 有机肥 地块配置","撒哈拉以南非洲 土壤肥力 精准施肥 小农户","小农户有机肥地块配置-3269",2,"10.3389\u002Ffsufs.2026.1938147",{"doi":93,"openalex_id":95,"authors":96,"venue":10,"cited_by_count":108,"oa_url":6,"card":109,"direction":113,"ingested_from":115},"W7214037932",[97,100,102,105],{"name":98,"orcid":99},"Yoshie Yageta","https:\u002F\u002Forcid.org\u002F0000-0001-8756-149X",{"name":101,"orcid":9},"Guenwoo Lee",{"name":103,"orcid":104},"Joseph Agebase Awuni","https:\u002F\u002Forcid.org\u002F0000-0003-0940-9462",{"name":106,"orcid":107},"Satoshi Nakamura","https:\u002F\u002Forcid.org\u002F0000-0002-0952-5618",0,{"tldr":110,"method":111,"finding":112,"direction":113,"opportunity":114},"研究加纳北部小农户在空间约束下有机肥的地块级分配决策。","2023-2024农户调查数据，计量模型分析地块可达性、作物与土壤特征。","地块越远越少施有机肥，禽粪集中于近地，堆肥分布更广。","数字乡村与农业信息化","可结合地块空间数据与遥感，构建小农户有机肥精准配置决策支持模型。","openalex","2026-09-23T23:30:07.582998Z",{"total":63,"page":22,"page_size":63,"items":118},[119,228,315,400,479,550],{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":133,"tags":135,"primary_topic":139,"secondary_topics":144,"classification":152,"search_phrases":211,"slug":214,"view_count":92,"doi":215,"paper":216,"created_at":227},1888,"Smart Technologies for Precision Nutrient Management in Smallholder Farming: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.4038\u002Fjur.v14i1.8134","Smallholder farmers with less than 2 hectares contribute more than 1\u002F3rd of the world food supply but continue to suffer the challenges of low nutrient use efficiency (30-40%), reduced soil fertility and lack of access to precision agriculture technologies. The aim of the review is to synthesize the evidence of 235 peer-reviewed articles (2010-2025) to assess the precision nutrient management (PNM) technologies, namely, in terms of their applicability, economic feasibility, and adoption capacity within the smallholder farming systems. Systematic literature review according to PRISMA, the analysis of articles on satellite and UAV-based remote sensing, IoT-enabled soil sensors, variable rate application systems, machine learning algorithms, and case studies worldwide were summarized. The application of variable rates can cut fertilizer use by 10-25 % and boost yields by 7-15 % but only small holders adopt it globally because of capital limitations, fragmented land holdings (1-2 ha), poor rural connectivity and insufficient extension services. Multi-source data forecasts nutrient needs with 85-92 % accuracy using machine learning algorithms (Random Forests, Support Vector Machines. Open-source sensor development, offline mobile decision support systems, equipment sharing cooperatives, blended finance (30–50% tech subsidies), and farmer involvement must all be part of the democratization of PNM for smallholders. Institutional adjustments, unified extension services, public-private cooperation, and supportive nutrient management regulations are all necessary for effective scaling. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. Economic analyses indicated that farms over 10 ha have positive returns, though smallholders required special support to overcome initial costs and limitations.","拥有不足2公顷土地的小农户贡献了全球粮食供应总量的三分之一以上，却持续面临养分利用效率低下（30-40%）、土壤肥力下降以及缺乏精准农业技术获取途径等挑战。本综述旨在综合235篇同行评审论文（2010-2025年）的证据，评估精准养分管理（PNM）技术在小农农业系统中的适用性、经济可行性和采纳能力。依据PRISMA方法进行系统性文献综述，总结了基于卫星和无人机遥感、物联网土壤传感器、变量施肥系统、机器学习算法及全球案例研究的分析结果。变量施肥技术的应用可减少化肥使用量10-25%，提高产量7-15%，但由于资金限制、土地碎片化（1-2公顷）、农村网络覆盖不足和推广服务欠缺，全球仅有少数小农户采纳该技术。利用机器学习算法（随机森林、支持向量机）进行多源数据预测，养分需求预测准确率可达85-92%。实现小农户PNM技术的普及化，必须包括开源传感器开发、离线移动决策支持系统、设备共享合作社、混合融资（30-50%技术补贴）以及农户参与等多方面措施。有效的规模化推广还需要制度调整、统一的推广服务、公私合作以及支持性养分管理法规的配套。PNM技术的采纳可减少养分径流15-30%，降低温室气体排放10-20%，并实现每公顷0.4-1.2吨二氧化碳当量的土壤碳封存。经济分析表明，10公顷以上的农场可获得正收益，但小农户需要特殊支持以克服初始成本和限制因素。","Journal of the University of Ruhuna","2026-09-07T00:00:00Z",72,{"impact":129,"substance":130,"depth":129,"authority":131,"freshness":92,"relevant":22,"comment":132},18,22,12,"系统综述小农户精准养分管理技术，数据详实，但时效性低，适合专题参考。",[134],{"name":125,"url":122},[136,27,137,138,29],"智慧农业","农业遥感","机器学习",{"code":77,"slug":140,"name":141,"path":142,"parent_code":143},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[145,146],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":147,"slug":148,"name":149,"path":150,"parent_code":151},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":153},{"candidates":154,"agricultural_evidence":47},[155,160,166,172,179,186,193,197,204],{"code":77,"score":156,"supported":47,"title":157,"abstract":158,"tags":159},15,[81],[29,81],[29],{"code":33,"score":13,"supported":47,"title":161,"abstract":162,"tags":165},[],[163,164,49],"施肥","养分管理",[27],{"code":147,"score":13,"supported":47,"title":167,"abstract":168,"tags":171},[],[138,169,170],"machine learning","decision support",[138],{"code":173,"score":55,"supported":47,"title":174,"abstract":175,"tags":178},"I1",[],[176,177],"精准农业","precision agriculture",[136],{"code":180,"score":55,"supported":47,"title":181,"abstract":182,"tags":185},"I3",[],[183,184],"遥感","remote sensing",[137],{"code":187,"score":63,"supported":47,"title":188,"abstract":189,"tags":192},"B7",[],[190,191],"温室","greenhouse",[],{"code":54,"score":63,"supported":47,"title":194,"abstract":195,"tags":196},[],[58,59],[],{"code":198,"score":63,"supported":47,"title":199,"abstract":200,"tags":203},"I5",[],[201,202],"物联网","IoT",[],{"code":205,"score":206,"supported":47,"title":207,"abstract":208,"tags":210},"H4",3,[],[209],"carbon sequestration",[],[212,213],"农业遥感 智慧农业 机器学习 精准施肥","农业遥感 智慧农业","农业遥感智慧农业机器学习精准施肥-1888","10.4038\u002Fjur.v14i1.8134",{"doi":215,"openalex_id":217,"authors":218,"venue":125,"cited_by_count":108,"oa_url":122,"card":221,"direction":225,"ingested_from":115},"W7211915396",[219],{"name":220,"orcid":9},"Anuga Liyanage",{"tldr":222,"method":223,"finding":224,"direction":225,"opportunity":226},"综述小农户精准养分管理技术，分析其适用性、经济可行性与采纳障碍。","PRISMA系统综述235篇文献，分析遥感、物联网、变量施肥及机器学习案例。","变量施肥可减肥10-25%并增产7-15%，但小农户因资金、土地细碎等采纳率低。","智慧农业 \u002F 农业物联网","小农户精准养分管理技术民主化路径，如低成本传感器、离线决策支持系统及合作社共享模式。","2026-09-08T23:30:08.156887Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":9,"source_name":10,"source_url":231,"published_at":234,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":235,"score_detail":236,"sources":238,"tags":240,"primary_topic":244,"secondary_topics":245,"classification":250,"search_phrases":279,"slug":282,"view_count":92,"doi":283,"paper":284,"created_at":314},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":131,"substance":129,"depth":46,"authority":131,"freshness":55,"relevant":22,"comment":237},"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。",[239],{"name":10,"url":231},[30,241,242,31,243],"无机肥","秋葵","产量",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[246],{"code":54,"slug":59,"name":247,"path":248,"parent_code":249},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":251},{"candidates":252,"agricultural_evidence":47},[253,258,262,269,273],{"code":33,"score":129,"supported":47,"title":254,"abstract":256,"tags":257},[255],"fertilization",[163,51,164,49,255],[30],{"code":54,"score":156,"supported":47,"title":259,"abstract":260,"tags":261},[59],[58,59],[31],{"code":263,"score":63,"supported":47,"title":264,"abstract":265,"tags":268},"B1",[],[266,267],"蔬菜","vegetable",[],{"code":70,"score":206,"supported":47,"title":270,"abstract":271,"tags":272},[],[74],[],{"code":274,"score":206,"supported":47,"title":275,"abstract":276,"tags":278},"H6",[],[277],"农业生态",[],[280,281],"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":283,"openalex_id":285,"authors":286,"venue":10,"cited_by_count":108,"oa_url":231,"card":309,"direction":225,"ingested_from":115},"W7206149898",[287,289,292,294,296,298,300,302,305,307],{"name":288,"orcid":9},"Judith Y. F. A. Honfoga",{"name":290,"orcid":291},"Sognigbé N’Danikou","https:\u002F\u002Forcid.org\u002F0000-0002-4163-8567",{"name":293,"orcid":9},"Eric C. Legba",{"name":295,"orcid":9},"Armand Agbannoussou",{"name":297,"orcid":9},"Jordan S. Houegban",{"name":299,"orcid":9},"Morel Avlessi",{"name":301,"orcid":9},"Ramasamy Srinivasan",{"name":303,"orcid":304},"Lukas Pawera","https:\u002F\u002Forcid.org\u002F0000-0001-8548-3491",{"name":306,"orcid":9},"Enoch G. Achigan-Dako",{"name":308,"orcid":9},"Christophe Gandonou",{"tldr":310,"method":311,"finding":312,"direction":225,"opportunity":313},"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。","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",{"id":316,"title":317,"url":318,"summary":319,"summary_zh":320,"content":9,"source_name":321,"source_url":318,"published_at":322,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":323,"score_detail":324,"sources":326,"tags":328,"primary_topic":333,"secondary_topics":334,"classification":335,"search_phrases":382,"slug":385,"view_count":92,"doi":386,"paper":387,"created_at":399},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":130,"substance":130,"depth":129,"authority":20,"freshness":17,"relevant":22,"comment":325},"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。",[327],{"name":321,"url":318},[136,329,27,330,331,332],"无人机","变量施肥","遥感监测","养分利用效率",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":336},{"candidates":337,"agricultural_evidence":47},[338,345,349,356,363,367,371,378],{"code":33,"score":339,"supported":47,"title":340,"abstract":341,"tags":344},20,[255],[163,51,342,164,343],"水肥一体化","fertigation",[332,330,27],{"code":180,"score":55,"supported":47,"title":346,"abstract":347,"tags":348},[],[183,184],[331],{"code":350,"score":63,"supported":47,"title":351,"abstract":352,"tags":355},"A1",[],[353,354],"水稻","rice",[],{"code":357,"score":63,"supported":47,"title":358,"abstract":359,"tags":362},"A2",[],[360,361],"小麦","wheat",[],{"code":62,"score":63,"supported":47,"title":364,"abstract":365,"tags":366},[],[66,67],[],{"code":54,"score":63,"supported":47,"title":368,"abstract":369,"tags":370},[],[58,59],[],{"code":372,"score":63,"supported":47,"title":373,"abstract":374,"tags":377},"I4",[],[375,376],"地理信息","geographic information",[],{"code":173,"score":22,"supported":14,"title":379,"abstract":380,"tags":381},[],[],[136],[383,384],"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":386,"openalex_id":388,"authors":389,"venue":321,"cited_by_count":108,"oa_url":318,"card":394,"direction":225,"ingested_from":115},"W7222838957",[390,392],{"name":391,"orcid":9},"S. T. Panusha",{"name":393,"orcid":9},"T. M. Soumya",{"tldr":395,"method":396,"finding":397,"direction":225,"opportunity":398},"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":401,"title":402,"url":403,"summary":404,"summary_zh":405,"content":9,"source_name":406,"source_url":403,"published_at":407,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":408,"score_detail":409,"sources":411,"tags":413,"primary_topic":418,"secondary_topics":424,"classification":426,"search_phrases":448,"slug":451,"view_count":22,"doi":452,"paper":453,"created_at":478},4685,"Farm management and yield gaps in Robusta (Coffea canephora) coffee production across the three largest Asian origins","https:\u002F\u002Fdoi.org\u002F10.25186\u002F.v21i.2515","Robusta coffee is a key livelihood crop in Asia, and its productivity differs largely across main regions. However, the factors underlying these differences remain limited. This study examines yield variation across Vietnam, Indonesia, and India, the three largest Robusta-producing countries in Asia. Using 13,525 farm-level surveys, the study applied descriptive statistics, correlation analysis, and regression-based threshold methods to assess the relationships between yield and key management practices. The results show that median yields reach 2,643 kg GBE ha⁻¹ in Vietnam, compared with 1,359 kg ha⁻¹ in India-Karnataka, 509 kg ha⁻¹ in India-Kerala, and 383 kg ha⁻¹ in Indonesia. Correlation analysis shows that inorganic fertilizer use is positively associated with yield across the three countries, with coefficients ranging from 0.326 to 0.408. Pruning practices are also associated with higher yields in several regions, whereas shade levels are negatively related to yield in Vietnam. These findings indicate that yield gaps in Asian Robusta systems arise from different constraints across countries. Input scarcity appears to constrain productivity in Indonesia, while inefficiencies in nutrient management and structural characteristics of production systems play a larger role in Vietnam and India. The results highlight the need for context-specific strategies to improve productivity and sustainability in Robusta coffee production. Key words: Yield differences; fertilizer efficiency; shade management; plantation rejuvenation; Robusta coffee.","罗布斯塔咖啡（Robusta coffee）是亚洲重要的生计作物，但其生产力在主产区之间差异显著。然而，造成这些差异的因素仍缺乏充分研究。本研究考察了亚洲三大罗布斯塔咖啡生产国——越南、印度尼西亚和印度之间的产量差异。研究利用13,525份农场层面调查数据，采用描述性统计、相关分析和基于回归的阈值方法，评估产量与关键管理措施之间的关系。结果表明，越南的产量中位数达到2,643 kg GBE ha⁻¹，而印度卡纳塔克邦为1,359 kg ha⁻¹，印度喀拉拉邦为509 kg ha⁻¹，印度尼西亚为383 kg ha⁻¹。相关分析显示，无机肥料施用与三国产量均呈正相关，系数范围为0.326至0.408。修剪措施在若干地区也与较高产量相关，而越南的遮阴水平与产量呈负相关。这些发现表明，亚洲罗布斯塔咖啡系统的产量差距源于各国不同的限制因素。投入品短缺似乎制约了印度尼西亚的生产力，而养分管理效率低下和生产系统的结构性特征在越南和印度发挥了更大作用。研究结果凸显了制定因地制宜策略以提高罗布斯塔咖啡生产力和可持续性的必要性。关键词：产量差异；肥料效率；遮阴管理；种植园更新；罗布斯塔咖啡。","Coffee Science","2026-10-09T00:00:00Z",77,{"impact":46,"substance":130,"depth":129,"authority":20,"freshness":17,"relevant":22,"comment":410},"基于13,525份农户调查揭示亚洲三大罗布斯塔产区的产量差与管理约束，数据规模大、结论有区域针对性，对咖啡产业信息化与精准施肥有参考价值。",[412],{"name":406,"url":403},[29,414,415,416,417],"咖啡产业","施肥管理","产量差","遮荫栽培",{"code":419,"slug":420,"name":421,"path":422,"parent_code":423},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[425],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":427},{"candidates":428,"agricultural_evidence":47},[429,435,439,444],{"code":419,"score":156,"supported":47,"title":430,"abstract":432,"tags":434},[431],"coffee",[433,431],"咖啡",[414],{"code":33,"score":21,"supported":47,"title":436,"abstract":437,"tags":438},[],[51,164,49],[],{"code":180,"score":206,"supported":47,"title":440,"abstract":441,"tags":443},[],[442],"估产",[],{"code":77,"score":22,"supported":14,"title":445,"abstract":446,"tags":447},[],[],[29],[449,450],"越南 印尼 印度 罗布斯塔 咖啡","罗布斯塔 咖啡 产量差 施肥","越南印尼印度罗布斯塔咖啡-4685","10.25186\u002F.v21i.2515",{"doi":452,"openalex_id":454,"authors":455,"venue":406,"cited_by_count":108,"oa_url":471,"card":472,"direction":477,"ingested_from":115},"W7221151398",[456,459,462,465,468],{"name":457,"orcid":458},"Hue Tran","https:\u002F\u002Forcid.org\u002F0000-0003-0606-0525",{"name":460,"orcid":461},"Thuy Phan Thi","https:\u002F\u002Forcid.org\u002F0000-0001-5451-7185",{"name":463,"orcid":464},"Long Nhat Cao","https:\u002F\u002Forcid.org\u002F0000-0002-5345-7472",{"name":466,"orcid":467},"Jacob Schenthal","https:\u002F\u002Forcid.org\u002F0000-0002-1265-033X",{"name":469,"orcid":470},"Ketan Patel","https:\u002F\u002Forcid.org\u002F0009-0007-1813-2449","https:\u002F\u002Fcoffeescience.ufla.br\u002Findex.php\u002FCoffeescience\u002Farticle\u002Fdownload\u002F2515\u002F2608",{"tldr":473,"method":474,"finding":475,"direction":113,"opportunity":476},"基于13525份农户调查，分析越南、印尼、印度罗布斯塔咖啡产量差异及管理因素。","13,525份农户调查，描述统计、相关分析与回归阈值法。","无机肥与产量正相关；印尼受投入不足限制，越南和印度则受养分管理低效与结构因素制约。","可构建分区域咖啡产量差诊断与精准施肥决策模型，结合遥感与物联网实现管理处方推荐。","农业遥感与作物表型","2026-10-11T23:30:31.779147Z",{"id":480,"title":481,"url":482,"summary":483,"summary_zh":484,"content":9,"source_name":485,"source_url":482,"published_at":486,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":487,"score_detail":488,"sources":490,"tags":492,"primary_topic":496,"secondary_topics":497,"classification":503,"search_phrases":529,"slug":532,"view_count":108,"doi":9,"paper":533,"created_at":549},4670,"SMART FERTILIZER DISTRIBUTION SYSTEM: A COMPREHENSIVE REVIEW","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F334","Agriculture is an important sector for food production and economic development, and the efficient use of fertilizers is essential for achieving better crop growth and productivity. Conventional fertilizer distribution methods largely depend on manual application, which can lead to uneven distribution, excessive fertilizer usage, increased labor requirements, and wastage of agricultural resources. The development of automation and Internet of Things (IoT) technologies has provided new opportunities for improving fertilizer distribution and management in agricultural fields. This review paper presents a comprehensive study of Smart Fertilizer Distribution Systems, focusing on the technologies, control methods, communication techniques, and approaches reported in existing research. Various systems using microcontrollers, IoT devices, wireless communication, web-based platforms, sensors, motor or pump control, driver circuits, and solenoid valves are reviewed. The working principles of automated fertilizer distribution systems and their role in controlling fertilizer flow and reducing manual intervention are discussed. Different communication technologies, including Wi-Fi and other wireless methods, are compared based on their applicability, cost, operating range, reliability, and control capability. The review also examines the major advantages and limitations of existing systems, such as automation, remote operation, improved fertilizer management, reduced labor, network dependency, power requirements, sensor accuracy, and system complexity. A comparative analysis of selected research works is presented to identify the techniques used, their contributions, and the limitations that remain to be addressed. The review further identifies research gaps and discusses future developments such as automatic fertilizer scheduling, multi-zone fertilizer distribution, real-time monitoring, cloud-based data storage, artificial intelligence, machine learning, and integration with renewable energy sources. The study concludes that smart fertilizer distribution can improve the efficiency, accuracy, and sustainability of fertilizer application and has significant potential for future precision agriculture applications.","农业是粮食生产和经济发展的重要部门，而肥料的高效利用对于实现作物更好生长和生产力提升至关重要。传统的肥料分配方法在很大程度上依赖人工施用，这可能导致分配不均、肥料过量使用、劳动力需求增加以及农业资源浪费。自动化和物联网（IoT）技术的发展为改善农田肥料分配与管理提供了新的机遇。本文综述对智能肥料分配系统进行了全面研究，重点关注现有研究中报道的技术、控制方法、通信技术和方法。本文回顾了使用微控制器、物联网设备、无线通信、基于网络的平台、传感器、电机或泵控制、驱动电路和电磁阀的各种系统。讨论了自动化肥料分配系统的工作原理及其在控制肥料流量和减少人工干预方面的作用。基于适用性、成本、工作范围、可靠性和控制能力，比较了包括Wi-Fi和其他无线方法在内的不同通信技术。本综述还考察了现有系统的主要优势和局限性，如自动化、远程操作、改善肥料管理、减少劳动力、网络依赖、功率需求、传感器精度和系统复杂性。本文对选定的研究工作进行了比较分析，以识别所采用的技术、其贡献以及仍有待解决的局限性。本综述进一步指出了研究空白，并讨论了未来发展方向，如自动肥料调度、多区域肥料分配、实时监测、基于云的数据存储、人工智能、机器学习以及与可再生能源的集成。该研究得出结论，智能肥料分配可以提高肥料施用的效率、准确性和可持续性，并在未来精准农业应用中具有巨大潜力。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",61,{"impact":131,"substance":46,"depth":156,"authority":131,"freshness":63,"relevant":22,"comment":489},"综述性论文系统梳理智能施肥系统的技术路线与研究空白，对智慧农业领域有参考价值，但属文献综述而非原创突破，时效性一般。",[491],{"name":485,"url":482},[136,493,27,494,495],"农业人工智能","农业物联网","智能农机",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[498,499],{"code":147,"slug":148,"name":149,"path":150,"parent_code":151},{"code":198,"slug":500,"name":501,"path":502,"parent_code":151},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":40,"locked":14,"version":41,"method":42,"evidence":504},{"candidates":505,"agricultural_evidence":47},[506,510,515,520,524],{"code":33,"score":156,"supported":47,"title":507,"abstract":508,"tags":509},[49],[51,49],[27],{"code":147,"score":13,"supported":47,"title":511,"abstract":512,"tags":514},[],[138,169,513],"artificial intelligence",[493],{"code":198,"score":13,"supported":47,"title":516,"abstract":517,"tags":519},[],[201,518,202],"internet of things",[494],{"code":173,"score":55,"supported":47,"title":521,"abstract":522,"tags":523},[],[176,177],[136],{"code":525,"score":22,"supported":14,"title":526,"abstract":527,"tags":528},"F5",[],[],[495],[530,531],"智能施肥系统 物联网","精准施肥 自动化","智能施肥系统物联网-4670",{"doi":9,"openalex_id":534,"authors":535,"venue":485,"cited_by_count":108,"oa_url":482,"card":544,"direction":225,"ingested_from":115},"W7221394563",[536,538,540,542],{"name":537,"orcid":9},"Anjali Manoj Kumbhar",{"name":539,"orcid":9},"Shruti Prashant Kulkarni",{"name":541,"orcid":9},"Snehal Santosh Khune",{"name":543,"orcid":9},"B. B. Godbole",{"tldr":545,"method":546,"finding":547,"direction":225,"opportunity":548},"综述智能施肥系统的技术、控制与通信方法，并指出研究空白与未来方向。","综述基于微控制器、物联网、无线通信、传感器与电磁阀的自动施肥系统研究。","智能施肥可提升施肥效率、精度与可持续性，但存在网络依赖、功耗与传感器精度等局限。","可探索AI驱动的自动施肥调度、多区域分配与云边协同的实时精准施肥系统。","2026-10-11T23:30:16.128775Z",{"id":551,"title":552,"url":553,"summary":554,"summary_zh":555,"content":9,"source_name":10,"source_url":553,"published_at":556,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":557,"score_detail":558,"sources":560,"tags":562,"primary_topic":567,"secondary_topics":568,"classification":570,"search_phrases":596,"slug":599,"view_count":206,"doi":600,"paper":601,"created_at":651},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。","2026-10-05T00:00:00Z",66,{"impact":17,"substance":339,"depth":46,"authority":20,"freshness":21,"relevant":22,"comment":559},"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。",[561],{"name":10,"url":553},[563,564,30,565,566],"生菜","土壤改良","蚯蚓粪","堆肥",{"code":54,"slug":59,"name":247,"path":248,"parent_code":249},[569],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":571},{"candidates":572,"agricultural_evidence":47},[573,577,581,586,590],{"code":54,"score":156,"supported":47,"title":574,"abstract":575,"tags":576},[59],[58,59],[564],{"code":33,"score":13,"supported":47,"title":578,"abstract":579,"tags":580},[],[163,51,49,255],[30],{"code":263,"score":55,"supported":47,"title":582,"abstract":583,"tags":585},[],[563,584],"萝卜",[563],{"code":187,"score":63,"supported":47,"title":587,"abstract":588,"tags":589},[],[190,191],[],{"code":591,"score":206,"supported":47,"title":592,"abstract":593,"tags":595},"B2",[],[594],"葡萄",[],[597,598],"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":600,"openalex_id":602,"authors":603,"venue":10,"cited_by_count":108,"oa_url":553,"card":645,"direction":649,"ingested_from":115},"W7220437154",[604,607,610,613,616,618,621,624,627,630,633,636,639,642],{"name":605,"orcid":606},"Maria Cristina Morais","https:\u002F\u002Forcid.org\u002F0000-0002-6598-7454",{"name":608,"orcid":609},"Elisabete Nascimento-Gonçalves","https:\u002F\u002Forcid.org\u002F0000-0001-6430-1513",{"name":611,"orcid":612},"Lia‐Tânia Dinis","https:\u002F\u002Forcid.org\u002F0000-0002-9612-0909",{"name":614,"orcid":615},"Sandra Pereira","https:\u002F\u002Forcid.org\u002F0000-0003-2998-814X",{"name":617,"orcid":9},"Zélia Branco",{"name":619,"orcid":620},"Henda Lopes","https:\u002F\u002Forcid.org\u002F0000-0002-2830-1783",{"name":622,"orcid":623},"Tiago Azevedo","https:\u002F\u002Forcid.org\u002F0000-0002-3030-4510",{"name":625,"orcid":626},"Catarina Medeiros","https:\u002F\u002Forcid.org\u002F0009-0005-2706-7023",{"name":628,"orcid":629},"Ana Maria Coimbra","https:\u002F\u002Forcid.org\u002F0000-0003-3110-7153",{"name":631,"orcid":632},"João Claro","https:\u002F\u002Forcid.org\u002F0000-0001-5936-1036",{"name":634,"orcid":635},"Guilhermina Marques","https:\u002F\u002Forcid.org\u002F0000-0003-0963-5785",{"name":637,"orcid":638},"João Ricardo Sousa","https:\u002F\u002Forcid.org\u002F0000-0003-4365-9086",{"name":640,"orcid":641},"Paula A. Oliveira","https:\u002F\u002Forcid.org\u002F0000-0001-9519-4044",{"name":643,"orcid":644},"Marta Roboredo","https:\u002F\u002Forcid.org\u002F0000-0002-6177-4988",{"tldr":646,"method":647,"finding":648,"direction":649,"opportunity":650},"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。","2026-10-06T23:30:06.098411Z"]