[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4416":3,"related-4416":112},{"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":23,"tags":28,"primary_topic":34,"secondary_topics":39,"classification":46,"search_phrases":85,"slug":88,"view_count":89,"doi":90,"paper":91,"created_at":111},4416,"Agricultural blockchain readiness environment and carbon emission intensity in maize production: evidence from provincial panel data in major maize-producing areas of China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1942225","China’s major maize-producing regions face the joint challenge of sustaining food production and reducing agricultural emissions. Existing research on digital agriculture often uses broad digitalization measures and therefore does not distinguish the regional conditions that may enable information management, traceability, and verification applications associated with blockchain. This study constructs an Agricultural Blockchain Readiness Environment Index (ABRE Index, block_index) using entropy weighting across digital infrastructure, the agricultural industrial base, and policy support. The index measures regional readiness and enabling conditions rather than realized blockchain adoption. Using 180 province-year observations for 12 major maize-producing provinces from 2010 to 2024, we estimate two-way fixed-effects models with province-clustered inference and conduct supplementary robustness analyses. The ABRE Index is negatively associated with carbon emissions per unit of maize-sown area, with the baseline coefficient statistically significant at the 10% level under conventional clustered inference and supported by supplementary inference procedures. The coefficient remains negative in all 12 leave-one-out specifications, is statistically significant at the 10% level or better in 10 specifications, and the wild cluster bootstrap test yields p = 0.0415. The index is positively correlated with blockchain-related patent activity ( r = 0.1883, p = 0.0114), although this correlation does not establish that the index measures realized adoption. The mechanism analysis is consistent with an organizational pathway through agricultural socialized services: the ABRE Index is positively associated with service_ratio (0.1099, p \u003C 0.01), and agricultural socialized services are negatively associated with ce_area (−2.2765, p \u003C 0.05). The interaction with the high-development indicator is negative but does not reach the conventional 10% significance threshold (−0.1499, p = 0.103). Overall, the evidence supports an associational relationship between blockchain-readiness conditions and maize-production carbon-emission intensity; it does not establish a strictly causal effect of blockchain adoption.","中国主要玉米产区面临保障粮食生产与减少农业排放的双重挑战。现有数字农业研究多采用宽泛的数字化指标，因而未能区分可能支撑与区块链相关的信息管理、追溯和验证应用的区域条件。本研究基于数字基础设施、农业产业基础和政策支持三个维度，采用熵权法构建了农业区块链就绪环境指数（ABRE Index，block_index）。该指数衡量的是区域就绪程度和使能条件，而非已实现的区块链采用。利用2010年至2024年12个主要玉米生产省份的180个省份—年份观测值，我们估计了双向固定效应模型，采用省份聚类推断，并进行了补充稳健性分析。ABRE指数与单位玉米播种面积碳排放呈负相关，基准系数在常规聚类推断下于10%水平上统计显著，并得到补充推断程序的支持。在全部12个逐一剔除（leave-one-out）设定中，该系数均保持为负，其中10个设定在10%或更优水平上统计显著，野生聚类自举检验（wild cluster bootstrap test）得出p = 0.0415。该指数与区块链相关专利活动呈正相关（r = 0.1883，p = 0.0114），尽管这一相关性并不能证明该指数衡量的是已实现的采用。机制分析与通过农业社会化服务发挥作用的组织路径相一致：ABRE指数与service_ratio呈正相关（0.1099，p \u003C 0.01），农业社会化服务与ce_area呈负相关（−2.2765，p \u003C 0.05）。与高发展指标的交互项为负，但未达到常规10%显著性阈值（−0.1499，p = 0.103）。总体而言，证据支持区块链就绪条件与玉米生产碳排放强度之间存在关联关系；但并未确立区块链采用的严格因果效应。",null,"Frontiers in Sustainable Food Systems","2026-10-07T00:00:00Z","论文",25,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于12个玉米主产省2010—2024年面板数据构建农业区块链就绪环境指数，发现其与玉米碳排放强度负相关，方法规范、数据规模可观，但结论仅为关联性而非因果，属细分领域实证进展。",[24,25],{"name":10,"url":6},{"name":26,"url":27},"Frontiers in Sustainable Food Systems 2026 年 10 月 07 日 Volume 10 Sec. Land, Livelihoods and Food Security","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1942225\u002Ffull",[29,30,31,32,33],"数字乡村","智慧农业","玉米","农业碳排放","区块链",{"code":35,"slug":36,"name":31,"path":37,"parent_code":38},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[40],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",{"status":47,"locked":14,"version":48,"method":49,"evidence":50},"classified","2026-10-22.2","rules",{"candidates":51,"agricultural_evidence":54},[52,58,65,73,80],{"code":35,"score":53,"supported":54,"title":55,"abstract":56,"tags":57},15,true,[36],[31,36],[31],{"code":41,"score":59,"supported":54,"title":60,"abstract":62,"tags":64},12,[61],"carbon emission",[63],"碳排放",[32],{"code":66,"score":67,"supported":54,"title":68,"abstract":69,"tags":72},"I1",7,[],[70,71],"数字农业","digital agriculture",[30],{"code":74,"score":75,"supported":54,"title":76,"abstract":77,"tags":79},"G2",3,[],[78],"社会化服务",[],{"code":81,"score":21,"supported":14,"title":82,"abstract":83,"tags":84},"I6",[],[],[29],[86,87],"农业区块链 玉米 碳排放","玉米主产区 区块链 碳强度","农业区块链玉米碳排放-4416",0,"10.3389\u002Ffsufs.2026.1942225",{"doi":90,"openalex_id":92,"authors":93,"venue":10,"cited_by_count":89,"oa_url":102,"card":103,"direction":109,"ingested_from":110},"W7220869193",[94,97,99],{"name":95,"orcid":96},"Pengfei Zhou","https:\u002F\u002Forcid.org\u002F0000-0003-2117-7967",{"name":98,"orcid":9},"Zhiyi Hu",{"name":100,"orcid":101},"Yang Shen","https:\u002F\u002Forcid.org\u002F0000-0002-6781-6915","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1942225\u002Fpdf",{"tldr":104,"method":105,"finding":106,"direction":107,"opportunity":108},"构建农业区块链就绪环境指数，分析其与玉米生产碳排放强度的关联。","熵权法构建ABRE指数，用2010-2024年12省面板数据做双向固定效应模型。","ABRE指数与玉米单位播种面积碳排放负相关，机制可能通过农业社会化服务实现。","农业绿色发展与碳","可探究区块链就绪环境通过社会化服务减碳的因果机制，并拓展到其他主粮作物或区域。","智慧农业 \u002F 农业物联网","openalex","2026-10-08T23:30:07.668939Z",{"total":113,"page":21,"page_size":113,"items":114},6,[115,170,225,277,343,422],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":9,"content":9,"source_name":120,"source_url":9,"published_at":121,"category":12,"cover_url":9,"hotness":122,"is_selected":14,"score":123,"score_detail":124,"sources":128,"tags":130,"primary_topic":133,"secondary_topics":134,"classification":135,"search_phrases":157,"slug":160,"view_count":21,"doi":9,"paper":161,"created_at":169},5227,"China's farm digitization reduces agricultural carbon emissions but the relationship is nonlinear (U-shaped)","https:\u002F\u002Fcommonplace.workforcefutures.net\u002Fpaper\u002Fopenalex:W7221809067","Gao、Feng、Yuan、Sun 在 OpenAlex 2026-10-16 索引的最新研究,基于中国31个省份2010—2023年平衡面板数据,采用双向固定效应面板与SEM中介模型,发现农业数字技术通过精准管理与间接融资\u002F绿色创新两条路径降低碳排放,但呈U型非线性,转折点为数字化指数0.540,劳动生产率提升可推迟转折点,数字化在区域、规模、排放来源维度呈现异质性。","OpenAlex W7221809067(2026-10-16)","2026-10-16T00:00:00Z",10,84,{"impact":18,"substance":125,"depth":17,"authority":19,"freshness":126,"relevant":21,"comment":127},23,8,"基于31省2010—2023年面板数据的全国性实证研究，揭示数字化与农业碳排放的U型关系及0.540转折点，结论新颖且具政策参考价值，值得进入每日精选。",[129],{"name":120,"url":118},[29,30,131,32,132],"农业数字化","绿色创新",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[],{"status":47,"locked":14,"version":48,"method":49,"evidence":136},{"candidates":137,"agricultural_evidence":54},[138,143,148,153],{"code":41,"score":139,"supported":54,"title":140,"abstract":141,"tags":142},4,[],[63],[32],{"code":66,"score":144,"supported":14,"title":145,"abstract":146,"tags":147},2,[],[],[131,30],{"code":149,"score":21,"supported":14,"title":150,"abstract":151,"tags":152},"I",[],[],[131],{"code":81,"score":21,"supported":14,"title":154,"abstract":155,"tags":156},[],[],[29],[158,159],"农业数字化 碳排放 U型","数字技术 农业碳减排","农业数字化碳排放U型-5227",{"doi":9,"openalex_id":9,"authors":162,"venue":9,"cited_by_count":89,"oa_url":9,"card":163,"direction":107,"ingested_from":168},[],{"tldr":164,"method":165,"finding":166,"direction":107,"opportunity":167},"基于中国31省2010—2023年面板数据，发现农业数字化与碳排放呈U型非线性关系。","双向固定效应面板模型与SEM中介模型，31省平衡面板数据。","数字化通过精准管理和绿色创新降碳，但存在0.540的U型转折点。","可探究转折点后数字化增碳的机制，及劳动生产率等调节因素如何推迟拐点。","agent","2026-10-18T00:05:30.735414Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":9,"source_name":176,"source_url":173,"published_at":177,"category":12,"cover_url":9,"hotness":122,"is_selected":14,"score":15,"score_detail":178,"sources":180,"tags":182,"primary_topic":185,"secondary_topics":186,"classification":187,"search_phrases":204,"slug":207,"view_count":139,"doi":208,"paper":209,"created_at":224},4520,"Agricultural Industry Agglomeration and Carbon Emission Intensity: A Systems Perspective on Resilience and Digital Development","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsystems14101263","Decarbonizing agriculture depends not only on the efficiency of individual production activities but also on coordination among production, processing, and service activities. From a systems perspective, this study treats provincial agriculture as an interconnected industrial system and examines the relationship between agricultural industry agglomeration and agricultural carbon emission intensity. Moving beyond conventional measures of production concentration, agricultural industry agglomeration is measured as coordination between horizontal agglomeration in agricultural production and vertical agglomeration across production, processing, and agricultural services. Based on a balanced panel of 390 observations across 30 Chinese provinces from 2011 to 2023, the study uses province and year fixed effects, mediation and moderation analyses, robustness checks, and an endogeneity check. Results show that agricultural industry agglomeration is significantly associated with lower agricultural carbon emission intensity. The mediation results are consistent with a partial intermediary role for agricultural industry resilience in this relationship. The interaction and simple slope results show that the positive association between agricultural industry agglomeration and agricultural industry resilience is larger at higher levels of digital village development. These findings connect subsystem coordination, adaptive capacity, digital conditions, and carbon performance, thereby extending research on agricultural transformation toward lower carbon intensity from a systems perspective.","农业脱碳不仅取决于单个生产活动的效率，还取决于生产、加工和服务活动之间的协调。本研究从系统视角出发，将省级农业视为一个相互关联的产业系统，考察农业产业集聚与农业碳排放强度之间的关系。本研究超越传统的生产集中度衡量方式，将农业产业集聚界定为农业生产横向集聚与生产、加工及农业服务纵向集聚之间的协调。基于2011年至2023年中国30个省份390个观测值的平衡面板数据，本研究采用省份和年份固定效应、中介与调节分析、稳健性检验以及内生性检验。结果表明，农业产业集聚与较低的农业碳排放强度显著相关。中介结果表明，农业产业韧性在这一关系中发挥部分中介作用。交互效应和简单斜率结果表明，在数字乡村发展水平较高时，农业产业集聚与农业产业韧性之间的正向关联更强。这些发现将子系统协调、适应能力、数字条件与碳绩效联系起来，从而从系统视角拓展了农业向低碳强度转型的研究。","Systems","2026-10-08T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":179},"基于30省2011-2023年面板数据，从系统视角揭示农业产业集聚通过韧性降低碳排放强度，并受数字乡村发展调节，方法规范、结论有政策参考价值。",[181],{"name":176,"url":173},[29,30,183,32,184],"农业韧性","农业产业集聚",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[],{"status":47,"locked":14,"version":48,"method":49,"evidence":188},{"candidates":189,"agricultural_evidence":54},[190,195,200],{"code":41,"score":17,"supported":54,"title":191,"abstract":192,"tags":194},[61],[63,193,61],"农业碳",[32],{"code":81,"score":67,"supported":54,"title":196,"abstract":197,"tags":199},[],[29,198],"digital village",[29],{"code":66,"score":21,"supported":14,"title":201,"abstract":202,"tags":203},[],[],[30],[205,206],"农业产业集聚 碳排放强度","数字乡村 农业韧性","农业产业集聚碳排放强度-4520","10.3390\u002Fsystems14101263",{"doi":208,"openalex_id":210,"authors":211,"venue":176,"cited_by_count":89,"oa_url":173,"card":218,"direction":223,"ingested_from":110},"W7220924322",[212,214,216],{"name":213,"orcid":9},"Jinghui Hu",{"name":215,"orcid":9},"Jun He",{"name":217,"orcid":9},"Junzhong He",{"tldr":219,"method":220,"finding":221,"direction":107,"opportunity":222},"从系统视角研究中国省级农业产业集聚对农业碳排放强度的影响及韧性与数字化的作用。","30省2011-2023年面板数据，固定效应、中介与调节分析。","农业产业集聚显著降低碳排放强度，产业韧性部分中介，数字乡村发展正向调节。","可探究产业集聚不同模式与数字化协同对碳减排的非线性及空间溢出效应。","数字乡村与农业信息化","2026-10-09T23:30:32.435467Z",{"id":226,"title":227,"url":228,"summary":229,"summary_zh":9,"content":9,"source_name":230,"source_url":9,"published_at":231,"category":12,"cover_url":9,"hotness":122,"is_selected":14,"score":232,"score_detail":233,"sources":235,"tags":237,"primary_topic":240,"secondary_topics":241,"classification":246,"search_phrases":265,"slug":268,"view_count":144,"doi":9,"paper":269,"created_at":276},2495,"China's Digital Farming Push Cuts Carbon Emissions:13 年省级面板数字乡村技术对农业碳排放效率影响(西北农林)","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F09\u002F12\u002Fchinas-digital-farming-push-cuts-carbon-emissions","西北农林科技大学 Yuming Li 在《Applied Sciences》发表 13 年省级数据研究,构建首个量化数字乡村技术(DVT)对农业碳排放效率影响的框架,使用超效率 SBM 模型同时量化期望产出(如作物产量)和非期望产出(如碳排放),发现数字融合可通过简化资源使用、启用数据驱动决策来提高农业碳效率。技术创新和社会化农业服务(如合作社平台)充当数字采纳与排放降低之间的桥梁。在地形平坦、农业集约化地区和靠近中大型城市的地区观察到最强效益。","MDPI Applied Sciences 2026-09-12","2026-09-11T16:00:00Z",79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":126,"relevant":21,"comment":234},"基于13年省级面板数据构建数字乡村技术影响农业碳排放效率的量化框架，方法新颖、结论有政策参考价值，但属学术论文层面，影响范围限于细分领域。",[236],{"name":230,"url":228},[29,30,32,238,239],"农业社会化服务","农业绿色低碳",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[242],{"code":81,"slug":243,"name":244,"path":245,"parent_code":149},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",{"status":47,"locked":14,"version":48,"method":49,"evidence":247},{"candidates":248,"agricultural_evidence":54},[249,253,257,261],{"code":41,"score":125,"supported":54,"title":250,"abstract":251,"tags":252},[63,193],[63,193],[32],{"code":81,"score":59,"supported":54,"title":254,"abstract":255,"tags":256},[29],[29],[29],{"code":74,"score":21,"supported":14,"title":258,"abstract":259,"tags":260},[],[],[238],{"code":66,"score":21,"supported":14,"title":262,"abstract":263,"tags":264},[],[],[30],[266,267],"农业社会化服务 农业绿色低碳 农业碳排放 数字乡村","农业社会化服务 农业绿色低碳","农业社会化服务农业绿色低碳农业碳排放数字乡村-2495",{"doi":9,"openalex_id":9,"authors":270,"venue":9,"cited_by_count":89,"oa_url":9,"card":271,"direction":107,"ingested_from":168},[],{"tldr":272,"method":273,"finding":274,"direction":107,"opportunity":275},"基于13年省级面板数据，研究数字乡村技术对农业碳排放效率的影响。","超效率SBM模型量化期望与非期望产出，省级面板数据分析。","数字融合通过优化资源与数据决策提升碳效率，技术创新与合作社平台起桥梁作用。","可探究数字技术对碳效率影响的非线性阈值及不同作物系统的异质性机制。","2026-09-15T00:04:27.428323Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":9,"source_name":283,"source_url":280,"published_at":284,"category":12,"cover_url":9,"hotness":122,"is_selected":14,"score":285,"score_detail":286,"sources":289,"tags":291,"primary_topic":295,"secondary_topics":296,"classification":302,"search_phrases":322,"slug":325,"view_count":89,"doi":326,"paper":327,"created_at":342},4759,"Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2870-2879.9043","Background: Accurate estimation of seedling vigor indices (SVI) is critical for monitoring plant health and predicting yields in precision agriculture. This study comparatively evaluates the performance of different machine learning algorithms in estimating the SVI parameter. Methods: Germination data were collected from 39 maize genotypes under three different salinity conditions (0 mM, 100 mM, and 200 mM NaCl), including the parameters Gmax, MGT, GI, SL, RL, SdL, SMR, RMR, and SVI. The dataset was preprocessed through normalization to the [0–1] range. The SVI parameter was estimated using four different machine learning methods: Random Forest, CatBoost, LightGBM, and Support Vector Regression (SVR). To evaluate the performance of the models, both 10-fold cross-validation and 80%–20% hold-out validation techniques were employed. The model performance was examined using RMSE, MSE, MAE, and R² metrics. Results: The findings show that all models have a high explanatory power in the training dataset. Specifically, the CatBoost model showed the highest success in the training phase (R²=0.9962), whereas the SVR model achieved the highest generalization performance on the test dataset (R²=0.9712, RMSE=0.0582). Feature importance analysis results revealed that the 'App' parameter had the highest impact on the target variable in most models, but different structural interactions were prominent in the LightGBM model. The results demonstrate that machine learning techniques provide high accuracy in SVI estimation and that the tendency towards overfitting is limited. Conclusions: In conclusion, this study confirms that machine learning techniques are powerful and reliable tools for estimating spectral indices in precision agriculture applications. The integration of SVR and ensemble-based models into agricultural decision support systems is expected to significantly contribute to optimizing resource use and improving yield predictions.","背景：准确估计幼苗活力指数（SVI）对于精准农业中监测植物健康和预测产量至关重要。本研究比较评估了不同机器学习算法在估计SVI参数方面的性能。方法：从39个玉米基因型在三种不同盐度条件（0 mM、100 mM和200 mM NaCl）下收集发芽数据，包括Gmax、MGT、GI、SL、RL、SdL、SMR、RMR和SVI等参数。数据集通过归一化至[0–1]范围进行预处理。采用四种不同的机器学习方法估计SVI参数：随机森林、CatBoost、LightGBM和支持向量回归（SVR）。为评估模型性能，采用了10折交叉验证和80%–20%留出验证技术。使用RMSE、MSE、MAE和R²指标检验模型性能。结果：研究结果表明，所有模型在训练数据集中均具有较高的解释力。具体而言，CatBoost模型在训练阶段表现出最高的成功率（R²=0.9962），而SVR模型在测试数据集上取得了最高的泛化性能（R²=0.9712，RMSE=0.0582）。特征重要性分析结果显示，在大多数模型中，“App”参数对目标变量的影响最大，但在LightGBM模型中不同的结构交互作用更为突出。结果表明，机器学习技术在SVI估计中提供了高精度，且过拟合倾向有限。结论：总之，本研究证实机器学习技术是精准农业应用中估计光谱指数的强大而可靠的工具。将SVR和基于集成的模型整合到农业决策支持系统中，预计将显著有助于优化资源利用和改善产量预测。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z",63,{"impact":126,"substance":17,"depth":287,"authority":59,"freshness":20,"relevant":21,"comment":288},16,"方法对比扎实、结论可靠，但属细分领域论文，产业影响有限，适合主题聚合而非头条精选。",[290],{"name":283,"url":280},[30,292,293,31,294],"农业人工智能","机器学习","耐盐育种",{"code":35,"slug":36,"name":31,"path":37,"parent_code":38},[297],{"code":298,"slug":299,"name":300,"path":301,"parent_code":149},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":47,"locked":14,"version":48,"method":49,"evidence":303},{"candidates":304,"agricultural_evidence":54},[305,312,316],{"code":298,"score":306,"supported":54,"title":307,"abstract":309,"tags":311},19,[308],"machine learning",[293,308,310],"decision support",[292,293],{"code":35,"score":53,"supported":54,"title":313,"abstract":314,"tags":315},[36],[31,36],[31],{"code":66,"score":67,"supported":54,"title":317,"abstract":318,"tags":321},[],[319,320],"精准农业","precision agriculture",[30],[323,324],"玉米 SVI 机器学习","玉米 耐盐 幼苗活力指数","玉米SVI机器学习-4759","10.24925\u002Fturjaf.v14i10.2870-2879.9043",{"doi":326,"openalex_id":328,"authors":329,"venue":283,"cited_by_count":89,"oa_url":280,"card":336,"direction":340,"ingested_from":110},"W7221462188",[330,333],{"name":331,"orcid":332},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":334,"orcid":335},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":337,"method":338,"finding":339,"direction":340,"opportunity":341},"用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。","39个玉米基因型在三种盐浓度下的发芽数据，归一化后用RF、CatBoost、Li","SVR泛化最优（R²=0.9712），CatBoost训练拟合最高；App参数对SVI影响最大。","农业人工智能与决策模型","可扩展到多作物多胁迫环境，结合光谱或图像表型数据提升SVI预测的田间适用性。","2026-10-12T23:30:44.762497Z",{"id":344,"title":345,"url":346,"summary":347,"summary_zh":348,"content":9,"source_name":349,"source_url":346,"published_at":350,"category":12,"cover_url":9,"hotness":122,"is_selected":14,"score":351,"score_detail":352,"sources":356,"tags":358,"primary_topic":362,"secondary_topics":363,"classification":370,"search_phrases":404,"slug":407,"view_count":21,"doi":408,"paper":409,"created_at":421},4694,"Precision Agriculture Approaches for Nutrient Management in Maize: A Comprehensive Review of Productivity, Efficiency and Economic Benefits","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104559","Maize nutrient management is an unusually demanding target for precision agriculture because crop demand, soil nutrient supply, weather, rooting conditions and management history vary simultaneously across space and time. Uniform fertiliser programmes can therefore be efficient in one part of a field and inefficient in another, while a recommendation that is appropriate before planting may become suboptimal after rainfall, mineralisation and crop establishment have altered nutrient availability. This critical narrative review evaluates precision approaches for nutrient management in maize, with emphasis on productivity, nutrient-use efficiency and farm-level economic performance. Literature published from 1 January 1990 to 2 August 2026 was examined using agricultural and multidisciplinary scholarly sources, supplemented by citation searching and bibliographic verification. The evidence was organised around the decision chain linking measurement, diagnosis, prescription and nutrient application. Strongest empirical support exists for site-specific and in-season nitrogen management because nitrogen is mobile, strongly coupled to weather and readily expressed in canopy signals. Chlorophyll meters, active optical sensors, aerial and unmanned aerial vehicle imagery, management zones, crop models and machine-learning approaches can improve the timing or spatial targeting of nitrogen, but diagnostic accuracy does not automatically translate into an economically superior fertiliser prescription. Multi-nutrient decision-support systems broaden precision management beyond nitrogen and can improve the balance of nitrogen, phosphorus and potassium, particularly where farmer practice is poorly aligned with crop demand. Across the evidence base, yield gains are often modest when conventional fertilisation is already adequate; benefits more consistently arise through reduced excess nutrient use, improved nutrient-use efficiency, better risk management or correction of under-fertilisation. Profitability is highly conditional on field heterogeneity, fertiliser and grain prices, baseline management, technology cost, scale and weather. Future progress requires multi-year on-farm validation of complete decision chains, stronger treatment of uncertainty and multi-stress confounding, improved multi-nutrient diagnostics, and economic evaluations that include fixed, transaction and information costs. Precision nutrient management is best understood as an adaptive decision framework rather than a single sensor or variable-rate technology.","玉米养分管理对精准农业而言是一个异常苛刻的目标，因为作物需求、土壤养分供应、天气、根系条件和管理历史在空间和时间上同时变化。因此，均匀施肥方案在田块某一部分可能高效，而在另一部分则可能低效；而播种前适宜的推荐方案，在降雨、矿化和作物建成改变养分有效性之后，也可能变得次优。这篇批判性叙述综述评估了玉米养分管理的精准方法，重点关注生产力、养分利用效率和农场层面经济表现。利用农业与多学科学术来源，检索了1990年1月1日至2026年8月2日发表的文献，并以引文检索和书目核验作为补充。证据围绕连接测量、诊断、处方和养分施用的决策链进行组织。最强的实证支持存在于位点特异性和季内氮管理，因为氮具有移动性、与天气强耦合，并易于在冠层信号中表达。叶绿素仪、主动光学传感器、航空和无人机影像、管理分区、作物模型和机器学习方法可以改善氮的施用时机或空间靶向，但诊断准确性并不会自动转化为经济上更优的施肥处方。多养分决策支持系统将精准管理扩展到氮之外，并能改善氮、磷、钾的平衡，尤其是在农户实践与作物需求严重不匹配的情况下。在整个证据基础中，当常规施肥已经充足时，产量增益往往有限；效益更一致地来自减少过量养分使用、提高养分利用效率、改善风险管理或纠正施肥不足。盈利能力高度取决于田块异质性、肥料和谷物价格、基线管理、技术成本、规模和天气。未来进展需要对完整决策链进行多年农场验证，更有力地处理不确定性和多胁迫混杂，改进多养分诊断，以及纳入固定成本、交易成本和信息成本的经济评价。精准养分管理最好被理解为一个适应性决策框架","Journal of Experimental Agriculture International","2026-10-10T00:00:00Z",71,{"impact":59,"substance":353,"depth":354,"authority":19,"freshness":20,"relevant":21,"comment":355},20,17,"系统综述精准养分管理在玉米上的产量、效率与经济性证据，方法学扎实但属学术综述，产业落地价值需长期验证。",[357],{"name":349,"url":346},[30,359,31,319,360,361],"变量施肥","遥感监测","养分管理",{"code":35,"slug":36,"name":31,"path":37,"parent_code":38},[364],{"code":365,"slug":366,"name":367,"path":368,"parent_code":369},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":47,"locked":14,"version":48,"method":49,"evidence":371},{"candidates":372,"agricultural_evidence":54},[373,377,381,388,395,399],{"code":66,"score":287,"supported":54,"title":374,"abstract":375,"tags":376},[320],[319,320],[30,319],{"code":35,"score":53,"supported":54,"title":378,"abstract":379,"tags":380},[36],[31,36],[31],{"code":365,"score":382,"supported":54,"title":383,"abstract":384,"tags":387},11,[],[385,386,361],"施肥","肥料",[361,359],{"code":389,"score":113,"supported":54,"title":390,"abstract":391,"tags":394},"H2",[],[392,393],"土壤","soil",[],{"code":298,"score":75,"supported":54,"title":396,"abstract":397,"tags":398},[],[293],[],{"code":400,"score":21,"supported":14,"title":401,"abstract":402,"tags":403},"I3",[],[],[360],[405,406],"玉米 精准施肥 氮肥管理","遥感 变量施肥 养分利用效率","玉米精准施肥氮肥管理-4694","10.9734\u002Fjeai\u002F2026\u002Fv48i104559",{"doi":408,"openalex_id":410,"authors":411,"venue":349,"cited_by_count":89,"oa_url":346,"card":416,"direction":340,"ingested_from":110},"W7221366291",[412,414],{"name":413,"orcid":9},"Piyali Pal",{"name":415,"orcid":9},"Disha Chattopadhyay",{"tldr":417,"method":418,"finding":419,"direction":340,"opportunity":420},"综述玉米精准养分管理在生产力、效率与经济效益上的证据与局限。","系统检索1990-2026年文献，按测量-诊断-处方-施用决策链组织证据。","氮素精准管理证据最强，但诊断精度不必然带来经济更优的施肥处方。","亟需多年多点田间验证完整决策链，并纳入不确定性、多养分诊断与信息成本的经济评价。","2026-10-11T23:30:51.450082Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":9,"content":9,"source_name":427,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":122,"is_selected":14,"score":428,"score_detail":429,"sources":431,"tags":433,"primary_topic":436,"secondary_topics":437,"classification":438,"search_phrases":462,"slug":465,"view_count":144,"doi":9,"paper":466,"created_at":473},4477,"玉米生产精准农业系统综述：技术、应用与产量优化（2020—2026）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37500368","依据PRISMA 2020指南，系统检索Scopus与Web of Science共464篇文献，最终纳入129项研究；样本构成为田间试验（28.6%）、遥感与精准农业整合研究（25.5%）、机器学习应用（19.4%）、气候知情精准农业策略（23.4%）和土壤退化研究（3.1%）；研究表明精准农业技术通过定点管理和数据驱动决策支持，提升了玉米生产力、养分利用效率、水利用效率和产量预测准确性；指出采用精准农业仍受制于实施成本高、技术复杂、数据有限等因素。","Precision Agriculture in Maize Production Systematic Review 2026",74,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":75,"relevant":21,"comment":430},"基于PRISMA 2020的系统综述，纳入129项研究，数据规模与方法规范性强，对玉米精准农业技术路径与瓶颈有实质参考价值，但发布日期不明、时效性偏弱。",[432],{"name":427,"url":425},[30,434,293,31,319,435],"产量预测","遥感",{"code":35,"slug":36,"name":31,"path":37,"parent_code":38},[],{"status":47,"locked":14,"version":48,"method":49,"evidence":439},{"candidates":440,"agricultural_evidence":54},[441,445,449,454,458],{"code":66,"score":19,"supported":54,"title":442,"abstract":443,"tags":444},[319],[319],[30,319],{"code":35,"score":59,"supported":54,"title":446,"abstract":447,"tags":448},[31],[31],[31],{"code":400,"score":450,"supported":54,"title":451,"abstract":452,"tags":453},5,[],[435],[434,435],{"code":298,"score":139,"supported":54,"title":455,"abstract":456,"tags":457},[],[293],[293],{"code":389,"score":75,"supported":54,"title":459,"abstract":460,"tags":461},[],[392],[],[463,464],"玉米 精准农业 系统综述","玉米 遥感 机器学习 产量","玉米精准农业系统综述-4477",{"doi":9,"openalex_id":9,"authors":467,"venue":9,"cited_by_count":89,"oa_url":9,"card":468,"direction":109,"ingested_from":168},[],{"tldr":469,"method":470,"finding":471,"direction":109,"opportunity":472},"系统综述2020—2026年玉米精准农业技术、应用与产量优化研究。","按PRISMA 2020检索Scopus与WoS，纳入129项研究做分类分析。","精准农业通过定点管理与数据决策提升玉米产量、养分和水利用效率。","成本高、技术复杂与数据不足制约推广，低成本轻量化方案与本地化数据共享是研究空白。","2026-10-09T00:05:02.451957Z"]