[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1713":3,"related-1713":79},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":35,"classification":36,"search_phrases":62,"slug":65,"view_count":21,"doi":66,"paper":67,"created_at":78},1713,"Nitrogen management on coarse Sandy soils: A DSSAT-based mechanistic evaluation of controlled-release and conventional fertilizers for maize","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104954","Nitrogen management on coarse Sandy soils: A DSSAT-based mechanistic evaluation of controlled-release and conventional fertilizers for maize。Agricultural Systems",null,"Agricultural Systems","2026-09-04T00:00:00Z","论文",10,false,62,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,16,14,2,1,"基于DSSAT模型对粗砂土玉米控释肥与常规肥的机制评估，方法新颖，数据可靠，但时效性较低。",[24],{"name":9,"url":6},[26,27,28,29],"智慧农业","玉米","氮肥管理","模型模拟",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":43},[42,47,52,57],{"code":31,"score":16,"supported":43,"title":44,"abstract":45,"tags":46},true,[32],[32],[27],{"code":48,"score":21,"supported":13,"title":49,"abstract":50,"tags":51},"F3",[],[],[28],{"code":53,"score":21,"supported":13,"title":54,"abstract":55,"tags":56},"I1",[],[],[26],{"code":58,"score":21,"supported":13,"title":59,"abstract":60,"tags":61},"I2",[],[],[29],[63,64],"智慧农业 模型模拟 氮肥管理 玉米","智慧农业 模型模拟","智慧农业模型模拟氮肥管理玉米-1713","10.1016\u002Fj.agsy.2026.104954",{"doi":66,"openalex_id":68,"authors":69,"venue":9,"cited_by_count":76,"oa_url":6,"card":8,"direction":8,"ingested_from":77},"W7208709428",[70,72,74],{"name":71,"orcid":8},"Rakesh K. Singh",{"name":73,"orcid":8},"Gerrit Hoogenboom",{"name":75,"orcid":8},"Vivek Sharma",0,"openalex","2026-09-05T23:30:05.498995Z",{"total":80,"page":21,"page_size":80,"items":81},6,[82,172,248,326,392,463],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":87,"content":8,"source_name":88,"source_url":85,"published_at":89,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":90,"score_detail":91,"sources":96,"tags":98,"primary_topic":101,"secondary_topics":102,"classification":103,"search_phrases":136,"slug":139,"view_count":127,"doi":140,"paper":141,"created_at":171},3620,"Assessment of corn chlorophyll content via UAV‐derived vegetative indices across growth stages","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fagg2.70437","Abstract Traditional chlorophyll (Chl) assessment methods are labor‐intensive and spatially limited. This study evaluated unmanned aerial vehicle (UAV) multispectral imagery for nondestructive field‐scale canopy Chl estimation in corn ( Zea mays L.) to support precision nutrient management. Field experiments were conducted at two Virginia Tech research farms under two nitrogen (N) rates, 112 and 224 kg N ha − 1 , and five phosphorus (P) rates, 0, 56, 112, 168, and 224 kg P 2 O 5 ha − 1 . Canopy Chl was measured using a SPAD‐502 Plus chlorophyll meter (where SPAD is soil plant analysis development). UAV imagery was collected at three sampling times using a DJI Mavic 3 M: early vegetative (Ve‐E), late vegetative (Ve‐L), and reproductive stages, corresponding to V4–V6, V11–V13, and R3–R5 growth stages, respectively. Eight vegetation indices (VIs) were derived from green, red, red‐edge, and near‐infrared bands. Relationships between SPAD and spectral predictors were evaluated using Pearson correlation, simple and multiple linear regression, Lasso, and Elastic Net. Model performance was assessed using fivefold cross‐validated (CV) estimates repeated 30 times with CV R 2 , root mean square error, and relative RMSE. Predictive accuracy was moderate and varied by site and time, with strongest relationships during Ve‐L. At Ve‐L, Green Normalized Difference Vegetation Index (GNDVI) and Chlorophyll Index Red Edge were the best predictors at Orange and Kentland, respectively. Multivariate and regularized models performed comparably but did not improve accuracy over the best single‐VI models. Overall, GNDVI and red‐edge‐based indices were the most useful indicators of canopy Chl variation. These findings indicate that UAV‐derived GNDVI and red‐edge indices can provide moderately accurate and interpretable estimates of SPAD‐based Chl status.","传统叶绿素（Chl）评估方法费时费力且空间覆盖有限。本研究评估了无人机（UAV）多光谱影像在玉米（Zea mays L.）田块尺度冠层叶绿素无损估测中的应用，以支持精准养分管理。田间试验在弗吉尼亚理工大学的两个研究农场进行，设置两个氮（N）水平，分别为112和224 kg N ha⁻¹，以及五个磷（P）水平，分别为0、56、112、168和224 kg P₂O₅ ha⁻¹。使用SPAD-502 Plus叶绿素仪（SPAD即土壤植物分析开发）测定冠层叶绿素。使用DJI Mavic 3 M在三个采样时期采集无人机影像：营养生长早期（Ve-E）、营养生长晚期（Ve-L）和生殖生长阶段，分别对应V4–V6、V11–V13和R3–R5生育时期。从绿、红、红边和近红外波段提取了8种植被指数（VIs）。采用Pearson相关、简单和多元线性回归、Lasso及弹性网（Elastic Net）评估SPAD与光谱预测变量之间的关系。模型性能通过重复30次的五折交叉验证（CV）估计进行评估，指标包括交叉验证R²、均方根误差和相对均方根误差。预测精度为中等水平，且因地点和时间而异，其中Ve-L时期关系最强。在Ve-L时期，绿归一化差异植被指数（GNDVI）和红边叶绿素指数分别在Orange和Kentland表现最佳。多变量模型和正则化模型表现相当，但未能比最佳单植被指数模型提高精度。总体而言，GNDVI和基于红边的指数是冠层叶绿素变异最有用的指标。这些结果表明，无人机获取的GNDVI和红边指数能够对基于SPAD的叶绿素状况提供中等精度且可解释的估测。","Agrosystems Geosciences & Environment","2026-09-24T00:00:00Z",69,{"impact":16,"substance":92,"depth":18,"authority":93,"freshness":94,"relevant":21,"comment":95},20,13,8,"基于无人机多光谱影像评估玉米冠层叶绿素，方法规范、结论明确，对精准养分管理有参考价值，但属细分领域研究，公共影响有限。",[97],{"name":88,"url":85},[26,27,99,28,100],"遥感","植被指数",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":104},{"candidates":105,"agricultural_evidence":43},[106,112,119,126,132],{"code":31,"score":107,"supported":43,"title":108,"abstract":110,"tags":111},15,[109],"corn",[27,109],[27],{"code":113,"score":80,"supported":43,"title":114,"abstract":115,"tags":118},"H2",[],[116,117],"土壤","soil",[],{"code":120,"score":121,"supported":43,"title":122,"abstract":123,"tags":125},"I3",5,[],[124],"multispectral",[100,99],{"code":48,"score":127,"supported":43,"title":128,"abstract":129,"tags":131},4,[],[130],"养分管理",[28],{"code":53,"score":21,"supported":13,"title":133,"abstract":134,"tags":135},[],[],[26],[137,138],"无人机 玉米 叶绿素 遥感","Virginia Tech 玉米 氮肥 SPAD","无人机玉米叶绿素遥感-3620","10.1002\u002Fagg2.70437",{"doi":140,"openalex_id":142,"authors":143,"venue":88,"cited_by_count":76,"oa_url":164,"card":165,"direction":169,"ingested_from":77},"W7214232972",[144,147,150,153,155,157,159,161],{"name":145,"orcid":146},"Aarati Khulal","https:\u002F\u002Forcid.org\u002F0000-0003-4405-1320",{"name":148,"orcid":149},"Huijie Gan","https:\u002F\u002Forcid.org\u002F0000-0001-6634-5704",{"name":151,"orcid":152},"Jitender Rathore","https:\u002F\u002Forcid.org\u002F0009-0006-5764-1652",{"name":154,"orcid":8},"Sheetal Kumari",{"name":156,"orcid":8},"Ivy Flory",{"name":158,"orcid":8},"Caleb Bishop",{"name":160,"orcid":8},"Santosh Rijal",{"name":162,"orcid":163},"Olga S. Walsh","https:\u002F\u002Forcid.org\u002F0000-0002-2958-931X","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fagg2.70437",{"tldr":166,"method":167,"finding":168,"direction":169,"opportunity":170},"用无人机多光谱植被指数评估玉米不同生育期冠层叶绿素含量。","DJI Mavic 3 M多光谱影像，8个植被指数，SPAD实测，线性回归与La","GNDVI与红边指数在营养生长后期预测SPAD最准，多变量模型未优于单指数。","农业遥感与作物表型","可探索多生育期时序融合与多源数据耦合，提升跨站点跨年份叶绿素估算泛化能力。","2026-09-27T23:30:35.468236Z",{"id":173,"title":174,"url":175,"summary":176,"summary_zh":8,"content":8,"source_name":9,"source_url":175,"published_at":177,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":178,"score_detail":179,"sources":181,"tags":183,"primary_topic":186,"secondary_topics":187,"classification":194,"search_phrases":224,"slug":227,"view_count":20,"doi":228,"paper":229,"created_at":247},2291,"Optimal management practices of nitrogen and irrigation for the trade-off between economy and environment for maize in China","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104970","Optimal management practices of nitrogen and irrigation for the trade-off between economy and environment for maize in China。Agricultural Systems","2026-09-12T00:00:00Z",78,{"impact":17,"substance":92,"depth":17,"authority":19,"freshness":94,"relevant":21,"comment":180},"核心期刊论文，提出中国玉米氮肥与灌溉协同优化管理策略，兼顾经济与环境权衡，对农业绿色生产与精准水肥管理有参考价值。",[182],{"name":9,"url":175},[26,27,184,185,28],"农业绿色发展","水肥一体化",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},[188],{"code":189,"slug":190,"name":191,"path":192,"parent_code":193},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":37,"locked":13,"version":38,"method":39,"evidence":195},{"candidates":196,"agricultural_evidence":43},[197,201,206,210,215,220],{"code":31,"score":16,"supported":43,"title":198,"abstract":199,"tags":200},[32],[32],[27],{"code":189,"score":202,"supported":43,"title":203,"abstract":204,"tags":205},11,[190],[190],[],{"code":48,"score":20,"supported":13,"title":207,"abstract":208,"tags":209},[],[],[28,185],{"code":211,"score":21,"supported":13,"title":212,"abstract":213,"tags":214},"H4",[],[],[184],{"code":216,"score":21,"supported":13,"title":217,"abstract":218,"tags":219},"H8",[],[],[184],{"code":53,"score":21,"supported":13,"title":221,"abstract":222,"tags":223},[],[],[26],[225,226],"农业绿色发展 水肥一体化 智慧农业 氮肥管理","农业绿色发展 水肥一体化","农业绿色发展水肥一体化智慧农业氮肥管理-2291","10.1016\u002Fj.agsy.2026.104970",{"doi":228,"openalex_id":230,"authors":231,"venue":9,"cited_by_count":76,"oa_url":175,"card":8,"direction":8,"ingested_from":77},"W7212404025",[232,235,237,239,241,243,245],{"name":233,"orcid":234},"Yu Ye","https:\u002F\u002Forcid.org\u002F0000-0002-6744-3338",{"name":236,"orcid":8},"Xingrui Liu",{"name":238,"orcid":8},"Yuanyuan Chen",{"name":240,"orcid":8},"Shuzhu Shi",{"name":242,"orcid":8},"Zhaocong Wu",{"name":244,"orcid":8},"Kaiping Zhang",{"name":246,"orcid":8},"Rui Xiao","2026-09-13T23:30:05.153684Z",{"id":249,"title":250,"url":251,"summary":252,"summary_zh":253,"content":8,"source_name":9,"source_url":251,"published_at":254,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":255,"score_detail":256,"sources":259,"tags":261,"primary_topic":264,"secondary_topics":265,"classification":270,"search_phrases":294,"slug":297,"view_count":298,"doi":299,"paper":300,"created_at":325},1383,"Pareto-front-based optimization of irrigation and nitrogen application schedule in Sandy-soil maize: Trade-offs among yield, water productivity, nitrogen physiological efficiency, and grain nitrogen concentration","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104953","Context Developing appropriate irrigation and nitrogen application schedules (INASs) is essential for maize production on sandy soils, where low water-retention and nutrient-holding capacities make crop performance highly sensitive to within-season water and nitrogen management. However, most crop-model-based optimization studies have focused mainly on yield and resource-use efficiency, while grain nutritional quality and the economic qualifications of optimized schedules have received less attention. Objective This study aimed to develop a CERES-Maize–NSGA-III framework to optimize INASs for shallow-buried drip-irrigated maize on sandy soil, while clarifying trade-offs among yield, crop water productivity (WP c ), nitrogen physiological efficiency (PE N ), and grain nitrogen concentration (GNC), and evaluate the economic performance of selected candidate schedules through partial-budget analysis. Methods CERES-Maize was calibrated and validated using two years of field observations and then coupled with NSGA-III to optimize irrigation timing, irrigation amount, nitrogen application timing, and nitrogen rate. Pareto-optimal schedules were further evaluated through candidate-schedule selection, weather-year stress testing, and partial-budget economic sensitivity analysis. Results and conclusions CERES-Maize reproduced maize growth, yield, seasonal water use, and total nitrogen uptake with acceptable accuracy. The Pareto front revealed clear trade-offs among yield, WP c , PE N , and GNC. The 16 high-yield (HY) candidates selected using the historical farmer-yield benchmark exceeded the corresponding weather-matched farmer yield in all five annual simulations and maintained positive ΔPNR across all tested price–cost combinations. Among these candidates, HY01 showed the strongest combined yield and partial-budget performance, with the highest five-year mean simulated yield (12,003.2 kg\u002Fha) and the highest mean ΔPNR relative to the corresponding farmer-managed schedules (1307.83 yuan\u002Fha) across the tested weather-year and price–cost combinations. In contrast, the distance-to-utopia compromise schedule produced lower yield than the corresponding farmer-managed schedule in each weather year and showed weather- and price–cost-dependent economic performance. Significance The proposed framework provides a biophysical optimization tool for generating candidate INASs and clarifying trade-offs among yield, WP c , PE N , and GNC in sandy-soil maize. Weather variability was evaluated only through ex-post simulations of selected candidates and was not incorporated directly into the optimization. Further field-level, operational, economic, and environmental validation is therefore required before practical implementation.","背景 制定合理的灌溉与施氮方案（INASs）对砂质土壤上的玉米生产至关重要，因为砂质土壤的保水性和保肥能力较低，作物表现对季内水分和氮素管理高度敏感。然而，大多数基于作物模型的优化研究主要关注产量和资源利用效率，而籽粒营养品质及优化方案的经济可行性受到的关注较少。目的 本研究旨在构建CERES-Maize–NSGA-III耦合框架，优化砂质土壤浅埋滴灌玉米的灌溉与施氮方案，明确产量、作物水分生产力（WPc）、氮素生理效率（PEN）和籽粒氮浓度（GNC）之间的权衡关系，并通过部分预算分析评估所选候选方案的经济表现。方法 利用两年田间观测数据对CERES-Maize模型进行校准和验证，随后与NSGA-III算法耦合，优化灌溉时间、灌溉量、施氮时间和施氮量。对Pareto最优方案进一步开展候选方案筛选、天气年胁迫测试和部分预算经济敏感性分析。结果与结论 CERES-Maize模型能够以可接受的精度再现玉米生长、产量、季节性水分利用和总氮吸收量。Pareto前沿揭示了产量、WPc、PEN和GNC之间明显的权衡关系。以历史农户产量基准筛选出的16个高产（HY）候选方案在全部五个模拟年份中均超过了相应天气匹配的农户产量，并在所有测试的价格—成本组合下保持正的ΔPNR。在这些候选方案中，HY01表现出最强的产量和部分预算综合表现，其五年平均模拟产量最高（12,003.2 kg\u002Fha），且在测试的天气年和价格—成本组合下，相对于相应农户管理方案的平均ΔPNR最高（1,307.83元\u002Fha）。相比之下，距离理想点折中方案在每个天气年的产量均低于相应的农户管理方案，且其经济表现依赖于天气和价格—成本条件。意义 所提出的框架为砂质土壤玉米生成候选灌溉施氮方案并阐明产量、WPc、PEN和GNC之间的权衡关系提供了生物物理优化工具。天气变异性","2026-09-02T00:00:00Z",80,{"impact":17,"substance":257,"depth":17,"authority":19,"freshness":94,"relevant":21,"comment":258},22,"研究提出CERES-Maize-NSGA-III框架优化沙土玉米水氮管理，兼顾产量、水氮效率与品质，方法新颖，结论可靠，对精准农业有参考价值。",[260],{"name":9,"url":251},[26,27,262,263,29],"水肥优化","多目标优化",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},[266,269],{"code":113,"slug":117,"name":267,"path":268,"parent_code":193},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":189,"slug":190,"name":191,"path":192,"parent_code":193},{"status":37,"locked":13,"version":38,"method":39,"evidence":271},{"candidates":272,"agricultural_evidence":43},[273,277,281,286,290],{"code":31,"score":107,"supported":43,"title":274,"abstract":275,"tags":276},[32],[27,32],[27],{"code":113,"score":19,"supported":43,"title":278,"abstract":279,"tags":280},[117],[116,117],[],{"code":189,"score":19,"supported":43,"title":282,"abstract":283,"tags":285},[190],[284,190],"灌溉",[],{"code":53,"score":21,"supported":13,"title":287,"abstract":288,"tags":289},[],[],[26],{"code":58,"score":21,"supported":13,"title":291,"abstract":292,"tags":293},[],[],[29],[295,296],"多目标优化 智慧农业 模型模拟 水肥优化","多目标优化 智慧农业","多目标优化智慧农业模型模拟水肥优化-1383",3,"10.1016\u002Fj.agsy.2026.104953",{"doi":299,"openalex_id":301,"authors":302,"venue":9,"cited_by_count":76,"oa_url":251,"card":319,"direction":323,"ingested_from":77},"W7205003045",[303,305,308,311,313,314,316,318],{"name":304,"orcid":8},"Yongqiang Wang",{"name":306,"orcid":307},"Fugui Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8349-8398",{"name":309,"orcid":310},"Lanfang Bai","https:\u002F\u002Forcid.org\u002F0000-0001-7862-2844",{"name":312,"orcid":8},"Zhen Wang",{"name":312,"orcid":8},{"name":315,"orcid":8},"Haofang Yan",{"name":317,"orcid":8},"Zhigang Wang",{"name":317,"orcid":8},{"tldr":320,"method":321,"finding":322,"direction":323,"opportunity":324},"构建CERES-Maize-NSGA-III框架，优化沙土玉米灌溉施氮方案，权衡产量、水生产力、氮效","CERES-Maize模型与NSGA-III多目标优化，结合田间数据校准验证，并","高产候选方案在产量和经济效益上优于农民方案，但折中方案表现不稳定。","农业人工智能与决策模型","可探索将多目标优化与实时气象预测结合，开发动态灌溉施氮决策支持系统，兼顾产量、品质与经济效益。","2026-09-02T23:30:05.454493Z",{"id":327,"title":328,"url":329,"summary":330,"summary_zh":331,"content":8,"source_name":332,"source_url":329,"published_at":333,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":334,"score_detail":335,"sources":338,"tags":340,"primary_topic":344,"secondary_topics":345,"classification":351,"search_phrases":372,"slug":375,"view_count":76,"doi":376,"paper":377,"created_at":391},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":94,"substance":17,"depth":18,"authority":16,"freshness":336,"relevant":21,"comment":337},9,"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。",[339],{"name":332,"url":329},[26,341,342,27,343],"农业人工智能","机器学习","耐盐育种",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},[346],{"code":58,"slug":347,"name":348,"path":349,"parent_code":350},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":37,"locked":13,"version":38,"method":39,"evidence":352},{"candidates":353,"agricultural_evidence":43},[354,361,365],{"code":58,"score":355,"supported":43,"title":356,"abstract":358,"tags":360},19,[357],"machine learning",[342,357,359],"decision support",[341,342],{"code":31,"score":107,"supported":43,"title":362,"abstract":363,"tags":364},[32],[27,32],[27],{"code":53,"score":366,"supported":43,"title":367,"abstract":368,"tags":371},7,[],[369,370],"精准农业","precision agriculture",[26],[373,374],"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":376,"openalex_id":378,"authors":379,"venue":332,"cited_by_count":76,"oa_url":329,"card":386,"direction":323,"ingested_from":77},"W7221462188",[380,383],{"name":381,"orcid":382},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":384,"orcid":385},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":387,"method":388,"finding":389,"direction":323,"opportunity":390},"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。","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":393,"title":394,"url":395,"summary":396,"summary_zh":397,"content":8,"source_name":398,"source_url":395,"published_at":399,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":400,"score_detail":401,"sources":404,"tags":406,"primary_topic":409,"secondary_topics":410,"classification":416,"search_phrases":445,"slug":448,"view_count":21,"doi":449,"paper":450,"created_at":462},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":16,"substance":92,"depth":402,"authority":93,"freshness":336,"relevant":21,"comment":403},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。",[405],{"name":398,"url":395},[26,407,27,369,408,130],"变量施肥","遥感监测",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},[411],{"code":48,"slug":412,"name":413,"path":414,"parent_code":415},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":37,"locked":13,"version":38,"method":39,"evidence":417},{"candidates":418,"agricultural_evidence":43},[419,423,427,433,437,441],{"code":53,"score":18,"supported":43,"title":420,"abstract":421,"tags":422},[370],[369,370],[26,369],{"code":31,"score":107,"supported":43,"title":424,"abstract":425,"tags":426},[32],[27,32],[27],{"code":48,"score":202,"supported":43,"title":428,"abstract":429,"tags":432},[],[430,431,130],"施肥","肥料",[130,407],{"code":113,"score":80,"supported":43,"title":434,"abstract":435,"tags":436},[],[116,117],[],{"code":58,"score":298,"supported":43,"title":438,"abstract":439,"tags":440},[],[342],[],{"code":120,"score":21,"supported":13,"title":442,"abstract":443,"tags":444},[],[],[408],[446,447],"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":449,"openalex_id":451,"authors":452,"venue":398,"cited_by_count":76,"oa_url":395,"card":457,"direction":323,"ingested_from":77},"W7221366291",[453,455],{"name":454,"orcid":8},"Piyali Pal",{"name":456,"orcid":8},"Disha Chattopadhyay",{"tldr":458,"method":459,"finding":460,"direction":323,"opportunity":461},"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":464,"title":465,"url":466,"summary":467,"summary_zh":8,"content":8,"source_name":468,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":469,"score_detail":470,"sources":472,"tags":474,"primary_topic":476,"secondary_topics":477,"classification":478,"search_phrases":501,"slug":504,"view_count":20,"doi":8,"paper":505,"created_at":514},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":257,"depth":17,"authority":93,"freshness":298,"relevant":21,"comment":471},"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。",[473],{"name":468,"url":466},[26,475,342,27,369,99],"产量预测",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":479},{"candidates":480,"agricultural_evidence":43},[481,485,489,493,497],{"code":53,"score":93,"supported":43,"title":482,"abstract":483,"tags":484},[369],[369],[26,369],{"code":31,"score":16,"supported":43,"title":486,"abstract":487,"tags":488},[27],[27],[27],{"code":120,"score":121,"supported":43,"title":490,"abstract":491,"tags":492},[],[99],[475,99],{"code":58,"score":127,"supported":43,"title":494,"abstract":495,"tags":496},[],[342],[342],{"code":113,"score":298,"supported":43,"title":498,"abstract":499,"tags":500},[],[116],[],[502,503],"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":8,"openalex_id":8,"authors":506,"venue":8,"cited_by_count":76,"oa_url":8,"card":507,"direction":511,"ingested_from":513},[],{"tldr":508,"method":509,"finding":510,"direction":511,"opportunity":512},"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。","智慧农业 \u002F 农业物联网","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。","agent","2026-10-09T00:05:02.451957Z"]