[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3523":3,"related-3523":108},{"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":32,"secondary_topics":37,"classification":50,"search_phrases":83,"slug":86,"view_count":87,"doi":88,"paper":89,"created_at":107},3523,"Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10458-y","Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system。Precision Agriculture",null,"Precision Agriculture","2026-09-25T00:00:00Z","论文",10,false,69,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,17,14,8,1,"精准农业核心期刊论文，探讨土壤空间变异对变量播种与施肥效果的影响，方法新颖、结论可靠，对智慧农业田间管理有参考价值，但属细分领域研究，影响范围有限。",[24],{"name":9,"url":6},[26,27,28,29,30,31],"智慧农业","变量施肥","玉米","精准农业","向日葵","变量播种",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":51,"locked":13,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":57},[56,61,66,71,78],{"code":33,"score":16,"supported":57,"title":58,"abstract":59,"tags":60},true,[34],[34],[28],{"code":39,"score":16,"supported":57,"title":62,"abstract":64,"tags":65},[63],"fertilization",[63],[27],{"code":45,"score":67,"supported":57,"title":68,"abstract":69,"tags":70},11,[46],[46],[],{"code":72,"score":73,"supported":57,"title":74,"abstract":75,"tags":77},"I1",5,[],[76],"precision agriculture",[26,29],{"code":79,"score":21,"supported":13,"title":80,"abstract":81,"tags":82},"A4",[],[],[30],[84,85],"Precision Agriculture 变量播种 变量施肥","灌溉玉米 向日葵 土壤变异","PrecisionAgriculture变量播种变量施肥-3523",6,"10.1007\u002Fs11119-026-10458-y",{"doi":88,"openalex_id":90,"authors":91,"venue":9,"cited_by_count":105,"oa_url":8,"card":8,"direction":8,"ingested_from":106},"W7214357812",[92,95,97,99,101,103],{"name":93,"orcid":94},"María Videgain","https:\u002F\u002Forcid.org\u002F0000-0002-3630-7931",{"name":96,"orcid":8},"J. A. Martínez-Casasnovas",{"name":98,"orcid":8},"S. Artero",{"name":100,"orcid":8},"A. Vigo",{"name":102,"orcid":8},"M. Vidal",{"name":104,"orcid":8},"F. J. García-Ramos",0,"openalex","2026-09-26T23:30:03.127069Z",{"total":87,"page":21,"page_size":87,"items":109},[110,187,249,302,394,473],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":8,"source_name":116,"source_url":113,"published_at":117,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":118,"score_detail":119,"sources":124,"tags":126,"primary_topic":129,"secondary_topics":130,"classification":132,"search_phrases":168,"slug":171,"view_count":21,"doi":172,"paper":173,"created_at":186},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":120,"depth":18,"authority":121,"freshness":122,"relevant":21,"comment":123},20,13,9,"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。",[125],{"name":116,"url":113},[26,27,28,29,127,128],"遥感监测","养分管理",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[131],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":13,"version":52,"method":53,"evidence":133},{"candidates":134,"agricultural_evidence":57},[135,140,145,151,156,163],{"code":72,"score":136,"supported":57,"title":137,"abstract":138,"tags":139},16,[76],[29,76],[26,29],{"code":33,"score":141,"supported":57,"title":142,"abstract":143,"tags":144},15,[34],[28,34],[28],{"code":39,"score":67,"supported":57,"title":146,"abstract":147,"tags":150},[],[148,149,128],"施肥","肥料",[128,27],{"code":45,"score":87,"supported":57,"title":152,"abstract":153,"tags":155},[],[154,46],"土壤",[],{"code":157,"score":158,"supported":57,"title":159,"abstract":160,"tags":162},"I2",3,[],[161],"机器学习",[],{"code":164,"score":21,"supported":13,"title":165,"abstract":166,"tags":167},"I3",[],[],[127],[169,170],"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":172,"openalex_id":174,"authors":175,"venue":116,"cited_by_count":105,"oa_url":113,"card":180,"direction":184,"ingested_from":106},"W7221366291",[176,178],{"name":177,"orcid":8},"Piyali Pal",{"name":179,"orcid":8},"Disha Chattopadhyay",{"tldr":181,"method":182,"finding":183,"direction":184,"opportunity":185},"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。","农业人工智能与决策模型","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":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":8,"source_name":193,"source_url":190,"published_at":194,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":195,"score_detail":196,"sources":199,"tags":201,"primary_topic":203,"secondary_topics":204,"classification":206,"search_phrases":226,"slug":229,"view_count":230,"doi":231,"paper":232,"created_at":248},1754,"Low-cost autonomous unmanned ground vehicle for in-situ phosphorus screening to support zone-based fertilization in maize crops","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112349","Low-cost autonomous unmanned ground vehicle for in-situ phosphorus screening to support zone-based fertilization in maize crops。Computers and Electronics in Agriculture","低成本自主无人地面车辆用于玉米作物原位磷筛查以支持分区施肥。农业计算机与电子","Computers and Electronics in Agriculture","2026-09-05T00:00:00Z",62,{"impact":16,"substance":17,"depth":136,"authority":19,"freshness":197,"relevant":21,"comment":198},2,"低成本无人车用于磷素原位检测，支持玉米分区施肥，技术可行但时效性低。",[200],{"name":193,"url":190},[26,202,27,28],"农业机器人",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[205],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":13,"version":52,"method":53,"evidence":207},{"candidates":208,"agricultural_evidence":57},[209,213,217,222],{"code":33,"score":141,"supported":57,"title":210,"abstract":211,"tags":212},[34],[28,34],[28],{"code":39,"score":141,"supported":57,"title":214,"abstract":215,"tags":216},[63],[148,63],[27],{"code":218,"score":21,"supported":13,"title":219,"abstract":220,"tags":221},"F6",[],[],[202],{"code":72,"score":21,"supported":13,"title":223,"abstract":224,"tags":225},[],[],[26],[227,228],"农业机器人 变量施肥 智慧农业 玉米","农业机器人 变量施肥","农业机器人变量施肥智慧农业玉米-1754",4,"10.1016\u002Fj.compag.2026.112349",{"doi":231,"openalex_id":233,"authors":234,"venue":193,"cited_by_count":105,"oa_url":8,"card":242,"direction":246,"ingested_from":106},"W7208817219",[235,237,240],{"name":236,"orcid":8},"Lalo Villacorta",{"name":238,"orcid":239},"Adrian Medrano","https:\u002F\u002Forcid.org\u002F0009-0009-2010-3828",{"name":241,"orcid":8},"Miguel Lara",{"tldr":243,"method":244,"finding":245,"direction":246,"opportunity":247},"研发低成本自主地面机器人，原位筛查玉米磷含量，支持分区施肥。","低成本UGV搭载原位传感器，结合自主导航与磷筛查技术。","实现低成本、自主的田间磷检测，为分区施肥提供依据。","智慧农业 \u002F 农业物联网","可延伸研究多养分原位传感与自主决策，或结合无人机与地面机器人协同，提升精准施肥效率。","2026-09-06T23:30:01.426286Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":8,"content":8,"source_name":9,"source_url":252,"published_at":254,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":255,"score_detail":256,"sources":258,"tags":260,"primary_topic":263,"secondary_topics":264,"classification":266,"search_phrases":285,"slug":288,"view_count":197,"doi":289,"paper":290,"created_at":301},1211,"A map-based decision-support framework for variable-depth seeding of fodder maize integrating proximal soil sensing and multi-temporal satellite NDVI data","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10437-3","A map-based decision-support framework for variable-depth seeding of fodder maize integrating proximal soil sensing and multi-temporal satellite NDVI data。Precision Agriculture","2026-08-25T00:00:00Z",70,{"impact":141,"substance":120,"depth":17,"authority":141,"freshness":197,"relevant":21,"comment":257},"论文提出结合土壤传感与卫星NDVI的变量播种决策框架，方法新颖，数据可靠，但时效性较低。",[259],{"name":9,"url":252},[26,28,29,261,262],"遥感","土壤传感",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[265],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":13,"version":52,"method":53,"evidence":267},{"candidates":268,"agricultural_evidence":57},[269,273,277,281],{"code":33,"score":16,"supported":57,"title":270,"abstract":271,"tags":272},[34],[34],[28],{"code":45,"score":67,"supported":57,"title":274,"abstract":275,"tags":276},[46],[46],[],{"code":72,"score":73,"supported":57,"title":278,"abstract":279,"tags":280},[],[76],[26,29],{"code":164,"score":21,"supported":13,"title":282,"abstract":283,"tags":284},[],[],[261],[286,287],"土壤传感 智慧农业 精准农业 玉米","土壤传感 智慧农业","土壤传感智慧农业精准农业玉米-1211","10.1007\u002Fs11119-026-10437-3",{"doi":289,"openalex_id":291,"authors":292,"venue":9,"cited_by_count":105,"oa_url":8,"card":8,"direction":8,"ingested_from":106},"W7204189193",[293,295,298],{"name":294,"orcid":8},"Jialu Sun",{"name":296,"orcid":297},"Manuel Vázquez-Arellano","https:\u002F\u002Forcid.org\u002F0000-0001-7820-5143",{"name":299,"orcid":300},"Abdul Mounem Mouazen","https:\u002F\u002Forcid.org\u002F0000-0002-0354-0067","2026-09-01T04:03:03.736293Z",{"id":303,"title":304,"url":305,"summary":306,"summary_zh":307,"content":8,"source_name":9,"source_url":305,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":57,"score":308,"score_detail":309,"sources":313,"tags":315,"primary_topic":318,"secondary_topics":319,"classification":321,"search_phrases":356,"slug":359,"view_count":87,"doi":360,"paper":361,"created_at":393},3525,"Multi-source data assimilation of Sentinel-2 reflectance and SMAP soil moisture into APSIM for maize biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10442-6","Crop growth models (CGMs) are valuable tools for agricultural monitoring. However, the need for many input parameters, the uncertainties related to model parametrization and structure, and the lack of spatial information motivate the application of techniques such as data assimilation (DA). This paper proposes a DA framework to improve maize biomass estimation. A particle filter (PF) was used to assimilate remotely sensed reflectance and soil moisture (SM) data, both independently and simultaneously, into the Agricultural Production Systems sIMulator (APSIM) model. Reflectance observations from Sentinel-2 were assimilated through coupling APSIM with the radiative transfer model (RTM) PROSAIL, while SMAP L-band SM products were directly assimilated into APSIM. The synthetic experiment, designed to evaluate the reliability of the proposed procedure, highlighted the strength of assimilating reflectance to constrain crop traits and of SM to provide complementary information on crop water status and to contribute to more robust ensemble trajectories. Real-case results confirmed these findings. DA assimilation of SM proved valuable particularly under data gaps and drought conditions. Although it did not consistently surpass single-source assimilation strategies, the joint assimilation yielded consistent results, especially in 2023, where RMSE, nRMSE and bias were 1258.83 kg\u002Fha, 16.62%, and - 220.52 kg\u002Fha, respectively. The proposed framework demonstrates the potential of multi-source DA to enhance biomass estimation and support robust, spatially explicit crop monitoring.","作物生长模型（CGM）是农业监测的重要工具。然而，其对大量输入参数的需求、模型参数化与结构相关的不确定性以及空间信息的缺乏，推动了数据同化（DA）等技术的应用。本文提出了一种数据同化框架，以改进玉米生物量估算。采用粒子滤波（PF）将遥感反射率和土壤水分（SM）数据分别独立及同时同化进入农业生产系统模拟器（APSIM）模型。Sentinel-2反射率观测数据通过将APSIM与辐射传输模型（RTM）PROSAIL耦合进行同化，而SMAP L波段土壤水分产品则直接同化进入APSIM。为评估所提方法可靠性而设计的合成实验，凸显了同化反射率在约束作物性状方面的优势，以及同化土壤水分在提供作物水分状况补充信息和促进更稳健集合轨迹方面的作用。实际案例结果证实了这些发现。土壤水分数据同化在数据缺失和干旱条件下尤为有价值。尽管联合同化未能持续超越单源同化策略，但其结果具有一致性，尤其在2023年，RMSE、nRMSE和偏差分别为1258.83 kg\u002Fha、16.62%和-220.52 kg\u002Fha。所提出的框架展示了多源数据同化在增强生物量估算和支持稳健、空间显式作物监测方面的潜力。",82,{"impact":17,"substance":310,"depth":311,"authority":19,"freshness":122,"relevant":21,"comment":312},22,19,"将Sentinel-2反射率与SMAP土壤水分同化进APSIM模型估算玉米生物量，方法新颖、结论可靠，对作物遥感监测有参考价值。",[314],{"name":9,"url":305},[26,28,316,127,317],"作物模型","数据同化",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[320],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":13,"version":52,"method":53,"evidence":322},{"candidates":323,"agricultural_evidence":57},[324,328,332,339,343,347,351],{"code":33,"score":141,"supported":57,"title":325,"abstract":326,"tags":327},[34],[28,34],[28],{"code":45,"score":19,"supported":57,"title":329,"abstract":330,"tags":331},[46],[154,46],[],{"code":333,"score":87,"supported":57,"title":334,"abstract":335,"tags":338},"H7",[],[336,337],"干旱","drought",[],{"code":164,"score":230,"supported":57,"title":340,"abstract":341,"tags":342},[],[261],[127],{"code":72,"score":21,"supported":13,"title":344,"abstract":345,"tags":346},[],[],[26],{"code":157,"score":21,"supported":13,"title":348,"abstract":349,"tags":350},[],[],[316],{"code":352,"score":21,"supported":13,"title":353,"abstract":354,"tags":355},"I5",[],[],[317],[357,358],"Sentinel-2 SMAP APSIM 玉米生物量","多源数据同化 玉米 遥感","Sentinel-2SMAPAPSIM玉米生物量-3525","10.1007\u002Fs11119-026-10442-6",{"doi":360,"openalex_id":362,"authors":363,"venue":9,"cited_by_count":105,"oa_url":305,"card":387,"direction":391,"ingested_from":106},"W7214405444",[364,366,369,372,375,377,379,382,384],{"name":365,"orcid":8},"Manuela Montella",{"name":367,"orcid":368},"Christian Bossung","https:\u002F\u002Forcid.org\u002F0000-0003-4651-2645",{"name":370,"orcid":371},"Thanh Huy Nguyen","https:\u002F\u002Forcid.org\u002F0000-0003-2471-350X",{"name":373,"orcid":374},"Marco Chini","https:\u002F\u002Forcid.org\u002F0000-0002-9094-0367",{"name":376,"orcid":8},"Jean François Iffly",{"name":378,"orcid":8},"Thomas Udelhoven",{"name":380,"orcid":381},"Julia Kubanek","https:\u002F\u002Forcid.org\u002F0000-0001-9597-2029",{"name":383,"orcid":8},"Zoltan Szantoi",{"name":385,"orcid":386},"Miriam Machwitz","https:\u002F\u002Forcid.org\u002F0000-0002-4999-673X",{"tldr":388,"method":389,"finding":390,"direction":391,"opportunity":392},"将Sentinel-2反射率与SMAP土壤水分同化进APSIM模型，提升玉米生物量估算精度。","粒子滤波同化Sentinel-2反射率（经PROSAIL耦合）与SMAP土壤水分","联合同化结果稳健，2023年RMSE为1258.83 kg\u002Fha，nRMSE 16.62%，偏差-2","农业遥感与作物表型","可探索多源数据同化在数据缺失与干旱条件下的自适应权重策略，并推广至其他作物与区域。","2026-09-26T23:30:03.247019Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":8,"source_name":400,"source_url":397,"published_at":401,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":402,"score_detail":403,"sources":405,"tags":407,"primary_topic":411,"secondary_topics":412,"classification":419,"search_phrases":443,"slug":446,"view_count":158,"doi":447,"paper":448,"created_at":472},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","Agricultural Systems","2026-09-02T00:00:00Z",80,{"impact":17,"substance":310,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":404},"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。",[406],{"name":400,"url":397},[26,28,408,409,410],"水肥优化","多目标优化","模型模拟",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[413,414],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"code":415,"slug":416,"name":417,"path":418,"parent_code":49},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":51,"locked":13,"version":52,"method":53,"evidence":420},{"candidates":421,"agricultural_evidence":57},[422,426,430,435,439],{"code":33,"score":141,"supported":57,"title":423,"abstract":424,"tags":425},[34],[28,34],[28],{"code":45,"score":19,"supported":57,"title":427,"abstract":428,"tags":429},[46],[154,46],[],{"code":415,"score":19,"supported":57,"title":431,"abstract":432,"tags":434},[416],[433,416],"灌溉",[],{"code":72,"score":21,"supported":13,"title":436,"abstract":437,"tags":438},[],[],[26],{"code":157,"score":21,"supported":13,"title":440,"abstract":441,"tags":442},[],[],[410],[444,445],"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","10.1016\u002Fj.agsy.2026.104953",{"doi":447,"openalex_id":449,"authors":450,"venue":400,"cited_by_count":105,"oa_url":397,"card":467,"direction":184,"ingested_from":106},"W7205003045",[451,453,456,459,461,462,464,466],{"name":452,"orcid":8},"Yongqiang Wang",{"name":454,"orcid":455},"Fugui Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8349-8398",{"name":457,"orcid":458},"Lanfang Bai","https:\u002F\u002Forcid.org\u002F0000-0001-7862-2844",{"name":460,"orcid":8},"Zhen Wang",{"name":460,"orcid":8},{"name":463,"orcid":8},"Haofang Yan",{"name":465,"orcid":8},"Zhigang Wang",{"name":465,"orcid":8},{"tldr":468,"method":469,"finding":470,"direction":184,"opportunity":471},"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。","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":474,"title":475,"url":476,"summary":477,"summary_zh":8,"content":478,"source_name":479,"source_url":8,"published_at":480,"category":481,"cover_url":8,"hotness":12,"is_selected":13,"score":482,"score_detail":483,"sources":485,"tags":487,"primary_topic":492,"secondary_topics":493,"classification":495,"search_phrases":507,"slug":510,"view_count":105,"doi":8,"paper":8,"created_at":511},5049,"河北省农林科学院滨海所举办盐碱地旱作农业示范观摩会 唐山丰南重度盐碱地改良后玉米亩产484.1公斤","https:\u002F\u002Fcnews.chinadaily.com.cn\u002Fa\u002F202610\u002F12\u002FWS6acc91e2e4b09a165c78ed7a.html","依托京津冀环境综合治理国家科技重大专项,河北省农林科学院滨海农业研究所聚焦耕层土壤盐分高、结构差、土壤肥力低等典型问题,在唐山市丰南区黑沿子镇示范基地开展盐碱地分区分类综合治理。曾经土壤含盐量超8‰的盐碱荒滩,通过暗管排盐、鼠道深翻、滴灌与有机培肥改良等\"下排+中阻+上改\"立体降盐模式,治理后耕层盐分降至3‰以下,2026年玉米现场测产亩产484.1公斤。","本次观摩会由河北省产业体系盐碱地改造利用综合试验站站长鲁雪林研究员主持。吴哲研究员向参会人员详细讲解耐盐改土植物绿色修复、生物有机肥培肥、“上改下排”、深翻耕鼠道暗管排盐等配套盐碱地改良技术体系，结合田间展板，对各项技术原理、实施要点和实际应用效果进行细致解读，参会人员深入田间实地察看作物长势，围绕盐碱地实际生产问题开展技术研讨交流。\n\n唐山市丰南区黑沿子镇示范基地曾经是盐碱荒滩，开垦之初土壤含盐量超过8‰，土质黏重、有机质匮乏，作物常年绝收。省农林科学院滨海农业研究所专家组实地调研研判，确立改土降盐、培肥地力核心目标，结合当地自然条件，制定分区分类分级综合开发利用方案。在高盐碱区域，应用暗管排盐技术，部分地块配套鼠道深翻，同时配套滴灌、有机培肥改良等绿色农艺措施，构建起“下排 中阻 上改” 立体降盐模式，加快盐分下渗。治理后耕层盐分降至 3‰以下，2026 年玉米现场测产亩产484.1 公斤。该体系可调控地下水位，有效改善强降雨季节田间积水、积涝问题，实现了盐碱改良、提质增产、防涝保收的多重治理成效。\n\n综合生态效益与改良成本，科研团队面向植被稀疏盐碱区域，构建耐盐植物生态改良技术路径。基于作物耐盐能力、改土效应综合筛选，种植了田菁、油葵等经济耐盐植物，规模200余亩。对比开垦初期玉米、高粱长势差、绝收的状况，当前地块植被长势旺盛，田菁平均亩鲜生物量1852公斤。监测表明，种植田菁一年后，根区土壤有机质提升至 8.52g\u002Fkg以上，耕作季根区盐分降至4.3‰以下。搭配有机培肥、滴灌等农艺措施，改良地块可满足耐盐高粱、油葵等作物种植。鉴于此，团队后续将继续试验田菁与高粱、油葵、油菜、玉米、小麦等多类型作物轮作，为盐碱地生态治理与资源化利用提供可复制的技术模式。","中国日报网 河北省农林科学院滨海农业研究所 2026年10月12日","2026-10-12T00:00:00Z","报道",75,{"impact":17,"substance":310,"depth":136,"authority":121,"freshness":87,"relevant":21,"comment":484},"省级科研机构盐碱地改良示范，含亩产484.1公斤等实测数据与技术体系细节，具行业参考价值。",[486],{"name":479,"url":476},[28,488,489,490,491],"土壤改良","盐碱地治理","暗管排盐","耐盐植物",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[494],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":13,"version":52,"method":53,"evidence":496},{"candidates":497,"agricultural_evidence":57},[498,503],{"code":45,"score":136,"supported":57,"title":499,"abstract":501,"tags":502},[500],"盐碱地",[154,500],[488,489],{"code":33,"score":16,"supported":57,"title":504,"abstract":505,"tags":506},[28],[28],[28],[508,509],"河北滨海所 盐碱地 玉米","唐山丰南 暗管排盐 田菁","河北滨海所盐碱地玉米-5049","2026-10-16T00:13:11.872759Z"]