[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3563":3,"related-3563":53},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":52},3563,"Can information increase farmers’ willingness to pay for digital agricultural technologies? A survey experiment on agricultural drones in rural China","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jrurstud.2026.104423","Can information increase farmers’ willingness to pay for digital agricultural technologies? A survey experiment on agricultural drones in rural China。Journal of Rural Studies",null,"Journal of Rural Studies","2026-09-25T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,21,18,14,9,1,"基于中国农村调查实验，实证信息干预对农户数字农业技术支付意愿的影响，方法规范、结论有政策参考价值，值得进入每日精选。",[24],{"name":9,"url":6},[26,27,28,29,30],"数字农业","智慧农业","农业技术推广","农业无人机","农户支付意愿",[32,33],"农业无人机 农户 支付意愿","信息干预 调查实验","农业无人机农户支付意愿-3563",0,"10.1016\u002Fj.jrurstud.2026.104423",{"doi":36,"openalex_id":38,"authors":39,"venue":9,"cited_by_count":35,"oa_url":8,"card":8,"direction":50,"ingested_from":51},"W7214363671",[40,42,45,48],{"name":41,"orcid":8},"Zhixian Lu",{"name":43,"orcid":44},"Huang Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2284-8083",{"name":46,"orcid":47},"Yi-Xiang Wang","https:\u002F\u002Forcid.org\u002F0000-0001-5697-0717",{"name":49,"orcid":8},"Kaixing Huang","数字乡村与农业信息化","openalex","2026-09-26T23:30:38.738490Z",{"total":54,"page":21,"page_size":54,"items":55},6,[56,94,128,166,200,246],{"id":57,"title":58,"url":59,"summary":60,"summary_zh":61,"content":8,"source_name":62,"source_url":59,"published_at":63,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":64,"score_detail":65,"sources":70,"tags":72,"search_phrases":75,"slug":78,"view_count":35,"doi":79,"paper":80,"created_at":93},3273,"Determinants of the Use and Extent of Digital Agriculture Among Moroccan Farmers","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.412776","Digital agriculture, driven by advancements in financial engineering, holds significant potential to enhance productivity and sustainability in agricultural production. However, the adoption and extent of these technologies fundamentally depend on farmers’ willingness to accept and use them. While recent studies have identified key factors influencing the adoption of digital agriculture, to the best of our knowledge, no academic study has specifically examined the determinants of both the use and the extent of adoption, particularly within the Moroccan context. This study investigates both the adoption and intensity of digital agriculture among a sample of 250 Moroccan farmers, utilizing a paper-based survey and two econometric approaches: a multinomial logit model and the Heckman model. The findings reveal that farmer age has a negative and significant impact on digital agriculture adoption. At the same time, crop type and risk aversion emerge as significant positive determinants of both the adoption and the extent of smart farming use. Specifically, technology adoption is mainly influenced by age, crop type, and risk aversion, whereas the extent of use is primarily driven by risk aversion and the type of crops cultivated. These results highlight the importance of implementing targeted policies and training programs to promote broader and more intensive use of digital agriculture technologies. Additionally, these findings open up avenues for further research aimed at better understanding the underlying factors that shape Moroccan farmers' behavior toward digital agriculture adoption.","由金融工程进步所驱动的数字农业，在提升农业生产率与可持续性方面具有巨大潜力。然而，这些技术的采用及其程度从根本上取决于农民接受和使用它们的意愿。尽管近期研究已识别出影响数字农业采用的关键因素，但据我们所知，尚无学术研究专门考察使用与采用程度的决定因素，尤其是在摩洛哥背景下。本研究基于250名摩洛哥农民的样本，采用纸质问卷调查和两种计量经济学方法——多项Logit模型与Heckman模型——考察了数字农业的采用情况及使用强度。研究发现，农民年龄对数字农业采用具有显著负向影响。与此同时，作物类型与风险规避对智慧农业的采用及使用程度均呈现显著正向决定作用。具体而言，技术采用主要受年龄、作物类型和风险规避影响，而使用程度则主要由风险规避和所种植作物类型驱动。这些结果凸显了实施有针对性的政策与培训项目以促进数字农业技术更广泛、更深入应用的重要性。此外，这些发现为后续研究开辟了方向，有助于更深入理解塑造摩洛哥农民数字农业采用行为的潜在因素。","AgEcon Search (University of Minnesota, USA)","2026-09-21T00:00:00Z",72,{"impact":66,"substance":17,"depth":67,"authority":68,"freshness":20,"relevant":21,"comment":69},12,17,13,"基于250户摩洛哥农户调查，用多项Logit与Heckman模型揭示年龄、作物类型与风险规避对数字农业采纳及使用强度的差异化影响，方法规范、结论有新意，对发展中国家数字农业推广有借鉴价值。",[71],{"name":62,"url":59},[26,27,28,73,74],"农户采纳","摩洛哥农业",[76,77],"摩洛哥 农户 数字农业","Heckman 模型 智慧农业 采纳","摩洛哥农户数字农业-3273","10.22004\u002Fag.econ.412776",{"doi":79,"openalex_id":81,"authors":82,"venue":62,"cited_by_count":35,"oa_url":59,"card":87,"direction":92,"ingested_from":51},"W7213999238",[83,85],{"name":84,"orcid":8},"Adil Jouamaa Mohammed",{"name":86,"orcid":8},"I. Mubarak Abdulilah",{"tldr":88,"method":89,"finding":90,"direction":50,"opportunity":91},"研究摩洛哥250位农民采用数字农业及其使用程度的决定因素。","纸质问卷，多项Logit模型与Heckman模型。","年龄负向影响采用，作物类型和风险规避正向影响采用与使用程度。","可针对不同作物和风险偏好农民设计差异化推广策略，并开展跨区域比较研究。","智慧农业 \u002F 农业物联网","2026-09-23T23:30:11.088448Z",{"id":95,"title":96,"url":97,"summary":98,"summary_zh":8,"content":8,"source_name":99,"source_url":97,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":100,"score_detail":101,"sources":104,"tags":106,"search_phrases":110,"slug":113,"view_count":35,"doi":114,"paper":115,"created_at":127},3520,"Factors influencing digital technology investments on French farms","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10429-3","Factors influencing digital technology investments on French farms。Precision Agriculture","Precision Agriculture",68,{"impact":66,"substance":18,"depth":16,"authority":19,"freshness":102,"relevant":21,"comment":103},8,"核心期刊论文，方法数据扎实，但研究对象为法国农场，对国内三农实践的直接参考价值有限，时效性尚可。",[105],{"name":99,"url":97},[26,27,107,108,109],"精准农业","农业投资","法国农业",[111,112],"法国农场 数字技术 投资","Precision Agriculture 法国","法国农场数字技术投资-3520","10.1007\u002Fs11119-026-10429-3",{"doi":114,"openalex_id":116,"authors":117,"venue":99,"cited_by_count":35,"oa_url":8,"card":8,"direction":50,"ingested_from":51},"W7214319682",[118,121,124],{"name":119,"orcid":120},"Maha Ben Jaballah","https:\u002F\u002Forcid.org\u002F0000-0002-4489-278X",{"name":122,"orcid":123},"Aude Ridier","https:\u002F\u002Forcid.org\u002F0000-0002-7893-735X",{"name":125,"orcid":126},"Karine Daniel","https:\u002F\u002Forcid.org\u002F0000-0001-9550-5666","2026-09-26T23:30:02.695117Z",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":8,"source_name":134,"source_url":131,"published_at":135,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":136,"score_detail":137,"sources":140,"tags":142,"search_phrases":146,"slug":149,"view_count":35,"doi":150,"paper":151,"created_at":165},3512,"AI-Driven Agricultural Advisory and Diagnostic Systems for Smallholder Farming: Technical Architectures, Evidence and Deployment Priorities for North-East India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Farja\u002F2026\u002Fv19i4919","Artificial intelligence (AI) is being introduced into agricultural advisory services through machine learning, computer vision, conversational large language models, retrieval-augmented generation and multimodal interfaces. For smallholder farming, the central question is not whether these technologies can produce technically plausible outputs, but whether they can provide locally correct, actionable and safe recommendations under heterogeneous agronomic, linguistic and connectivity conditions. This critical narrative review integrates evidence on digital extension, AI-enabled agricultural advice, image-based diagnosis and responsible digital agriculture, with particular reference to North-East India. Literature published from 1 January 2010 to 17 July 2026 was considered, with emphasis on peer-reviewed field evaluations, technical validation studies, reviews and regionally relevant research. Evidence from digital extension provides the strongest causal baseline: mobile and personalised advisory services frequently improve information recall, agronomic knowledge and adoption of recommended practices, yet effects on yield, profit and welfare are inconsistent. Recent generative-AI studies show that large language models can produce useful agricultural responses, but site-specific rates, timing and local practice remain recurrent failure points. Retrieval grounding and expert feedback improve local relevance, although multi-season farm-level effectiveness evidence remains scarce. Image-based plant-disease systems achieve high accuracy in curated datasets, but performance can deteriorate sharply under field domain shift, class novelty and variable image quality. North-East Indian studies of mobile advisory systems in Meghalaya, Nagaland and Tripura demonstrate a valuable institutional foundation based on interactive voice response, local expert networks and user-centred service design; they do not, however, establish the effectiveness of autonomous AI. The most defensible deployment model is therefore an offline-tolerant, multilingual, multimodal and human-supervised architecture that grounds recommendations in curated regional knowledge, represents uncertainty, preserves provenance and escalates high-risk or out-of-distribution cases. Future research should prioritise prospective district- and season-spanning evaluations that connect model quality to farmer decisions, agronomic outcomes, equity, safety and cost-effectiveness.","人工智能（AI）正通过机器学习、计算机视觉、对话式大语言模型、检索增强生成和多模态界面被引入农业咨询服务。对于小农户而言，核心问题不在于这些技术能否产生技术上看似合理的输出，而在于它们能否在异质的农艺、语言和网络连接条件下提供本地正确、可操作且安全的建议。本批判性叙事综述整合了数字推广、AI赋能的农业建议、基于图像的诊断和负责任数字农业方面的证据，并特别关注印度东北部。本文考察了2010年1月1日至2026年7月17日期间发表的文献，重点关注同行评议的田间评估、技术验证研究、综述及区域相关研究。来自数字推广的证据提供了最强的因果基线：移动化和个性化咨询服务经常改善信息记忆、农艺知识和对推荐措施的采纳，但对产量、利润和福利的影响并不一致。近期生成式AI研究表明，大语言模型能够产生有用的农业回答，但针对具体地点的用量、时机和本地实践仍是反复出现的失败点。检索 grounding 和专家反馈可提高本地相关性，但多季农场层面的有效性证据仍然稀缺。基于图像的植物病害系统在精选数据集上达到高准确率，但在田间域偏移、类别新颖性和图像质量多变的情况下，性能可能急剧下降。印度东北部在梅加拉亚邦、那加兰邦和特里普拉邦开展的移动咨询系统研究展示了基于交互式语音应答、本地专家网络和以用户为中心的服务设计的宝贵制度基础；然而，这些研究并未确立自主AI的有效性。因此，最可辩护的部署模式是一种容忍离线、多语言、多模态且有人工监督的架构，该架构将建议建立在精选的区域知识之上，表征不确定性，保留来源信息，并对高风险或分布外案例进行升级处理。未来研究应优先开展前瞻性的跨区县和跨季节评估，将模型质量与农户决策、农艺结果、公平性、安全性和成本效益联系起来。","Asian Research Journal of Agriculture","2026-09-24T00:00:00Z",80,{"impact":18,"substance":138,"depth":18,"authority":68,"freshness":20,"relevant":21,"comment":139},22,"系统综述AI农业咨询与诊断系统在印度东北小农场景的技术架构与落地证据，指出人机协同、离线多语言与检索增强是可行路径，对智慧农业落地有参考价值。",[141],{"name":134,"url":131},[143,27,144,28,145],"数字乡村","农业人工智能","小农户",[147,148],"印度东北部 农业AI 小农户","农业智能诊断 多语言 离线","印度东北部农业AI小农户-3512","10.9734\u002Farja\u002F2026\u002Fv19i4919",{"doi":150,"openalex_id":152,"authors":153,"venue":134,"cited_by_count":35,"oa_url":131,"card":159,"direction":50,"ingested_from":51},"W7214205238",[154,156],{"name":155,"orcid":8},"Pravangkar Boruah",{"name":157,"orcid":158},"Rubul Kumar Bania","https:\u002F\u002Forcid.org\u002F0000-0001-6294-0231",{"tldr":160,"method":161,"finding":162,"direction":163,"opportunity":164},"综述AI农业咨询与诊断系统，聚焦印度东北小农，提出人监督多模态部署架构。","批判性叙述综述，整合2010-2026年数字推广、生成式AI与图像诊断证据。","AI输出技术可行但本地化、安全与田间效果证据不足，需人监督与检索增强。","农业人工智能与决策模型","可开展跨区跨季前瞻评估，连接模型质量与农户决策、产量、公平及成本效益。","2026-09-25T23:30:39.745514Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":171,"content":8,"source_name":172,"source_url":169,"published_at":135,"category":11,"cover_url":8,"hotness":173,"is_selected":13,"score":174,"score_detail":175,"sources":178,"tags":182,"search_phrases":184,"slug":187,"view_count":35,"doi":188,"paper":189,"created_at":199},3482,"Digital Transformation and the Reconfiguration of Farming Systems: Understanding How Tech-nological Adoption and Institutional Conditions Shape Sustainable and Inclusive Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932900","Digital agriculture is progressively reshaping farming through the integration of sensors, artificial intelligence, Internet of Things technologies, digital platforms, data analytics, robotics and automated decision-support systems. This paper examines this transformation by connecting three complementary dimensions: the socio-technical development of digital agriculture, the determinants and processes of technological adoption, and the economic, organizational and inclusive consequences of digitalization for farming systems. The literature indicates that agricultural digitalization cannot be reduced to the availability of increasingly sophisticated technologies. Adoption and sustained use depend on farmers’ perceptions of usefulness and compatibility, farm resources, human capital, infrastructure, institutional support, advisory systems and the capacity to integrate technologies into established production routines. Digital technologies may improve resource efficiency, information management, productivity and sustainability, while also modifying labor organization, farmer autonomy, data governance and relationships among actors within agricultural value chains. These effects remain uneven across farms and territories, particularly where smallholders face financial, infrastructural and capability constraints. The paper therefore interprets digital agriculture as a multidimensional transformation whose outcomes depend on the interaction between technological capabilities, farmer behavior and institutional conditions. Inclusive and sustainable digitalization requires attention not only to innovation diffusion but also to governance, skills, accessibility and the distribution of technological benefits.","数字农业正通过传感器、人工智能、物联网技术、数字平台、数据分析、机器人技术和自动化决策支持系统的整合，逐步重塑农业生产方式。本文通过连接三个互补维度来考察这一转型：数字农业的社会技术发展、技术采纳的决定因素与过程，以及数字化对农业系统产生的经济、组织和包容性后果。文献表明，农业数字化不能简化为日益复杂的技术供给。采纳和持续使用取决于农民对有用性和兼容性的认知、农场资源、人力资本、基础设施、制度支持、咨询系统以及将技术整合到既定生产惯例中的能力。数字技术可能提高资源效率、信息管理、生产力和可持续性，同时也会改变劳动组织、农民自主性、数据治理以及农业价值链中参与者之间的关系。这些影响在农场和区域之间仍不均衡，尤其是小农户面临资金、基础设施和能力约束的地方。因此，本文将数字农业解释为一种多维转型，其结果取决于技术能力、农民行为和制度条件之间的相互作用。包容和可持续的数字化不仅需要关注创新扩散，还需要关注治理、技能、可及性和技术收益的分配。","Zenodo (CERN European Organization for Nuclear Research)",25,77,{"impact":18,"substance":176,"depth":67,"authority":68,"freshness":20,"relevant":21,"comment":177},20,"系统梳理数字农业技术采纳与制度条件的研究综述，对智慧农业政策与推广有参考价值，但属文献综述类，非重大突破。",[179,180],{"name":172,"url":169},{"name":172,"url":181},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932899",[26,27,144,145,183],"技术采纳",[185,186],"农业人工智能 技术采纳 数字农业 智慧农业","农业人工智能 技术采纳","农业人工智能技术采纳数字农业智慧农业-3482","10.5281\u002Fzenodo.22932900",{"doi":188,"openalex_id":190,"authors":191,"venue":172,"cited_by_count":35,"oa_url":169,"card":194,"direction":92,"ingested_from":51},"W7214187008",[192],{"name":193,"orcid":8},"Chilufya Banda",{"tldr":195,"method":196,"finding":197,"direction":50,"opportunity":198},"综述数字农业转型，分析技术采纳与制度条件如何共同塑造可持续包容性农业。","文献综述，整合社会技术、技术采纳与数字化经济组织后果三维度。","数字化成效取决于技术能力、农户行为与制度条件的交互，小农面临多重约束。","可实证检验制度支持与农户能力如何调节数字技术对包容性和可持续性的影响。","2026-09-25T23:30:18.463733Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":205,"content":8,"source_name":206,"source_url":203,"published_at":135,"category":11,"cover_url":8,"hotness":173,"is_selected":13,"score":207,"score_detail":208,"sources":212,"tags":217,"search_phrases":220,"slug":223,"view_count":35,"doi":224,"paper":225,"created_at":245},3470,"AI-generated advice as a reinforcement layer in climate-smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.landusepol.2026.108336","Climate-smart agricultural (CSA) practices are central to food-system decarbonisation, yet adoption often falls short of farmers’ stated intentions, weakening the impact of incentives and extension under capacity constraints. We test whether spatially targeted, AI-generated advice can narrow this intention-action gap in a randomised field experiment with 1529 row crop farmers in Iowa, Illinois and Indiana during the cover crop decision window. Farmers assigned to receive four AI-generated emails were 4.45 %age points more likely to plant cover crops than controls (z = 2.81, p = 0.005), despite high baseline intentions in both groups. Effects operated on the extensive margin: there was no detectable change in the share of land planted among adopters. Survey responses indicate high engagement and a shift from untested optimism to more calibrated trust after exposure. Supervised AI advice can provide a low-cost, scalable complement to existing extension, improving follow-through and modestly expanding uptake without displacing human expertise.","气候智慧型农业（CSA）实践是食品系统脱碳的核心，然而在能力受限的情况下，农户的实际采用往往低于其声称的意愿，削弱了激励措施和推广服务的效果。我们在爱荷华州、伊利诺伊州和印第安纳州开展了一项随机田间试验，覆盖1529名大田作物种植户，在覆盖作物决策窗口期测试了空间靶向的AI生成建议能否缩小这一意愿—行动差距。被分配接收四封AI生成电子邮件的农户种植覆盖作物的概率比对照组高4.45个百分点（z = 2.81，p = 0.005），尽管两组基线意愿均较高。效应体现在广延边际上：采用者中种植土地比例未检测到显著变化。调查回复表明参与度较高，且在接触建议后，农户从未经检验的乐观转向更为校准的信任。有监督的AI建议可作为现有推广服务的低成本、可扩展补充，改善后续落实并适度扩大采用，而不会取代人类专业知识。","Land Use Policy",87,{"impact":138,"substance":209,"depth":210,"authority":19,"freshness":20,"relevant":21,"comment":211},23,19,"随机对照试验证实AI生成建议可低成本缩小农户意愿与行动差距，对智慧农业推广具参考价值。",[213,214],{"name":206,"url":203},{"name":215,"url":216},"Apollo","https:\u002F\u002Fdoi.org\u002F10.17863\u002Fcam.134741",[27,144,28,218,219],"覆盖作物","气候智慧农业",[221,222],"AI生成建议 覆盖作物","爱荷华 伊利诺伊 印第安纳 覆盖作物","AI生成建议覆盖作物-3470","10.1016\u002Fj.landusepol.2026.108336",{"doi":224,"openalex_id":226,"authors":227,"venue":206,"cited_by_count":35,"oa_url":203,"card":240,"direction":92,"ingested_from":51},"W7214223143",[228,231,233,235,238],{"name":229,"orcid":230},"Callum Alexander","https:\u002F\u002Forcid.org\u002F0009-0007-5275-4583",{"name":232,"orcid":8},"Aiora Zabala",{"name":234,"orcid":8},"Andreas Kontoleon",{"name":236,"orcid":237},"Shalamar Armstrong","https:\u002F\u002Forcid.org\u002F0000-0002-1326-9936",{"name":239,"orcid":8},"Anuoluwa Sangotayo",{"tldr":241,"method":242,"finding":243,"direction":163,"opportunity":244},"随机试验检验AI生成建议能否缩小农户覆盖作物种植的意图-行动差距。","1529户美国中西部农户随机对照试验，四次AI生成邮件干预。","AI建议使覆盖作物种植率提高4.45个百分点，效果体现在是否采纳而非种植面积。","可探索AI建议与人工推广协同、长期持续效果及不同作物区域的异质性影响。","2026-09-25T23:30:09.887867Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":251,"content":8,"source_name":252,"source_url":249,"published_at":135,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":253,"score_detail":254,"sources":256,"tags":258,"search_phrases":262,"slug":265,"view_count":35,"doi":266,"paper":267,"created_at":288},3455,"Model-based optimization of agricultural drainage design: comparing orthogonal design, range analysis, and surrogate-assisted evolutionary optimization","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1942112","Artificial drainage is essential for agricultural productivity, but current design methods struggle to incorporate changing climate conditions and evolving policies and economic constraints. We propose a model-based optimization framework employing orthogonal design, range analysis, numerical simulations, and economic cost assessment. To benchmark the simplified approach, a Gaussian Process surrogate model (trained on the 16 orthogonal treatments) coupled with NSGA-II multi-objective evolutionary optimization was implemented to search for Pareto-optimal designs in continuous space. The framework was evaluated through a case study in Eastern China. The orthogonal design reduced the required simulations by 75% (64 to 16). The identified optimum (12 m spacing, 140 cm depth, 3 cm surface roughness) was corroborated through a continuous-space NSGA-II search, where the Pareto front knee point achieved a model-validated water table depth of 86.82 cm vs. 87.93 cm for the orthogonal optimum. The GP surrogate predictions agreed well with the numerical model (mean absolute error 4.5 cm, 4.4%). Global sensitivity analysis identified the drainage efficiency weight (67%) as the most influential factor. Systematic optimization methods, from orthogonal designs to surrogate-assisted evolutionary algorithms, can effectively identify optimal drainage designs while substantially reducing simulation effort, bridging the gap between optimization theory and practical drainage engineering.","人工排水对农业生产至关重要，但当前的设计方法难以纳入变化的气候条件以及不断演变的政策和经济约束。我们提出了一种基于模型的优化框架，采用正交设计、极差分析、数值模拟和经济成本评估。为对简化方法进行基准测试，我们实现了一种高斯过程代理模型（基于16个正交处理训练）与NSGA-II多目标进化优化相结合的方法，以在连续空间中搜索帕累托最优设计。该框架通过中国东部的一个案例研究进行了评估。正交设计将所需模拟次数减少了75%（从64次降至16次）。所确定的最优方案（间距12 m、埋深140 cm、地表粗糙度3 cm）通过连续空间NSGA-II搜索得到了验证，其中帕累托前沿拐点实现了模型验证的地下水位埋深86.82 cm，而正交最优方案为87.93 cm。高斯过程代理模型预测与数值模型吻合良好（平均绝对误差4.5 cm，4.4%）。全局敏感性分析表明，排水效率权重（67%）是最具影响力的因素。从正交设计到代理辅助进化算法的系统优化方法，能够有效识别最优排水设计，同时大幅减少模拟工作量，弥合了优化理论与实际排水工程之间的差距。","Frontiers in Sustainable Food Systems",74,{"impact":66,"substance":138,"depth":18,"authority":68,"freshness":20,"relevant":21,"comment":255},"方法学扎实、数据可信的农业排水优化研究，但属细分领域技术论文，公共影响有限，适合主题聚合而非每日精选。",[257],{"name":252,"url":249},[26,27,259,260,261],"多目标优化","农业排水","农业模型优化",[263,264],"农业排水设计 正交试验 NSGA-II","GP代理模型 排水工程 优化","农业排水设计正交试验NSGA-II-3455","10.3389\u002Ffsufs.2026.1942112",{"doi":266,"openalex_id":268,"authors":269,"venue":252,"cited_by_count":35,"oa_url":249,"card":283,"direction":163,"ingested_from":51},"W7214176721",[270,273,275,277,279,281],{"name":271,"orcid":272},"Yi Gong","https:\u002F\u002Forcid.org\u002F0000-0002-6768-5417",{"name":274,"orcid":8},"Yuehua Ma",{"name":276,"orcid":8},"Lidong Chen",{"name":278,"orcid":8},"Ruinan Li",{"name":280,"orcid":8},"Jingsen Liu",{"name":282,"orcid":8},"Hao Liu",{"tldr":284,"method":285,"finding":286,"direction":163,"opportunity":287},"提出基于模型的农田排水设计优化框架，比较正交设计与代理辅助进化优化。","正交设计、极差分析、数值模拟、高斯过程代理模型与NSGA-II多目标优化。","正交设计减少75%模拟，最优设计经连续空间搜索验证，排水效率权重影响最大。","可探索代理模型与进化算法在多变气候和政策约束下的动态排水设计优化。","2026-09-25T23:30:06.531154Z"]