[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2167":3,"related-2167":151},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":118,"slug":121,"view_count":122,"doi":123,"paper":124,"created_at":150},2167,"Crop Simulation Model: A Digital Tool for Sustainable Water Management in Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i95667","Efficient management of agricultural water resources is increasingly important under climate variability and freshwater scarcity. Accurate estimation of crop water requirements (CWR) supports crop productivity, irrigation scheduling and water-use efficiency. Conventional approaches, including crop-coefficient, Penman–Monteith and soil-water-balance methods, remain widely used but can require extensive field observations and may have limited capacity to represent dynamic soil–crop–atmosphere interactions. Crop simulation models provide a process-based digital framework that integrates weather, soil, crop and management information to simulate crop growth, evapotranspiration, yield and water productivity under different environmental and management conditions. This review synthesises the roles of widely used models, including DSSAT, APSIM, AquaCrop, CROPWAT and CropSyst, in estimating crop water requirements and supporting irrigation management. Applications across maize, wheat, rice, cotton and other crops show that these models can evaluate irrigation schedules, deficit-irrigation strategies, water-saving practices and potential responses to future climate conditions. Model performance, however, depends on appropriate input data, local calibration and validation. The review also identifies opportunities to strengthen crop simulation through integration with hydrological information, remote sensing, artificial intelligence and modules for biotic stresses. Overall, crop simulation models provide useful decision-support tools for evaluating water-management options and improving the evidence base for sustainable agricultural water-resource planning.","在气候变率和淡水稀缺背景下，农业水资源的高效管理日益重要。准确估算作物需水量（CWR）有助于支撑作物生产力、灌溉调度和水分利用效率。传统方法，包括作物系数法、Penman–Monteith法和土壤水量平衡法，仍被广泛使用，但可能需要大量田间观测，且在表征动态的土壤–作物–大气相互作用方面能力有限。作物模拟模型提供了一种基于过程的数字化框架，可整合天气、土壤、作物和管理信息，以模拟不同环境和管理条件下的作物生长、蒸散发、产量和水分生产力。本文综述了广泛使用的模型，包括DSSAT、APSIM、AquaCrop、CROPWAT和CropSyst，在估算作物需水量和支持灌溉管理中的作用。在玉米、小麦、水稻、棉花及其他作物上的应用表明，这些模型能够评估灌溉制度、亏缺灌溉策略、节水措施以及对未来气候条件的潜在响应。然而，模型性能取决于适当的输入数据、本地校准和验证。本综述还指出了通过与水文信息、遥感、人工智能及生物胁迫模块集成来加强作物模拟的机遇。总体而言，作物模拟模型为评估水资源管理方案和改善可持续农业水资源规划的证据基础提供了有用的决策支持工具。",null,"International Journal of Environment and Climate Change","2026-09-10T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,8,1,"系统综述DSSAT、APSIM、AquaCrop等作物模拟模型在作物需水估算与灌溉决策中的应用，并提出与遥感、AI融合方向，对农业水资源数字化管理有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智慧农业","农业人工智能","农业水资源","遥感","灌溉管理","作物模拟模型",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46,56,63,71,78,85,92,99,106,113],{"code":34,"score":47,"supported":48,"title":49,"abstract":50,"tags":55},11,true,[],[51,52,53,35,54],"灌溉","水资源","蒸散发","evapotranspiration",[29,31],{"code":57,"score":58,"supported":48,"title":59,"abstract":60,"tags":62},"I3",7,[],[30,61],"remote sensing",[30],{"code":64,"score":65,"supported":48,"title":66,"abstract":67,"tags":70},"A1",6,[],[68,69],"水稻","rice",[],{"code":72,"score":65,"supported":48,"title":73,"abstract":74,"tags":77},"A2",[],[75,76],"小麦","wheat",[],{"code":79,"score":65,"supported":48,"title":80,"abstract":81,"tags":84},"A3",[],[82,83],"玉米","maize",[],{"code":86,"score":65,"supported":48,"title":87,"abstract":88,"tags":91},"A6",[],[89,90],"棉花","cotton",[],{"code":93,"score":65,"supported":48,"title":94,"abstract":95,"tags":98},"H2",[],[96,97],"土壤","soil",[],{"code":100,"score":101,"supported":48,"title":102,"abstract":103,"tags":105},"I2",4,[],[104],"artificial intelligence",[28],{"code":107,"score":108,"supported":48,"title":109,"abstract":110,"tags":112},"H8",3,[],[111],"可持续农业",[],{"code":114,"score":22,"supported":14,"title":115,"abstract":116,"tags":117},"I1",[],[],[27],[119,120],"作物模拟模型 农业人工智能 农业水资源 智慧农业","作物模拟模型 农业人工智能","作物模拟模型农业人工智能农业水资源智慧农业-2167",2,"10.9734\u002Fijecc\u002F2026\u002Fv16i95667",{"doi":123,"openalex_id":125,"authors":126,"venue":10,"cited_by_count":141,"oa_url":6,"card":142,"direction":148,"ingested_from":149},"W7212194041",[127,129,131,133,135,137,139],{"name":128,"orcid":9},"P. S. Manju",{"name":130,"orcid":9},"N. Manikandan",{"name":132,"orcid":9},"A.P. Ramaraj",{"name":134,"orcid":9},"V. S. Jinsy",{"name":136,"orcid":9},"K. V. Sumesh",{"name":138,"orcid":9},"V. Dhanalakshmi",{"name":140,"orcid":9},"N. Gopika",0,{"tldr":143,"method":144,"finding":145,"direction":146,"opportunity":147},"综述作物模拟模型在估算作物需水与灌溉管理中的应用及改进方向。","综述DSSAT、APSIM、AquaCrop、CROPWAT、CropSyst等","模型可评估灌溉策略与节水实践，但精度依赖本地校准与输入数据。","农业人工智能与决策模型","可探索作物模型与遥感、AI及水文信息耦合，提升需水估算与灌溉决策精度。","农业遥感与作物表型","openalex","2026-09-11T23:30:30.176174Z",{"total":65,"page":22,"page_size":65,"items":152},[153,228,339,415,505,572],{"id":154,"title":155,"url":156,"summary":157,"summary_zh":158,"content":9,"source_name":159,"source_url":156,"published_at":160,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":161,"score_detail":162,"sources":165,"tags":167,"primary_topic":169,"secondary_topics":174,"classification":176,"search_phrases":211,"slug":214,"view_count":122,"doi":215,"paper":216,"created_at":227},2667,"A systematic review of remote sensing applications for agricultural water management in a water-stressed South Africa","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04728-y","Abstract Recent advancements in earth observation and computational technologies have enabled the prudent use of remote sensing (RS) for monitoring and managing water resources. RS is a spatially explicit and effective approach for field-level to large-scale monitoring through data-driven insights on key agricultural water management (AWM) aspects of water consumption, productivity, agricultural drought and irrigated area extent mapping. However, the progress made in the South African context is not well documented. Hence, this review aimed to assess how RS is utilized for AWM in South Africa. Article search was conducted in the Scopus and Web of Science databases using keywords remote sensing, earth observation, agricultural water management, irrigation, evapotranspiration, water use, water productivity, agricultural drought, and South Africa. The bibliometric analysis from 165 articles revealed that number of publications have a 8.3% annual growth over 26 years. RS data have been utilized to assess evapotranspiration (ET), crop water productivity (CWP), agricultural drought, scheme performance, and irrigation water quality. Data limitations, insufficient technical capacity, and limited computational infrastructure were the notable challenges. Future research should focus on integrated use of multi-temporal and multi-source data to minimize gaps across sensors and improve accuracy. Overall, RS has substantial potential to address water management issues in the agricultural sector of South Africa. Addressing the challenges through continued innovation, data integration, and capacity building is key to utilizing RS for sustainable agriculture and water security.","摘要 地球观测与计算技术的最新进展使得遥感（RS）能够被审慎地应用于水资源监测与管理。遥感是一种空间显式且有效的方法，可通过数据驱动的洞察，从田块尺度到大规模监测农业水资源管理（AWM）的关键方面，包括耗水量、生产力、农业干旱及灌溉面积范围制图。然而，南非在这一领域的进展尚未得到充分记录。因此，本综述旨在评估遥感在南非农业水资源管理中的应用情况。研究在Scopus和Web of Science数据库中使用遥感、地球观测、农业水资源管理、灌溉、蒸散发、用水量、水分生产力、农业干旱和南非等关键词进行文献检索。对165篇文章的文献计量分析显示，过去26年间发表数量年增长率为8.3%。遥感数据已被用于评估蒸散发（ET）、作物水分生产力（CWP）、农业干旱、灌区性能及灌溉水质。数据局限性、技术能力不足和计算基础设施有限是显著挑战。未来研究应侧重于多时相和多源数据的集成使用，以缩小传感器之间的差距并提高精度。总体而言，遥感在解决南非农业部门水资源管理问题方面具有巨大潜力。通过持续创新、数据集成和能力建设来应对这些挑战，是利用遥感实现可持续农业和水安全的关键。","Discover Sustainability","2026-09-15T00:00:00Z",74,{"impact":163,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":164},16,"以南非为对象的遥感农业水资源管理系统性综述，方法规范、数据规模明确，对干旱区灌溉与遥感应用有参考价值，但属区域案例研究，公共影响层级有限。",[166],{"name":159,"url":156},[27,29,30,31,168],"农业干旱",{"code":57,"slug":170,"name":171,"path":172,"parent_code":173},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[175],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":177},{"candidates":178,"agricultural_evidence":48},[179,184,188,195,200,207],{"code":57,"score":180,"supported":48,"title":181,"abstract":182,"tags":183},15,[61],[30,61],[30],{"code":34,"score":47,"supported":48,"title":185,"abstract":186,"tags":187},[],[51,52,53,35,54],[29,31],{"code":189,"score":65,"supported":48,"title":190,"abstract":191,"tags":194},"H7",[],[192,193],"干旱","drought",[],{"code":107,"score":65,"supported":48,"title":196,"abstract":197,"tags":199},[],[111,198],"sustainable agriculture",[],{"code":201,"score":65,"supported":48,"title":202,"abstract":203,"tags":206},"I7",[],[204,205],"文献计量","bibliometric",[],{"code":114,"score":22,"supported":14,"title":208,"abstract":209,"tags":210},[],[],[27],[212,213],"农业水资源 农业干旱 智慧农业 灌溉管理","农业水资源 农业干旱","农业水资源农业干旱智慧农业灌溉管理-2667","10.1007\u002Fs43621-026-04728-y",{"doi":215,"openalex_id":217,"authors":218,"venue":159,"cited_by_count":141,"oa_url":156,"card":222,"direction":148,"ingested_from":149},"W7213266434",[219],{"name":220,"orcid":221},"Yilkal Gebeyehu Mekonnen","https:\u002F\u002Forcid.org\u002F0000-0001-9450-1081",{"tldr":223,"method":224,"finding":225,"direction":148,"opportunity":226},"系统综述遥感在南非农业水资源管理中的应用进展与挑战。","Scopus与Web of Science检索165篇文献，做文献计量分析。","遥感用于蒸散、水分生产力、干旱与灌溉监测，但受数据与能力限制。","多源多时相遥感数据融合以填补传感器空缺、提升精度，是值得切入的方向。","2026-09-16T23:30:29.241938Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":9,"source_name":234,"source_url":231,"published_at":235,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":236,"score_detail":237,"sources":242,"tags":244,"primary_topic":247,"secondary_topics":248,"classification":258,"search_phrases":309,"slug":312,"view_count":141,"doi":313,"paper":314,"created_at":338},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":17,"substance":238,"depth":17,"authority":239,"freshness":240,"relevant":22,"comment":241},22,12,9,"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[243],{"name":234,"url":231},[27,28,30,245,246],"作物水分胁迫","精准灌溉",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[249,253],{"code":100,"slug":250,"name":251,"path":252,"parent_code":173},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":254,"slug":255,"name":256,"path":257,"parent_code":173},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":41,"locked":14,"version":42,"method":43,"evidence":259},{"candidates":260,"agricultural_evidence":48},[261,265,271,278,284,288,292,296,303],{"code":34,"score":180,"supported":48,"title":262,"abstract":263,"tags":264},[35],[51,35],[246],{"code":100,"score":13,"supported":48,"title":266,"abstract":267,"tags":270},[],[268,269,104],"机器学习","machine learning",[28],{"code":254,"score":240,"supported":48,"title":272,"abstract":273,"tags":277},[],[274,275,276],"物联网","internet of things","IoT",[],{"code":114,"score":58,"supported":48,"title":279,"abstract":280,"tags":283},[],[281,282],"数字孪生","digital twin",[27],{"code":57,"score":58,"supported":48,"title":285,"abstract":286,"tags":287},[],[30,61],[30],{"code":72,"score":65,"supported":48,"title":289,"abstract":290,"tags":291},[],[75,76],[],{"code":79,"score":65,"supported":48,"title":293,"abstract":294,"tags":295},[],[82,83],[],{"code":297,"score":65,"supported":48,"title":298,"abstract":299,"tags":302},"A5",[],[300,301],"马铃薯","potato",[],{"code":304,"score":108,"supported":48,"title":305,"abstract":306,"tags":308},"B2",[],[307],"葡萄",[],[310,311],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":313,"openalex_id":315,"authors":316,"venue":234,"cited_by_count":141,"oa_url":231,"card":332,"direction":337,"ingested_from":149},"W7223088921",[317,320,323,326,329],{"name":318,"orcid":319},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":321,"orcid":322},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":324,"orcid":325},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":327,"orcid":328},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":330,"orcid":331},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":333,"method":334,"finding":335,"direction":148,"opportunity":336},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":9,"source_name":345,"source_url":342,"published_at":346,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":161,"score_detail":347,"sources":350,"tags":352,"primary_topic":355,"secondary_topics":356,"classification":361,"search_phrases":386,"slug":389,"view_count":108,"doi":390,"paper":391,"created_at":414},3790,"Evaluation of the accuracy and response to recorded irrigation events of remote sensing and reanalysis soil moisture products in a typical irrigation district","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110822","Soil moisture (SM) plays a critical role in the land–atmosphere water cycle and agricultural water resource management. However, the applicability of prevalent SM products in typical irrigation districts remains insufficiently assessed. In this study, two in situ SM observation networks—an automatic meteorological station network (AMS) and an agro-meteorological experimental station network (AES)—were used to comprehensively evaluate the accuracy and irrigation-associated response characteristics of seven microwave and reanalysis SM products in the North China Plain. The results indicated that: (1) AES-based evaluations generally showed a higher overall correlation (R = 0.58) and smaller errors with the SM products than AMS-based evaluations (R = 0.54). However, when restricted to common observation dates, the correlation coefficients decreased while the biases were close to zero in the AES-based evaluation. (2) Significant differences were observed among the evaluated products under different monitoring objectives. The ERA5-Land reanalysis product performed best for continuous SM monitoring, while the European Space Agency Climate Change Initiative (ESA CCI) Passive product achieved the highest correlation coefficient with in situ observations (R = 0.69) and the lowest unbiased root mean square error (ubRMSE = 0.043 m 3 m −3 ) on recorded irrigation days and exhibited a comparatively pronounced positive SM response under precipitation-controlled conditions, indicating greater sensitivity to short-term irrigation-associated wetting. (3) Strict precipitation control substantially affected the interpretation of irrigation-associated responses. ERA5-Land and GLDAS-Noah showed little systematic positive change when precipitation was controlled using both station observations and model precipitation forcing, while threshold-based probability of detection (POD) varied substantially with the selected SM response threshold. These findings demonstrate that product suitability depends on the monitoring objective and that precipitation attribution and threshold sensitivity should be explicitly considered when using SM products for irrigation-related applications.","土壤水分（SM）在陆气水循环和农业水资源管理中起着至关重要的作用。然而，主流SM产品在典型灌区的适用性仍缺乏充分评估。本研究利用两套原位SM观测网络——自动气象站网络（AMS）和农业气象实验站网络（AES），综合评估了七种微波和再分析SM产品在华北平原的精度及与灌溉相关的响应特征。结果表明：（1）基于AES的评估总体上显示出比基于AMS的评估更高的整体相关性（R = 0.58）和更小的误差（R = 0.54）。然而，当限定于共同观测日期时，基于AES的评估中相关系数下降，而偏差接近于零。（2）在不同监测目标下，所评估产品之间存在显著差异。ERA5-Land再分析产品在连续SM监测中表现最佳，而欧洲空间局气候变化倡议（ESA CCI）被动产品在记录灌溉日与原位观测的相关系数最高（R = 0.69），无偏均方根误差最低（ubRMSE = 0.043 m³ m⁻³），并在降水控制条件下表现出相对明显的SM正响应，表明其对短期灌溉相关增湿具有更高的敏感性。（3）严格的降水控制显著影响了对灌溉相关响应的解释。当利用站点观测和模型降水强迫控制降水时，ERA5-Land和GLDAS-Noah几乎未表现出系统性正变化，而基于阈值的检测概率（POD）随所选SM响应阈值的变化而显著变化。这些发现表明，产品适用性取决于监测目标，且在将SM产品用于灌溉相关应用时，应明确考虑降水归因和阈值敏感性。","Agricultural Water Management","2026-09-28T00:00:00Z",{"impact":239,"substance":238,"depth":17,"authority":348,"freshness":21,"relevant":22,"comment":349},14,"基于华北平原实测网络的七种土壤水分产品评估，为灌溉监测产品选型提供实证依据，专业价值较高但属细分领域研究。",[351],{"name":345,"url":342},[29,30,353,31,354],"土壤墒情","华北平原",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[357,360],{"code":93,"slug":97,"name":358,"path":359,"parent_code":38},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":57,"slug":170,"name":171,"path":172,"parent_code":173},{"status":41,"locked":14,"version":42,"method":43,"evidence":362},{"candidates":363,"agricultural_evidence":48},[364,370,374,379],{"code":34,"score":365,"supported":48,"title":366,"abstract":367,"tags":369},19,[35],[51,52,35,368],"water resource",[29,31],{"code":93,"score":180,"supported":48,"title":371,"abstract":372,"tags":373},[97],[96,97],[353],{"code":57,"score":239,"supported":48,"title":375,"abstract":376,"tags":378},[61],[377],"估产",[30],{"code":189,"score":240,"supported":48,"title":380,"abstract":381,"tags":385},[],[382,383,384],"气候变化","农业气象","climate change",[],[387,388],"ESA CCI 土壤水分 灌溉","华北平原 土壤水分 遥感","ESACCI土壤水分灌溉-3790","10.1016\u002Fj.agwat.2026.110822",{"doi":390,"openalex_id":392,"authors":393,"venue":345,"cited_by_count":141,"oa_url":408,"card":409,"direction":148,"ingested_from":149},"W7214549221",[394,397,399,401,403,405],{"name":395,"orcid":396},"Rui Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-6114-6412",{"name":398,"orcid":9},"Yu Li",{"name":400,"orcid":9},"Jiawei Lv",{"name":402,"orcid":9},"Lei Wang",{"name":404,"orcid":9},"Zhiqing Cheng",{"name":406,"orcid":407},"Dong Wu","https:\u002F\u002Forcid.org\u002F0000-0002-3202-255X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0378377426007043\u002Fpdf",{"tldr":410,"method":411,"finding":412,"direction":148,"opportunity":413},"评估七种遥感与再分析土壤湿度产品在典型灌区的精度及对灌溉事件的响应。","用自动气象站与农业气象站两套实测网络，对比七种微波\u002F再分析产品。","ESA CCI Passive对灌溉日响应最灵敏，ERA5-Land适合连续监测，降水控制影响响应判","可针对灌溉事件构建降水控制下的阈值自适应土壤湿度响应检测方法，提升灌区遥感监测可靠性。","2026-09-29T23:30:28.424080Z",{"id":416,"title":417,"url":418,"summary":419,"summary_zh":420,"content":9,"source_name":421,"source_url":418,"published_at":422,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":423,"score_detail":424,"sources":426,"tags":428,"primary_topic":430,"secondary_topics":431,"classification":434,"search_phrases":469,"slug":472,"view_count":108,"doi":473,"paper":474,"created_at":504},1895,"Continuous monitoring of deficit irrigation in avocado across two contrasting rainfall years using sensor networks, telemetry, and machine learning","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.03.745400","Water scarcity and increasingly irregular rainfall threaten avocado production in Mediterranean regions, yet the long term physiological responses of mature trees to sustained deficit irrigation remain poorly understood. We conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping in a mature avocado orchard subjected to three irrigation regimes. The two study years differed markedly in rainfall, providing a unique opportunity to evaluate how environmental conditions modulate tree responses to water limitation. Trees under severe deficit irrigation showed depletion of water in deeper soil layers and a flattened physiological profile, with near-zero diel variation in leaf thickness and trunk water potential, indicating minimal transpiration and decoupling of tree water status from environmental demand. Drone telemetry via NDVI detected stress during fruit growth and maturation, but not during flowering or the new summer leaf flush, revealing greater drought sensitivity at later maturation stages. Although canopy area did not differ among irrigation treatments, canopy surface roughness increased significantly under deficit irrigation, thereby identifying a novel structural indicator of drought stress. Despite large physiological differences among treatments, fruit number remained stable, while fruit weight decreased significantly under severe deficit irrigation, particularly in the wetter year, suggesting that annual rainfall modulates the trade-off between fruit retention and fruit growth. This study provides the first continuous, multi-scale characterization of avocado performance under sustained deficit irrigation in Mediterranean conditions. By integrating plant-based sensors, remote sensing, and artificial intelligence, we reveal previously undescribed stress dynamics and identify new indicators for precision irrigation management in fruit crops.","水资源短缺和日益不规律的降雨威胁着地中海地区鳄梨的生产，然而，长期亏缺灌溉下成熟树木的生理响应仍知之甚少。我们开展了一项为期两年的田间研究，在成熟鳄梨园中设置了三种灌溉制度，整合了土壤-植物-大气连续体的连续监测、无人机多光谱成像、冠层结构分析以及果实表型鉴定。两个研究年份的降雨量差异显著，为评估环境条件如何调节树木对水分限制的响应提供了独特机会。严重亏缺灌溉下的树木表现出深层土壤水分耗竭和生理特征趋于平缓的现象，叶片厚度和树干水势的昼夜变化接近于零，表明蒸腾作用极低，树木水分状态与环境需求脱钩。通过NDVI进行的无人机遥测在果实生长和成熟期检测到胁迫，但在开花期或夏季新梢生长期未检测到，揭示了后期成熟阶段对干旱的敏感性更高。尽管各灌溉处理间冠层面积无显著差异，但亏缺灌溉下冠层表面粗糙度显著增加，从而识别出一种新的干旱胁迫结构指标。尽管各处理间生理差异显著，果实数量保持稳定，而严重亏缺灌溉下果实重量显著下降，尤其在较湿润的年份更为明显，表明年降雨量调节着果实保留与果实生长之间的权衡。本研究首次提供了地中海条件下持续亏缺灌溉下鳄梨表现的多尺度连续表征。通过整合基于植物的传感器、遥感技术和人工智能，我们揭示了此前未被描述的胁迫动态，并为果树精准灌溉管理识别了新指标。","bioRxiv (Cold Spring Harbor Laboratory)","2026-09-07T00:00:00Z",73,{"impact":17,"substance":238,"depth":17,"authority":239,"freshness":108,"relevant":22,"comment":425},"研究利用传感器网络与机器学习持续监测牛油果缺水胁迫，揭示新结构指标，对精准灌溉有重要参考价值。",[427],{"name":421,"url":418},[27,28,30,246,429],"牛油果",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[432,433],{"code":100,"slug":250,"name":251,"path":252,"parent_code":173},{"code":57,"slug":170,"name":171,"path":172,"parent_code":173},{"status":41,"locked":14,"version":42,"method":43,"evidence":435},{"candidates":436,"agricultural_evidence":48},[437,441,445,451,457,461,465],{"code":34,"score":17,"supported":48,"title":438,"abstract":439,"tags":440},[35],[51,52,35],[246],{"code":100,"score":239,"supported":48,"title":442,"abstract":443,"tags":444},[269],[104],[28],{"code":57,"score":13,"supported":48,"title":446,"abstract":447,"tags":450},[],[30,448,61,449],"无人机多光谱","multispectral",[30],{"code":304,"score":58,"supported":48,"title":452,"abstract":453,"tags":456},[],[454,455],"果树","orchard",[429],{"code":93,"score":65,"supported":48,"title":458,"abstract":459,"tags":460},[],[96,97],[],{"code":189,"score":65,"supported":48,"title":462,"abstract":463,"tags":464},[],[192,193],[],{"code":114,"score":22,"supported":14,"title":466,"abstract":467,"tags":468},[],[],[27],[470,471],"农业人工智能 智慧农业 精准灌溉 牛油果","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉牛油果-1895","10.64898\u002F2026.09.03.745400",{"doi":473,"openalex_id":475,"authors":476,"venue":421,"cited_by_count":141,"oa_url":498,"card":499,"direction":148,"ingested_from":149},"W7211887459",[477,480,483,486,489,492,495],{"name":478,"orcid":479},"Alberto Férez-Gómez","https:\u002F\u002Forcid.org\u002F0000-0002-8545-9237",{"name":481,"orcid":482},"Lucia Soler-Escamez","https:\u002F\u002Forcid.org\u002F0009-0004-1361-6928",{"name":484,"orcid":485},"Cristina Ferrer-Blanco","https:\u002F\u002Forcid.org\u002F0000-0002-3456-917X",{"name":487,"orcid":488},"Adrián Pérez Aguilar","https:\u002F\u002Forcid.org\u002F0000-0001-7012-1551",{"name":490,"orcid":491},"J.I. Hormaza","https:\u002F\u002Forcid.org\u002F0000-0001-5449-7444",{"name":493,"orcid":494},"Almudena Díaz Zayas","https:\u002F\u002Forcid.org\u002F0000-0002-1226-6135",{"name":496,"orcid":497},"Juan M. Losada","https:\u002F\u002Forcid.org\u002F0000-0002-7966-5018","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F09\u002F07\u002F2026.09.03.745400.full.pdf",{"tldr":500,"method":501,"finding":502,"direction":337,"opportunity":503},"通过传感器、遥感和机器学习连续监测两年不同降雨下鳄梨的亏缺灌溉响应。","集成土壤-植物-大气连续体监测、无人机多光谱、冠层分析及机器学习。","严重亏缺灌溉导致深层土壤水分耗尽、生理活动减弱，果实重量下降，冠层表面粗糙度增加。","可研究不同降雨年份下亏缺灌溉对果实品质的影响，或开发基于冠层粗糙度的干旱胁迫预警模型。","2026-09-08T23:30:14.886372Z",{"id":506,"title":507,"url":508,"summary":509,"summary_zh":510,"content":9,"source_name":511,"source_url":508,"published_at":512,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":513,"score_detail":514,"sources":516,"tags":518,"primary_topic":521,"secondary_topics":522,"classification":524,"search_phrases":554,"slug":557,"view_count":22,"doi":558,"paper":559,"created_at":571},1759,"Integrating Geospatial Information and Artificial Intelligence for Irrigation Planning in Kwali Area Council, Federal Capital Territory, Nigeria","https:\u002F\u002Fdoi.org\u002F10.70382\u002Fhujaeed.v13i4.026","Agriculture in Nigeria is increasingly threatened by climate variability, irregular rainfall, inefficient water management and land degradation, which collectively affect food security and rural livelihoods. This study evaluated irrigation suitability within the Wako Irrigation Site, Kwali Area Council, Federal Capital Territory (FCT), Nigeria, using an integrated GeoAI framework combining geospatial technology and artificial intelligence for precision irrigation planning. Environmental variables comprising slope, Soil Moisture Index (SMI), drainage density and vegetation condition represented by the Normalised Difference Vegetation Index (NDVI) were analysed and integrated using a Multilayer Perceptron Artificial Neural Network (MLP-ANN). Topographic analysis indicated that nearly level terrain dominated the study area, covering 8.72 km² (58.23%), while gentle and moderate slopes accounted for 4.68 km² (31.22%) and 1.58 km² (10.55%), respectively. Soil-moisture analysis showed that moist areas constituted the dominant class, covering 6.52 km² (43.52%), followed by wet areas at 4.96 km² (33.13%) and dry areas at 3.50 km² (23.35%). Low drainage density was the dominant drainage class, occupying 6.62 km² (44.19%), while moderate and very low drainage densities covered 4.85 km² (32.38%) and 3.51 km² (23.43%), respectively. NDVI values ranged from 0.031 to 0.473, indicating vegetation conditions ranging from no vegetation to low vegetation, with comparatively higher values observed around Dagara. The MLP-ANN model integrated the environmental variables to delineate irrigation-suitability zones. Highly irrigable areas constituted the dominant class, covering 9.91 km² (66.20%) of the 14.98 km² study area. Moderately irrigable areas occupied 3.68 km² (24.50%), while low irrigable areas accounted for 1.39 km² (9.27%). Model validation using 100 field-reference observations produced an overall accuracy of 79.00%, sensitivity of 80.00%, specificity of 76.00% and ROC–AUC of 0.780, indicating acceptable and reasonably balanced predictive performance. The findings demonstrate that GeoAI-based irrigation-suitability modelling can support evidence-based irrigation planning, improve water-use efficiency and promote climate-smart agricultural development in Nigeria.","尼日利亚农业日益受到气候变率、降雨不规律、水资源管理效率低下和土地退化的威胁，这些因素共同影响着粮食安全和农村生计。本研究采用地理空间技术与人工智能相结合的GeoAI综合框架，对尼日利亚联邦首都特区（FCT）夸利地区委员会瓦科灌溉区进行了灌溉适宜性评价，以服务于精准灌溉规划。研究分析并整合了包括坡度、土壤湿度指数（SMI）、排水密度以及以归一化植被指数（NDVI）表示的植被状况等环境变量，并采用多层感知器人工神经网络（MLP-ANN）进行集成分析。地形分析表明，研究区以近水平地形为主，面积8.72平方公里（占58.23%），缓坡和中坡分别占4.68平方公里（31.22%）和1.58平方公里（10.55%）。土壤湿度分析显示，湿润区为主导类别，面积6.52平方公里（占43.52%），其次为潮湿区4.96平方公里（33.13%）和干燥区3.50平方公里（23.35%）。低排水密度为主导排水类别，面积6.62平方公里（占44.19%），中等和极低排水密度分别覆盖4.85平方公里（32.38%）和3.51平方公里（23.43%）。NDVI值介于0.031至0.473之间，表明植被状况从无植被到低植被不等，其中达加拉周边地区观测值相对较高。MLP-ANN模型整合了各环境变量以划分灌溉适宜性分区。高度可灌区为主导类别，占14.98平方公里研究区的9.91平方公里（66.20%）。中度可灌区占3.68平方公里（24.50%），低度可灌区占1.39平方公里（9.27%）。利用100个实地参考观测点进行模型验证，总体精度为79.00%，敏感性为80.00%，特异性为76.00%，ROC–AUC为0.780，表明模型预测性能可接受且具有合理均衡性。研究结果表明，基于GeoAI的灌溉适宜性建模能够支持循证灌溉规划，提高用水效率，并促进尼日利亚气候智慧型农业发展。","Journal of Applied Ecology and Environmental Design","2026-09-05T00:00:00Z",64,{"impact":239,"substance":18,"depth":17,"authority":239,"freshness":122,"relevant":22,"comment":515},"GeoAI用于灌溉适宜性评估，方法新颖且数据详实，但地域性强，时效性低。",[517],{"name":511,"url":508},[27,28,30,519,520],"尼日利亚","灌溉规划",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[523],{"code":100,"slug":250,"name":251,"path":252,"parent_code":173},{"status":41,"locked":14,"version":42,"method":43,"evidence":525},{"candidates":526,"agricultural_evidence":48},[527,531,535,542,546,550],{"code":34,"score":19,"supported":48,"title":528,"abstract":529,"tags":530},[35],[51,52,35],[],{"code":100,"score":239,"supported":48,"title":532,"abstract":533,"tags":534},[104],[104],[28],{"code":536,"score":65,"supported":48,"title":537,"abstract":538,"tags":541},"A8",[],[539,540],"粮食安全","food security",[],{"code":93,"score":65,"supported":48,"title":543,"abstract":544,"tags":545},[],[96,97],[],{"code":114,"score":22,"supported":14,"title":547,"abstract":548,"tags":549},[],[],[27],{"code":57,"score":22,"supported":14,"title":551,"abstract":552,"tags":553},[],[],[30],[555,556],"农业人工智能 尼日利亚 智慧农业 灌溉规划","农业人工智能 尼日利亚","农业人工智能尼日利亚智慧农业灌溉规划-1759","10.70382\u002Fhujaeed.v13i4.026",{"doi":558,"openalex_id":560,"authors":561,"venue":511,"cited_by_count":141,"oa_url":508,"card":566,"direction":337,"ingested_from":149},"W7208817716",[562,564],{"name":563,"orcid":9},"Audu Matthew John",{"name":565,"orcid":9},"Nanpon Zitta",{"tldr":567,"method":568,"finding":569,"direction":148,"opportunity":570},"结合地理空间与AI评估尼日利亚Wako灌区适宜性，划分灌溉分区。","集成坡度、土壤湿度、排水密度、NDVI，用MLP-ANN建模。","高适宜区占66.2%，模型精度79%，AUC 0.78。","可扩展至其他地区，结合实时气象与土壤数据，优化模型并验证实际灌溉效益。","2026-09-06T23:30:08.695704Z",{"id":573,"title":574,"url":575,"summary":576,"summary_zh":577,"content":9,"source_name":578,"source_url":575,"published_at":235,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":579,"score_detail":580,"sources":582,"tags":584,"primary_topic":588,"secondary_topics":589,"classification":591,"search_phrases":611,"slug":614,"view_count":141,"doi":615,"paper":616,"created_at":628},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。","Zenodo (CERN European Organization for Nuclear Research)",69,{"impact":21,"substance":238,"depth":17,"authority":239,"freshness":240,"relevant":22,"comment":581},"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[583],{"name":578,"url":575},[27,28,585,586,587],"灌溉决策","安全保证","试验设计",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[590],{"code":100,"slug":250,"name":251,"path":252,"parent_code":173},{"status":41,"locked":14,"version":42,"method":43,"evidence":592},{"candidates":593,"agricultural_evidence":48},[594,598,603,607],{"code":34,"score":180,"supported":48,"title":595,"abstract":596,"tags":597},[35],[51,35],[585],{"code":100,"score":239,"supported":48,"title":599,"abstract":600,"tags":602},[104],[601],"decision support",[28],{"code":93,"score":65,"supported":48,"title":604,"abstract":605,"tags":606},[],[96,97],[],{"code":114,"score":22,"supported":14,"title":608,"abstract":609,"tags":610},[],[],[27],[612,613],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":615,"openalex_id":617,"authors":618,"venue":578,"cited_by_count":141,"oa_url":575,"card":623,"direction":146,"ingested_from":149},"W7223131205",[619,621],{"name":620,"orcid":9},"Nassima Benkhelifa",{"name":622,"orcid":9},"Yacine Aït Ahmed",{"tldr":624,"method":625,"finding":626,"direction":146,"opportunity":627},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z"]