[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3508":3,"related-3508":52},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":51},3508,"Environmental Threats to Human Security: Groundwater Collapse, Maladaptive Deepening, And Distressed Agrarian Migration in South India","https:\u002F\u002Fdoi.org\u002F10.55041\u002Fijcope.v2i9.139","Accelerated groundwater depletion and erratic monsoons across South India [IMD Official Portal] have driven a 78.4% borewell failure rate and an average fourfold increase in agrarian debt, structurally forcing a 64.2% permanent out-migration of rural families into low-wage urban informal labour sectors [UNDP Human Development Report]. Environmental degradation and climate change are no longer just ecological concerns; they have transformed into direct, systemic threats to human security. Traditional frameworks often view environmental issues through a scientific or macroeconomic lens. However, a human security paradigm shifts the focus entirely to the vulnerability of individuals and communities. As global temperatures rise, the compounding effects of severe droughts, catastrophic flooding, and accelerating biodiversity loss are actively destabilizing the foundational pillars of human survival. This paper explores the multifaceted pathways through which environmental disruption undermines human security, focusing empirically on the hard-rock aquifer zones of South India. Integrating decadal remote sensing data (2016–2026) with a field survey of \\(N = 450\\) agrarian households, this study examines how climate-induced resource scarcity triggers acute food and water insecurity, drives unprecedented patterns of forced migration, and introduces novel public health crises. Our results indicate a severe hydro-social crisis: average operational borewell depths plummeted from 450 to 1,150 feet, yielding a 78.4% primary borewell failure rate and forcing a 64.2% permanent out-migration rate to regional megacities, where 91.5% of displaced individuals were absorbed into the low-wage informal labor market. Multivariate logistic regression demonstrates that crop failure frequencies (\\(p \u003C 0.001\\)) and borewell-induced debt (\\(p \u003C 0.001\\)) serve as the strongest structural predictors of household displacement. Ultimately, this study argues that safeguarding human security in the 21st century requires a critical shift from reactive crisis management to proactive, decentralized environmental governance, crop diversification, and resilient sub-surface infrastructure development. Keywords: Human security, Environmental security, Climate vulnerability, Threat multiplier, Resource scarcity, Climate-induced migration, Groundwater governance, South India.","南印度地区加速的地下水枯竭与季风异常[IMD官方门户]已导致78.4%的钻井失败率和农业债务平均增长四倍，从结构上迫使64.2%的农村家庭永久外迁至低工资城市非正规劳动部门[UNDP人类发展报告]。环境退化与气候变化已不再仅仅是生态问题；它们已转变为对人类安全的直接、系统性威胁。传统框架往往从科学或宏观经济视角审视环境问题。然而，人类安全范式将焦点完全转向个体与社区的脆弱性。随着全球气温上升，严重干旱、灾难性洪水与加速的生物多样性丧失的复合效应正在积极动摇人类生存的基础支柱。本文探讨环境破坏削弱人类安全的多重路径，并以南印度硬岩含水层地带为实证研究重点。本研究将十年期遥感数据（2016—2026年）与对\\(N = 450\\)户农业家庭的实地调查相结合，考察气候引发的资源稀缺如何触发严重的粮食与水不安全、驱动前所未有的被迫迁移模式，并引入新型公共卫生危机。研究结果表明存在严重的水—社会危机：平均运行钻井深度从450英尺骤降至1,150英尺，导致78.4%的初级钻井失败率，并迫使64.2%的永久外迁率流向区域大城市，其中91.5%的迁移人口被低工资非正规劳动力市场吸收。多变量逻辑回归表明，作物歉收频率（\\(p \u003C 0.001\\)）和钻井引发的债务（\\(p \u003C 0.001\\)）是家庭迁移最强的结构性预测因子。最终，本研究认为，保障21世纪的人类安全需要从被动危机管理向主动、分散化的环境治理、作物多样化及具有韧性的地下基础设施建设进行关键性转变。关键词：人类安全，环境安全，气候脆弱性，威胁倍增器，资源稀缺，气候诱发迁移，地下水治理，南印度。",null,"International Journal of Creative and Open Research in Engineering and Management","2026-09-22T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,6,9,1,"以十年遥感与450户实地调查量化地下水枯竭驱动的农业移民，数据扎实但期刊权威性偏低，可作为国际农业资源安全参考。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","地下水治理","气候移民","农村水资源","小农生计",[32,33],"南印度 地下水 农业移民","硬岩含水层 井灌 农户","南印度地下水农业移民-3508",0,"10.55041\u002Fijcope.v2i9.139",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":42,"card":43,"direction":49,"ingested_from":50},"W7213966502",[40],{"name":41,"orcid":9},"Dr. Lalith Kumar Dharavath","https:\u002F\u002Fijcope.org\u002Fwp-content\u002Fuploads\u002F2026\u002F09\u002FEnvironmental-threats-to-human-security-Groundwater-Collapse-maladaptive-deepening-and-distressed-agrarian-migration-in-south-India.pdf",{"tldr":44,"method":45,"finding":46,"direction":47,"opportunity":48},"研究印度南部地下水枯竭如何通过农业失败与债务迫使农民永久迁入城市非正规部门。","整合2016–2026年遥感数据与450户农户调查，用多元逻辑回归分析。","井深从450英尺降至1150英尺，78.4%水井失效，64.2%家庭永久外迁，91.5%进入低薪非正","数字乡村与农业信息化","可结合遥感与农户调查构建地下水—债务—迁移预警模型，探索分散式水治理与作物多样化干预。","农业遥感与作物表型","openalex","2026-09-25T23:30:34.009974Z",{"total":19,"page":21,"page_size":19,"items":53},[54,119,157,213,263,287],{"id":55,"title":56,"url":57,"summary":58,"summary_zh":59,"content":9,"source_name":60,"source_url":57,"published_at":61,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":62,"score_detail":63,"sources":67,"tags":69,"search_phrases":74,"slug":77,"view_count":35,"doi":78,"paper":79,"created_at":118},3507,"OCO‐3 Meets ECOSTRESS: Insights Into Ecosystem Diurnal Water‐Use Efficiency From Co‐Located Solar‐Induced Fluorescence and Thermal Observations","https:\u002F\u002Fdoi.org\u002F10.1029\u002F2026gl124105","Abstract Terrestrial ecosystem regulation of carbon and water fluxes is critical for constraining climate–biosphere feedbacks but remains poorly quantified across space and time. Here, we evaluate whether co‐located observations from NASA's Orbiting Carbon Observatory‐3 (OCO‐3) and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can reproduce ecosystem water use efficiency (WUE) dynamics observed at FLUXNET sites across temporal scales, vegetation types, climates, and drought conditions. We created the ECOCO3 data set, which harmonizes OCO‐3 and ECOSTRESS observations in space and time. ECOCO3 captures broad seasonal and diurnal carbon and water flux patterns including midday drought responses. Sampling sensitivity analysis shows that ECOCO3 is primarily limited by available sample size for distinguishing vegetation and climate driven differences in WUE. Our findings highlight both the promise and limitations of remote sensing for resolving sub‐daily carbon–water coupling.","陆地生态系统对碳通量和水通量的调节对于约束气候–生物圈反馈至关重要，但在空间和时间尺度上仍缺乏充分的量化。在此，我们评估了来自NASA轨道碳观测站-3（OCO-3）和空间站生态系统星载热辐射计实验（ECOSTRESS）的同位观测能否在时间尺度、植被类型、气候条件和干旱状况下重现FLUXNET站点观测到的生态系统水分利用效率（WUE）动态。我们创建了ECOCO3数据集，该数据集在空间和时间上协调了OCO-3和ECOSTRESS的观测。ECOCO3能够捕捉广泛的季节性和日间碳通量与水通量模式，包括正午干旱响应。采样敏感性分析表明，ECOCO3主要受限于可用样本量，难以区分植被和气候驱动的WUE差异。我们的研究结果既凸显了遥感在解析亚日尺度碳–水耦合方面的前景，也揭示了其局限性。","Geophysical Research Letters","2026-09-23T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":64,"freshness":65,"relevant":21,"comment":66},15,8,"NASA两颗卫星协同观测提升生态系统碳水耦合监测能力，方法新颖、数据可靠，对农业遥感与水资源管理有参考价值。",[68],{"name":60,"url":57},[26,70,71,72,73],"遥感","水资源利用","生态监测","碳汇",[75,76],"OCO-3 ECOSTRESS 水分利用效率","ECOCO3 数据集 碳水通量","OCO-3ECOSTRESS水分利用效率-3507","10.1029\u002F2026gl124105",{"doi":78,"openalex_id":80,"authors":81,"venue":60,"cited_by_count":35,"oa_url":112,"card":113,"direction":49,"ingested_from":50},"W7214122316",[82,85,88,91,94,97,100,103,106,109],{"name":83,"orcid":84},"Zoe Pierrat","https:\u002F\u002Forcid.org\u002F0000-0002-6726-2406",{"name":86,"orcid":87},"Thomas P. Kurosu","https:\u002F\u002Forcid.org\u002F0000-0003-2555-7780",{"name":89,"orcid":90},"Abhishek Chatterjee","https:\u002F\u002Forcid.org\u002F0000-0002-3680-0160",{"name":92,"orcid":93},"Joshua B. Fisher","https:\u002F\u002Forcid.org\u002F0000-0003-4734-9085",{"name":95,"orcid":96},"Margaret C. Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1481-9706",{"name":98,"orcid":99},"Le Kuai","https:\u002F\u002Forcid.org\u002F0000-0001-6406-1150",{"name":101,"orcid":102},"Kaniska Mallick","https:\u002F\u002Forcid.org\u002F0000-0002-2735-930X",{"name":104,"orcid":105},"Nicholas Cody Parazoo","https:\u002F\u002Forcid.org\u002F0000-0002-4424-7780",{"name":107,"orcid":108},"Benjamin C. Wiebe","https:\u002F\u002Forcid.org\u002F0000-0002-9325-1540",{"name":110,"orcid":111},"Kerry A. Cawse-Nicholson","https:\u002F\u002Forcid.org\u002F0000-0002-0510-4066","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1029\u002F2026GL124105",{"tldr":114,"method":115,"finding":116,"direction":49,"opportunity":117},"融合OCO-3与ECOSTRESS观测评估生态系统日间水分利用效率动态。","构建ECOCO3数据集，结合FLUXNET站点验证与采样敏感性分析。","ECOCO3能捕捉季节与日间碳-水通量模式，但样本量限制其区分植被与气候差异。","可探索多源遥感融合提升亚日尺度碳水耦合估算精度，并扩展至农田生态系统。","2026-09-25T23:30:33.881463Z",{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":126,"category":12,"cover_url":9,"hotness":127,"is_selected":14,"score":128,"score_detail":129,"sources":132,"tags":136,"search_phrases":141,"slug":144,"view_count":35,"doi":145,"paper":146,"created_at":156},3498,"DRON VA MASOFADAN MONITORING TEXNOLOGIYALARIGA OID AGRAR TERMINLARNING INGLIZ TILIDAN O'ZBEK TILIGA KIRIB KELISH MEXANIZMLARI","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22942951","Ushbu maqolada dron va masofadan monitoring texnologiyalari bilan bog‘liq zamonaviy agrar terminlarning ingliz tilidan o‘zbek tiliga kirib kelish mexanizmlari leksik-semantik va tarjimashunoslik nuqtayi nazaridan tahlil qilinadi. Tadqiqot materiali sifatida precision agriculture, drone, unmanned aerial vehicle, remote sensing, crop monitoring, multispectral imaging, hyperspectral imaging, thermal imaging, vegetation index, orthomosaic, georeferencing, payload, flight planning, variable-rate application singari xalqaro ilmiy-texnik birliklar va ularning o‘zbekcha qo‘llanish shakllari tanlandi. Tahlil natijasida o‘zlashishning besh asosiy modeli: bevosita fonetik-grafik o‘zlashtirish, kalkalash, tavsifiy-ekvivalent tarjima, qisqartmalarni saqlash hamda gibrid termin hosil qilish faol ekanligi aniqlandi. Agrar raqamlashtirish tezlashgani sari inglizcha terminlarning o‘zbek tiliga kirib kelishi faqat lug‘aviy jarayon emas, balki texnologik transfer, professional kommunikatsiya va terminologik me’yorlash bilan uzviy bog‘liq jarayon sifatida namoyon bo‘lmoqda. Maqolada variantdorlikni kamaytirish, o‘zbek tilining so‘z yasash imkoniyatlaridan oqilona foydalanish va xalqaro qisqartmalar bilan milliy ekvivalentlar o‘rtasida funksional muvozanatni ta’minlash bo‘yicha takliflar ilgari suriladi.","本文从词汇语义学和翻译学的角度，分析了与无人机和遥感监测技术相关的现代涉农术语从英语进入乌兹别克语的机制。研究以precision agriculture（精准农业）、drone（无人机）、unmanned aerial vehicle（无人驾驶飞行器）、remote sensing（遥感）、crop monitoring（作物监测）、multispectral imaging（多光谱成像）、hyperspectral imaging（高光谱成像）、thermal imaging（热成像）、vegetation index（植被指数）、orthomosaic（正射影像图）、georeferencing（地理配准）、payload（有效载荷）、flight planning（飞行规划）、variable-rate application（变量施用）等国际科技统一术语及其乌兹别克语使用形式为材料。分析结果表明，借入的五种主要模式较为活跃：直接语音—字形借入、仿译、描述—等值翻译、保留缩略语以及混合术语构词。随着农业数字化加速，英语术语进入乌兹别克语不仅是一个词汇过程，而且表现为与工艺转移、专业交流和术语规范化密切相关的过程。文中就减少变体现象、合理利用乌兹别克语的构词能力以及在国标缩略语与民族语对应词之间保持功能平衡提出了建议。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-24T00:00:00Z",25,62,{"impact":65,"substance":17,"depth":64,"authority":130,"freshness":20,"relevant":21,"comment":131},12,"论文系统分析无人机与遥感农业术语从英语进入乌兹别克语的五种借入机制，属农业信息化语言标准化研究，专业性强但影响面偏窄。",[133,134],{"name":125,"url":122},{"name":125,"url":135},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22942950",[137,138,26,139,140],"智慧农业","无人机","农业术语","乌兹别克语",[142,143],"乌兹别克语 农业术语 农业遥感 智慧农业","乌兹别克语 农业术语","乌兹别克语农业术语农业遥感智慧农业-3498","10.5281\u002Fzenodo.22942951",{"doi":145,"openalex_id":147,"authors":148,"venue":125,"cited_by_count":35,"oa_url":122,"card":151,"direction":49,"ingested_from":50},"W7214177750",[149],{"name":150,"orcid":9},"Qodirova Gulbahor Turdiyevna",{"tldr":152,"method":153,"finding":154,"direction":47,"opportunity":155},"分析无人机与遥感农业术语从英语借入乌兹别克语的五种机制。","选取精准农业等15个术语，做词汇语义与翻译学分析。","借入机制含音译、仿译、描述翻译、缩写保留和混合构词五类。","可构建多语言农业术语库，研究术语标准化对技术扩散的影响。","2026-09-25T23:30:30.787630Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":9,"source_name":163,"source_url":160,"published_at":61,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":164,"score_detail":165,"sources":168,"tags":170,"search_phrases":175,"slug":178,"view_count":35,"doi":179,"paper":180,"created_at":212},3370,"A National Depth-Resolved Soil-Moisture-to-Electromagnetic Proxy Database for Hydrogeophysical Monitoring","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-2026-563","Abstract. Soil moisture is a key control on drought, recharge, crop water availability, agricultural water management, and land-atmosphere exchange, but its depth-resolved spatial variability remains difficult to monitor over large areas. Non-invasive methods such as ground-penetrating radar (GPR), electrical resistivity, and electromagnetic surveys can support soil-moisture estimation, yet their interpretation depends strongly on soil texture and hydraulic-retention properties. This study develops a national six-depth soil-moisture-to-electromagnetic proxy database to support future GPR- and resistivity-based root-zone monitoring and decision support. We used multi-year soil-moisture, porosity, saturation, texture, and hydraulic-retention data from 117 monitoring stations across Hungary at 10, 20, 30, 45, 60, and 75 cm depth. Dielectric permittivity, EM-wave velocity, electrical conductivity, attenuation, and apparent resistivity were derived as proxy variables from observed soil moisture using established petrophysical relationships, including the Topp equation and an Archie-type formulation. The proxy database identified a consistent 30-45 cm buffering zone, where soil moisture increased to 22.25%, dielectric permittivity peaked at 12.29, EM-wave velocity reached a minimum of 0.0935 m ns⁻¹, and apparent resistivity decreased to 545 Ω m. Texture strongly shaped the translated EM response: sandy soils were drier and more resistive, whereas clayey soils retained more water and showed higher dielectric response. Hydraulic-retention variables provided calibration-relevant prior information for explaining why similar EM or resistivity signals may represent different true soil-moisture values across soil types. The framework provides a transferable national calibration layer for drought-monitoring agencies, irrigation planners, precision-agriculture users, and future drone-based geophysical surveys in regions with comparable monitoring and soil databases.","摘要：土壤水分是干旱、补给、作物可用水量、农业水资源管理和陆气交换的关键控制因素，但其深度分辨的空间变异性在大范围区域仍难以监测。探地雷达（GPR）、电阻率和电磁勘探等非侵入式方法可支持土壤水分估算，但其解译在很大程度上取决于土壤质地和水力保持特性。本研究构建了一个全国尺度的六深度土壤水分—电磁代用指标数据库，以支持未来基于GPR和电阻率的根区监测与决策支持。我们使用了匈牙利全国117个监测站多年在10、20、30、45、60和75 cm深度的土壤水分、孔隙度、饱和度、质地和水力保持数据。基于观测土壤水分，利用成熟的地球物理关系（包括Topp方程和Archie型公式），推导出介电常数、电磁波速度、电导率、衰减和视电阻率作为代用变量。该代用指标数据库识别出一致的30–45 cm缓冲带，其中土壤水分增至22.25%，介电常数峰值达12.29，电磁波速度最低为0.0935 m ns⁻¹，视电阻率降至545 Ω m。质地对转换后的电磁响应具有强烈影响：砂质土壤更干、电阻率更高，而黏质土壤持水更多并表现出更高的介电响应。水力保持变量为解释为何相似的电磁或电阻率信号在不同土壤类型中可能代表不同真实土壤水分值提供了与校准相关的先验信息。该框架为干旱监测机构、灌溉规划者、精准农业用户以及未来在具有可比监测和土壤数据库区域开展的无人机地球物理调查提供了一个可迁移的全国校准层。","Earth System Science Data Discussions",80,{"impact":17,"substance":18,"depth":17,"authority":166,"freshness":20,"relevant":21,"comment":167},13,"基于匈牙利117个站点六层深度构建的土壤水分—电磁代理数据库，为根区墒情遥感与精准灌溉决策提供可迁移校准层，方法新颖、数据扎实，对农业信息化有参考价值。",[169],{"name":163,"url":160},[26,171,172,173,174],"精准农业","遥感监测","土壤墒情","智慧灌溉",[176,177],"匈牙利 土壤水分 电磁代理数据库","探地雷达 根区土壤水分 监测","匈牙利土壤水分电磁代理数据库-3370","10.5194\u002Fessd-2026-563",{"doi":179,"openalex_id":181,"authors":182,"venue":163,"cited_by_count":35,"oa_url":160,"card":206,"direction":211,"ingested_from":50},"W7214076150",[183,186,188,190,193,195,197,200,203],{"name":184,"orcid":185},"Diaa Sheishah","https:\u002F\u002Forcid.org\u002F0000-0003-2050-6474",{"name":187,"orcid":9},"Enas Abdelsamei",{"name":189,"orcid":9},"Viktoria Blanka-Vegi",{"name":191,"orcid":192},"Károly Barta","https:\u002F\u002Forcid.org\u002F0000-0001-9450-5277",{"name":194,"orcid":9},"Ahmed M. Ali",{"name":196,"orcid":9},"Khaldoun Abualhin",{"name":198,"orcid":199},"Djamil Al‐Halbouni","https:\u002F\u002Forcid.org\u002F0000-0003-2254-3914",{"name":201,"orcid":202},"Wouter Arnoud Dorigo","https:\u002F\u002Forcid.org\u002F0000-0001-8054-7572",{"name":204,"orcid":205},"György Sípos","https:\u002F\u002Forcid.org\u002F0000-0001-6224-2361",{"tldr":207,"method":208,"finding":209,"direction":49,"opportunity":210},"构建匈牙利全国六深度土壤水分-电磁代理数据库，支持根区水文地球物理监测。","利用117站多年土壤水分、质地与水力参数，经Topp和Archie模型推导电磁代","发现30-45 cm缓冲带水分与介电常数峰值，质地显著影响电磁响应解释。","可扩展至无人机地球物理调查与多区域迁移校准，解决土壤质地导致的电磁信号歧义。","农业人工智能与决策模型","2026-09-24T23:30:36.864649Z",{"id":214,"title":215,"url":216,"summary":217,"summary_zh":218,"content":9,"source_name":219,"source_url":216,"published_at":61,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":62,"score_detail":220,"sources":223,"tags":225,"search_phrases":229,"slug":232,"view_count":35,"doi":233,"paper":234,"created_at":262},3350,"Precision agriculture for water saving: The case of processing tomato and table grape","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppp3.70253","Societal Impact Statement Agriculture faces increasing pressure to produce high‐quality food while reducing water use under intensifying climate change and water scarcity. This study compared sensor‐based precision irrigation, drone monitoring, and near‐infrared spectroscopy with conventional farmer management in table grape and processing tomato production systems. Precision agriculture improved crop water status, photosynthetic performance, yield, and marketable quality, while reducing irrigation by 8%–15% in table grape and approximately 15% in processing tomato. These findings support wider adoption of integrated digital tools through targeted incentives and farmer training, offering a scalable strategy to strengthen long‐term water security, farm resilience, and sustainable food production globally. Summary The study aimed to evaluate the effectiveness of precision agriculture (PA) technologies, specifically sensor‐based irrigation and drones, on table grape (cv. Allison ) and processing tomato (cv. Taylor ) production compared to traditional farming methods (control). The research involved field experiments in two locations in the Puglia region, southeastern Italy, in 2023 and 2024. For table grape and processing tomato, two different irrigation managements (PA vs. Control\u002FFarmer) were compared, monitoring physiological, morphological, yield, and quality parameters. For processing tomato, drone imagery, and ground measurements were also conducted. Predictive models for fruit ripeness and quality traits of both species were also developed using near‐infrared (NIR) spectroscopy data, preprocessing techniques, and PLS regression. For table grape, the PA vines showed greater water potential stability, more uniform stomatal conductance, and higher chlorophyll content, resulting in higher and more consistent production with 8%–15% water savings. For processing tomato, PA management improved plant vegetative indicators, total and marketable yields, and reduced water consumption by approximately 15%. Three out of four calibrated models using NIR showed predictive performance suitable for future practical applications. The findings highlight the potential of PA to improve water resource utilization, crop development, yield, and fruit quality, contributing to more sustainable agricultural systems in regions facing water scarcity and climate change. Moreover, this work demonstrates how sensor‐driven irrigation of the two crops (with more equilibrated plants) directly influenced high‐accuracy predictive modelling. The integration of environmental sensors (for irrigation) and optical sensors (for quality) will represent the core of modern smart farming.","社会影响声明 在气候变化加剧和水资源短缺的背景下，农业面临着在生产高质量食品的同时减少用水量的日益增大的压力。本研究在鲜食葡萄和加工番茄生产系统中，将基于传感器的精准灌溉、无人机监测和近红外光谱技术与传统农户管理进行了比较。精准农业改善了作物水分状况、光合性能、产量和商品品质，同时在鲜食葡萄中减少了8%–15%的灌溉用水，在加工番茄中减少了约15%。这些发现支持通过有针对性的激励措施和农户培训来更广泛地采用集成数字工具，为增强全球长期水资源安全、农场韧性和可持续食品生产提供了一种可推广的策略。摘要 本研究旨在评估精准农业（PA）技术，特别是基于传感器的灌溉和无人机，在鲜食葡萄（品种Allison）和加工番茄（品种Taylor）生产中相较于传统耕作方法（对照）的有效性。研究于2023年和2024年在意大利东南部普利亚地区的两个地点进行了田间试验。对于鲜食葡萄和加工番茄，比较了两种不同的灌溉管理方式（PA vs. 对照\u002F农户），监测了生理、形态、产量和品质参数。对于加工番茄，还进行了无人机影像采集和地面测量。研究还利用近红外（NIR）光谱数据、预处理技术和PLS回归，开发了两种作物果实成熟度和品质性状的预测模型。对于鲜食葡萄，PA处理的葡萄藤表现出更强的水势稳定性、更均匀的气孔导度和更高的叶绿素含量，从而实现了更高且更稳定的产量，并节约了8%–15%的用水。对于加工番茄，PA管理改善了植株营养指标、总产量和商品产量，并减少了约15%的耗水量。使用NIR校准的四个模型中有三个显示出适合未来实际应用的预测性能。研究结果凸显了PA在改善水资源利用、作物发育、产量和果实品质方面的潜力，有助于在水资源短缺和气候变化地区构建更可持续的农业系统。此外，本研究还表明，两种作物的传感器驱动灌溉（使植株更加均衡）……","Plants People Planet",{"impact":17,"substance":18,"depth":17,"authority":221,"freshness":20,"relevant":21,"comment":222},14,"意大利田间试验证实传感器与无人机精准灌溉可节水8%–15%并提升产量品质，对缺水地区智慧农业推广有实证参考价值。",[224],{"name":219,"url":216},[137,26,226,227,228],"精准灌溉","节水农业","近红外光谱",[230,231],"加工番茄 精准灌溉 节水","鲜食葡萄 无人机 灌溉","加工番茄精准灌溉节水-3350","10.1002\u002Fppp3.70253",{"doi":233,"openalex_id":235,"authors":236,"venue":219,"cited_by_count":35,"oa_url":216,"card":256,"direction":260,"ingested_from":50},"W7214079746",[237,240,243,245,247,250,253],{"name":238,"orcid":239},"Giuseppe Ferrara","https:\u002F\u002Forcid.org\u002F0000-0002-2129-6723",{"name":241,"orcid":242},"Alessandro Pesole","https:\u002F\u002Forcid.org\u002F0009-0007-1292-4517",{"name":244,"orcid":9},"Rita De Marco",{"name":246,"orcid":9},"Sara Bisceglie",{"name":248,"orcid":249},"Giovanni Popeo","https:\u002F\u002Forcid.org\u002F0009-0003-6796-3880",{"name":251,"orcid":252},"Simone Pascuzzi","https:\u002F\u002Forcid.org\u002F0000-0002-6699-3485",{"name":254,"orcid":255},"Luigi Tedone","https:\u002F\u002Forcid.org\u002F0000-0003-4398-3820",{"tldr":257,"method":258,"finding":259,"direction":260,"opportunity":261},"对比传感器精准灌溉、无人机与近红外光谱和传统管理在葡萄与番茄上的节水增产效果。","意大利普利亚两年田间试验，传感器灌溉、无人机监测与NIR光谱PLS建模。","精准农业改善水分与光合状态，葡萄节水8%–15%、番茄约15%，并提高产量与品质。","智慧农业 \u002F 农业物联网","可探索多源传感与NIR模型跨品种跨区域迁移，并量化农户采纳激励与培训的长期节水效益。","2026-09-24T23:30:10.070697Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":9,"content":268,"source_name":269,"source_url":9,"published_at":270,"category":271,"cover_url":9,"hotness":13,"is_selected":14,"score":272,"score_detail":273,"sources":275,"tags":277,"search_phrases":282,"slug":285,"view_count":35,"doi":9,"paper":9,"created_at":286},3297,"山区油菜智能化监测成果在十堰国家农业科技园区落地推广——天空地一体化监测技术与山区油菜大数据平台","https:\u002F\u002Fwww.iarrp.cn\u002Fysdt\u002Fkydt\u002Fd6e5d598bf9f4f998f7f08d7c811699c.htm","近日，由中国农业科学院农业资源与农业区划研究所牵头实施的国家重点研发计划课题山区油菜产业智能化关键技术和机械装备研究示范成果应用推广会在十堰国家农业科技园区召开。项目首席科学家梅德圣研究员及行业专家，十堰市农技中心及下属各县农技中心、长阳县等秦巴山区县基层农技推广部门、区域内油菜种植大户代表参会。课题组精准锚定秦巴山区油菜生产实际需求，构建天空地一体化监测技术与山区油菜大数据平台，能够对区域气候环境、油菜长势、菌核病发生态势开展动态监测与智能研判；研发适配山地小地块、不规则地块的油菜播种、田间管护、无人植保等专用系统装备。","近日，由中国农业科学院农业资源与农业区划研究所牵头实施的国家重点研发计划课题“山区油菜产业智能化关键技术和机械装备研究示范”成果应用推广会在十堰国家农业科技园区顺利召开。本次会议聚焦山区油菜产业智能化转型痛点难点，集中交付项目核心科技成果，开展大数据平台实操培训，搭建成果落地、技术普及、产业赋能的产学研交流平台，助力秦巴山区油菜产业摆脱传统生产模式，全面迈向数字化、智能化、高效化高质量发展新阶段。项目首席科学家梅德圣研究员等行业专家，十堰市农技中心及下属各县农技中心、长阳县等秦巴山市区县基层农技推广部门，区域内油菜种植大户用户代表参会参训。\n\n我国秦巴等山区油菜种植体量较大，是保障区域油料供给、带动农民增收、助推乡村产业振兴的特色优势产业。但受山地、坡地为主的特殊地形制约，区域内耕地零散、地块不规则，长期存在机械化作业难、生产管理粗放、长势监测滞后、病虫害防控被动、生产数据缺失等突出问题，生产成本偏高、产量稳定性不足，严重制约山区油菜产业提质增效与绿色可持续发展。课题组精准锚定秦巴山区油菜生产实际需求，依托高水平产学研协同攻关体系，针对性开展智能技术改良、专用装备适配、数字体系构建与本地化示范推广，形成一套适配山地生产场景的成熟技术成果与应用模式。\n\n会上，课题组详细介绍了课题研发成果、技术创新亮点与本地化示范成效，顺利完成系列核心成果集中交付。中国农业科学院农业资源与农业区划研究所宋茜研究员表示，课题创新构建天空地一体化监测技术与山区油菜大数据平台，能够对区域气候环境、油菜长势、菌核病发生态势开展动态监测与智能研判，可为田间科学施肥、绿色防控、适时收获、精准植保提供精准数据支撑，改变山区油菜传统凭经验、粗放式的生产管理模式。同时，课题研发适配山地小地块、不规则地块的油菜播种、田间管护、无人植保等专用系统装备，有效破解山地农机作业难度大、精度低、损耗高的产业难题，全面提升山区油菜机械化、标准化作业水平。\n\n为切实打通科技成果落地“最后一公里”，会议同步开展山区油菜大数据平台专项实操培训。项目技术专家围绕平台功能架构、操作流程、数据填报、智能分析、后台运维等核心内容开展系统讲解，结合秦巴山区油菜生产真实场景进行实操演示，现场解答基层农技人员、种植大户在平台使用、技术落地、田间实操中的各类问题。培训内容贴合一线生产需求、实操性极强，有效提升了基层技术人员数字化应用能力，为成果规模化落地推广夯实人才基础。\n\n据了解，课题以十堰柳陂镇示范基地为核心辐射支点，持续拓展示范应用版图，辐射带动五峰、安阳、大柳等多个乡镇开展规模化示范种植，常态化开展技术培训、智能设备实操演示、田间观摩交流、入户指导等配套服务，不断优化完善技术应用模式，成功探索形成一套适配秦巴山区、可复制、可推广的智慧油菜绿色高效生产模式。通过智能化技术与装备的落地应用，有效实现油菜稳产提质、生产降本增效，显著提升山区油菜产业标准化、智能化、现代化发展水平。\n\n与会代表一致认为，该课题成果针对性解决了山区油菜生产的系列瓶颈问题，构建了适配山地特征的智慧油菜生产体系，为山区油料产业高质量发展提供了优秀示范样板。下一步，课题各方将持续深化产学研协同合作，依托十堰国家农业科技园区平台优势，持续迭代优化大数据平台功能与智能装备性能，持续扩大示范推广覆盖面，常态化开展基层技术赋能服务，全力推动智慧油菜生产技术在秦巴武陵山区落地普及、提质增效，持续助力区域油料稳产保供、农业绿色发展、农民增收致富与乡村全面振兴。","中国农业科学院农业资源与农业区划研究所 2026年09月18日","2026-09-18T00:00:00Z","报道",70,{"impact":17,"substance":17,"depth":221,"authority":221,"freshness":19,"relevant":21,"comment":274},"国家重点研发计划课题成果在十堰国家农业科技园区落地推广，天空地一体化监测与山区油菜大数据平台具备实质技术内容与示范价值，值得进入每日精选。",[276],{"name":269,"url":266},[137,26,278,279,280,281],"成果转化","油菜","天空地一体化","大数据平台",[283,284],"十堰 山区油菜 智能化监测","天空地一体化 油菜大数据平台","十堰山区油菜智能化监测-3297","2026-09-24T00:03:59.027363Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":9,"source_name":293,"source_url":290,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":294,"score_detail":295,"sources":298,"tags":300,"search_phrases":304,"slug":307,"view_count":35,"doi":308,"paper":309,"created_at":350},3278,"Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-15959-x","Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring。Environmental Monitoring and Assessment","基于地物高光谱数据与机器学习的稻叶铅含量反演最优建模路径及跨尺度遥感监测研究。环境监测与评估","Environmental Monitoring and Assessment",64,{"impact":65,"substance":17,"depth":296,"authority":166,"freshness":20,"relevant":21,"comment":297},16,"基于地物高光谱与机器学习的水稻叶片铅含量反演建模研究，方法有创新但属细分领域学术进展，公共影响有限。",[299],{"name":293,"url":290},[137,26,301,302,303],"水稻","机器学习","高光谱遥感",[305,306],"水稻叶片 铅含量 高光谱 反演","稻米 重金属 遥感 监测","水稻叶片铅含量高光谱反演-3278","10.1007\u002Fs10661-026-15959-x",{"doi":308,"openalex_id":310,"authors":311,"venue":293,"cited_by_count":35,"oa_url":9,"card":345,"direction":49,"ingested_from":50},"W7214027889",[312,315,318,321,324,327,329,331,334,337,339,341,343],{"name":313,"orcid":314},"Zhenlong Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-2354-9123",{"name":316,"orcid":317},"Zhe Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1266-7251",{"name":319,"orcid":320},"Chengxia Wang","https:\u002F\u002Forcid.org\u002F0009-0001-0820-2462",{"name":322,"orcid":323},"Wenxue Lin","https:\u002F\u002Forcid.org\u002F0000-0002-8245-9063",{"name":325,"orcid":326},"Jingyan Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-4567-5316",{"name":328,"orcid":9},"Ying Luo",{"name":330,"orcid":9},"Jiaqian Zhang",{"name":332,"orcid":333},"Kai Ye","https:\u002F\u002Forcid.org\u002F0000-0002-2851-6741",{"name":335,"orcid":336},"Yiming Chen","https:\u002F\u002Forcid.org\u002F0000-0002-8121-3109",{"name":338,"orcid":9},"Chaoliang Peng",{"name":340,"orcid":9},"Duan Tian",{"name":342,"orcid":9},"Weihao Wang",{"name":344,"orcid":9},"Jiaxin Liu",{"tldr":346,"method":347,"finding":348,"direction":49,"opportunity":349},"研究基于地面高光谱与机器学习反演水稻叶片铅含量，并探索跨尺度遥感监测的最优建模路径。","地面高光谱数据结合机器学习建模，开展跨尺度遥感监测。","明确了水稻叶片铅含量反演的最优建模路径，实现跨尺度遥感监测。","可探索多尺度遥感数据融合与迁移学习，提升重金属胁迫反演的普适性与精度。","2026-09-23T23:30:19.291361Z"]