[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3761":3,"related-3761":77},{"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,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":50,"slug":53,"view_count":54,"doi":55,"paper":56,"created_at":76},3761,"Multidimensional climate physical risk and reported farmland abandonment among rural farm households in China: evidence from household panel data","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1948860","Introduction For family-operated farms, keeping contracted land in cultivation is one practical dimension of resilience to climate stress. This study asks whether multidimensional climate physical risk is associated with reported farmland abandonment among rural households in China. Methods We matched the 2014, 2016, and 2018 China Labor-force Dynamics Survey (CLDS) waves to province-level Climate Physical Risk Index (CPRI) values from the preceding agricultural years. Abandonment is coded from a household report that some contracted cropland was uncultivated during the reference period; the survey does not show whether that status was temporary or persistent or the reason for it. The resulting unbalanced panel contains 19,311 household-year observations for 11,982 households in 27 provincial-level units. Household fixed-effects linear probability models include survey-wave fixed effects, province-clustered standard errors, and Webb-weight wild cluster bootstrap inference. Results A one-standard-deviation within-province increase in CPRI is associated with a 3.02-percentage-point increase in reported abandonment, approximately one-fifth of the sample mean. The association remains positive under alternative outcomes, exposure dates, sample restrictions, and model choices. The four hazards are jointly significant; only extreme low temperature is individually significant in the exploratory decomposition. Discussion Although the estimates are observational rather than causal, they suggest that multidimensional climate exposure may weaken land-use continuity in household farming systems.","引言 对于家庭经营的农场而言，保持承包地处于耕作状态是应对气候压力的一种实际韧性维度。本研究旨在探讨多维气候物理风险是否与中国农村家庭报告的耕地撂荒存在关联。方法 我们将2014年、2016年和2018年中国劳动力动态调查（CLDS）数据与前一农业年份的省级气候物理风险指数（CPRI）值进行匹配。撂荒的编码依据是家庭报告在参考期内部分承包耕地未进行耕作；该调查并未显示该状态是暂时性的还是持续性的，也未显示其原因。由此得到的非平衡面板包含27个省级单位中11,982个家庭的19,311个家庭-年份观测值。家庭固定效应线性概率模型纳入了调查波次固定效应、省级聚类标准误以及Webb权重野生聚类自举推断。结果 省内CPRI每增加一个标准差，与报告撂荒增加3.02个百分点相关，约为样本均值的五分之一。在替代结果变量、暴露日期、样本限制和模型选择下，该关联仍为正向。四种灾害联合显著；在探索性分解中，仅极端低温单独显著。讨论 尽管这些估计是观察性的而非因果性的，但它们表明多维气候暴露可能削弱家庭农业系统中土地利用的连续性。",null,"Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于CLDS三期面板与省级气候风险指数，量化气候物理风险与农户耕地撂荒的关联，方法规范、数据规模大，对农业风险管理有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"气候变化","农业风险","农户生计","耕地撂荒","CLDS",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"unclassified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":48,"reason":49},[38,43],{"code":39,"score":21,"supported":14,"title":40,"abstract":41,"tags":42},"G6",[],[],[28],{"code":44,"score":21,"supported":14,"title":45,"abstract":46,"tags":47},"H7",[],[],[26],true,"缺少足够的主题证据",[51,52],"中国劳动力动态调查 耕地撂荒","气候物理风险 农户 撂荒","中国劳动力动态调查耕地撂荒-3761",6,"10.3389\u002Ffsufs.2026.1948860",{"doi":55,"openalex_id":57,"authors":58,"venue":10,"cited_by_count":68,"oa_url":6,"card":69,"direction":73,"ingested_from":75},"W7214624950",[59,62,64,66],{"name":60,"orcid":61},"Ruofan Liao","https:\u002F\u002Forcid.org\u002F0009-0009-5058-7368",{"name":63,"orcid":9},"Kexin Zhang",{"name":65,"orcid":9},"Mi Tan",{"name":67,"orcid":9},"Jianxu Liu",0,{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"研究中国农户多维气候物理风险与报告耕地撂荒的关联。","匹配CLDS 2014-2018面板与省级气候物理风险指数，用固定效应模型。","省内CPRI每增一个标准差，报告撂荒率上升3.02个百分点。","农业绿色发展与碳","可结合遥感验证撂荒、区分暂时\u002F持续撂荒，并探究极端低温等具体致灾机制。","openalex","2026-09-29T23:30:07.148843Z",{"total":54,"page":21,"page_size":54,"items":78},[79,128,194,279,333,402],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":84,"content":9,"source_name":10,"source_url":82,"published_at":85,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":86,"score_detail":87,"sources":91,"tags":93,"primary_topic":9,"secondary_topics":96,"classification":97,"search_phrases":109,"slug":112,"view_count":21,"doi":113,"paper":114,"created_at":127},1605,"Assessing the impacts of spatial shifts in grain production on agricultural vulnerability in China: implications for nature-based solutions","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1921926","Against the backdrop of economic expansion and climate warming, spatial shifts in grain production in China have continuously moved toward relatively economically underdeveloped and climatically marginal regions. However, how these spatial shifts affect agricultural vulnerability and what underlying economic-geographical mechanisms drive these effects remain insufficiently explored. Using prefecture-level city data in China and applying a counterfactual decomposition approach together with nonparametric testing, this study investigates these issues. The results show that, during 2010–2022, spatial shifts in grain production in China exhibited a U-shaped relationship with both grain yield variability and the agricultural vulnerability index, with most regions concentrated on the right side of the turning point of the curve. In other words, many grain yield growth regions were areas with high agricultural vulnerability. Spatial shifts in grain production during 2010–2022 increased grain yield variability and the agricultural vulnerability index by 3.07% and 5.07%, respectively. Rapid grain yield growth areas (e.g., emerging grain-production bases) face relatively high climate risks, low crop diversity and multiple cropping index, labor outmigration and limited agricultural inputs, weak local fiscal capacity for agricultural investment, and low rural incomes, which are the key mechanisms through which spatial shifts aggravate agricultural vulnerability. Finally, this study proposes a “U-shaped spatial continuum model” to provide theoretical insights into the impacts of spatial shifts in grain production on agricultural vulnerability, and on this basis offers policy implications for governing agricultural vulnerability in China from a spatial perspective that integrates nature-based, engineered, and input-based adaptation.","在经济扩张与气候变暖的背景下，中国粮食生产的空间格局持续向经济相对欠发达、气候边缘区域转移。然而，这种空间转移如何影响农业脆弱性，以及其背后的经济地理机制尚待深入探究。本研究利用中国地级市数据，结合反事实分解方法与非参数检验，对上述问题进行了考察。结果表明，2010—2022年间，中国粮食生产的空间转移与粮食产量变异性和农业脆弱性指数均呈U型关系，且多数地区集中于曲线拐点右侧。换言之，许多粮食增产区域恰是农业脆弱性较高的地区。2010—2022年的粮食生产空间转移分别使粮食产量变异性和农业脆弱性指数提高了3.07%和5.07%。快速增产区域（如新兴粮食生产基地）面临较高的气候风险、较低的作物多样性和复种指数、劳动力外流与农业投入不足、地方财政对农业投资能力薄弱以及农村收入偏低等问题，这些正是空间转移加剧农业脆弱性的关键机制。最后，本研究提出“U型空间连续体模型”，为粮食生产空间转移对农业脆弱性影响提供理论洞见，并在此基础上，从整合自然本底、工程措施和投入型适应的空间视角，提出治理中国农业脆弱性的政策启示。","2026-09-03T00:00:00Z",73,{"impact":17,"substance":18,"depth":17,"authority":88,"freshness":89,"relevant":21,"comment":90},12,3,"研究揭示中国粮食生产空间转移加剧农业脆弱性，提出U型模型与政策启示，对农业可持续发展有重要参考价值。",[92],{"name":10,"url":82},[94,26,27,95],"粮食安全","空间布局",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":98},{"candidates":99,"agricultural_evidence":48,"reason":49},[100,105],{"code":101,"score":21,"supported":14,"title":102,"abstract":103,"tags":104},"A8",[],[],[94],{"code":44,"score":21,"supported":14,"title":106,"abstract":107,"tags":108},[],[],[26],[110,111],"农业风险 气候变化 空间布局 粮食安全","农业风险 气候变化","农业风险气候变化空间布局粮食安全-1605","10.3389\u002Ffsufs.2026.1921926",{"doi":113,"openalex_id":115,"authors":116,"venue":10,"cited_by_count":68,"oa_url":121,"card":122,"direction":73,"ingested_from":75},"W7207553105",[117,119],{"name":118,"orcid":9},"Jianzhi Liu",{"name":120,"orcid":9},"Ruru Wang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1921926\u002Fpdf",{"tldr":123,"method":124,"finding":125,"direction":73,"opportunity":126},"研究中国粮食生产空间转移对农业脆弱性的影响，发现呈U型关系并加剧脆弱性。","使用地级市数据，反事实分解和非参数检验。","2010-2022年粮食生产空间转移使产量变异性和脆弱性分别增加3.07%和5.07%。","可探索基于自然的解决方案在缓解粮食生产空间转移导致的农业脆弱性中的应用与效果评估。","2026-09-04T23:30:07.804994Z",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":9,"source_name":134,"source_url":131,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":139,"tags":141,"primary_topic":146,"secondary_topics":152,"classification":153,"search_phrases":173,"slug":176,"view_count":68,"doi":177,"paper":178,"created_at":193},5584,"Distributionally-Robust Evaluation of Computer Vision Agriculture Disease Classifiers Under Climate-Driven Pathogen Range Shifts: An Artificial Intelligence Worst-Case Generalization Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544150","Computer vision classifiers for crop disease are evaluated almost exclusively on held-out samples drawn from the same distribution as their training data, yet climate-driven pathogen range shifts mean that deployment environments are systematically out of distribution. The framework treats the deployment distribution as an adversarial perturbation of the training distribution within a Wasserstein ball whose radius is calibrated to observed climate displacement. Using NASA POWER monthly reanalysis for Harare, Zimbabwe (2000-2023, 312 monthly observations), the analysis estimates a mean air temperature of 18.914 C, a standard deviation of 3.107 C, and a warming trend of 0.2001 C per decade. These estimates anchor the radius of the ambiguity set and the resulting bound. The bound decomposes worst-case risk into an empirical risk term, a Wasserstein radius term scaled by the Lipschitz constant of the classifier, and a concentration term. Under plausible Lipschitz constants for convolutional backbones, the radius term dominates the empirical risk term once the climate displacement exceeds a threshold that the Harare record already approaches. The contribution is a formal evaluation criterion that replaces the i. i. d. assumption with a climate-calibrated ambiguity set, together with the analytical machinery needed to compute it.","作物病害的计算机视觉分类器几乎完全在与训练数据同分布的留出样本上进行评估，然而气候驱动的病原体分布范围变化意味着部署环境系统性地处于分布之外。该框架将部署分布视为训练分布在Wasserstein球内的对抗性扰动，球的半径根据观测到的气候位移进行校准。利用NASA POWER对津巴布韦哈拉雷的月度再分析数据（2000—2023年，312个月度观测值），分析估计平均气温为18.914°C，标准差为3.107°C，每十年升温趋势为0.2001°C。这些估计值锚定了模糊集的半径及由此得到的界。该界将最坏情况风险分解为经验风险项、由分类器Lipschitz常数缩放后的Wasserstein半径项以及集中项。在卷积骨干网络合理的Lipschitz常数下，一旦气候位移超过哈拉雷记录已接近的阈值，半径项便主导经验风险项。本文的贡献在于提出了一种形式化评估准则，以气候校准的模糊集替代独立同分布（i.i.d.）假设，并提供了计算该准则所需的分析工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",75,{"impact":88,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":138},"方法新颖、数据具体，但属方法论预印论文，产业影响有限，适合作为专业精选而非头条。",[140],{"name":134,"url":131},[142,143,144,145,26],"智慧农业","农业人工智能","计算机视觉","作物病害",{"code":147,"slug":148,"name":149,"path":150,"parent_code":151},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":154,"locked":14,"version":34,"method":35,"evidence":155},"classified",{"candidates":156,"agricultural_evidence":48},[157,164,168],{"code":147,"score":158,"supported":48,"title":159,"abstract":162,"tags":163},24,[160,161],"artificial intelligence","computer vision",[144,161],[143,144],{"code":44,"score":21,"supported":14,"title":165,"abstract":166,"tags":167},[],[],[26],{"code":169,"score":21,"supported":14,"title":170,"abstract":171,"tags":172},"I1",[],[],[142],[174,175],"NASA POWER 哈拉雷 气温","Wasserstein 分布鲁棒 病害分类","NASAPOWER哈拉雷气温-5584","10.5281\u002Fzenodo.23544150",{"doi":177,"openalex_id":179,"authors":180,"venue":134,"cited_by_count":68,"oa_url":131,"card":187,"direction":191,"ingested_from":75},"W7223087892",[181,183,185],{"name":182,"orcid":9},"Kossi Afeli",{"name":184,"orcid":9},"Akouvi Amegashie",{"name":186,"orcid":9},"Komlan Sélom Dossou",{"tldr":188,"method":189,"finding":190,"direction":191,"opportunity":192},"提出气候校准的分布鲁棒评估准则，替代i.i.d.假设评价作物病害视觉分类器。","用Wasserstein球建模部署分布偏移，以NASA POWER哈拉雷气温数据","气候位移超阈值后，半径项主导经验风险，现有评估会高估模型泛化能力。","农业人工智能与决策模型","可将气候校准的鲁棒评估扩展到多作物多区域，并研究适应分布偏移的鲁棒训练方法。","2026-10-22T11:21:00.036937Z",{"id":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":9,"source_name":200,"source_url":197,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":48,"score":201,"score_detail":202,"sources":206,"tags":208,"primary_topic":213,"secondary_topics":219,"classification":225,"search_phrases":258,"slug":261,"view_count":68,"doi":262,"paper":263,"created_at":278},5569,"A four-decadal (1981–2024) quality-assured ground-based soybean phenology dataset from Northeast China: a FAIR-compliant resource for climate impact and agricultural adaptation studies","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20964471.2026.2735138","Although satellite remote sensing has been widely used to monitor land surface phenology, long-term, high-quality ground-based observations are essential for validating and calibrating these products, particularly for understanding agricultural ecosystem responses to climate change. However, such ground-truth datasets remain scarce, limiting the full potential of Earth system data applications. Here we present a comprehensive ground-based soybean phenology dataset for Northeast China, a region critical to national food security. The dataset integrates historical paper-based logs (1980s–1990s) with modern electronic records (2000s onwards) from 43 national agrometeorological stations operated by the China Meteorological Administration (CMA), spanning a 15° latitudinal gradient (38–53°N) across Heilongjiang, Jilin, and Liaoning provinces over 44 years (1981–2024). It provides the timing (day of year, DOY) of eight key phenophases (sowing, emergence, three-leaf, branching, flowering, pod formation, seed filling, maturity) and the durations of six intervening growth intervals. A multi-tiered quality assurance framework combining automated consistency checks, statistical outlier detection, and expert arbitration against agronomic principles and auxiliary climate data ensures high data integrity and completeness (>99%; paper era: 93.8%; electronic era: 98.1%). To facilitate reuse, we provide pre-computed kernel density estimates (probability distributions) and univariate linear regression trends for each station and phenological variable, along with 656 diagnostic plots. The dataset is openly available in the Science Data Bank under a CC BY 4.0 license as primary data tables (XLSX) and diagnostic plots (JPEG, 1200 dpi) (https:\u002F\u002Fdoi.org\u002F10.57760\u002Fsciencedb.37245). As a long-term, spatially explicit, FAIR-compliant ground-truth resource, this dataset is valuable for validating satellite-derived phenology products, calibrating crop modules, and investigating climate-driven impacts on agricultural systems.","尽管卫星遥感已被广泛用于监测地表物候，但长期、高质量的地面观测对于验证和校准这些产品至关重要，尤其是对于理解农业生态系统对气候变化的响应而言。然而，此类地面真值数据集仍然稀缺，限制了地球系统数据应用潜力的充分发挥。本文介绍了一套面向中国东北地区的综合性大豆物候地面数据集，该地区对国家粮食安全至关重要。该数据集整合了中国气象局（CMA）运营的43个国家农业气象站的历史纸质记录（20世纪80—90年代）与现代电子记录（2000年代至今），覆盖黑龙江、吉林和辽宁三省跨越15°纬度梯度（38—53°N）、时间跨度44年（1981—2024年）。数据集提供了8个关键物候期（播种、出苗、三叶、分枝、开花、结荚、鼓粒、成熟）的发生时间（年积日，DOY）以及6个中间生长间隔的持续时间。一套多层质量保证框架结合了自动一致性检查、统计异常值检测以及依据农学原理和辅助气候数据进行的专家仲裁，确保了高数据完整性和完整性（>99%；纸质时代：93.8%；电子时代：98.1%）。为便于重复使用，我们提供了每个站点和物候变量的预计算核密度估计（概率分布）和单变量线性回归趋势，以及656幅诊断图。该数据集以CC BY 4.0许可在Science Data Bank公开提供，包括主数据表（XLSX）和诊断图（JPEG，1200 dpi）（https:\u002F\u002Fdoi.org\u002F10.57760\u002Fsciencedb.37245）。作为一个长期、空间明确、符合FAIR原则的地面真值资源，该数据集对于验证卫星反演的物候产品、校准作物模块以及研究气候驱动对农业系统的影响具有重要价值。","Big Earth Data",89,{"impact":18,"substance":158,"depth":203,"authority":204,"freshness":13,"relevant":21,"comment":205},19,14,"覆盖东北44年、43站的大豆物候地面观测数据集，质控严格且FAIR开放，对遥感验证与气候影响研究有实质价值，值得入选。",[207],{"name":200,"url":197},[209,26,210,211,212],"大豆","东北农业","物候数据","遥感验证",{"code":214,"slug":215,"name":216,"path":217,"parent_code":218},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[220],{"code":44,"slug":221,"name":222,"path":223,"parent_code":224},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":154,"locked":14,"version":34,"method":35,"evidence":226},{"candidates":227,"agricultural_evidence":48},[228,234,240,245,252],{"code":214,"score":229,"supported":48,"title":230,"abstract":232,"tags":233},15,[231],"soybean",[209,231],[209],{"code":44,"score":13,"supported":48,"title":235,"abstract":236,"tags":239},[],[26,237,238],"农业气象","climate change",[26],{"code":101,"score":54,"supported":48,"title":241,"abstract":242,"tags":244},[],[94,243],"food security",[],{"code":246,"score":54,"supported":48,"title":247,"abstract":248,"tags":251},"I3",[],[249,250],"遥感","remote sensing",[],{"code":253,"score":89,"supported":48,"title":254,"abstract":255,"tags":257},"H6",[],[256],"农业生态",[],[259,260],"东北 大豆 物候 数据集","大豆 物候 遥感 验证","东北大豆物候数据集-5569","10.1080\u002F20964471.2026.2735138",{"doi":262,"openalex_id":264,"authors":265,"venue":200,"cited_by_count":68,"oa_url":197,"card":272,"direction":276,"ingested_from":75},"W7223091615",[266,269],{"name":267,"orcid":268},"Quanjun Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-6087-3928",{"name":270,"orcid":271},"Dongli Wu","https:\u002F\u002Forcid.org\u002F0000-0003-1676-2264",{"tldr":273,"method":274,"finding":275,"direction":276,"opportunity":277},"发布东北大豆1981–2024年44年地面物候数据集，含8个物候期与6个间隔期。","整合43个农气站纸质与电子记录，经自动校验、统计离群检测和专家仲裁质控。","数据完整度超99%，并提供核密度分布、线性趋势与656张诊断图。","农业遥感与作物表型","可基于该地面真值校准遥感物候产品与作物模型，量化气候变化对大豆物候的影响。","2026-10-22T11:20:39.146445Z",{"id":280,"title":281,"url":282,"summary":283,"summary_zh":284,"content":9,"source_name":285,"source_url":282,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":68,"score_detail":287,"sources":289,"tags":291,"primary_topic":9,"secondary_topics":295,"classification":296,"search_phrases":312,"slug":315,"view_count":68,"doi":316,"paper":317,"created_at":332},5560,"How do weather, topography and fuel condition drive the extreme behavior of a Mediterranean megafire? A case study of the 2025 Jarilla wildfire (western Spain)","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fffgc.2026.1953360","The 2025 wildfire season was the most destructive on record in the European Union, with more than 1 million hectares burned, and the Iberian Peninsula was the hardest-hit region, accounting for 43% of that area. The Jarilla wildfire (Cáceres, Spain; 17,317 ha, august 2025) exemplifies how meteorology, topography, and fuel condition jointly controlled the behavior of these Iberian megafires. Our aim was to analyze the role of these three factors in the fire’s behavior and to evaluate their interpretation against the post-fire severity footprint, within a reproducible workflow relying entirely on open-access data. We combined ERA5 hourly reanalysis, the fire weather index (FWI), Sentinel-2 spectral indices (NDVI, NDII, dNBR), VIIRS active-fire detections, and the 5 m PNOA digital elevation model. During the active phase (august 12–17), the atmosphere was extremely desiccating—vapor pressure deficit peaking at 5.9 kPa, minimum relative humidity near 15%, and FWI in the “extreme” class—acting on a dense but already water-stressed fuel cover (mean NDVI 0.53, mean NDII 0.05). The wind regime was bimodal: a frequent but weak nocturnal north-easterly flow and a diurnal south–south-westerly (SSW) flow that drove the observed SW–NE progression. Fire spread corridors—steep, wind-aligned, fuel-laden terrain delineated with the SSW driving wind—covered 13.7% of the burned area and showed higher mean severity (dNBR 665) than their surroundings (525); however, 84% of the high-severity area lay outside them. These results indicate that, in such “hot-drought” megafires, severity is driven primarily by extreme atmospheric drying acting on continuous, dry fuel, whereas topography and wind spatially organize its peaks rather than determining them. Beyond reconstructing a single event, the study provides a transferable, open-data framework for diagnosing megafire drivers and suggests that fuel- and weather-based prevention, more than topographic targeting, is key to mitigating Mediterranean megafires.","2025年野火季是欧盟有记录以来破坏性最强的一个野火季，过火面积超过100万公顷，其中伊比利亚半岛是受灾最严重的地区，占该面积的43%。Jarilla野火（西班牙卡塞雷斯，17,317公顷，2025年8月）体现了气象、地形和可燃物条件如何共同控制这些伊比利亚特大火灾的行为。我们的目标是分析这三个因素在火灾行为中的作用，并在完全依赖开放获取数据的可重复工作流程中，评估它们与火后严重程度足迹的对应解释。我们结合了ERA5逐小时再分析数据、火险天气指数（FWI）、Sentinel-2光谱指数（NDVI、NDII、dNBR）、VIIRS活跃火点检测以及5米PNOA数字高程模型。在活跃阶段（8月12日至17日），大气极度干燥——饱和水汽压差峰值达5.9 kPa，最低相对湿度接近15%，FWI处于“极端”等级——作用于密集但已受水分胁迫的可燃物覆盖层（平均NDVI为0.53，平均NDII为0.05）。风况呈双峰分布：夜间频繁但微弱的东北气流，以及白天驱动观测到的西南—东北方向蔓延的南—西南（SSW）气流。火势蔓延走廊——陡峭、与风向一致、可燃物载量高的地形，以SSW驱动风 delineated——覆盖了过火面积的13.7%，并显示出比周围区域更高的平均严重程度（dNBR 665对525）；然而，84%的高严重程度区域位于这些走廊之外。这些结果表明，在此类“高温—干旱”特大火灾中，严重程度主要由极端大气干燥作用于连续、干燥的可燃物所驱动，而地形和风在空间上组织其峰值而非决定峰值。除重建单一事件外，该研究提供了一个可迁移的开放数据框架，用于诊断特大火灾驱动因素，并表明基于可燃物和天气的预防，而非地形靶向，是缓解地中海特大火灾的关键。","Frontiers in Forests and Global Change","2026-10-20T00:00:00Z",{"impact":68,"substance":68,"depth":68,"authority":68,"freshness":68,"relevant":68,"comment":288},"该研究聚焦西班牙地中海野火的气象与地形驱动机制，属于林火生态与遥感应用，与三农、农业信息化、智慧农业主题无直接关联，不建议进入每日精选。",[290],{"name":285,"url":282},[26,292,293,294],"遥感监测","野火研究","西班牙",[],{"status":297,"locked":14,"version":34,"method":35,"evidence":298},"needs_review",{"candidates":299,"agricultural_evidence":14,"reason":311},[300,307],{"code":44,"score":301,"supported":48,"title":302,"abstract":303,"tags":306},7,[],[304,305],"干旱","drought",[26],{"code":246,"score":21,"supported":14,"title":308,"abstract":309,"tags":310},[],[],[292],"农业相关性需要复核",[313,314],"气候变化 遥感监测 野火研究 西班牙","气候变化 遥感监测","气候变化遥感监测野火研究西班牙-5560","10.3389\u002Fffgc.2026.1953360",{"doi":316,"openalex_id":318,"authors":319,"venue":285,"cited_by_count":68,"oa_url":325,"card":326,"direction":331,"ingested_from":75},"W7223002169",[320,322],{"name":321,"orcid":9},"Luis Hernández-Sánchez",{"name":323,"orcid":324},"Marcos Francos","https:\u002F\u002Forcid.org\u002F0000-0002-3311-5686","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fforests-and-global-change\u002Farticles\u002F10.3389\u002Fffgc.2026.1953360\u002Fpdf",{"tldr":327,"method":328,"finding":329,"direction":276,"opportunity":330},"分析2025年西班牙Jarilla特大火灾，揭示气象、地形与燃料条件如何共同驱动火行为。","融合ERA5再分析、FWI、Sentinel-2指数、VIIRS火点和5米DEM","极端大气干燥叠加连续干旱燃料主导火强度，地形与风仅空间组织高严重度峰值而非决定其分布。","可延伸至多源遥感融合的野火驱动因子归因与火险预警，并检验该开源框架在其他地中海火区的可迁移性。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:20.437834Z",{"id":334,"title":335,"url":336,"summary":337,"summary_zh":338,"content":9,"source_name":339,"source_url":336,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":340,"score_detail":341,"sources":345,"tags":347,"primary_topic":9,"secondary_topics":352,"classification":353,"search_phrases":378,"slug":381,"view_count":68,"doi":382,"paper":383,"created_at":401},5486,"Parasites as environmental bioindicators and climate-sensitive sentinels in arid ecosystems: a one health synthesis with a focus on Saudi Arabia","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffvets.2026.1978111","Parasites integrate environmental conditions across trophic levels and may therefore provide sensitive, mechanistically interpretable signals of ecosystem change. This narrative synthesis evaluates the potential of parasites and parasite communities as environmental bioindicators and climate-sensitive sentinels, with particular emphasis on arid ecosystems and Saudi Arabia. We integrate evidence from terrestrial, aquatic, marine, and zoonotic systems to examine how warming, drought, hydrological change, salinity, pollution, land-use transformation, and host-mediated vector movement influence parasite occurrence, transmission, phenology, community composition, and contaminant accumulation. The evidence supports two complementary bioindicator functions: accumulation indicators, in which parasites concentrate environmental contaminants, and effect indicators, in which parasite prevalence, phenology, richness, or community structure responds to environmental disturbance. Saudi marine systems provide particularly strong examples, including trace-metal accumulation by acanthocephalans and nematodes, community-level changes associated with habitat degradation, and seasonal transmission of Kudoa sp. in Priacanthus tayenus . Terrestrial and waterborne systems provide additional candidate signals through tick phenology and the distribution of Cryptosporidium and Giardia. However, the current evidence base is dominated by cross-sectional studies, heterogeneous diagnostic methods, and geographically fragmented datasets; consequently, parasites should be regarded as promising candidate sentinels rather than validated early-warning indicators. We identify priority research needs and propose a One Health surveillance architecture integrating standardised parasitological sampling, environmental and meteorological measurements, molecular identification, remote sensing, livestock-movement data, and multivariate community analysis. Long-term, coupled datasets will be essential to distinguish climate-driven signals from concurrent anthropogenic pressures and to translate parasite bioindication from an ecological concept into an operational monitoring tool for arid and peri-arid regions.","寄生虫整合了跨营养级的环境条件，因而可能提供敏感且机制上可解释的生态系统变化信号。本叙述性综述评估了寄生虫及寄生虫群落作为环境生物指示物和气候敏感哨兵的潜力，尤其聚焦于干旱生态系统和沙特阿拉伯。我们整合了陆地、淡水、海洋及人兽共患系统的证据，考察变暖、干旱、水文变化、盐度、污染、土地利用转变以及宿主介导的媒介移动如何影响寄生虫的发生、传播、物候、群落组成和污染物蓄积。证据支持两种互补的生物指示功能：蓄积指示物，即寄生虫富集环境污染物；效应指示物，即寄生虫的流行率、物候、丰富度或群落结构对环境扰动做出响应。沙特海洋系统提供了尤为有力的例证，包括棘头虫和线虫对微量金属的蓄积、与栖息地退化相关的群落层面变化，以及 Kudoa sp. 在 Priacanthus tayenus 中的季节性传播。陆生和水源性系统通过蜱的物候以及隐孢子虫（Cryptosporidium）和贾第虫（Giardia）的分布提供了额外的候选信号。然而，当前证据基础以横断面研究为主，诊断方法异质，数据集在地理上碎片化；因此，寄生虫应被视为有前景的候选哨兵，而非经过验证的早期预警指示物。我们确定了优先研究需求，并提出了一种“同一健康”（One Health）监测架构，整合标准化寄生虫学采样、环境与气象测量、分子鉴定、遥感、牲畜移动数据和多变量群落分析。长期耦合数据集对于区分气候驱动信号与同期人为压力，以及将寄生虫生物指示从生态学概念转化为干旱和半干旱区域的可操作监测工具，将至关重要。","Frontiers in Veterinary Science",64,{"impact":342,"substance":17,"depth":343,"authority":19,"freshness":20,"relevant":21,"comment":344},8,16,"综述性论文，提出寄生虫作为干旱区生态与气候哨兵的One Health监测框架，具方法学参考价值，但属概念性综合、非实证突破，公共影响有限。",[346],{"name":339,"url":336},[26,348,349,350,351],"One Health","干旱农业","生态系统健康","寄生虫监测",[],{"status":297,"locked":14,"version":34,"method":35,"evidence":354},{"candidates":355,"agricultural_evidence":48,"reason":377},[356,363,367,371],{"code":357,"score":301,"supported":48,"title":358,"abstract":359,"tags":362},"C5",[],[360,361],"人兽共患","zoonotic",[348],{"code":44,"score":301,"supported":48,"title":364,"abstract":365,"tags":366},[],[304,305],[26],{"code":246,"score":54,"supported":48,"title":368,"abstract":369,"tags":370},[],[249,250],[],{"code":372,"score":89,"supported":48,"title":373,"abstract":374,"tags":376},"C",[],[375],"livestock",[],"多个产业证据相当，需要确认主归属",[379,380],"沙特阿拉伯 寄生虫 生物指示","干旱生态系统 气候敏感 哨兵","沙特阿拉伯寄生虫生物指示-5486","10.3389\u002Ffvets.2026.1978111",{"doi":382,"openalex_id":384,"authors":385,"venue":339,"cited_by_count":68,"oa_url":395,"card":396,"direction":276,"ingested_from":75},"W7223056541",[386,389,392],{"name":387,"orcid":388},"Hanadi B. A. Baghdadi","https:\u002F\u002Forcid.org\u002F0000-0003-0012-1964",{"name":390,"orcid":391},"Mohamed Abdelsalam","https:\u002F\u002Forcid.org\u002F0000-0003-1406-7030",{"name":393,"orcid":394},"Mohamed Abdo Rizk","https:\u002F\u002Forcid.org\u002F0000-0002-5250-6172","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fveterinary-science\u002Farticles\u002F10.3389\u002Ffvets.2026.1978111\u002Fpdf",{"tldr":397,"method":398,"finding":399,"direction":73,"opportunity":400},"综述寄生虫作为干旱生态系统环境生物指示物与气候敏感哨兵，聚焦沙特。","整合陆地、水生、海洋及人兽共患系统的文献证据，提出One Health监测框架。","寄生虫有累积与效应两类指示功能，但证据多为横断面研究，尚属候选哨兵。","可建立标准化寄生虫采样与遥感、气象耦合的长期监测网，验证其早期预警能力。","2026-10-21T23:30:23.356941Z",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":9,"source_name":408,"source_url":405,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":409,"score_detail":410,"sources":412,"tags":414,"primary_topic":418,"secondary_topics":419,"classification":430,"search_phrases":483,"slug":486,"view_count":68,"doi":487,"paper":488,"created_at":511},5483,"Soil Salinity under Climate Change in Pakistan: Emerging Challenges and Sustainable Management Approaches for Agriculture","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.883","Soil salinity is a major constraint to agricultural productivity, soil health, and food security in Pakistan, particularly under changing climatic conditions. Rising temperatures, increased evapotranspiration, irregular precipitation, droughts, flooding, and sea-level rise can intensify salt accumulation and alter soil–water interactions. This review examines the emerging challenges associated with climate change and soil salinity in Pakistan, focusing on their impacts on soil properties, crop physiology, agricultural production, and rural livelihoods. It discusses the mechanisms of soil salinity and sodicity, their spatial distribution, and the interactions among climate variability, irrigation practices, groundwater quality, and secondary salinization. Furthermore, sustainable management approaches are evaluated, including gypsum application, organic amendments, integrated nutrient management, efficient irrigation, drainage improvement, and appropriate leaching strategies. Climate-resilient approaches, such as conventional crop breeding, modern biotechnology, halophyte utilization, plant growth-promoting microorganisms, and mycorrhizal associations, are also explored. The review highlights the potential of remote sensing, geographic information systems, precision agriculture, and predictive modelling to support salinity monitoring and site-specific management. Despite these opportunities, important challenges remain, including insufficient long-term monitoring, limited region-specific data, economic constraints, and gaps in farmer awareness and institutional coordination. Addressing these challenges requires integrated research, effective agricultural policies, improved extension services, and collaboration among researchers, policymakers, and farming communities. Prioritizing locally adapted, economically feasible, and environmentally sustainable interventions can strengthen agricultural resilience, restore salt-affected soils, and protect freshwater resources. Overall, integrating climate adaptation with sustainable soil and water management is essential for maintaining agricultural productivity and improving long-term food security in Pakistan.","土壤盐渍化是制约巴基斯坦农业生产力、土壤健康和粮食安全的主要因素，在气候变化条件下尤为突出。气温升高、蒸散加剧、降水不规律、干旱、洪水以及海平面上升可能加剧盐分积累并改变土壤—水相互作用。本文综述了巴基斯坦气候变化与土壤盐渍化相关的新兴挑战，重点关注其对土壤性质、作物生理、农业生产和农村生计的影响。文章讨论了土壤盐渍化和钠质化的机制、其空间分布，以及气候变率、灌溉实践、地下水质量和次生盐渍化之间的相互作用。此外，评估了可持续管理途径，包括施用石膏、有机改良、综合养分管理、高效灌溉、排水改良和适宜的淋洗策略。还探讨了气候韧性方法，如常规作物育种、现代生物技术、盐生植物利用、植物促生微生物和菌根共生。本文强调了遥感、地理信息系统、精准农业和预测建模在支持盐渍化监测和因地制宜管理方面的潜力。尽管存在这些机遇，重要挑战依然存在，包括长期监测不足、区域特定数据有限、经济制约以及农民意识和制度协调方面的差距。应对这些挑战需要综合研究、有效的农业政策、改进的推广服务以及研究人员、政策制定者和农业社区之间的协作。优先采取适应当地、经济可行且环境可持续的干预措施，可增强农业韧性、修复盐渍化土壤并保护淡水资源。总体而言，将气候适应与可持续水土管理相结合，对于维持巴基斯坦农业生产力和改善长期粮食安全至关重要。","Kashmir Journal of Academic Research and Development",66,{"impact":19,"substance":17,"depth":229,"authority":88,"freshness":342,"relevant":21,"comment":411},"综述性论文，系统梳理气候变化下巴基斯坦土壤盐渍化成因与可持续治理路径，兼具遥感与精准农业视角，对盐碱地治理议题有参考价值，但属区域性综述、非突破性成果。",[413],{"name":408,"url":405},[26,415,292,416,417],"盐碱地治理","智慧灌溉","土壤盐渍化",{"code":44,"slug":221,"name":222,"path":223,"parent_code":224},[420,425],{"code":421,"slug":422,"name":423,"path":424,"parent_code":224},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":426,"slug":427,"name":428,"path":429,"parent_code":224},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":154,"locked":14,"version":34,"method":35,"evidence":431},{"candidates":432,"agricultural_evidence":48},[433,438,443,450,454,458,464,471,477],{"code":44,"score":17,"supported":48,"title":434,"abstract":435,"tags":437},[238],[26,436,304,238],"气候适应",[26],{"code":421,"score":229,"supported":48,"title":439,"abstract":440,"tags":442},[422],[441,422],"土壤",[415],{"code":426,"score":13,"supported":48,"title":444,"abstract":445,"tags":449},[],[446,447,427,448],"灌溉","水资源","evapotranspiration",[416],{"code":246,"score":301,"supported":48,"title":451,"abstract":452,"tags":453},[],[249,250],[292],{"code":101,"score":54,"supported":48,"title":455,"abstract":456,"tags":457},[],[94,243],[],{"code":169,"score":54,"supported":48,"title":459,"abstract":460,"tags":463},[],[461,462],"精准农业","precision agriculture",[],{"code":465,"score":54,"supported":48,"title":466,"abstract":467,"tags":470},"I4",[],[468,469],"地理信息","geographic information",[],{"code":472,"score":89,"supported":48,"title":473,"abstract":474,"tags":476},"F3",[],[475],"养分管理",[],{"code":478,"score":89,"supported":48,"title":479,"abstract":480,"tags":482},"J1",[],[481],"农业政策",[],[484,485],"巴基斯坦 土壤盐渍化","盐碱地 遥感监测","巴基斯坦土壤盐渍化-5483","10.71317\u002Fkjard.2.10.2026.883",{"doi":487,"openalex_id":489,"authors":490,"venue":408,"cited_by_count":68,"oa_url":405,"card":506,"direction":276,"ingested_from":75},"W7222999158",[491,493,495,497,499,501,504],{"name":492,"orcid":9},"Asifa Naz",{"name":494,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":496,"orcid":9},"Syed Asghar Hussain Shah",{"name":498,"orcid":9},"Shahrish Nawaz",{"name":500,"orcid":9},"Muhmmad Irfan",{"name":502,"orcid":503},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":505,"orcid":9},"Muhammad Nouman Khalid",{"tldr":507,"method":508,"finding":509,"direction":276,"opportunity":510},"综述气候变化下巴基斯坦土壤盐渍化的挑战与可持续管理途径。","文献综述，整合盐渍化机制、空间分布及遥感GIS与预测模型应用。","需将气候适应与水土管理结合，优先本地化、经济可行的干预措施。","可填补长期盐渍化监测与区域数据空白，发展遥感+预测模型的精准分区治理。","2026-10-21T23:30:23.059448Z"]