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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)","2026-10-21T00:00:00Z","论文",false,{"impact":173,"substance":321,"depth":361,"authority":416,"freshness":583,"relevant":455,"comment":584},9,"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。",[586],{"name":578,"url":575},[588,589,590,591,592],"智慧农业","农业人工智能","灌溉决策","安全保证","试验设计",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[595],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":597,"locked":581,"version":4,"method":598,"evidence":599},"classified","rules",{"candidates":600,"agricultural_evidence":603},[601,608,614,620],{"code":152,"score":602,"supported":603,"title":604,"abstract":605,"tags":607},15,true,[153],[606,153],"灌溉",[590],{"code":24,"score":416,"supported":603,"title":609,"abstract":611,"tags":613},[610],"artificial intelligence",[612],"decision support",[589],{"code":144,"score":615,"supported":603,"title":616,"abstract":617,"tags":619},6,[],[618,145],"土壤",[],{"code":16,"score":455,"supported":581,"title":621,"abstract":622,"tags":623},[],[],[588],[625,626],"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":628,"openalex_id":630,"authors":631,"venue":578,"cited_by_count":424,"oa_url":575,"card":636,"direction":640,"ingested_from":642},"W7223131205",[632,634],{"name":633,"orcid":11},"Nassima Benkhelifa",{"name":635,"orcid":11},"Yacine Aït Ahmed",{"tldr":637,"method":638,"finding":639,"direction":640,"opportunity":641},"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。","农业人工智能与决策模型","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。","openalex","2026-10-22T11:21:10.283561Z",{"id":645,"title":646,"url":647,"summary":648,"summary_zh":649,"content":11,"source_name":578,"source_url":647,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":650,"score_detail":651,"sources":654,"tags":656,"primary_topic":660,"secondary_topics":661,"classification":663,"search_phrases":681,"slug":684,"view_count":424,"doi":685,"paper":686,"created_at":700},5583,"Active-Labeling Evaluation for Computer Agriculture Nematode-Cyst Detection Under Sparse Soil-Sample Ground Truth: An Artificial Intelligence Annotation-Efficiency Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544145","Active learning promises to reduce the annotation burden of training nematode-cyst detection models for precision agriculture, but the size of that reduction is not known when ground truth comes from sparse soil samples rather than exhaustive visual inspection. the present analysis derives an annotation-efficiency bound for pool-based active learning under sparse soil-sample ground truth. The detection task is formalised as a two-stage process in which a classifier selects candidate cysts from soil imagery and a laboratory assay confirms a small labelled subset. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, the bound is evaluated across a design grid of 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per cell. The annotation-efficiency gain is bounded by the ratio of label noise to cluster-level variance, and that 52 clusters are required to detect a 5-percentage-point improvement at 80% power. The bound provides a design rule for allocating annotation effort between active selection and confirmatory soil sampling.","主动学习有望减轻精准农业中训练线虫囊检测模型所需的标注负担，但当真实标签来自稀疏土壤样本而非详尽的视觉检查时，这种负担减轻的幅度尚不清楚。本分析推导了在稀疏土壤样本真实标签下基于池的主动学习的标注效率界限。检测任务被形式化为两阶段过程：分类器从土壤图像中筛选候选线虫囊，实验室检测确认一个小的已标注子集。在设定的方差结构下——组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64——该界限在8、16、24和32个簇的设计网格以及3、5和8个百分点的效应量下进行评估，每个单元格重复2000次。标注效率增益受标签噪声与簇级方差之比的约束，并且在80%检验功效下检测5个百分点的改进需要52个簇。该界限为在主动选择与验证性土壤采样之间分配标注工作量提供了设计规则。",64,{"impact":173,"substance":508,"depth":652,"authority":583,"freshness":385,"relevant":455,"comment":653},17,"该研究给出稀疏土壤样本下主动学习标注效率的理论边界与聚类设计规则，方法新颖、结论具体，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。",[655],{"name":578,"url":647},[588,589,657,658,659],"主动学习","线虫检测","土壤采样",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[662],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":597,"locked":581,"version":4,"method":598,"evidence":664},{"candidates":665,"agricultural_evidence":603},[666,670,674],{"code":144,"score":345,"supported":603,"title":667,"abstract":668,"tags":669},[145],[618,145],[],{"code":24,"score":583,"supported":603,"title":671,"abstract":672,"tags":673},[610],[],[589],{"code":16,"score":675,"supported":603,"title":676,"abstract":677,"tags":680},7,[],[678,679],"精准农业","precision agriculture",[588],[682,683],"线虫孢囊 检测 主动学习","稀疏土壤样本 标注效率","线虫孢囊检测主动学习-5583","10.5281\u002Fzenodo.23544145",{"doi":685,"openalex_id":687,"authors":688,"venue":578,"cited_by_count":424,"oa_url":647,"card":695,"direction":640,"ingested_from":642},"W7223082195",[689,691,693],{"name":690,"orcid":11},"Amine Ben Salah",{"name":692,"orcid":11},"Ines Trabelsi",{"name":694,"orcid":11},"Mohamed Aziz Khelifi",{"tldr":696,"method":697,"finding":698,"direction":640,"opportunity":699},"推导稀疏土壤样本真值下主动学习标注效率的理论上界。","基于方差结构与设计网格的统计模拟，2000次重复。","标注效率增益受标签噪声与簇间方差之比限制，检测5个百分点提升需52簇。","可将该上界推广到其他稀疏真值农业检测任务，并探索主动选择与确认采样的最优分配策略。","2026-10-22T11:20:58.901747Z",{"id":702,"title":703,"url":704,"summary":705,"summary_zh":706,"content":11,"source_name":707,"source_url":704,"published_at":708,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":709,"score_detail":710,"sources":713,"tags":715,"primary_topic":721,"secondary_topics":722,"classification":723,"search_phrases":746,"slug":749,"view_count":424,"doi":750,"paper":751,"created_at":784},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA","2026-10-20T00:00:00Z",79,{"impact":711,"substance":321,"depth":361,"authority":345,"freshness":583,"relevant":455,"comment":712},16,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[714],{"name":707,"url":704},[716,717,718,719,720],"遥感监测","莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":724},{"candidates":725,"agricultural_evidence":603},[726,730,736,742],{"code":144,"score":345,"supported":603,"title":727,"abstract":728,"tags":729},[145],[618,145],[],{"code":32,"score":675,"supported":603,"title":731,"abstract":732,"tags":735},[],[733,734],"遥感","remote sensing",[716],{"code":184,"score":615,"supported":603,"title":737,"abstract":738,"tags":741},[],[739,740],"气候适应","干旱",[],{"code":176,"score":455,"supported":581,"title":743,"abstract":744,"tags":745},[],[],[719],[747,748],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":750,"openalex_id":752,"authors":753,"venue":707,"cited_by_count":424,"oa_url":704,"card":778,"direction":782,"ingested_from":642},"W7223069171",[754,756,758,760,763,765,768,771,773,775],{"name":755,"orcid":11},"Yue Li",{"name":757,"orcid":11},"Mengfei Shi",{"name":759,"orcid":11},"Fei Zhang",{"name":761,"orcid":762},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":764,"orcid":11},"Gowhar Meraj",{"name":766,"orcid":767},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":769,"orcid":770},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":772,"orcid":11},"Yuanfang Chai",{"name":774,"orcid":11},"Hongtao Xu",{"name":776,"orcid":777},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":779,"method":780,"finding":781,"direction":782,"opportunity":783},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","农业遥感与作物表型","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":786,"title":787,"url":788,"summary":789,"summary_zh":790,"content":11,"source_name":791,"source_url":788,"published_at":708,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":792,"score_detail":793,"sources":795,"tags":797,"primary_topic":803,"secondary_topics":804,"classification":805,"search_phrases":828,"slug":831,"view_count":424,"doi":832,"paper":833,"created_at":848},5575,"Wetland vegetation dynamics and its linkage with climatic, hydrological, and land-use\u002F land cover changes in the Foket floodplain of the Fetam River Watershed, Northwestern Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01136-x","Timely monitoring of wetland changes is vital for effective conservation and management of the landscape. This study, therefore, examines vegetation dynamics and their climatic, hydrological, and land-use drivers in the Foket floodplain wetland, Upper Blue Nile basin, Ethiopia. The LULC analysis was employed based on 1985, 2002, and 2020 cloud-free Landsat images. The maximum likelihood classification algorithm was applied to classify the images into eight LULC classes. The spatial and temporal dynamics of the floodplain’s surface water were further analyzed using the Global Surface Water Explorer (1984–2020), focusing on water occurrence, seasonality, transitions, and changes in surface-water dynamics. The Normalized Difference Vegetation Index (NDVI) was used to assess the temporal dynamics of wetland vegetation, while annual rainfall, temperature, and evapotranspiration data were used to examine the climatic context of vegetation change. The results reveal that the area coverage of water body and wetland decreased by 63.2% and 29%, respectively, over the study period. In contrast, cultivated land, settlement, and eucalyptus plantation had expanded by 39.8%, 280.6%, and 380%, respectively, at the expense of wetland, woodland, and shrubland. Surface-water dynamics showed a substantial decline in seasonally inundated areas and changes in the spatial distribution and persistence of surface water. This indicates the reduction of the floodplain’s hydrological connectivity. The NDVI analysis also revealed a significant increasing of wetland vegetation over 1985–2020\u002F21. Vegetation growth was very fast during 1985–2000, slowed down between 2001 and 2012, and dropped slightly from 2013 to 2020\u002F21. Analysis of climatic variables showed no major change in annual rainfall or evapotranspiration, whereas temperature exhibited a significant upward trend. The overall findings revealed the association of the floodplain vegetation dynamics with land-use land cover, hydrological, and climatic changes. This indicates that land-use land cover, hydrological, and climatic changes have contributed to the degradation of the floodplain vegetation, highlighting the urgent need for integrated management interventions that regulate agricultural expansion, grazing, and water diversion while maintaining hydrological connectivity and wetland vegetation. Clinical trial number Not applicable (NA).","及时监测湿地变化对于景观的有效保护和管理至关重要。因此，本研究考察了埃塞俄比亚上青尼罗河流域福凯特洪泛平原湿地的植被动态及其气候、水文和土地利用驱动因素。基于1985年、2002年和2020年无云Landsat影像进行土地利用\u002F土地覆盖（LULC）分析，采用最大似然分类算法将影像分为八个LULC类别。进一步利用全球地表水探索器（1984—2020年）分析洪泛平原地表水的时空动态，重点关注水的出现频率、季节性、转换及地表水动态变化。采用归一化差异植被指数（NDVI）评估湿地植被的时间动态，同时利用年降水量、气温和蒸散发数据考察植被变化的气候背景。结果表明，研究期间水体和湿地的面积覆盖率分别下降了63.2%和29%。相比之下，耕地、居民点和桉树种植园分别扩张了39.8%、280.6%和380%，侵占了湿地、林地和灌丛。地表水动态显示季节性淹没区域大幅减少，地表水的空间分布和持续性发生变化，表明洪泛平原水文连通性降低。NDVI分析还揭示1985—2020\u002F21年间湿地植被显著增加。植被生长在1985—2000年间非常迅速，2001—2012年间放缓，2013—2020\u002F21年间略有下降。气候变量分析显示年降水量或蒸散发无重大变化，而气温呈显著上升趋势。总体研究结果揭示了洪泛平原植被动态与土地利用\u002F土地覆盖、水文和气候变化的关联。这表明土地利用\u002F土地覆盖、水文和气候变化共同导致了洪泛平原植被退化，凸显了迫切需要综合管理干预措施，以调控农业扩张、放牧和引水，同时维持水文连通性和湿地植被。临床试验编号：不适用（NA）。","Discover Environment",65,{"impact":173,"substance":508,"depth":711,"authority":416,"freshness":583,"relevant":455,"comment":794},"基于Landsat与NDVI的湿地植被动态实证研究，数据扎实但属区域案例，国际参考价值有限，不宜进入每日精选。",[796],{"name":791,"url":788},[798,799,800,801,802],"NDVI","埃塞俄比亚","土地利用变化","湿地遥感","水文连通性",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":806},{"candidates":807,"agricultural_evidence":603},[808,814,820,824],{"code":152,"score":615,"supported":603,"title":809,"abstract":810,"tags":813},[],[811,812],"蒸散发","evapotranspiration",[],{"code":184,"score":815,"supported":603,"title":816,"abstract":817,"tags":819},3,[],[818],"气候变化",[],{"code":176,"score":455,"supported":581,"title":821,"abstract":822,"tags":823},[],[],[800],{"code":32,"score":455,"supported":581,"title":825,"abstract":826,"tags":827},[],[],[798],[829,830],"土地利用变化 埃塞俄比亚 水文连通性 湿地遥感","土地利用变化 埃塞俄比亚","土地利用变化埃塞俄比亚水文连通性湿地遥感-5575","10.1007\u002Fs44274-026-01136-x",{"doi":832,"openalex_id":834,"authors":835,"venue":791,"cited_by_count":424,"oa_url":842,"card":843,"direction":782,"ingested_from":642},"W7223064056",[836,839],{"name":837,"orcid":838},"Workiyie Worie Assefa","https:\u002F\u002Forcid.org\u002F0000-0002-4029-9022",{"name":840,"orcid":841},"Belachew Getnet Eneyew","https:\u002F\u002Forcid.org\u002F0000-0003-1196-9004","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01136-x.pdf",{"tldr":844,"method":845,"finding":846,"direction":782,"opportunity":847},"分析埃塞俄比亚Foket洪泛湿地植被动态及其气候、水文与土地利用驱动因素。","用Landsat影像做最大似然分类，结合全球地表水数据与NDVI及气候数据分析。","水体与湿地面积大幅减少，耕地、居民点和桉树种植扩张，温度显著上升。","可结合多源遥感与水文连通性指标，量化湿地退化对农业灌溉与乡村生态服务的影响。","2026-10-22T11:20:42.476484Z",{"id":850,"title":851,"url":852,"summary":853,"summary_zh":854,"content":11,"source_name":855,"source_url":852,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":603,"score":856,"score_detail":857,"sources":861,"tags":863,"primary_topic":868,"secondary_topics":869,"classification":871,"search_phrases":900,"slug":903,"view_count":424,"doi":904,"paper":905,"created_at":919},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":321,"substance":858,"depth":859,"authority":345,"freshness":385,"relevant":455,"comment":860},24,19,"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。",[862],{"name":855,"url":852},[864,818,865,866,867],"大豆","东北农业","物候数据","遥感验证",{"code":232,"slug":233,"name":234,"path":235,"parent_code":200},[870],{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"status":597,"locked":581,"version":4,"method":598,"evidence":872},{"candidates":873,"agricultural_evidence":603},[874,879,885,891,895],{"code":232,"score":602,"supported":603,"title":875,"abstract":877,"tags":878},[876],"soybean",[864,876],[864],{"code":184,"score":385,"supported":603,"title":880,"abstract":881,"tags":884},[],[818,882,883],"农业气象","climate change",[818],{"code":263,"score":615,"supported":603,"title":886,"abstract":887,"tags":890},[],[888,889],"粮食安全","food security",[],{"code":32,"score":615,"supported":603,"title":892,"abstract":893,"tags":894},[],[733,734],[],{"code":176,"score":815,"supported":603,"title":896,"abstract":897,"tags":899},[],[898],"农业生态",[],[901,902],"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":904,"openalex_id":906,"authors":907,"venue":855,"cited_by_count":424,"oa_url":852,"card":914,"direction":782,"ingested_from":642},"W7223091615",[908,911],{"name":909,"orcid":910},"Quanjun Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-6087-3928",{"name":912,"orcid":913},"Dongli Wu","https:\u002F\u002Forcid.org\u002F0000-0003-1676-2264",{"tldr":915,"method":916,"finding":917,"direction":782,"opportunity":918},"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。","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":921,"title":922,"url":923,"summary":924,"summary_zh":925,"content":11,"source_name":926,"source_url":923,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":709,"score_detail":927,"sources":929,"tags":931,"primary_topic":934,"secondary_topics":935,"classification":938,"search_phrases":987,"slug":990,"view_count":424,"doi":991,"paper":992,"created_at":1016},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",{"impact":361,"substance":321,"depth":361,"authority":416,"freshness":583,"relevant":455,"comment":928},"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。",[930],{"name":926,"url":923},[588,589,733,932,933],"作物水分胁迫","精准灌溉",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[936,937],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"status":597,"locked":581,"version":4,"method":598,"evidence":939},{"candidates":940,"agricultural_evidence":603},[941,945,951,958,964,968,972,976,982],{"code":152,"score":602,"supported":603,"title":942,"abstract":943,"tags":944},[153],[606,153],[933],{"code":24,"score":385,"supported":603,"title":946,"abstract":947,"tags":950},[],[948,949,610],"机器学习","machine learning",[589],{"code":48,"score":583,"supported":603,"title":952,"abstract":953,"tags":957},[],[954,955,956],"物联网","internet of things","IoT",[],{"code":16,"score":675,"supported":603,"title":959,"abstract":960,"tags":963},[],[961,962],"数字孪生","digital twin",[588],{"code":32,"score":675,"supported":603,"title":965,"abstract":966,"tags":967},[],[733,734],[733],{"code":216,"score":615,"supported":603,"title":969,"abstract":970,"tags":971},[],[218,217],[],{"code":224,"score":615,"supported":603,"title":973,"abstract":974,"tags":975},[],[226,225],[],{"code":240,"score":615,"supported":603,"title":977,"abstract":978,"tags":981},[],[979,980],"马铃薯","potato",[],{"code":286,"score":815,"supported":603,"title":983,"abstract":984,"tags":986},[],[985],"葡萄",[],[988,989],"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":991,"openalex_id":993,"authors":994,"venue":926,"cited_by_count":424,"oa_url":923,"card":1010,"direction":1015,"ingested_from":642},"W7223088921",[995,998,1001,1004,1007],{"name":996,"orcid":997},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":999,"orcid":1000},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":1002,"orcid":1003},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":1005,"orcid":1006},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":1008,"orcid":1009},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":1011,"method":1012,"finding":1013,"direction":782,"opportunity":1014},"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":1018,"title":1019,"url":1020,"summary":1021,"summary_zh":1022,"content":11,"source_name":1023,"source_url":1020,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1024,"score_detail":1025,"sources":1029,"tags":1031,"primary_topic":1034,"secondary_topics":1035,"classification":1038,"search_phrases":1068,"slug":1071,"view_count":424,"doi":1072,"paper":1073,"created_at":1090},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy",77,{"impact":711,"substance":1026,"depth":652,"authority":1027,"freshness":385,"relevant":455,"comment":1028},21,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[1030],{"name":1023,"url":1020},[588,948,716,1032,1033],"土壤墒情","冬小麦",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[1036,1037],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":597,"locked":581,"version":4,"method":598,"evidence":1039},{"candidates":1040,"agricultural_evidence":603},[1041,1047,1051,1055,1060,1064],{"code":216,"score":1042,"supported":603,"title":1043,"abstract":1045,"tags":1046},26,[1044,217],"winter wheat",[218,1044,217],[1033],{"code":144,"score":602,"supported":603,"title":1048,"abstract":1049,"tags":1050},[145],[618,145],[1032],{"code":24,"score":602,"supported":603,"title":1052,"abstract":1053,"tags":1054},[949],[948,949],[948],{"code":32,"score":385,"supported":603,"title":1056,"abstract":1057,"tags":1059},[],[733,734,1058],"multispectral",[716],{"code":152,"score":615,"supported":603,"title":1061,"abstract":1062,"tags":1063},[],[606,153],[],{"code":16,"score":400,"supported":603,"title":1065,"abstract":1066,"tags":1067},[],[588],[588],[1069,1070],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":1072,"openalex_id":1074,"authors":1075,"venue":1023,"cited_by_count":424,"oa_url":1020,"card":1085,"direction":1015,"ingested_from":642},"W7223156204",[1076,1079,1081,1083],{"name":1077,"orcid":1078},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":1080,"orcid":11},"Yi Zhang",{"name":1082,"orcid":11},"WEI Lei",{"name":1084,"orcid":11},"Chengcai Zhang",{"tldr":1086,"method":1087,"finding":1088,"direction":782,"opportunity":1089},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z",{"id":1092,"title":1093,"url":1094,"summary":1095,"summary_zh":1096,"content":11,"source_name":1097,"source_url":1094,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1098,"score_detail":1099,"sources":1101,"tags":1103,"primary_topic":1105,"secondary_topics":1106,"classification":1109,"search_phrases":1153,"slug":1156,"view_count":424,"doi":1157,"paper":1158,"created_at":1176},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":361,"substance":321,"depth":361,"authority":1027,"freshness":583,"relevant":455,"comment":1100},"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[1102],{"name":1097,"url":1094},[588,589,954,678,1104],"气候韧性",{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},[1107,1108],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},{"status":597,"locked":581,"version":4,"method":598,"evidence":1110},{"candidates":1111,"agricultural_evidence":603},[1112,1120,1124,1130,1135,1139,1143,1148],{"code":24,"score":361,"supported":603,"title":1113,"abstract":1114,"tags":1119},[610],[1115,948,1116,949,1117,1118],"深度学习","计算机视觉","deep learning","computer vision",[589],{"code":16,"score":711,"supported":603,"title":1121,"abstract":1122,"tags":1123},[679],[678,679],[588,678],{"code":48,"score":385,"supported":603,"title":1125,"abstract":1126,"tags":1129},[],[954,1127,956,1128],"边缘计算","edge computing",[954],{"code":152,"score":583,"supported":603,"title":1131,"abstract":1132,"tags":1134},[],[606,1133,153],"水资源",[],{"code":184,"score":675,"supported":603,"title":1136,"abstract":1137,"tags":1138},[],[818,883],[1104],{"code":144,"score":615,"supported":603,"title":1140,"abstract":1141,"tags":1142},[],[618,145],[],{"code":256,"score":815,"supported":603,"title":1144,"abstract":1145,"tags":1147},[],[1146],"间作",[],{"code":96,"score":815,"supported":603,"title":1149,"abstract":1150,"tags":1152},[],[1151],"养分管理",[],[1154,1155],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":1157,"openalex_id":1159,"authors":1160,"venue":1097,"cited_by_count":424,"oa_url":1094,"card":1171,"direction":1015,"ingested_from":642},"W7223121456",[1161,1164,1166,1168],{"name":1162,"orcid":1163},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":1165,"orcid":11},"Oluwadare OYEBODE",{"name":1167,"orcid":11},"Iretiayo Adelaiye",{"name":1169,"orcid":1170},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":1172,"method":1173,"finding":1174,"direction":640,"opportunity":1175},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":1178,"title":1179,"url":1180,"summary":1181,"summary_zh":1182,"content":11,"source_name":578,"source_url":1180,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1183,"score_detail":1184,"sources":1186,"tags":1188,"primary_topic":1192,"secondary_topics":1193,"classification":1196,"search_phrases":1221,"slug":1224,"view_count":424,"doi":1225,"paper":1226,"created_at":1236},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":361,"substance":508,"depth":711,"authority":416,"freshness":583,"relevant":455,"comment":1185},"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[1187],{"name":578,"url":1180},[589,1189,733,1190,1191],"农业物联网","智慧灌溉","节水农业",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[1194,1195],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":597,"locked":581,"version":4,"method":598,"evidence":1197},{"candidates":1198,"agricultural_evidence":603},[1199,1203,1207,1211,1215],{"code":152,"score":859,"supported":603,"title":1200,"abstract":1201,"tags":1202},[153],[606,1133,153],[1190,1191],{"code":48,"score":361,"supported":603,"title":1204,"abstract":1205,"tags":1206},[956],[954,955,956],[1189],{"code":32,"score":602,"supported":603,"title":1208,"abstract":1209,"tags":1210},[734],[733,734],[733],{"code":24,"score":416,"supported":603,"title":1212,"abstract":1213,"tags":1214},[610],[610],[589],{"code":192,"score":615,"supported":603,"title":1216,"abstract":1217,"tags":1220},[],[1218,1219],"可持续农业","sustainable agriculture",[],[1222,1223],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":1225,"openalex_id":1227,"authors":1228,"venue":578,"cited_by_count":424,"oa_url":1180,"card":1231,"direction":1015,"ingested_from":642},"W7223090389",[1229],{"name":1230,"orcid":11},"Patil",{"tldr":1232,"method":1233,"finding":1234,"direction":1015,"opportunity":1235},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":1238,"title":1239,"url":1240,"summary":1241,"summary_zh":1242,"content":11,"source_name":1243,"source_url":1240,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1098,"score_detail":1244,"sources":1246,"tags":1248,"primary_topic":1254,"secondary_topics":1255,"classification":1256,"search_phrases":1275,"slug":1278,"view_count":424,"doi":1279,"paper":1280,"created_at":1304},5549,"Characterising climate mitigation and adaptation performance in oil palm plantation: evidence from Indonesia","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1871877","Despite growing demand for ‘climate-friendly’ palm oil, little is known about how existing soil and management conditions align with climate mitigation and adaptation goals. This study characterised the climate mitigation and adaptation performance of oil palm (OP) production across different soil and management conditions based on simulated carbon balance and productivity. The Agricultural Production Systems SIMulator (APSIM)-OilPalm model was used to simulate productivity and carbon balance in 25 industrial OP sites on mineral soils in North Sumatra, Indonesia. K-means clustering was used to group similarity patterns between simulated carbon balance and productivity with observed soil and management conditions data. The model has good skill in simulating OP productivity at sites with standard plant density, achieving coefficient of determination (R 2 ) yield up to 0.79, though performance is poor at plantations with plant density outside the standard range or very low observed yields. Simulated OP productivity exhibits moderate variability from 15.38 to 28.67 t fresh fruit bunch (FFB) ha −1 yr. −1 . Simulated carbon balance, which is comparable to measured values in the region, indicates that all observed OP sites serve as a carbon sink with low variability from −2.09 to −3.86 t C eq. ha −1 yr. −1 , driven by uniformity in climate factors and plant density. Eleven of 25 sites have good mitigation and adaptation performance, indicated by high carbon sinks, yield and soil organic carbon (SOC) increment. These sites are characterised by greater plant density and moderate levels of N fertilisation, sand content and Ganoderma boninense (G. boninense) infection, with low carbon nitrogen (CN) ratio. Over half of sites face climate adaptation challenges, indicated by either low productivity or SOC increment and characterised by low plant density and nitrogen fertiliser with high G. boninense infection and sandy soils. This study contributes information about mitigation and adaptation in determining the management of OP and supports the tracking of climate-friendly palm oil within broader supply chains.","尽管对“气候友好型”棕榈油的需求日益增长，但人们对现有土壤和管理条件如何与气候减缓及适应目标相协调知之甚少。本研究基于模拟碳平衡和生产力，表征了油棕（OP）生产在不同土壤和管理条件下的气候减缓与适应表现。研究采用农业生产系统模拟器（APSIM）-OilPalm模型，模拟了印度尼西亚北苏门答腊25个矿质土壤工业油棕样点的生产力和碳平衡。运用K均值聚类方法，将模拟碳平衡与生产力之间的相似性模式与观测到的土壤和管理条件数据进行分组。该模型在标准种植密度样点的油棕生产力模拟中具有良好能力，决定系数（R²）最高达0.79，但在种植密度超出标准范围或观测产量极低的种植园中表现较差。模拟油棕生产力呈现中等变异性，范围为15.38至28.67 t鲜果串（FFB）ha⁻¹ yr.⁻¹。模拟碳平衡与该地区实测值相当，表明所有观测油棕样点均为碳汇，变异性较低，范围为−2.09至−3.86 t C eq. ha⁻¹ yr.⁻¹，这归因于气候因子和种植密度的一致性。25个样点中有11个具有良好的减缓和适应表现，表现为高碳汇、产量和土壤有机碳（SOC）增量。这些样点的特征是种植密度较大，氮肥施用量、砂含量和灵芝（Ganoderma boninense，G. boninense）感染程度适中，碳氮（CN）比低。超过半数样点面临气候适应挑战，表现为生产力或SOC增量较低，其特征是种植密度和氮肥用量低、灵芝感染率高及砂质土壤。本研究为确定油棕管理中的减缓和适应提供了信息，并支持在更广泛供应链中追踪气候友好型棕榈油。","Frontiers in Sustainable Food Systems",{"impact":361,"substance":321,"depth":361,"authority":1027,"freshness":583,"relevant":455,"comment":1245},"基于APSIM模型对印尼25个油棕园碳平衡与生产力的模拟研究，方法清晰、数据具体，对气候友好型油棕管理与供应链追踪有参考价值，但属区域性论文，影响范围有限。",[1247],{"name":1243,"url":1240},[1249,1250,1251,1252,1253],"作物模型","气候智慧农业","油棕","碳汇","可持续种植",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":1257},{"candidates":1258,"agricultural_evidence":603},[1259,1263,1267,1271],{"code":144,"score":615,"supported":603,"title":1260,"abstract":1261,"tags":1262},[],[618,145],[],{"code":160,"score":400,"supported":603,"title":1264,"abstract":1265,"tags":1266},[],[1252],[1252],{"code":184,"score":400,"supported":603,"title":1268,"abstract":1269,"tags":1270},[],[739],[1250],{"code":24,"score":455,"supported":581,"title":1272,"abstract":1273,"tags":1274},[],[],[1249],[1276,1277],"APSIM-OilPalm 油棕 碳平衡","印尼 油棕 气候适应","APSIM-OilPalm油棕碳平衡-5549","10.3389\u002Ffsufs.2026.1871877",{"doi":1279,"openalex_id":1281,"authors":1282,"venue":1243,"cited_by_count":424,"oa_url":1240,"card":1298,"direction":1302,"ingested_from":642},"W7223112249",[1283,1286,1289,1292,1295],{"name":1284,"orcid":1285},"Lisma Safitri","https:\u002F\u002Forcid.org\u002F0000-0002-7188-1058",{"name":1287,"orcid":1288},"Marcelo Valadares Galdos","https:\u002F\u002Forcid.org\u002F0000-0002-6080-0726",{"name":1290,"orcid":1291},"Iput Pradiko","https:\u002F\u002Forcid.org\u002F0000-0001-7877-8400",{"name":1293,"orcid":1294},"Alexis Comber","https:\u002F\u002Forcid.org\u002F0000-0002-3652-7846",{"name":1296,"orcid":1297},"Andrew Juan Challinor","https:\u002F\u002Forcid.org\u002F0000-0002-8551-6617",{"tldr":1299,"method":1300,"finding":1301,"direction":1302,"opportunity":1303},"用APSIM模型模拟印尼油棕园碳平衡与产量，评估其气候减缓与适应表现。","APSIM-OilPalm模型模拟25个站点，结合K-means聚类分析土壤与管","所有站点均为碳汇，11个站点减缓与适应表现良好，过半站点面临适应挑战。","农业绿色发展与碳","可扩展至不同气候带与泥炭地油棕园，结合遥感数据构建区域尺度气候友好型油棕认证体系。","2026-10-22T11:20:15.883770Z",{"id":1306,"title":1307,"url":1308,"summary":1309,"summary_zh":1310,"content":11,"source_name":1243,"source_url":1308,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1311,"score_detail":1312,"sources":1314,"tags":1316,"primary_topic":1322,"secondary_topics":1323,"classification":1325,"search_phrases":1344,"slug":1347,"view_count":424,"doi":1348,"paper":1349,"created_at":1371},5547,"Apple quality and soil biological diversity in long-term organic and IPM apple orchards","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1943487","Introduction While several studies have compared fruit quality in organic and conventional apple production, integrated assessments of fruit quality and soil biological diversity remain limited. Majority of previous studies have been based on relatively short-term experimental comparisons, although responses of soil biota and soil properties often require many years of consistent management to become fully expressed. Methods This study compared yield, fruit quality, and soil biological diversity in long-term organic (ORG) apple orchard and long-term integrated pest management (IPM) orchard under Nordic conditions. The study was conducted in 2022–2024 at the Polli Horticultural Research Centre (Estonia) using the cultivars “Katre”, “Krista,” and “Alesja”. Apple, yield, fruit quality attributes (soluble solids and titratable acids content and their ratio, ascorbic acid content and total polyphenols, total anthocyanins, fruit firmness, fruit colour, fruit mineral elements content), soil chemical parameters, earthworm and springtail communities were analysed. Results and Discussion Higher total and fresh-market yields were observed in the IPM orchard in two of the three cultivars, whereas the proportion of fresh-market fruit did not differ consistently between the two orchards. When averaged across cultivars, apples from ORG orchard exhibited more intense peel coloration. Differences in taste-related traits and antioxidant composition between cultivation systems were cultivar- and year-dependent rather than consistently associated with cultivation system. Total polyphenol content was consistently higher under ORG management only in “Katre,” while “Alesja” exhibited the highest total polyphenol and anthocyanin contents irrespective of cultivation system, highlighting the dominant role of genotype. Soil in the ORG orchard had higher pH and greater phosphorus and calcium contents, supporting a higher proportion of anecic earthworms, particularly Aporrectodea longa . Total earthworm abundance was higher in the IPM orchard, springtail abundance did not differ significantly between systems. These results suggest that the conditions in the long-term ORG were more favourable for ecologically important earthworm groups without consistently affecting internal fruit quality compared to IPM orchard.","引言 尽管已有若干研究比较了有机与常规苹果生产中的果实品质，但结合果实品质与土壤生物多样性的综合评价仍较为有限。以往研究大多基于相对短期的试验比较，而土壤生物区系和土壤性质的响应往往需要多年持续一致的管理才能充分显现。方法 本研究在北欧条件下比较了长期有机（ORG）苹果园与长期综合病虫害管理（IPM）苹果园的产量、果实品质及土壤生物多样性。研究于2022—2024年在爱沙尼亚Polli园艺研究中心开展，供试品种为“Katre”“Krista”和“Alesja”。分析了苹果产量、果实品质指标（可溶性固形物与可滴定酸含量及其比值、抗坏血酸含量、总多酚、总花青素、果实硬度、果实色泽、果实矿质元素含量）、土壤化学参数、蚯蚓和跳虫群落。结果与讨论 在3个品种中的2个品种上，IPM果园的总产量和鲜食市场产量较高，而鲜食市场果实比例在两种果园间并无一致差异。按品种平均，ORG果园苹果果皮色泽更深。栽培系统间口感相关性状和抗氧化成分的差异因品种和年份而异，而非与栽培系统稳定相关。总多酚含量仅在“Katre”中于ORG管理下持续较高，而“Alesja”无论栽培系统如何均表现出最高的总多酚和花青素含量，凸显了基因型的主导作用。ORG果园土壤具有较高的pH以及较高的磷和钙含量，支持了较高比例的内栖型蚯蚓，尤其是长穴居蚯蚓（Aporrectodea longa）。IPM果园蚯蚓总丰度较高，跳虫丰度在系统间无显著差异。这些结果表明，与IPM果园相比，长期ORG的条件更有利于生态上重要的蚯蚓类群，但并未对果实内部品质产生一致影响。",66,{"impact":173,"substance":508,"depth":711,"authority":1027,"freshness":583,"relevant":455,"comment":1313},"北欧长期定位试验对比有机与IPM苹果园的果实品质与土壤生物多样性，结论有新意但属区域尺度研究，公共影响有限。",[1315],{"name":1243,"url":1308},[1317,1318,1319,1320,1321],"长期定位试验","有机果园","土壤生物多样性","苹果品质","IPM",{"code":286,"slug":287,"name":288,"path":289,"parent_code":271},[1324],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"status":597,"locked":581,"version":4,"method":598,"evidence":1326},{"candidates":1327,"agricultural_evidence":603},[1328,1335,1339],{"code":286,"score":652,"supported":603,"title":1329,"abstract":1331,"tags":1334},[1330],"apple",[1332,1333,1330],"苹果","orchard",[],{"code":144,"score":345,"supported":603,"title":1336,"abstract":1337,"tags":1338},[145],[618,145],[],{"code":176,"score":815,"supported":603,"title":1340,"abstract":1341,"tags":1343},[],[1342],"生物多样性",[],[1345,1346],"爱沙尼亚 Polli 苹果 有机 IPM","苹果园 蚯蚓 跳虫 土壤","爱沙尼亚Polli苹果有机IPM-5547","10.3389\u002Ffsufs.2026.1943487",{"doi":1348,"openalex_id":1350,"authors":1351,"venue":1243,"cited_by_count":424,"oa_url":1308,"card":1366,"direction":1302,"ingested_from":642},"W7223104118",[1352,1355,1358,1360,1363],{"name":1353,"orcid":1354},"Ulvi Moor","https:\u002F\u002Forcid.org\u002F0000-0002-8808-8494",{"name":1356,"orcid":1357},"Annely Kuu","https:\u002F\u002Forcid.org\u002F0000-0002-0335-6243",{"name":1359,"orcid":11},"K. Kahu",{"name":1361,"orcid":1362},"P. Põldma","https:\u002F\u002Forcid.org\u002F0000-0002-7906-823X",{"name":1364,"orcid":1365},"Reelika Rätsep","https:\u002F\u002Forcid.org\u002F0000-0002-8673-9978",{"tldr":1367,"method":1368,"finding":1369,"direction":1302,"opportunity":1370},"比较长期有机与IPM苹果园的果实品质和土壤生物多样性。","在爱沙尼亚长期试验园测定三个品种的产量、品质及蚯蚓和跳虫群落。","有机园果皮色泽更深、土壤pH和磷钙更高，但果实内在品质差异多随品种和年份变化。","可结合长期定位试验，量化土壤动物群落与果实品质的关联机制，并拓展多区域验证。","2026-10-22T11:20:15.557635Z",{"id":1373,"title":1374,"url":1375,"summary":1376,"summary_zh":1377,"content":11,"source_name":1243,"source_url":1375,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1378,"score_detail":1379,"sources":1381,"tags":1383,"primary_topic":1389,"secondary_topics":1390,"classification":1391,"search_phrases":1404,"slug":1407,"view_count":424,"doi":1408,"paper":1409,"created_at":1443},5546,"Transforming olive mill solid waste: innovative biochar production for sustainable organic pollutant removal in olive mill wastewater treatment","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1977076","Introduction Olive mill wastewater (OMW) is characterized by a high organic load and complex composition, requiring efficient and sustainable treatment strategies. This study investigates the potential of biochar (BC) derived from olive mill solid waste (OMSW) as an adsorbent for the removal of total phenols (TPh) and chemical oxygen demand (COD) from OMW. Methods OMSW-derived BC was produced by slow pyrolysis at 600°C for 5 h and characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The BC exhibited a carbon content of 67.21%, a BET surface area of 135.2 m 2 \u002Fg, and a total pore volume of 0.0055 cm 3 \u002Fg. Batch adsorption experiments were conducted to evaluate the effects of contact time, BC dosage, and temperature. Adsorption kinetics, isotherms, and thermodynamic parameters were evaluated using established models. Results TPh adsorption followed a pseudo-second-order kinetic model with an excellent fit (R 2 = 1), whereas COD showed a weaker fit (R 2 = 0.76), indicating more complex adsorption behavior. Increasing the BC dosage from 0.05 to 0.5 g resulted in only limited improvements in removal efficiency, with COD removal increasing from 73.79% to 77.45% and TPh removal from 75.87% to 79.30%. Temperature significantly affected adsorption performance. COD removal reached 75.61% at 25°C and 79.46% at 35°C. TPh removal from OMW increased from 0.12% at 25°C to 19.15% at 35°C and reached 24% at 45°C. For the gallic acid (GA) solution, TPh removal reached 72.28% at 35°C before declining at 45°C. Isotherm analysis showed that the Freundlich model provided a better description of TPh adsorption at elevated temperatures, with R 2 = 0.9833 at 45°C. Thermodynamic analysis indicated that TPh adsorption was spontaneous and endothermic (ΔH° = +71.37 kJ\u002Fmol), with negative entropy suggesting increased ordering at the BC surface. Discussion The results demonstrate that OMSW-derived BC has potential as a sustainable adsorbent for the treatment of OMW, particularly for the removal of COD and phenolic compounds. The conversion of OMSW into BC provides an approach for valorizing an agricultural waste stream while producing an adsorbent with useful physicochemical properties. These findings support the potential application of agricultural waste-derived BC in targeted OMW treatment and contribute to more sustainable wastewater management practices in the olive oil sector.","引言 橄榄油厂废水（OMW）具有有机负荷高、成分复杂的特点，需要高效且可持续的处理策略。本研究探讨了由橄榄油厂固体废弃物（OMSW）制备的生物炭（BC）作为吸附剂去除OMW中总酚（TPh）和化学需氧量（COD）的潜力。方法 采用600°C慢速热解5 h制备OMSW衍生BC，并通过傅里叶变换红外光谱（FTIR）、扫描电子显微镜（SEM）和X射线衍射（XRD）对其进行表征。该BC的碳含量为67.21%，BET比表面积为135.2 m²\u002Fg，总孔容为0.0055 cm³\u002Fg。通过批式吸附实验评估接触时间、BC投加量和温度的影响。采用已有模型评价吸附动力学、等温线和热力学参数。结果 TPh吸附符合准二级动力学模型，拟合效果极佳（R² = 1），而COD拟合较差（R² = 0.76），表明其吸附行为更为复杂。将BC投加量从0.05 g增至0.5 g仅带来有限的去除效率提升，COD去除率从73.79%增至77.45%，TPh去除率从75.87%增至79.30%。温度显著影响吸附性能。COD去除率在25°C时为75.61%，在35°C时为79.46%。OMW中TPh去除率从25°C时的0.12%增至35°C时的19.15%，并在45°C时达到24%。对于没食子酸（GA）溶液，TPh去除率在35°C时达到72.28%，随后在45°C时下降。等温线分析表明，Freundlich模型能更好地描述高温下的TPh吸附，45°C时R² = 0.9833。热力学分析表明，TPh吸附为自发且吸热过程（ΔH° = +71.37 kJ\u002Fmol），负熵变表明BC表面有序性增加。讨论 结果表明，OMSW衍生BC具有作为可持续吸附剂处理OMW的潜力，尤其适用于去除COD和酚类化合物。将OMSW转化为BC为农业废弃物流的资源化利用提供了途径，同时可制备具有有用理化性质的吸附剂。这些发现支持农业废弃物衍生BC在针对性OMW处理中的潜在应用，并有助于橄榄油行业实现更可持续的废水管理实践。",71,{"impact":416,"substance":508,"depth":711,"authority":1027,"freshness":385,"relevant":455,"comment":1380},"该研究以橄榄油固废制备生物炭处理橄榄油废水，方法扎实、数据完整，但属细分领域实验室研究，产业影响有限，可作为论文类资讯收录。",[1382],{"name":1243,"url":1375},[1384,1385,1386,1387,1388],"生物炭","废水处理","橄榄油废水","农业废弃物资源化","吸附材料",{"code":168,"slug":169,"name":170,"path":171,"parent_code":128},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":1392},{"candidates":1393,"agricultural_evidence":603},[1394,1400],{"code":168,"score":615,"supported":603,"title":1395,"abstract":1396,"tags":1399},[],[1397,1398],"农业废弃物","agricultural waste",[],{"code":144,"score":455,"supported":581,"title":1401,"abstract":1402,"tags":1403},[],[],[1384],[1405,1406],"橄榄油废水 生物炭 吸附","橄榄果渣 生物炭 酚类去除","橄榄油废水生物炭吸附-5546","10.3389\u002Ffsufs.2026.1977076",{"doi":1408,"openalex_id":1410,"authors":1411,"venue":1243,"cited_by_count":424,"oa_url":1437,"card":1438,"direction":1302,"ingested_from":642},"W7223089477",[1412,1415,1418,1421,1424,1427,1430,1432,1434],{"name":1413,"orcid":1414},"Emad Al-Karablieh","https:\u002F\u002Forcid.org\u002F0000-0002-9721-5109",{"name":1416,"orcid":1417},"Imad Hamadneh","https:\u002F\u002Forcid.org\u002F0000-0002-4446-112X",{"name":1419,"orcid":1420},"Zakaria Al‐Qodah","https:\u002F\u002Forcid.org\u002F0000-0002-1120-3087",{"name":1422,"orcid":1423},"Massimo DEL BUBBA","https:\u002F\u002Forcid.org\u002F0000-0002-6326-6549",{"name":1425,"orcid":1426},"Tharaa M. Al-Zghoul","https:\u002F\u002Forcid.org\u002F0000-0003-2925-1957",{"name":1428,"orcid":1429},"Faissal Aziz","https:\u002F\u002Forcid.org\u002F0000-0001-9144-6400",{"name":1431,"orcid":11},"Aiman Awadh Bin Mokaizh",{"name":1433,"orcid":11},"Aseel Al-Khraisha",{"name":1435,"orcid":1436},"Ahmad I. Jamrah","https:\u002F\u002Forcid.org\u002F0009-0006-0224-844X","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1977076\u002Fpdf",{"tldr":1439,"method":1440,"finding":1441,"direction":1302,"opportunity":1442},"用橄榄油渣在600°C慢热解制备生物炭，吸附去除橄榄油废水中的酚类与COD。","600°C慢热解制炭，FTIR\u002FSEM\u002FXRD表征，批次吸附动力学、等温线与热力","COD去除约74-79%，总酚去除受温度显著影响，35°C时从0.12%升至19.15%。","可探究生物炭改性或活化提升酚类去除率，并评估吸附饱和再生与真实废水长期稳定性。","2026-10-22T11:20:15.445966Z",{"id":1445,"title":1446,"url":1447,"summary":1448,"summary_zh":1449,"content":11,"source_name":1450,"source_url":1447,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":709,"score_detail":1451,"sources":1453,"tags":1455,"primary_topic":1461,"secondary_topics":1462,"classification":1464,"search_phrases":1484,"slug":1487,"view_count":424,"doi":1488,"paper":1489,"created_at":1509},5541,"Year-round modeling of soil water-heat-salt transport and crop growth under film-mulched drip irrigation with freeze-thaw cycles","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105024","Context Soil water-heat-salt migration and crop growth in film-mulched drip irrigation fields in seasonally frozen regions are influenced by partial mulching and drip irrigation during the growth period, as well as soil freeze-thaw processes during the fallow period. Understanding year-round soil water-heat-salt dynamics and crop growth is essential for sustainable agricultural water use. Objective However, existing year-round models often use one-dimensional (1D) approaches during the growth period, which neglect horizontal salt migration under partial mulching and drip irrigation, leading to an overestimation of salt leaching efficiency. This limitation can be addressed by developing a year-round model that incorporates the distinct dynamics of both growth and fallow periods. Methods In this study, we developed a year-round coupled model, CAWM-S, by integrating the previously developed Crop and Water Model based on HYDRUS-2D (CAWM-H2D) with the SHAW model, which considers soil freeze-thaw processes. The key features of CAWM-S include detailed depiction of 2D soil water-heat-salt migration, crop growth and yield formation, and energy balance at the film-mulched soil surface during the growth period, as well as the redistribution of soil water-salt due to freeze-thaw processes in the fallow period. The model was calibrated and validated using monitored data from two consecutive year-round periods in a film-mulched drip irrigation cotton field in southern Xinjiang, a region with seasonal frozen soils, under different irrigation water salinity levels. This validation is treatment-wise and limited to the same site and two-year period; independent validation across other sites, years, crops, and irrigation systems remains necessary. Results and conclusions The simulated results for soil water, temperature, salinity, crop growth indicators, and yield generally matched the measured data across the two years with varying salinity levels, with treatment-averaged R 2 values ranging from 0.48 to 0.98 and NSE values 0.42 to 0.97. Furthermore, we analyzed the effects of different irrigation volume and salinity combinations on soil desalination rates, evapotranspiration and its components, crop yield, and water productivity, revealing a quadratic relationship between optimal irrigation volume and water salinity under film-mulched drip irrigation. Validation results show that CAWM-S effectively quantifies coupled soil water-heat-salt dynamics and crop growth, and predicts salt redistribution across year-round cycles in seasonally frozen regions. Significance It provides an effective tool for supporting sustainable agriculture by aiding water saving, control of soil salinization and improvement of crop water productivity in the film-mulched drip irrigation fields within seasonally frozen regions.","背景 季节性冻土区覆膜滴灌农田的土壤水热盐迁移与作物生长，受生育期内部分覆膜和滴灌以及休止期土壤冻融过程的影响。理解全年土壤水热盐动态及作物生长对于农业可持续用水至关重要。目的 然而，现有全年模型在生育期多采用一维（1D）方法，忽略了部分覆膜滴灌条件下的水平盐分迁移，导致对盐分淋洗效率的高估。这一局限可通过开发兼顾生育期与休止期不同动态特征的全年模型加以解决。方法 本研究将前期开发的基于HYDRUS-2D的作物与水分模型（CAWM-H2D）与考虑土壤冻融过程的SHAW模型相耦合，构建了全年耦合模型CAWM-S。CAWM-S的主要特点包括：详细刻画生育期二维土壤水热盐迁移、作物生长与产量形成以及覆膜土壤表面能量平衡，同时描述休止期冻融过程引起的土壤水盐再分布。模型采用南疆季节性冻土区覆膜滴灌棉田连续两个全年周期的监测数据，在不同灌溉水矿化度水平下进行率定与验证。该验证按处理进行，且仅限于同一站点和两年时段；仍需在其他站点、年份、作物及灌溉系统下开展独立验证。结果与结论 在两个不同矿化度水平的年份中，土壤水分、温度、盐分、作物生长指标及产量的模拟结果与实测数据总体吻合，处理平均R²为0.48~0.98，NSE为0.42~0.97。此外，分析了不同灌水量与矿化度组合对土壤脱盐率、蒸散及其组分、作物产量和水分生产率的影响，揭示了覆膜滴灌条件下最优灌水量与灌溉水矿化度之间的二次关系。验证结果表明，CAWM-S能够有效量化土壤水热盐耦合动态与作物生长，并预测","Agricultural Systems",{"impact":711,"substance":321,"depth":361,"authority":345,"freshness":583,"relevant":455,"comment":1452},"构建了覆盖冻融期的全年水热盐与作物生长耦合模型，方法有新意、验证数据扎实，对盐碱地滴灌节水有实用价值，但属单点两年验证的学术成果，产业影响有限。",[1454],{"name":1450,"url":1447},[1456,1457,1458,1459,1460],"棉花","盐碱地治理","节水灌溉","覆膜滴灌","土壤水盐运移",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[1463],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"status":597,"locked":581,"version":4,"method":598,"evidence":1465},{"candidates":1466,"agricultural_evidence":603},[1467,1471,1475,1480],{"code":152,"score":361,"supported":603,"title":1468,"abstract":1469,"tags":1470},[153],[606,153,812],[1458],{"code":144,"score":602,"supported":603,"title":1472,"abstract":1473,"tags":1474},[145],[618,145],[1457],{"code":248,"score":400,"supported":603,"title":1476,"abstract":1477,"tags":1479},[],[1478],"cotton",[1456],{"code":192,"score":815,"supported":603,"title":1481,"abstract":1482,"tags":1483},[],[1219],[],[1485,1486],"南疆 覆膜滴灌 棉花","CAWM-S 模型 冻融","南疆覆膜滴灌棉花-5541","10.1016\u002Fj.agsy.2026.105024",{"doi":1488,"openalex_id":1490,"authors":1491,"venue":1450,"cited_by_count":424,"oa_url":1447,"card":1504,"direction":782,"ingested_from":642},"W7223120642",[1492,1495,1497,1500,1502],{"name":1493,"orcid":1494},"Dong Lin","https:\u002F\u002Forcid.org\u002F0000-0001-8433-9658",{"name":1496,"orcid":11},"Tongkai Guo",{"name":1498,"orcid":1499},"Zunqiu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-9393-3605",{"name":1501,"orcid":11},"Wenping Bi",{"name":1503,"orcid":11},"Xiaomin Mao",{"tldr":1505,"method":1506,"finding":1507,"direction":782,"opportunity":1508},"构建全年耦合模型CAWM-S，模拟覆膜滴灌棉田水热盐运移与作物生长。","将HYDRUS-2D的CAWM-H2D与冻融模型SHAW耦合，用南疆两年监测数据","模型精度较高（R²0.48-0.98），揭示最优灌溉量与灌水盐度呈二次关系。","可扩展至多站点、多作物与不同灌溉制度的独立验证，并耦合遥感数据实现区域尺度模拟。","2026-10-22T11:20:12.238655Z",{"id":1511,"title":1512,"url":1513,"summary":1514,"summary_zh":11,"content":11,"source_name":1515,"source_url":11,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1516,"score_detail":1517,"sources":1519,"tags":1521,"primary_topic":1526,"secondary_topics":1527,"classification":1530,"search_phrases":1550,"slug":1553,"view_count":455,"doi":11,"paper":1554,"created_at":1562},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21",82,{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":385,"relevant":455,"comment":1518},"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[1520],{"name":1515,"url":1513},[588,1522,1523,1524,1525],"农业遥感","作物表型","无人机多光谱","小麦渍害",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[1528,1529],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"status":597,"locked":581,"version":4,"method":598,"evidence":1531},{"candidates":1532,"agricultural_evidence":603},[1533,1537,1541,1546],{"code":216,"score":524,"supported":603,"title":1534,"abstract":1535,"tags":1536},[218],[218],[],{"code":32,"score":524,"supported":603,"title":1538,"abstract":1539,"tags":1540},[1524],[],[1523,1522,1524],{"code":184,"score":173,"supported":603,"title":1542,"abstract":1544,"tags":1545},[1543],"渍害",[],[],{"code":16,"score":455,"supported":581,"title":1547,"abstract":1548,"tags":1549},[],[],[588],[1551,1552],"江苏省农科院 小麦渍害 多光谱","VGG21 小麦 硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":11,"openalex_id":11,"authors":1555,"venue":11,"cited_by_count":424,"oa_url":11,"card":1556,"direction":782,"ingested_from":1561},[],{"tldr":1557,"method":1558,"finding":1559,"direction":782,"opportunity":1560},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","agent","2026-10-22T00:07:10.070916Z",{"id":1564,"title":1565,"url":1566,"summary":1567,"summary_zh":11,"content":1568,"source_name":1569,"source_url":11,"published_at":708,"category":1570,"cover_url":11,"hotness":385,"is_selected":581,"score":1571,"score_detail":1572,"sources":1574,"tags":1576,"primary_topic":1581,"secondary_topics":1582,"classification":1585,"search_phrases":1611,"slug":1614,"view_count":424,"doi":11,"paper":11,"created_at":1615},5519,"四川农科院资环所赴简阳调研 推进粗饲料加工与种养循环落地","https:\u002F\u002Fwww.chinawestagr.com\u002Ftszl\u002Fshowts.asp?id=67504","10月9日院资环所（草业所）副所长朱永群一行赴简阳市平泉街道调研座谈，汇报「天府农科」四川省平原丘陵区粗饲料加工利用研究院项目建设进展。三方就共建饲草料加工示范点、推进种草养猪等绿色循环项目达成共识，强调立足实际选准主导品种与技术模式，形成「饲料—养殖—粪肥—农田」闭环，把示范基地建成可学可复制的样板。","λԺʻ                ʱ䣺2026\u002F10\u002F16331\n\nΪ᳹ʵҹƶDzݲҵչľ߲𣬼ӿƼɹת2026109գԺʻҵȺоԱһиƽȪ̸ֵƽȪֵίֵ֣ίǡ´¼ֵũҵۺϷġͩйظͬ־пũƼ޹˾³¡ܾˣԺʻҵƹܿƿƳ޸㸱оԱҵ̷оʿȲμӡ\n\nϣ㱨“‘츮ũ’ĴʡƽԭϼӹоԺĿ”Ľ豳岼ֺ½չ¶³˹˾ҵͺϣDzϼӹʾ㣬ӿƽֲɫѭĿ˽ֵԴͲҵʾֵոԴḻֳϺãDzֲϻôַţϣڼ˲ϵõԺֵ֧֣õءùϡ\n\nȺڽָҪʵѡ׼ƷֺͼģʽḻDzϲƷߣͻ“Բݴ”ѭγ“—ֳ——ũ”ջעر׼ƶƷʾؽɿѧɸƵ塣","四川省农业科学院 2026-10-20","报道",49,{"impact":416,"substance":385,"depth":173,"authority":1027,"freshness":615,"relevant":455,"comment":1573},"省级科研机构赴地方推进粗饲料加工与种养循环落地的调研通稿，有具体合作与示范意图，但属区域性进展，信息增量有限，不宜进入每日精选。",[1575],{"name":1569,"url":1566},[1577,1578,1579,1580],"院企合作","科技成果转化","种养循环","粗饲料加工",{"code":364,"slug":365,"name":366,"path":367,"parent_code":332},[1583,1584],{"code":442,"slug":443,"name":444,"path":445,"parent_code":434},{"code":192,"slug":193,"name":194,"path":195,"parent_code":128},{"status":597,"locked":581,"version":4,"method":598,"evidence":1586},{"candidates":1587,"agricultural_evidence":603},[1588,1594,1598,1602,1607],{"code":364,"score":345,"supported":603,"title":1589,"abstract":1591,"tags":1593},[1590],"饲料",[1590,1592],"饲草",[],{"code":442,"score":524,"supported":603,"title":1595,"abstract":1596,"tags":1597},[1580],[1580],[],{"code":192,"score":583,"supported":603,"title":1599,"abstract":1600,"tags":1601},[1579],[],[1579],{"code":340,"score":815,"supported":603,"title":1603,"abstract":1604,"tags":1606},[],[1605],"养猪",[],{"code":488,"score":69,"supported":581,"title":1608,"abstract":1609,"tags":1610},[],[],[1578,1577],[1612,1613],"四川省农科院 简阳 粗饲料","简阳 种养循环 示范基地","四川省农科院简阳粗饲料-5519","2026-10-22T00:07:04.243033Z",{"id":1617,"title":1618,"url":1619,"summary":1620,"summary_zh":11,"content":1621,"source_name":1622,"source_url":11,"published_at":579,"category":1623,"cover_url":11,"hotness":385,"is_selected":581,"score":1098,"score_detail":1624,"sources":1627,"tags":1629,"primary_topic":1634,"secondary_topics":1635,"classification":1636,"search_phrases":1674,"slug":1677,"view_count":424,"doi":11,"paper":11,"created_at":1678},5498,"江西省「十五五」农业农村现代化规划印发 部署智慧农业与四季接续","https:\u002F\u002Fjx.ifeng.com\u002Fc\u002F8xPB4YCdgGF","《江西省「十五五」农业农村现代化规划》（赣府发〔2026〕16号）围绕32项重点任务展开，明确推进信息技术与农业深度融合，加快农业人工智能技术创新与场景拓展，建设农业农村大数据平台。规划还部署井冈山国家农业科技园区升建国家农高区，推广「村播」「田播」电商直播带货，并开展农产品碳足迹管理与农业碳交易。","（一）提升粮食等重要农产品供给保障水平。坚持产量产能、生产生态、增产增收一起抓，深入实施藏粮于地、藏粮于技战略，落实新一轮粮食产能提升行动，实施粮油作物大面积单产提升行动，建设吨粮片、高产片，促进良田良种良机良法集成增效。优化农产品种植结构，稳定水稻、油菜生产，大力发展油茶产业，提升大豆产能。优化生猪产能综合调控机制，推进牛羊产业扩量提质，促进畜禽养殖节本提质增效。推动农产品品种培优和品质提升，增加营养、健康、风味农产品供给。完善价格、补贴、保险“三位一体”的种粮农民收益保障机制，建立健全与周边省份粮食产销协作机制，强化粮食产购储加销协同保障。\n\n（二）构建多元化食物供给体系。践行大食物观，坚持农林牧渔并举，全方位、多用途开发食物资源。提升“菜篮子”产业质量和效益，推动蔬菜、水果、茶叶、中药材产业优质发展。丰富畜产品供给，发展蜂、兔、肉鸽、鹌鹑等特色养殖。积极发展大水面生态渔业，科学推进稻渔综合种养。发展林下经济，壮大林药和森林食品。发展富硒食品与功能食品，开发新型食品资源。\n\n（三）加强耕地保护与质量提升。严格落实耕地保护党政同责，严守耕地保护红线。统筹农用地布局优化，严格耕地占补平衡管理，规范补充耕地质量验收。科学开展红壤改良利用，推进酸化耕地治理，分阶段实施耕地有机质提升行动。分区分类高质量推进高标准农田建设，对新建和改造提升实行差异化投入，优先在鄱阳湖平原、赣抚平原、吉泰盆地等区域推进整区域建设，逐步把具备条件的永久基本农田建成高标准农田。完善高标准农田立项、建设、验收和管护机制，编制高标准农田建设全流程工作规范，统一建设标准、施工工艺、管理流程，压实管护责任。开展高标准农田整治提升行动，按照“集中修复一批、改造提升一批、疏通灌溉一批、置换退出一批”分类施策，逐步补齐设施短板、消除存量问题。健全完善问题发现响应处置机制。实施大中型灌区现代化建设与改造，推动井冈灌区、赣抚平原灌区等项目建设，强化高标准农田渠系与灌区骨干工程衔接。\n\n（四）提升农业防灾减灾救灾能力。发展韧性农业，完善农业气象、水文、地质灾害等监测预警机制。推进防洪排涝抗旱设施建设，持续开展农田沟渠修复整治、中小河流和平原涝区治理。完善农机应急救灾设施装备条件,健全和落实救灾机具配置、救灾备荒种子储备和应急调用机制，加快补齐烘干、仓储等设施装备短板。强化病虫疫情和重大动物疫病监测预警、联防联控和应急处置，建强县乡动植物疫病防控专业队伍。","凤凰网江西 2026-10-21","政策",{"impact":321,"substance":1625,"depth":361,"authority":173,"freshness":583,"relevant":455,"comment":1626},23,"省级「十五五」农业农村现代化规划全文，条款与数字细节充实，涉及智慧农业、高标准农田、粮食产能等实质部署，值得进入每日精选。",[1628],{"name":1622,"url":1619},[588,1630,1631,1632,1633],"高标准农田","十五五规划","大食物观","粮食产能",{"code":160,"slug":161,"name":162,"path":163,"parent_code":128},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":1637},{"candidates":1638,"agricultural_evidence":603},[1639,1644,1648,1654,1658,1662,1666,1670],{"code":549,"score":524,"supported":603,"title":1640,"abstract":1642,"tags":1643},[1641],"农业农村现代化",[1641],[],{"code":16,"score":583,"supported":603,"title":1645,"abstract":1646,"tags":1647},[588],[],[588],{"code":160,"score":615,"supported":603,"title":1649,"abstract":1650,"tags":1653},[],[1651,1652],"农业碳","碳足迹",[],{"code":24,"score":815,"supported":603,"title":1655,"abstract":1656,"tags":1657},[],[589],[],{"code":263,"score":455,"supported":581,"title":1659,"abstract":1660,"tags":1661},[],[],[1633],{"code":450,"score":455,"supported":581,"title":1663,"abstract":1664,"tags":1665},[],[],[1632],{"code":136,"score":455,"supported":581,"title":1667,"abstract":1668,"tags":1669},[],[],[1630],{"code":542,"score":455,"supported":581,"title":1671,"abstract":1672,"tags":1673},[],[],[1631],[1675,1676],"江西 十五五 农业农村现代化规划","江西 高标准农田 鄱阳湖平原","江西十五五农业农村现代化规划-5498","2026-10-22T00:06:58.106992Z",{"id":1680,"title":1681,"url":1682,"summary":1683,"summary_zh":1684,"content":11,"source_name":1685,"source_url":1682,"published_at":1686,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":45,"score_detail":1687,"sources":1689,"tags":1691,"primary_topic":1692,"secondary_topics":1693,"classification":1695,"search_phrases":1725,"slug":1728,"view_count":424,"doi":1729,"paper":1730,"created_at":1745},5496,"Multi-Crop Irrigation Need Classification Based on Machine Learning with an Indonesian-Language GenAI-UX Recommendation Layer","https:\u002F\u002Fdoi.org\u002F10.30871\u002Fjaic.v10i5.13774","Inefficient irrigation water use remains a critical challenge in Indonesian agriculture, and the complexity of raw sensor data hinders adoption by smallholder farmers who lack data-analysis skills. This study develops an intelligent irrigation Decision Support System that integrates interpretable Machine Learning with an Indonesian-language GenAI-UX recommendation layer, using a publicly available multi-crop irrigation scheduling dataset (501 records, nine crops) with an analytical focus on paddy and maize. Four components are combined into one pipeline: (i) irrigation-need classification compared across six algorithms; (ii) regression of a continuous water-requirement score; (iii) sensor-anomaly detection using Isolation Forest cross-validated with the Interquartile Range method; and (iv) a rule-based natural-language recommendation layer whose operating point is optimized by a Genetic Algorithm. Validity was safeguarded through stratified five-fold cross-validation and leakage-controlled preprocessing. Gradient Boosting achieved the best classification performance (F1-score 0.980; cross-validated F1 0.963), Random Forest Regressor reached R square of 0.807, Isolation Forest agreed with IQR outliers at 95.2%, and the Genetic Algorithm attained a confidence of 0.993 within realistic agronomic bounds. In an illustrative usability evaluation, the GenAI-UX interface obtained the highest System Usability Scale score (Good category). The results indicate that pairing accurate, interpretable models with a language-based delivery layer can transform complex sensor data into actionable irrigation recommendations, lowering the adoption barrier for non-technical users. Field validation with real respondents and edge deployment are identified as the next steps.","灌溉用水效率低下仍是印度尼西亚农业面临的一项关键挑战，而原始传感器数据的复杂性阻碍了缺乏数据分析技能的小农户采用相关技术。本研究开发了一种智能灌溉决策支持系统（Decision Support System），将可解释的机器学习与印度尼西亚语生成式人工智能用户体验（GenAI-UX）推荐层相结合，使用公开可用的多作物灌溉调度数据集（501条记录，九种作物），并以水稻和玉米为分析重点。四个组件被整合为一条流水线：（i）在六种算法之间比较灌溉需求分类；（ii）对连续需水量得分进行回归；（iii）使用孤立森林（Isolation Forest）进行传感器异常检测，并与四分位距（IQR）方法交叉验证；（iv）基于规则的自然语言推荐层，其操作点由遗传算法优化。通过分层五折交叉验证和防泄漏预处理保障了有效性。梯度提升（Gradient Boosting）取得了最佳分类性能（F1分数0.980；交叉验证F1为0.963），随机森林回归器（Random Forest Regressor）达到R平方0.807，孤立森林与IQR异常值的一致率为95.2%，遗传算法在现实农艺范围内达到0.993的置信度。在一项示例性可用性评估中，GenAI-UX界面获得了最高的系统可用性量表（System Usability Scale）得分（良好类别）。结果表明，将准确、可解释的模型与基于语言的交付层相结合，能够将复杂的传感器数据转化为可操作的灌溉建议，从而降低非技术用户的采用门槛。与真实受访者进行田间验证以及边缘部署被确定为下一步工作。","Journal of Applied Informatics and Computing","2026-10-19T00:00:00Z",{"impact":416,"substance":508,"depth":652,"authority":416,"freshness":173,"relevant":455,"comment":1688},"方法组合完整、指标扎实的农业AI决策支持论文，但属印尼小农户场景的实验室级研究，公共影响有限，适合作为技术参考而非每日精选头条。",[1690],{"name":1685,"url":1682},[588,589,210,226,590],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},[1694],{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},{"status":597,"locked":581,"version":4,"method":598,"evidence":1696},{"candidates":1697,"agricultural_evidence":603},[1698,1702,1706,1711,1715,1721],{"code":24,"score":361,"supported":603,"title":1699,"abstract":1700,"tags":1701},[949],[948,949,612],[589],{"code":152,"score":602,"supported":603,"title":1703,"abstract":1704,"tags":1705},[153],[606,153],[590],{"code":208,"score":675,"supported":603,"title":1707,"abstract":1708,"tags":1710},[],[210,1709],"paddy",[210],{"code":224,"score":675,"supported":603,"title":1712,"abstract":1713,"tags":1714},[],[226,225],[226],{"code":496,"score":615,"supported":603,"title":1716,"abstract":1717,"tags":1720},[],[1718,1719],"小农户","smallholder",[],{"code":16,"score":455,"supported":581,"title":1722,"abstract":1723,"tags":1724},[],[],[588],[1726,1727],"印尼 多作物 灌溉决策","GenAI 灌溉推荐","印尼多作物灌溉决策-5496","10.30871\u002Fjaic.v10i5.13774",{"doi":1729,"openalex_id":1731,"authors":1732,"venue":1685,"cited_by_count":424,"oa_url":1682,"card":1740,"direction":640,"ingested_from":642},"W7223016583",[1733,1736,1738],{"name":1734,"orcid":1735},"Muhammad Rusdi","https:\u002F\u002Forcid.org\u002F0000-0002-1048-5000",{"name":1737,"orcid":11},"Zaenuddin Zaenuddin",{"name":1739,"orcid":11},"Hendra Sanjaya",{"tldr":1741,"method":1742,"finding":1743,"direction":1015,"opportunity":1744},"构建融合可解释机器学习与印尼语GenAI推荐层的智能灌溉决策支持系统。","六种分类算法、回归、孤立森林异常检测与遗传算法优化规则推荐层。","梯度提升分类F1达0.980，GenAI界面可用性最佳，降低非技术用户使用门槛。","可探索多语言本地化GenAI推荐层与边缘部署，并开展真实农户田间验证。","2026-10-21T23:30:46.441864Z",{"id":1747,"title":1748,"url":1749,"summary":1750,"summary_zh":1751,"content":11,"source_name":1752,"source_url":1749,"published_at":1686,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1024,"score_detail":1753,"sources":1755,"tags":1757,"primary_topic":1761,"secondary_topics":1762,"classification":1763,"search_phrases":1784,"slug":1787,"view_count":424,"doi":1788,"paper":1789,"created_at":1809},5490,"Biochar for post-war land remediation in Ukraine: A critical review supported by bibliometric analysis","https:\u002F\u002Fdoi.org\u002F10.1093\u002Finteam\u002Fvjag167","This critical review analyzes the current state of scientific research on biochar in Europe and examines its potential use for post-war restoration and improvement of soil fertility in Ukraine. A bibliometric analysis of scientific publications in the Scopus and Web of Science databases for the period 2014-2023 was conducted using VOSviewer visualization and data processing methods. The analysis identified the main areas of research and gaps in knowledge that are relevant to the future application of this technology in Ukraine. Moreover, remote sensing methodologies were employed to identify potential remediation requirements in the Ukrainian territory. This involved the analysis of changes in the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) of a part of Ukraine's agricultural territory situated along the frontline, prior to and following a period of three years during a full-scale war. The results revealed changes in vegetation and moisture levels, indicating land degradation in the border area under study. However, these observations do not constitute direct evidence of soil contamination or the effectiveness of biochar; rather, they indicate areas where further field studies and an assessment of restoration measures are needed. A strengths, weaknesses, opportunities, and threats (SWOT) analysis was used as a strategic discussion tool to identify the opportunities and challenges associated with the implementation of biochar in Ukraine. This review also identifies the main scientific uncertainties, barriers, economic aspects, and research priorities that should be considered before the large-scale implementation of biochar-based technologies in Ukraine.","这篇批判性综述分析了欧洲生物炭（biochar）科学研究的现状，并探讨其在乌克兰战后恢复与土壤肥力提升中的潜在应用。研究采用VOSviewer可视化与数据处理方法，对Scopus和Web of Science数据库中2014—2023年的科学出版物进行了文献计量分析。分析识别了与该项技术在乌克兰未来应用相关的主要研究领域和知识空白。此外，研究采用遥感方法识别乌克兰境内潜在的修复需求，具体包括对乌克兰沿前线部分农业区域在全面战争前后三年期间的归一化植被指数（NDVI）和归一化水分指数（NDMI）变化进行分析。结果显示植被与水分水平发生变化，表明所研究边境地区存在土地退化。然而，这些观测结果并不构成土壤污染或生物炭有效性的直接证据，而是指示了需要进一步开展田间研究和评估修复措施的区域。研究采用优势、劣势、机会与威胁（SWOT）分析作为战略讨论工具，识别在乌克兰实施生物炭相关的机遇与挑战。本综述还明确了在乌克兰大规模实施基于生物炭的技术之前应考虑的主要科学不确定性、障碍、经济因素及研究优先事项。","Integrated Environmental Assessment and Management",{"impact":711,"substance":1026,"depth":361,"authority":345,"freshness":173,"relevant":455,"comment":1754},"该综述结合文献计量与遥感NDVI\u002FNDMI分析，评估生物炭用于乌克兰战后农田修复的潜力与不确定性，方法新颖、结论审慎，对农业遥感与土壤治理具参考价值，但属区域性研究，未达重大突破层级。",[1756],{"name":1752,"url":1749},[1384,1758,716,1759,1760],"土地退化","土壤修复","乌克兰农业",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":1764},{"candidates":1765,"agricultural_evidence":603},[1766,1772,1776,1780],{"code":64,"score":345,"supported":603,"title":1767,"abstract":1769,"tags":1771},[1768],"bibliometric",[1770,1768],"文献计量",[],{"code":144,"score":173,"supported":603,"title":1773,"abstract":1774,"tags":1775},[],[618,145],[1759,1384],{"code":32,"score":675,"supported":603,"title":1777,"abstract":1778,"tags":1779},[],[733,734],[716],{"code":136,"score":455,"supported":581,"title":1781,"abstract":1782,"tags":1783},[],[],[1758],[1785,1786],"生物炭 战后土壤修复 乌克兰","NDVI NDMI 遥感 土地退化","生物炭战后土壤修复乌克兰-5490","10.1093\u002Finteam\u002Fvjag167",{"doi":1788,"openalex_id":1790,"authors":1791,"venue":1752,"cited_by_count":424,"oa_url":11,"card":1804,"direction":782,"ingested_from":642},"W7223053668",[1792,1795,1798,1801],{"name":1793,"orcid":1794},"Olha Shomko","https:\u002F\u002Forcid.org\u002F0000-0003-2418-0985",{"name":1796,"orcid":1797},"Iryna Davydova","https:\u002F\u002Forcid.org\u002F0000-0001-6535-3948",{"name":1799,"orcid":1800},"Marco Bartoli","https:\u002F\u002Forcid.org\u002F0000-0002-5022-6206",{"name":1802,"orcid":1803},"Elena Maestri","https:\u002F\u002Forcid.org\u002F0000-0002-8745-9564",{"tldr":1805,"method":1806,"finding":1807,"direction":1302,"opportunity":1808},"综述生物炭用于乌克兰战后土地修复，结合文献计量与遥感分析。","Scopus\u002FWoS文献计量、VOSviewer、NDVI\u002FNDMI遥感与SWO","前线农区植被与湿度变化显示土地退化，但需田间验证生物炭效果。","可研究战区土地退化的遥感快速诊断与生物炭修复效果的田间验证体系。","2026-10-21T23:30:25.223030Z",{"id":1811,"title":1812,"url":1813,"summary":1814,"summary_zh":1815,"content":11,"source_name":1816,"source_url":1813,"published_at":708,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":45,"score_detail":1817,"sources":1819,"tags":1821,"primary_topic":1825,"secondary_topics":1826,"classification":1827,"search_phrases":1851,"slug":1854,"view_count":424,"doi":1855,"paper":1856,"created_at":1886},5487,"Quantification of soil loss by using RUSLE model under different land use and land cover conditions in AKANYARU catchment in Rwanda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44378-026-00347-1","This study quantified annual soil loss across the Akanyaru Catchment (3,402 km², Rwanda) under four land use and land cover types forest, agriculture, residential, and bare soil using the RUSLE model integrated with GIS. The Rainfall erosivity (R) is derived from 20 years of daily rainfall data obtained from the NASA database. The Soil erodibility (K) used is from global soil texture data; slope length and steepness (LS) from a 30 m DEM; and cover management (C) from Landsat 8 NDVI. The support practice factor (P) was set to 1.0, reflecting the absence of formal conservation structures in the catchment. The Soil loss range from 0 to 51.4391 t\u002Fha\u002Fyear . Near zero soil loss occurred under dense forest cover, while the highest rates were concentrated in bare and intensively farmed areas on steep slopes particularly in the northeastern and southwestern sub-catchments. The three RUSLE factors showing greatest spatial variationwere steep topography (LS: 0–1.678), degraded or absent vegetation cover (C: 0.0004–0.524), and high rainfall erosivity (R: 527.507–888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹). Findings call for prioritizing terracing and contour farming on steep agricultural slopes, expanding agroforestry and cover crops to reduce surface exposure, and improving soil organic matter in erodible sandy-silty soils. The spatially explicit outputs directly support evidence based targeting of conservation resources by Rwanda’s land and water management authorities.","本研究采用RUSLE模型并结合GIS，对阿卡尼亚鲁流域（面积3 402 km²，位于卢旺达）在森林、农业、居民区和裸地四种土地利用与土地覆盖类型下的年土壤流失量进行了定量评估。降雨侵蚀力（R）因子基于从NASA数据库获取的20年日降雨数据推算得出；土壤可蚀性（K）因子采用全球土壤质地数据；坡长与坡度（LS）因子基于30 m数字高程模型（DEM）提取；覆盖管理（C）因子由Landsat 8 NDVI数据获得。支持措施因子（P）设为1.0，以反映该流域缺乏正式的水土保持措施。土壤流失量范围为0～51.4391 t\u002Fha\u002F年。茂密森林覆盖下土壤流失量接近于零，而流失率最高的区域集中在陡坡上的裸地和集约耕作区，尤其是东北部和西南部子流域。空间变异最大的三个RUSLE因子分别为陡峭地形（LS：0～1.678）、退化或缺失的植被覆盖（C：0.0004～0.524）以及高降雨侵蚀力（R：527.507～888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹）。研究结果呼吁优先在陡坡农地实施梯田与等高耕作，扩大农林复合经营和覆盖作物以减少地表裸露，并改善易侵蚀砂质粉砂土壤的有机质含量。空间显式输出结果可直接为卢旺达土地与水资源管理部门基于证据的水土保持资源靶向配置提供支持。","Discover Soil.",{"impact":416,"substance":508,"depth":711,"authority":1027,"freshness":173,"relevant":455,"comment":1818},"基于RUSLE与GIS的流域土壤流失量化研究，方法规范、数据扎实，对水土保持与遥感应用有参考价值，但属区域性案例，公共影响有限。",[1820],{"name":1816,"url":1813},[733,1822,1823,1824],"土地利用","土壤侵蚀","水土保持",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[],{"status":597,"locked":581,"version":4,"method":598,"evidence":1828},{"candidates":1829,"agricultural_evidence":603},[1830,1834,1838,1842,1847],{"code":144,"score":345,"supported":603,"title":1831,"abstract":1832,"tags":1833},[145],[618,145],[],{"code":176,"score":408,"supported":603,"title":1835,"abstract":1836,"tags":1837},[],[1824],[1822,1824],{"code":152,"score":815,"supported":603,"title":1839,"abstract":1840,"tags":1841},[],[1133],[],{"code":40,"score":815,"supported":603,"title":1843,"abstract":1844,"tags":1846},[],[1845],"GIS",[],{"code":32,"score":455,"supported":581,"title":1848,"abstract":1849,"tags":1850},[],[],[733],[1852,1853],"RUSLE 模型 卢旺达","Akanyaru 流域 土壤流失","RUSLE模型卢旺达-5487","10.1007\u002Fs44378-026-00347-1",{"doi":1855,"openalex_id":1857,"authors":1858,"venue":1816,"cited_by_count":424,"oa_url":1880,"card":1881,"direction":782,"ingested_from":642},"W7223058039",[1859,1862,1865,1868,1871,1874,1877],{"name":1860,"orcid":1861},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":1863,"orcid":1864},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":1866,"orcid":1867},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":1869,"orcid":1870},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":1872,"orcid":1873},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":1875,"orcid":1876},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":1878,"orcid":1879},"Judith Uwihirwe","https:\u002F\u002Forcid.org\u002F0000-0002-0351-5726","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44378-026-00347-1.pdf",{"tldr":1882,"method":1883,"finding":1884,"direction":1302,"opportunity":1885},"用RUSLE与GIS量化卢旺达Akanyaru流域四类土地利用的年土壤流失量。","RUSLE模型结合GIS，采用NASA降雨、全球土壤、DEM与Landsat 8","土壤流失0–51.4 t\u002Fha\u002F年，裸地与陡坡集约农地最高，密林近零。","可结合遥感时序与保护措施情景，评估梯田与农林复合的减蚀效益及碳协同。","2026-10-21T23:30:23.420269Z",{"id":1888,"title":1889,"url":1890,"summary":1891,"summary_zh":1892,"content":11,"source_name":1893,"source_url":1890,"published_at":708,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1311,"score_detail":1894,"sources":1896,"tags":1898,"primary_topic":1899,"secondary_topics":1900,"classification":1903,"search_phrases":1945,"slug":1948,"view_count":424,"doi":1949,"paper":1950,"created_at":1973},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",{"impact":1027,"substance":361,"depth":602,"authority":416,"freshness":173,"relevant":455,"comment":1895},"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。",[1897],{"name":1893,"url":1890},[818,1457,716,1190,718],{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},[1901,1902],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},{"status":597,"locked":581,"version":4,"method":598,"evidence":1904},{"candidates":1905,"agricultural_evidence":603},[1906,1910,1914,1918,1922,1926,1930,1936,1940],{"code":184,"score":361,"supported":603,"title":1907,"abstract":1908,"tags":1909},[883],[818,739,740,883],[818],{"code":144,"score":602,"supported":603,"title":1911,"abstract":1912,"tags":1913},[145],[618,145],[1457],{"code":152,"score":385,"supported":603,"title":1915,"abstract":1916,"tags":1917},[],[606,1133,153,812],[1190],{"code":32,"score":675,"supported":603,"title":1919,"abstract":1920,"tags":1921},[],[733,734],[716],{"code":263,"score":615,"supported":603,"title":1923,"abstract":1924,"tags":1925},[],[888,889],[],{"code":16,"score":615,"supported":603,"title":1927,"abstract":1928,"tags":1929},[],[678,679],[],{"code":40,"score":615,"supported":603,"title":1931,"abstract":1932,"tags":1935},[],[1933,1934],"地理信息","geographic information",[],{"code":96,"score":815,"supported":603,"title":1937,"abstract":1938,"tags":1939},[],[1151],[],{"code":542,"score":815,"supported":603,"title":1941,"abstract":1942,"tags":1944},[],[1943],"农业政策",[],[1946,1947],"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":1949,"openalex_id":1951,"authors":1952,"venue":1893,"cited_by_count":424,"oa_url":1890,"card":1968,"direction":782,"ingested_from":642},"W7222999158",[1953,1955,1957,1959,1961,1963,1966],{"name":1954,"orcid":11},"Asifa Naz",{"name":1956,"orcid":11},"Muhammad Abu Baker Siddiq",{"name":1958,"orcid":11},"Syed Asghar Hussain Shah",{"name":1960,"orcid":11},"Shahrish Nawaz",{"name":1962,"orcid":11},"Muhmmad Irfan",{"name":1964,"orcid":1965},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":1967,"orcid":11},"Muhammad Nouman Khalid",{"tldr":1969,"method":1970,"finding":1971,"direction":782,"opportunity":1972},"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",{"technologies":1975,"keywords":2034},[1976,1978,1979,1981,1983,1985,1986,1987,1988,1989,1990,1992,1994,1995,1997,1999,2001,2003,2004,2006,2008,2010,2011,2012,2014,2016,2018,2020,2022,2024,2026,2028,2030,2032],{"name":589,"count":1977},99,{"name":716,"count":101},{"name":733,"count":1980},70,{"name":678,"count":1982},53,{"name":1522,"count":1984},31,{"name":948,"count":516},{"name":954,"count":321},{"name":1845,"count":1027},{"name":1189,"count":1027},{"name":961,"count":173},{"name":1991,"count":408},"传感器",{"name":1993,"count":408},"农业传感器",{"name":1127,"count":408},{"name":1996,"count":400},"无人机遥感",{"name":1998,"count":815},"农业机器人",{"name":2000,"count":815},"区块链",{"name":2002,"count":69},"人工智能",{"name":1523,"count":69},{"name":2005,"count":69},"农业大模型",{"name":2007,"count":69},"分子育种",{"name":2009,"count":69},"多光谱成像",{"name":1524,"count":69},{"name":1115,"count":69},{"name":2013,"count":455},"农产品溯源",{"name":2015,"count":455},"卫星遥感",{"name":2017,"count":455},"图像分割",{"name":2019,"count":455},"地理信息系统",{"name":2021,"count":455},"多模态",{"name":2023,"count":455},"多源遥感",{"name":2025,"count":455},"大模型",{"name":2027,"count":455},"大语言模型",{"name":2029,"count":455},"强化学习",{"name":2031,"count":455},"联邦学习",{"name":2033,"count":455},"高光谱",[2035,2037,2039,2040,2042,2044,2045,2047,2049,2051,2052,2054],{"name":588,"count":2036},221,{"name":888,"count":2038},62,{"name":818,"count":353},{"name":1250,"count":2041},32,{"name":1630,"count":2043},27,{"name":933,"count":858},{"name":2046,"count":1625},"数字农业",{"name":2048,"count":1625},"智能灌溉",{"name":2050,"count":1625},"种业振兴",{"name":1457,"count":321},{"name":2053,"count":1026},"数字乡村",{"name":2055,"count":1026},"防灾减灾"]