[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1140":3,"related-1140":60},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":22,"tags":24,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":54,"slug":57,"view_count":58,"doi":8,"paper":8,"created_at":59},1140,"河北农林科学院粮油所举办第二十九期冀丰作物科学青年学术交流 聚焦绿色种植与气候智慧型农业","http:\u002F\u002Fwww.hblys.com\u002Fhtml\u002F1\u002F3\u002F5613.html","8月26日,河北农林科学院粮油作物研究所举办第二十九期“冀丰作物科学青年学术交流”活动。中国农业大学农学院臧华栋教授以“绿色种植制度理论与实践探索”为题,系统阐述了绿色种植制度的理论内涵与发展趋势;温媛副教授以“推进气候智慧型农业:深层土壤管理提升作物产量与碳储存能力”为题,介绍深层土壤管理协同提升作物产量、土壤质量和碳储存能力的研究进展。",null,"为面向粮食安全与农业绿色低碳发展需求，聚焦绿色种植制度和气候智慧型农业前沿，8月26日，我所举办了第二十九期“冀丰作物科学青年学术交流”活动。中国农业大学农学院臧华栋教授、温媛副教授应邀作专题学术报告，研究所青年科研人员参加活动。\n\n臧华栋教授以“绿色种植制度理论与实践探索”为题，系统阐述了绿色种植制度的理论内涵、发展趋势与实践探索，分享了多样化种植促进作物增产、资源高效利用和生态功能提升的研究进展，并介绍了现代信息技术赋能种植制度优化的前沿方向。温媛副教授以“推进气候智慧型农业：深层土壤管理提升作物产量与碳储存能力”为题，介绍了深层土壤在作物生产和农田固碳中的重要作用，分享了深层土壤管理协同提升作物产量、土壤质量和碳储存能力的研究进展，探讨了其在气候智慧型农业发展中的应用潜力。\n\n报告结束后，两位专家与科研人员就多样化种植、根际过程、秸秆还田和深层土壤碳循环等内容进行了深入交流。本次活动进一步拓展了青年科研人员的研究视野，深化了对种植制度优化与土壤资源高效利用的认识，促进了相关研究方向的交流与融合。","河北省农林科学院粮油作物研究所","2026-08-27T01:00:00Z","报道",10,false,53,{"impact":17,"substance":18,"depth":13,"authority":13,"freshness":19,"relevant":20,"comment":21},12,14,7,1,"省级科研院所学术交流报道，涉及绿色种植与气候智慧型农业，有一定信息增量但深度一般。",[23],{"name":10,"url":6},[25,26,27,28],"河北农林科学院","粮油所","气候智慧型农业","青年学术交流",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49],{"code":30,"score":43,"supported":44,"title":45,"abstract":46,"tags":48},3,true,[],[47],"土壤",[],{"code":50,"score":20,"supported":14,"title":51,"abstract":52,"tags":53},"H7",[],[],[27],[55,56],"气候智慧型农业 河北农林科学院 青年学术交流 粮油所","气候智慧型农业 河北农林科学院","气候智慧型农业河北农林科学院青年学术交流粮油所-1140",2,"2026-08-31T00:08:56.214585Z",{"total":61,"page":20,"page_size":61,"items":62},6,[63,159,218,300,383,452],{"id":64,"title":65,"url":66,"summary":67,"summary_zh":68,"content":8,"source_name":69,"source_url":66,"published_at":70,"category":71,"cover_url":8,"hotness":72,"is_selected":14,"score":73,"score_detail":74,"sources":79,"tags":83,"primary_topic":88,"secondary_topics":89,"classification":96,"search_phrases":139,"slug":142,"view_count":143,"doi":8,"paper":144,"created_at":158},4351,"Rhizosphere Microbiome Engineering for Climate-Smart Agriculture: From Synthetic Consortia to Precision Decision Support","https:\u002F\u002Fmel.cgiar.org\u002Freporting\u002Fdownloadmelspace\u002Fhash\u002F095138d10184dc7b696f79e27e716f84","Rhizosphere microbiome engineering is a promising approach that can enhance crop resilience and input use efficiency by redirecting plant–microbe–soil interactions toward predictable functions. Here, we review the mechanistic bases underlying rhizosphere assembly and stability, including root exudate-mediated selection, priority effects, keystone taxa, and metabolite-driven signaling, and connect these principles to proposed design rules for microbial inoculants. We present a generalizable Design–Build–Test–Learn (DBTL) framework for engineering synthetic microbial consortia, covering trait-to-module mapping (nutrient acquisition, phytohormone modulation, ACC deaminase activity, stress-protective metabolites, and biocontrol), compatibility screening, minimal yet robust community architectures, and iterative optimization driven by multi-omics and high-throughput phenotyping. Translation to field settings is framed as an engineering challenge defined by formulation and administration limitations, including carrier type, seed coating and encapsulation methods, shelf life, strain invasiveness, and permanence of colonization amid environmental diversity. We also summarize how integrative measurement pipelines (amplicon and shotgun sequencing, transcriptomics, metabolomics, and network or causal analyses) can advance microbiome studies from correlation to actionability. We describe how precision agriculture (sensors, remote sensing, and variable-rate inputs) and AI\u002FML (split-sample comparisons, transfer learning, and active learning) approaches can accelerate strain discovery, mixture optimization, and adaptive experimentation, driven by the need for stringent controls, metadata-rich reporting, and cross-site comparability. Use cases focus on stress conditions (drought, salinity, thermal extremes, and biotic stress) to demonstrate how microbial functions translate to agronomic outcomes and to highlight critical bottlenecks for reproducible, scalable microbiome products.","根际微生物组工程是一种有前景的方法，通过将植物–微生物–土壤相互作用导向可预测的功能，能够增强作物抗逆性和投入品利用效率。本文综述了根际组装与稳定性背后的机制基础，包括根系分泌物介导的选择、优先效应、关键类群和代谢物驱动的信号传导，并将这些原理与微生物接种剂拟议的设计规则相联系。我们提出了一个可推广的设计–构建–测试–学习（DBTL）框架，用于工程化合成微生物群落，涵盖性状到模块的映射（养分获取、植物激素调节、ACC脱氨酶活性、胁迫保护性代谢物和生物防治）、兼容性筛选、最小但稳健的群落架构，以及由多组学和高通量表型驱动的迭代优化。向田间环境的转化被界定为一项工程挑战，其受限于制剂和施用方面的制约，包括载体类型、种子包衣和包埋方法、货架期、菌株入侵性以及在环境多样性中定殖的持久性。我们还总结了整合性测量流程（扩增子和鸟枪法测序、转录组学、代谢组学以及网络或因果分析）如何推动微生物组研究从相关性走向可操作性。我们描述了精准农业（传感器、遥感和变量投入）以及人工智能\u002F机器学习（分样本比较、迁移学习和主动学习）方法如何加速菌株发现、混合优化和自适应实验，其驱动力在于对严格对照、富含元数据的报告和跨站点可比性的需求。应用案例聚焦于胁迫条件（干旱、盐渍、极端温度和生物胁迫），以展示微生物功能如何转化为农艺结果，并突出可重复、可规模化微生物组产品的关键瓶颈。","MELSpace (ICARDA (The International Center for Agricultural Research in Dry Areas))","2026-10-05T00:00:00Z","论文",25,80,{"impact":75,"substance":76,"depth":75,"authority":18,"freshness":77,"relevant":20,"comment":78},18,22,8,"ICARDA 综述提出根际微生物组工程的 DBTL 框架并衔接精准农业与 AI，方法体系完整、结论可靠，对气候智慧型农业有参考价值，但属综述类论文、非全国性政策或重大突破。",[80,81],{"name":69,"url":66},{"name":69,"url":82},"https:\u002F\u002Fhdl.handle.net\u002F20.500.11766\u002F70900",[84,85,86,27,87],"智慧农业","农业人工智能","根际微生物","合成菌群",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[90],{"code":91,"slug":92,"name":93,"path":94,"parent_code":95},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":37,"locked":14,"version":38,"method":39,"evidence":97},{"candidates":98,"agricultural_evidence":44},[99,105,114,120,126,133],{"code":30,"score":75,"supported":44,"title":100,"abstract":102,"tags":104},[101],"rhizosphere",[47,103,31,101],"根际",[86],{"code":106,"score":107,"supported":44,"title":108,"abstract":110,"tags":113},"I1",15,[109],"smart agriculture",[111,112],"精准农业","precision agriculture",[84],{"code":91,"score":17,"supported":44,"title":115,"abstract":117,"tags":119},[116],"decision support",[118],"机器学习",[85],{"code":50,"score":19,"supported":44,"title":121,"abstract":122,"tags":125},[],[123,124],"干旱","drought",[27],{"code":127,"score":61,"supported":44,"title":128,"abstract":129,"tags":132},"I3",[],[130,131],"遥感","remote sensing",[],{"code":134,"score":43,"supported":44,"title":135,"abstract":136,"tags":138},"F4",[],[137],"生物防治",[],[140,141],"ICARDA 根际微生物 合成菌群","根际微生物 精准农业 决策支持","ICARDA根际微生物合成菌群-4351",4,{"doi":8,"openalex_id":145,"authors":146,"venue":69,"cited_by_count":149,"oa_url":66,"card":150,"direction":156,"ingested_from":157},"W7220812607",[147],{"name":148,"orcid":8},"Nourhan Fouad Mahmoud",0,{"tldr":151,"method":152,"finding":153,"direction":154,"opportunity":155},"综述根际微生物组工程，提出DBTL框架设计合成菌群，并融合精准农业与AI实现气候智慧型农业。","综述根际组装机制，提出DBTL框架，整合多组学、高通量表型、传感器与AI\u002FML方","微生物组工程需从相关走向可操作，精准农业与AI可加速菌株发现和菌群优化，但田间可重复性仍是瓶颈。","农业人工智能与决策模型","可研究AI驱动的根际微生物组设计-田间验证闭环，重点解决菌群定殖持久性与跨站点可迁移性。","智慧农业 \u002F 农业物联网","openalex","2026-10-07T23:30:12.909703Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":8,"source_name":165,"source_url":162,"published_at":166,"category":71,"cover_url":8,"hotness":13,"is_selected":14,"score":167,"score_detail":168,"sources":173,"tags":175,"primary_topic":179,"secondary_topics":180,"classification":182,"search_phrases":198,"slug":201,"view_count":149,"doi":202,"paper":203,"created_at":217},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",64,{"impact":77,"substance":169,"depth":170,"authority":171,"freshness":13,"relevant":20,"comment":172},20,17,9,"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。",[174],{"name":165,"url":162},[84,85,176,177,178],"主动学习","线虫检测","土壤采样",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[181],{"code":91,"slug":92,"name":93,"path":94,"parent_code":95},{"status":37,"locked":14,"version":38,"method":39,"evidence":183},{"candidates":184,"agricultural_evidence":44},[185,189,194],{"code":30,"score":18,"supported":44,"title":186,"abstract":187,"tags":188},[31],[47,31],[],{"code":91,"score":171,"supported":44,"title":190,"abstract":192,"tags":193},[191],"artificial intelligence",[],[85],{"code":106,"score":19,"supported":44,"title":195,"abstract":196,"tags":197},[],[111,112],[84],[199,200],"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":202,"openalex_id":204,"authors":205,"venue":165,"cited_by_count":149,"oa_url":162,"card":212,"direction":154,"ingested_from":157},"W7223082195",[206,208,210],{"name":207,"orcid":8},"Amine Ben Salah",{"name":209,"orcid":8},"Ines Trabelsi",{"name":211,"orcid":8},"Mohamed Aziz Khelifi",{"tldr":213,"method":214,"finding":215,"direction":154,"opportunity":216},"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":219,"title":220,"url":221,"summary":222,"summary_zh":223,"content":8,"source_name":224,"source_url":221,"published_at":225,"category":71,"cover_url":8,"hotness":13,"is_selected":14,"score":226,"score_detail":227,"sources":230,"tags":232,"primary_topic":238,"secondary_topics":239,"classification":240,"search_phrases":261,"slug":264,"view_count":149,"doi":265,"paper":266,"created_at":299},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":228,"substance":76,"depth":75,"authority":18,"freshness":171,"relevant":20,"comment":229},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。",[231],{"name":224,"url":221},[233,234,235,236,237],"遥感监测","莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":241},{"candidates":242,"agricultural_evidence":44},[243,247,251,256],{"code":30,"score":18,"supported":44,"title":244,"abstract":245,"tags":246},[31],[47,31],[],{"code":127,"score":19,"supported":44,"title":248,"abstract":249,"tags":250},[],[130,131],[233],{"code":50,"score":61,"supported":44,"title":252,"abstract":253,"tags":255},[],[254,123],"气候适应",[],{"code":257,"score":20,"supported":14,"title":258,"abstract":259,"tags":260},"H6",[],[],[236],[262,263],"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":265,"openalex_id":267,"authors":268,"venue":224,"cited_by_count":149,"oa_url":221,"card":293,"direction":297,"ingested_from":157},"W7223069171",[269,271,273,275,278,280,283,286,288,290],{"name":270,"orcid":8},"Yue Li",{"name":272,"orcid":8},"Mengfei Shi",{"name":274,"orcid":8},"Fei Zhang",{"name":276,"orcid":277},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":279,"orcid":8},"Gowhar Meraj",{"name":281,"orcid":282},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":284,"orcid":285},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":287,"orcid":8},"Yuanfang Chai",{"name":289,"orcid":8},"Hongtao Xu",{"name":291,"orcid":292},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":294,"method":295,"finding":296,"direction":297,"opportunity":298},"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":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":8,"source_name":306,"source_url":303,"published_at":166,"category":71,"cover_url":8,"hotness":13,"is_selected":14,"score":307,"score_detail":308,"sources":312,"tags":314,"primary_topic":317,"secondary_topics":323,"classification":326,"search_phrases":360,"slug":363,"view_count":149,"doi":364,"paper":365,"created_at":382},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":228,"substance":309,"depth":170,"authority":310,"freshness":13,"relevant":20,"comment":311},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。",[313],{"name":306,"url":303},[84,118,233,315,316],"土壤墒情","冬小麦",{"code":318,"slug":319,"name":320,"path":321,"parent_code":322},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[324,325],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"code":91,"slug":92,"name":93,"path":94,"parent_code":95},{"status":37,"locked":14,"version":38,"method":39,"evidence":327},{"candidates":328,"agricultural_evidence":44},[329,335,339,344,349,356],{"code":318,"score":330,"supported":44,"title":331,"abstract":333,"tags":334},26,[332,319],"winter wheat",[320,332,319],[316],{"code":30,"score":107,"supported":44,"title":336,"abstract":337,"tags":338},[31],[47,31],[315],{"code":91,"score":107,"supported":44,"title":340,"abstract":342,"tags":343},[341],"machine learning",[118,341],[118],{"code":127,"score":13,"supported":44,"title":345,"abstract":346,"tags":348},[],[130,131,347],"multispectral",[233],{"code":350,"score":61,"supported":44,"title":351,"abstract":352,"tags":355},"H3",[],[353,354],"灌溉","irrigation",[],{"code":106,"score":143,"supported":44,"title":357,"abstract":358,"tags":359},[],[84],[84],[361,362],"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":364,"openalex_id":366,"authors":367,"venue":306,"cited_by_count":149,"oa_url":303,"card":377,"direction":156,"ingested_from":157},"W7223156204",[368,371,373,375],{"name":369,"orcid":370},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":372,"orcid":8},"Yi Zhang",{"name":374,"orcid":8},"WEI Lei",{"name":376,"orcid":8},"Chengcai Zhang",{"tldr":378,"method":379,"finding":380,"direction":297,"opportunity":381},"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":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":8,"source_name":389,"source_url":386,"published_at":166,"category":71,"cover_url":8,"hotness":13,"is_selected":14,"score":73,"score_detail":390,"sources":392,"tags":394,"primary_topic":400,"secondary_topics":401,"classification":402,"search_phrases":422,"slug":425,"view_count":149,"doi":426,"paper":427,"created_at":451},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":75,"substance":76,"depth":75,"authority":310,"freshness":171,"relevant":20,"comment":391},"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。",[393],{"name":389,"url":386},[395,396,397,398,399],"作物模型","气候智慧农业","油棕","碳汇","可持续种植",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":403},{"candidates":404,"agricultural_evidence":44},[405,409,414,418],{"code":30,"score":61,"supported":44,"title":406,"abstract":407,"tags":408},[],[47,31],[],{"code":410,"score":143,"supported":44,"title":411,"abstract":412,"tags":413},"H4",[],[398],[398],{"code":50,"score":143,"supported":44,"title":415,"abstract":416,"tags":417},[],[254],[396],{"code":91,"score":20,"supported":14,"title":419,"abstract":420,"tags":421},[],[],[395],[423,424],"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":426,"openalex_id":428,"authors":429,"venue":389,"cited_by_count":149,"oa_url":386,"card":445,"direction":449,"ingested_from":157},"W7223112249",[430,433,436,439,442],{"name":431,"orcid":432},"Lisma Safitri","https:\u002F\u002Forcid.org\u002F0000-0002-7188-1058",{"name":434,"orcid":435},"Marcelo Valadares Galdos","https:\u002F\u002Forcid.org\u002F0000-0002-6080-0726",{"name":437,"orcid":438},"Iput Pradiko","https:\u002F\u002Forcid.org\u002F0000-0001-7877-8400",{"name":440,"orcid":441},"Alexis Comber","https:\u002F\u002Forcid.org\u002F0000-0002-3652-7846",{"name":443,"orcid":444},"Andrew Juan Challinor","https:\u002F\u002Forcid.org\u002F0000-0002-8551-6617",{"tldr":446,"method":447,"finding":448,"direction":449,"opportunity":450},"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":453,"title":454,"url":455,"summary":456,"summary_zh":457,"content":8,"source_name":389,"source_url":455,"published_at":166,"category":71,"cover_url":8,"hotness":13,"is_selected":14,"score":458,"score_detail":459,"sources":461,"tags":463,"primary_topic":469,"secondary_topics":475,"classification":477,"search_phrases":496,"slug":499,"view_count":149,"doi":500,"paper":501,"created_at":523},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":77,"substance":169,"depth":228,"authority":310,"freshness":171,"relevant":20,"comment":460},"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。",[462],{"name":389,"url":455},[464,465,466,467,468],"长期定位试验","有机果园","土壤生物多样性","苹果品质","IPM",{"code":470,"slug":471,"name":472,"path":473,"parent_code":474},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[476],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":478},{"candidates":479,"agricultural_evidence":44},[480,487,491],{"code":470,"score":170,"supported":44,"title":481,"abstract":483,"tags":486},[482],"apple",[484,485,482],"苹果","orchard",[],{"code":30,"score":18,"supported":44,"title":488,"abstract":489,"tags":490},[31],[47,31],[],{"code":257,"score":43,"supported":44,"title":492,"abstract":493,"tags":495},[],[494],"生物多样性",[],[497,498],"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":500,"openalex_id":502,"authors":503,"venue":389,"cited_by_count":149,"oa_url":455,"card":518,"direction":449,"ingested_from":157},"W7223104118",[504,507,510,512,515],{"name":505,"orcid":506},"Ulvi Moor","https:\u002F\u002Forcid.org\u002F0000-0002-8808-8494",{"name":508,"orcid":509},"Annely Kuu","https:\u002F\u002Forcid.org\u002F0000-0002-0335-6243",{"name":511,"orcid":8},"K. Kahu",{"name":513,"orcid":514},"P. Põldma","https:\u002F\u002Forcid.org\u002F0000-0002-7906-823X",{"name":516,"orcid":517},"Reelika Rätsep","https:\u002F\u002Forcid.org\u002F0000-0002-8673-9978",{"tldr":519,"method":520,"finding":521,"direction":449,"opportunity":522},"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"]