[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-27":3,"related-27":74},{"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":24,"tags":26,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":68,"slug":71,"view_count":72,"doi":8,"paper":8,"created_at":73},27,"大数据研究揭示东北黑土侵蚀沟增长的主要驱动","https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Fttxw\u002F700b0132ab834ff6a0c9dca5053ea9a3.htm","研究绘制东北侵蚀沟时空演变图谱，发现2000年后沟增长速率整体上升，主要关联规模机械化和高强度耕作，而非耕地大规模扩张。成果可为黑土保护、耕地碎片化治理和粮食安全提供依据。",null,"近日，中国农业科学院农业资源与农业区划研究所智慧农业团队、耕地质量保育团队联合中国科学院南京土壤研究所、中国科学院东北地理与农业生态研究所、比利时根特大学等多家机构，在《科学通报（Science Bulletin）》期刊上发表了题为“Drivers of the temporal and spatial variation in gully growth rates in Northeast China”的研究成果。研究覆盖过去60年间超过12万条永久性侵蚀沟的475组配对数据，在区域尺度上揭示了黑土地侵蚀沟发育的时空演变规律及其驱动机制。\n\n侵蚀沟作为土地退化的极端形式，一直是全球农业、生态以及可持续发展关注的前沿热点问题。黑土地作为我国粮食安全的压舱石，侵蚀沟问题尤为突出。尽管已有大量实地研究量化了沟蚀速率及其驱动因素，但受限于以往野外实地研究在时空尺度的局限性，区域侵蚀沟时空变异的规律认知仍然缺乏。\n\n研究明确沟长度、面积、体积、密度和土壤流失率5类指标长期平均速率。与超过10年观测期相比，短期（1-10年）沟长度和面积增长率明显要高得多。这说明短期发育极大受到极端事件的影响，超过10年的研究才能可靠计算长期平均速率。驱动因素分析表明，气温和降水季节性变异对沟发育贡献最大。进一步对比两种时间尺度的气候指标，发现采用长期气候变量对沟发育的影响大于短期，这表明极端降雨事件并非直接驱动因素，决定侵蚀沟增长快慢因素依然是长期气候指标，其通过塑造立地条件（如植被、土壤、汇流等）影响沟蚀的空间格局。空间分析发现，侵蚀沟发育存在明显的空间分异规律，长度和体积增长显著强于面积扩张。表明黑土地侵蚀沟主要通过长度增加来扩张，老沟的宽度变化不显著。进一步时序分析表明，2000年后沟增长速率整体上升，这表明导致沟剧烈增长的并非耕地大规模扩张，而是与规模机械化以及高强度耕作相关。\n\n该研究通过区域尺度大数据集成，克服了传统研究特定小流域的边界外推局限，为东北侵蚀沟绘制了一幅时空演变图谱，明确了黑土地这一宝贵土壤资源面临侵蚀沟“伤疤”，导致耕地碎片化的速率与驱动因素，为黑土资源利用与保障国家粮食安全提供了重要科学支撑。\n\n![Image 1](https:\u002F\u002Fiarrp.caas.cn\u002Fimages\u002F2026-07\u002Fa722a95719bf4999bc90b529091e835f.jpeg)\n\n图 1. 潜在影响侵蚀沟发育的13个自变量（对5种发育速率取平均）以及5种发育速率（对13 个自变量取平均）线性回归的平均决定系数（R2）和平均显著性星标数量。\n\n文章第一作者为中国农业科学院农业资源与农业区划研究所温艳茹副研究员，通讯作者为吴文斌研究员，云南师范大学联合培养硕士研究生李婧悦为共同第一作者。研究得到了北方干旱半干旱耕地高效利用全国重点实验室、国家重点研发计划项目、国家自然科学基金，中国农业科学院青年创新专项和国家留学基金资助。\n\n原文信息：Yanru Wen#, Jingyue Li#, Zhufeng Wang, Karl Auerswald, Peter Fiener, Wim Cornelis, Peng Qi, Ting-yong Li, Xinhua Peng, Liming Ye, Wenbin Wu*, Ming Jiang, Jiabao Zhang, Science Bulletin, 2026.\n\nDoi: 10.1016\u002Fj.scib.2026.07.043","中国农业科学院农业资源与农业区划研究所 · 2026-07-23","2026-07-23T00:00:00Z","报道",10,false,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},25,22,18,15,2,1,"基于12万条侵蚀沟大数据揭示东北黑土侵蚀沟增长驱动，为黑土地保护提供科学支撑，影响深远。",[25],{"name":10,"url":6},[27,28,29,30],"农业遥感","黑土地保护","土壤侵蚀","大数据",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":67},[38,46,52,57,62],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":45},"A8",3,true,[],[44],"粮食安全",[],{"code":47,"score":40,"supported":41,"title":48,"abstract":49,"tags":51},"F5",[],[50],"机械化",[],{"code":53,"score":22,"supported":14,"title":54,"abstract":55,"tags":56},"H1",[],[],[28],{"code":58,"score":22,"supported":14,"title":59,"abstract":60,"tags":61},"I3",[],[],[27],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"I6",[],[],[30],"多个产业证据相当，需要确认主归属",[69,70],"黑土地保护 农业遥感 土壤侵蚀 大数据","黑土地保护 农业遥感","黑土地保护农业遥感土壤侵蚀大数据-27",6,"2026-07-27T23:59:41.068118Z",{"total":72,"page":22,"page_size":72,"items":75},[76,154,225,329,397,458],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":81,"content":8,"source_name":82,"source_url":79,"published_at":83,"category":84,"cover_url":8,"hotness":13,"is_selected":14,"score":85,"score_detail":86,"sources":90,"tags":92,"primary_topic":8,"secondary_topics":96,"classification":97,"search_phrases":129,"slug":132,"view_count":133,"doi":134,"paper":135,"created_at":153},4584,"Farmland Transformation Reshapes Ecosystem Services Through Multi-Level Landscape Reconfiguration: Evidence from China’s Songnen Plain","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16202188","Global agricultural expansion has profoundly reshaped terrestrial landscapes, yet the ecological consequences of farmland transformation across multiple spatial scales remain insufficiently understood. This study investigates how farmland dynamics influence ecosystem service (ES) provision in China’s Songnen Plain by integrating long-term land-use trajectories, remote sensing data, and landscape ecological analysis. Using annual 30 m land-use\u002Fland-cover datasets from 2000 to 2020, we identified diverse farmland transformation pathways, including stable cultivation, reclamation, abandonment, fallow, afforestation, and urban encroachment. The InVEST model was applied to quantify three key ecosystem services: food supply, soil conservation, and carbon storage, while grid-level Partial Least Squares Regression (PLSR) was employed to identify their major drivers. Results revealed substantial spatial and temporal heterogeneity in ecosystem services. Food supply and soil conservation generally increased during the study period, whereas carbon storage exhibited a declining trend. Landscape-level characteristics, particularly Shannon diversity index (SHDI), aggregation index (AI), and patch density (PD), were identified as dominant factors regulating ecosystem service variation. Class-level indicators, including farmland area (FA) and mean patch area (MPA), also showed significant but comparatively weaker effects. This study provides new insights into the ecological consequences of intensive farmland transformation and highlights the importance of optimizing multi-scale landscape configurations to balance agricultural production and ecosystem sustainability.","全球农业扩张深刻重塑了陆地景观格局，但农田转型在多个空间尺度上的生态效应仍缺乏充分认识。本研究通过整合长期土地利用轨迹、遥感数据和景观生态分析，探讨了农田动态如何影响中国松嫩平原的生态系统服务（ES）供给。基于2000—2020年逐年30 m土地利用\u002F土地覆盖数据集，我们识别了多种农田转型路径，包括稳定耕作、开垦、撂荒、休耕、退耕还林和城市侵占。应用InVEST模型量化了粮食供给、土壤保持和碳储量三项关键生态系统服务，并采用格网级偏最小二乘回归（PLSR）识别其主要驱动因子。结果表明，生态系统服务存在显著的空间和时间异质性。粮食供给和土壤保持总体上在研究期间呈增加趋势，而碳储量则表现出下降趋势。景观尺度特征，特别是香农多样性指数（SHDI）、聚集度指数（AI）和斑块密度（PD），被识别为调控生态系统服务变化的主导因子。类型尺度指标，包括农田面积（FA）和平均斑块面积（MPA），也表现出显著但相对较弱的影响。本研究为集约化农田转型的生态后果提供了新见解，并强调了优化多尺度景观格局以平衡农业生产与生态系统可持续性的重要性。","Agriculture","2026-10-09T00:00:00Z","论文",79,{"impact":19,"substance":18,"depth":19,"authority":87,"freshness":88,"relevant":22,"comment":89},13,8,"基于2000—2020年30米土地利用数据与InVEST模型，揭示松嫩平原农田转型对生态系统服务的多尺度影响，方法与数据扎实，对黑土地保护与农业可持续布局有参考价值。",[91],{"name":82,"url":79},[27,28,93,94,95],"遥感监测","土地利用","生态系统服务",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":98},{"candidates":99,"agricultural_evidence":41,"reason":67},[100,106,112,119,125],{"code":101,"score":88,"supported":41,"title":102,"abstract":103,"tags":105},"H6",[],[95,104],"ecosystem service",[94,95],{"code":58,"score":88,"supported":41,"title":107,"abstract":108,"tags":111},[],[109,110],"遥感","remote sensing",[27,93],{"code":113,"score":72,"supported":41,"title":114,"abstract":115,"tags":118},"H2",[],[116,117],"土壤","soil",[],{"code":120,"score":40,"supported":41,"title":121,"abstract":122,"tags":124},"J3",[],[123],"全球农业",[],{"code":53,"score":22,"supported":14,"title":126,"abstract":127,"tags":128},[],[],[28],[130,131],"松嫩平原 农田转型 生态系统服务","InVEST模型 碳储量 土壤保持","松嫩平原农田转型生态系统服务-4584",0,"10.3390\u002Fagriculture16202188",{"doi":134,"openalex_id":136,"authors":137,"venue":82,"cited_by_count":133,"oa_url":79,"card":145,"direction":151,"ingested_from":152},"W7221099965",[138,140,143],{"name":139,"orcid":8},"Ruhao Xue",{"name":141,"orcid":142},"Bonoua Faye","https:\u002F\u002Forcid.org\u002F0000-0003-4465-6792",{"name":144,"orcid":8},"Hu Xie",{"tldr":146,"method":147,"finding":148,"direction":149,"opportunity":150},"基于松嫩平原2000-2020年土地利用数据，揭示耕地转型通过多尺度景观格局重塑生态系统服务。","30米土地利用数据、InVEST模型与网格级偏最小二乘回归（PLSR）。","食物供给与土壤保持增加而碳储量下降，景观格局指数是主导驱动因子。","农业绿色发展与碳","可探究不同耕地转型路径对碳储量的非线性影响及多尺度景观优化配置阈值。","农业遥感与作物表型","openalex","2026-10-10T23:30:27.812095Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":159,"content":8,"source_name":160,"source_url":157,"published_at":161,"category":84,"cover_url":8,"hotness":13,"is_selected":14,"score":162,"score_detail":163,"sources":167,"tags":169,"primary_topic":8,"secondary_topics":173,"classification":174,"search_phrases":198,"slug":201,"view_count":22,"doi":202,"paper":203,"created_at":224},3073,"Evolution of soil erosion and sediment delivery modelling over six decades: paradigms, limitations and new solutions","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15715124.2026.2717245","Modelling sediment delivery, namely the linkage between hillslope erosion and yield in river systems, is still a challenge. In this study, 1,075 publications from Elsevier's Scopus were analysed to trace the evolution from lumped to spatially distributed approaches. Results confirm that no single model type dominates, while several studies rely on simplified approaches, and data- and model-related errors remain unresolved. Artificial intelligence (AI) and remote sensing (RS) can address such limitations. However, although AI has emerged as either a complementary tool for generating new climate or land-use scenarios or a standalone framework, its adoption has not been transformative so far. Similarly, RS is increasingly used to characterise topography and land cover, but is poorly leveraged to determine other factors influencing sediment dynamics. This overview of existing and evolving methods provides useful insights to support river basin management and model selection. Overall, this study underscores the need for further integration of diverse data sources and processing methods, while future research should prioritise the trade-offs among model complexity, accuracy, and scalability.","模拟泥沙输移，即坡面侵蚀与河流系统产沙之间的联系，仍然是一项挑战。本研究分析了来自Elsevier Scopus数据库的1，075篇文献，以追溯从集总式方法到空间分布式方法的演变历程。结果证实，尚无单一模型类型占据主导地位，而若干研究依赖于简化方法，且与数据和模型相关的误差仍未得到解决。人工智能（AI）和遥感（RS）能够应对这些局限性。然而，尽管AI已作为生成新气候或土地利用情景的补充工具或独立框架出现，但其应用迄今尚未带来变革性影响。同样，RS越来越多地用于表征地形和土地覆盖，但在确定影响泥沙动力学的其他因素方面利用不足。本综述对现有及不断演变的方法进行了概述，为支持流域管理和模型选择提供了有益见解。总体而言，本研究强调了进一步整合多样化数据来源和处理方法的必要性，同时未来研究应优先考虑模型复杂性、精度和可扩展性之间的权衡。","International Journal of River Basin Management","2026-09-19T00:00:00Z",66,{"impact":88,"substance":164,"depth":165,"authority":87,"freshness":88,"relevant":22,"comment":166},20,17,"基于1075篇文献的六十年土壤侵蚀与泥沙输移建模综述，指出AI与遥感应用尚未形成变革性突破，对农业水土保持与流域管理有参考价值，但属学术综述、影响面有限。",[168],{"name":160,"url":157},[170,27,29,171,172],"农业人工智能","水土保持","流域管理",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":175},{"candidates":176,"agricultural_evidence":14,"reason":197},[177,181,186,193],{"code":113,"score":88,"supported":41,"title":178,"abstract":179,"tags":180},[117],[],[],{"code":58,"score":182,"supported":41,"title":183,"abstract":184,"tags":185},7,[],[109,110],[27],{"code":187,"score":188,"supported":41,"title":189,"abstract":190,"tags":192},"I2",4,[],[191],"artificial intelligence",[170],{"code":101,"score":22,"supported":14,"title":194,"abstract":195,"tags":196},[],[],[171],"农业相关性需要复核",[199,200],"土壤侵蚀 泥沙输移 模型","遥感 AI 流域管理","土壤侵蚀泥沙输移模型-3073","10.1080\u002F15715124.2026.2717245",{"doi":202,"openalex_id":204,"authors":205,"venue":160,"cited_by_count":133,"oa_url":218,"card":219,"direction":151,"ingested_from":152},"W7213758675",[206,209,212,215],{"name":207,"orcid":208},"Melissa Latella","https:\u002F\u002Forcid.org\u002F0000-0003-3678-6992",{"name":210,"orcid":211},"Monia Santini","https:\u002F\u002Forcid.org\u002F0000-0002-8041-8241",{"name":213,"orcid":214},"Pierfranco Costabile","https:\u002F\u002Forcid.org\u002F0000-0003-1147-9929",{"name":216,"orcid":217},"Roberta Padulano","https:\u002F\u002Forcid.org\u002F0000-0003-4881-4495","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15715124.2026.2717245?needAccess=true",{"tldr":220,"method":221,"finding":222,"direction":151,"opportunity":223},"分析1075篇文献，梳理六十年土壤侵蚀与泥沙输移模型从集总到分布式的演变。","Scopus文献计量分析，综述AI与遥感在泥沙输移建模中的应用。","无单一模型占主导，AI与遥感应用尚未变革性，需整合多源数据并权衡复杂度、精度与可扩展性。","可探索AI与多源遥感深度融合的分布式泥沙输移模型，兼顾精度与可扩展性。","2026-09-21T23:30:25.637249Z",{"id":226,"title":227,"url":228,"summary":229,"summary_zh":230,"content":8,"source_name":231,"source_url":228,"published_at":232,"category":84,"cover_url":8,"hotness":13,"is_selected":14,"score":233,"score_detail":234,"sources":238,"tags":240,"primary_topic":244,"secondary_topics":250,"classification":262,"search_phrases":297,"slug":300,"view_count":133,"doi":301,"paper":302,"created_at":328},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",80,{"impact":235,"substance":18,"depth":19,"authority":236,"freshness":13,"relevant":22,"comment":237},16,14,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[239],{"name":231,"url":228},[241,170,27,242,243],"智慧农业","氮素监测","紫花苜蓿",{"code":245,"slug":246,"name":247,"path":248,"parent_code":249},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[251,256],{"code":58,"slug":252,"name":253,"path":254,"parent_code":255},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":257,"slug":258,"name":259,"path":260,"parent_code":261},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":263,"locked":14,"version":34,"method":35,"evidence":264},"classified",{"candidates":265,"agricultural_evidence":41},[266,271,277,283,292],{"code":58,"score":19,"supported":41,"title":267,"abstract":269,"tags":270},[268],"hyperspectral",[109,110,268],[27],{"code":245,"score":20,"supported":41,"title":272,"abstract":274,"tags":276},[273],"alfalfa",[275,273],"苜蓿",[243],{"code":257,"score":13,"supported":41,"title":278,"abstract":279,"tags":282},[],[280,275,281],"饲草","forage",[243],{"code":187,"score":13,"supported":41,"title":284,"abstract":285,"tags":291},[],[286,287,288,289,290],"深度学习","机器学习","machine learning","deep learning","decision support",[170],{"code":293,"score":22,"supported":14,"title":294,"abstract":295,"tags":296},"I1",[],[],[241],[298,299],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":301,"openalex_id":303,"authors":304,"venue":231,"cited_by_count":133,"oa_url":228,"card":323,"direction":151,"ingested_from":152},"W7223154576",[305,307,309,311,313,316,318,321],{"name":306,"orcid":8},"Shuai Fu",{"name":308,"orcid":8},"Yunhao Li",{"name":310,"orcid":8},"Chunli Miao",{"name":312,"orcid":8},"Jie Liu",{"name":314,"orcid":315},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":317,"orcid":8},"Yongjia Hu",{"name":319,"orcid":320},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":322,"orcid":8},"吴彩霞",{"tldr":324,"method":325,"finding":326,"direction":151,"opportunity":327},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":330,"title":331,"url":332,"summary":333,"summary_zh":334,"content":8,"source_name":335,"source_url":332,"published_at":232,"category":84,"cover_url":8,"hotness":13,"is_selected":14,"score":336,"score_detail":337,"sources":341,"tags":343,"primary_topic":345,"secondary_topics":350,"classification":351,"search_phrases":378,"slug":381,"view_count":133,"doi":382,"paper":383,"created_at":396},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies",61,{"impact":88,"substance":19,"depth":236,"authority":338,"freshness":339,"relevant":22,"comment":340},12,9,"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[342],{"name":335,"url":332},[109,94,29,344,171],"GIS",{"code":346,"slug":347,"name":348,"path":349,"parent_code":255},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[],{"status":263,"locked":14,"version":34,"method":35,"evidence":352},{"candidates":353,"agricultural_evidence":41},[354,360,364,368,374],{"code":346,"score":13,"supported":41,"title":355,"abstract":356,"tags":359},[],[357,344,358],"地理信息","geographic information",[344],{"code":58,"score":182,"supported":41,"title":361,"abstract":362,"tags":363},[],[109,110],[109],{"code":113,"score":72,"supported":41,"title":365,"abstract":366,"tags":367},[],[116,117],[],{"code":369,"score":40,"supported":41,"title":370,"abstract":371,"tags":373},"H3",[],[372],"灌溉",[],{"code":101,"score":21,"supported":14,"title":375,"abstract":376,"tags":377},[],[],[94,171],[379,380],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":382,"openalex_id":384,"authors":385,"venue":335,"cited_by_count":133,"oa_url":8,"card":391,"direction":151,"ingested_from":152},"W7223117733",[386,388],{"name":387,"orcid":8},"MERİÇ KURT",{"name":389,"orcid":390},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":392,"method":393,"finding":394,"direction":149,"opportunity":395},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","2026-10-22T11:20:39.475037Z",{"id":398,"title":399,"url":400,"summary":401,"summary_zh":8,"content":8,"source_name":402,"source_url":8,"published_at":232,"category":84,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":403,"sources":405,"tags":407,"primary_topic":411,"secondary_topics":416,"classification":424,"search_phrases":445,"slug":448,"view_count":22,"doi":8,"paper":449,"created_at":457},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",{"impact":19,"substance":18,"depth":19,"authority":236,"freshness":13,"relevant":22,"comment":404},"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。",[406],{"name":402,"url":400},[241,27,408,409,410],"作物表型","无人机多光谱","小麦渍害",{"code":412,"slug":413,"name":414,"path":415,"parent_code":249},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[417,418],{"code":58,"slug":252,"name":253,"path":254,"parent_code":255},{"code":419,"slug":420,"name":421,"path":422,"parent_code":423},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":263,"locked":14,"version":34,"method":35,"evidence":425},{"candidates":426,"agricultural_evidence":41},[427,432,436,441],{"code":412,"score":428,"supported":41,"title":429,"abstract":430,"tags":431},11,[414],[414],[],{"code":58,"score":428,"supported":41,"title":433,"abstract":434,"tags":435},[409],[],[408,27,409],{"code":419,"score":88,"supported":41,"title":437,"abstract":439,"tags":440},[438],"渍害",[],[],{"code":293,"score":22,"supported":14,"title":442,"abstract":443,"tags":444},[],[],[241],[446,447],"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":8,"openalex_id":8,"authors":450,"venue":8,"cited_by_count":133,"oa_url":8,"card":451,"direction":151,"ingested_from":456},[],{"tldr":452,"method":453,"finding":454,"direction":151,"opportunity":455},"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":459,"title":460,"url":461,"summary":462,"summary_zh":8,"content":463,"source_name":464,"source_url":8,"published_at":465,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":466,"sources":468,"tags":470,"primary_topic":475,"secondary_topics":480,"classification":481,"search_phrases":507,"slug":510,"view_count":133,"doi":8,"paper":8,"created_at":511},5512,"中国农科院环发所黑土地抗逆技术吉林大安实收测产 玉米亩产1376.9公斤","https:\u002F\u002Fieda.caas.cn\u002Fxwzx\u002Fxzjl\u002F6365a18c2bc34a96863196ccf9e34af0.htm","国家重点研发计划「黑土地抗干旱冷害减灾增效关键技术与示范」项目在吉林大安开展测产观摩，核心示范田玉米产量达13768.8公斤每公顷，较传统垄作增产20.48%。示范区连续三年增产，从2024年12510.7公斤每公顷提升至2026年13768.8公斤每公顷，在春旱导致发芽率降低12.9%的背景下实现抗旱增产。","10月10日，由国家重点研发计划项目“黑土地抗干旱冷害减灾增效关键技术与示范”玉米现场测产与成果展示观摩会在项目示范区之一的吉林省大安市烧锅镇乡举行。经专家现场测产，项目集成技术模式核心示范田的玉米产量达到13768.8公斤\u002F公顷，较传统垄作增产20.48%，验证了研发技术模式应对主粮作物干旱与冷害胁迫实现减灾增效与地力协同提升的可行性。\n\n松嫩平原西部地区农田土壤瘠薄、春播期易遭遇干旱和低温冷害，影响玉米出苗及稳产。针对这一难题，在中国农业科学院农业环境与可持续发展研究所退化及及污染农田修复团队曾希柏研究员的带领下，团队研发集成了瘠薄黑土分层培肥、保墒增温等关键技术，并结合抗旱耐密农作物品种筛选，集成条耕宽窄行秸秆还田、浅埋滴灌水肥一体化、绿色病虫害防控和全程机械化等技术措施，有效破解了播前干旱、低温导致玉米发芽率低，以及耕层变浅、有机质降低、地力逐年下降等导致农作物生长不良和产量低下等问题，显著提升了作物抵御干旱、冷害等逆境胁迫的能力，降低了灾害损失，在2026年春旱严重、玉米发芽率较往年降低12.9%、减产17.1%的前提下，通过项目研发技术的示范应用，实现了作物抗旱增产的目标，为保障松嫩平原粮食主产区稳产丰产、减灾增效提供了有力支撑。\n\n团队依托中国科学院东北地理与农业生态研究所在大安市烧锅镇乡富国村建立了千亩示范区，已连续三年开展定位试验与效果验证。项目组研发的技术模式在近年极端气候条件影响下表现突出： 2024年，示范区种植期雨量充沛，玉米出苗率保持在95%以上，产量达12510.7公斤\u002F公顷；2025年，示范区玉米产量为13124.6公斤\u002F公顷；2026年，面对春旱和低温冷害双重胁迫，玉米产量进一步提升至13768.8公斤\u002F公顷，实现连年增产，同时较当地传统垄作产量增幅超20%，效果显著。\n\n团队与地方政府涉农部门多方联动，围绕示范区建设、现场观摩和技术推广等开展广泛协作。下一步，将继续发挥国家重点研发计划项目的科技引领作用，完善东北黑土区耕地地力提升与减灾增效协同技术体系，扩大示范应用范围，为气候变化下黑土区抗逆增产及地力提升提供技术保障。","中国农科院农业环境与可持续发展研究所 2026-10-19","2026-10-19T00:00:00Z",{"impact":18,"substance":18,"depth":165,"authority":20,"freshness":72,"relevant":22,"comment":467},"国家重点研发计划项目在吉林大安连续三年实测增产，数据翔实、信源权威，具备行业参考价值。",[469],{"name":464,"url":461},[28,471,472,473,474],"保护性耕作","水肥一体化","玉米单产","抗旱技术",{"code":476,"slug":477,"name":478,"path":479,"parent_code":249},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize",[],{"status":263,"locked":14,"version":34,"method":35,"evidence":482},{"candidates":483,"agricultural_evidence":41},[484,488,493,498,502],{"code":476,"score":428,"supported":41,"title":485,"abstract":486,"tags":487},[478],[478],[],{"code":419,"score":40,"supported":41,"title":489,"abstract":490,"tags":492},[],[491],"干旱",[],{"code":494,"score":22,"supported":14,"title":495,"abstract":496,"tags":497},"F3",[],[],[472],{"code":53,"score":22,"supported":14,"title":499,"abstract":500,"tags":501},[],[],[28],{"code":503,"score":22,"supported":14,"title":504,"abstract":505,"tags":506},"H8",[],[],[471],[508,509],"中国农科院 黑土地 玉米 大安","吉林大安 玉米 实收测产","中国农科院黑土地玉米大安-5512","2026-10-22T00:07:03.815140Z"]