[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4348":3,"related-4348":97},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":73,"slug":76,"view_count":77,"doi":78,"paper":79,"created_at":96},4348,"Soil and Water Conservation Mechanism in Oil Palm Plantations: A Review of Best Management Practices and Decision Support Strategies","https:\u002F\u002Fdoi.org\u002F10.37394\u002F232015.2026.22.93","Soil and water conservation on oil palm plantation is increasingly considered as a glimmer of hope for the future sustainability of land functionality and environment. Appropriate management practices and informed decision-making frameworks thus have a strong influence on conservation outcomes. This study aims to systematically identify, categorize, synthesize and quantitatively aggregate peer-reviewed evidence regarding soil and water conservation mechanisms in oil palm plantations, focusing on best management practice strategies and decision support. This study uses Systematic Litigation Review (SLR) the Scopus database is used as a reference in searching scientific publication. A keyword-based structured search was used to identify peer-reviewed articles published from 2020 to 2025 and subsequent screening in sequence based on relevance and language. Thirty-eight articles met all the selection criteria for analysis. Then we extracted data and analyzed it using thematic synthesis with descriptive quantitative aggregation of reported metrics (soil loss, SOC change, runoff reduction, or yield response). The results show six main interrelated themes: control of soil erosion; soil fertility and organic matter management; conservation of water and hydrological regulation; integrated land use and landscape management; agronomic best methods practices; and support for decision-making processes. Quantitative synthesis show that cover-based and structural BMPs decrease soil loss by 50–80%, organic amendments increase SOC by 10–35% and integrated water management decreases runoff by 20–60% across regions reported.","油棕种植园的水土保持日益被视为实现土地功能与环境未来可持续性的一线希望。因此，适当的管理实践和基于信息的决策框架对保护成效具有重要影响。本研究旨在系统识别、分类、综合并定量汇总关于油棕种植园水土保持机制的同行评审证据，重点关注最佳管理实践策略与决策支持。本研究采用系统性文献综述（SLR），以Scopus数据库作为科学出版物检索的参考依据。通过基于关键词的结构化检索，识别2020年至2025年发表的同行评审文章，并依据相关性和语言依次进行筛选。共有38篇文章符合所有分析筛选标准。随后，我们提取数据并采用主题综合法进行分析，同时对所报告指标（土壤流失量、土壤有机碳变化、径流减少量或产量响应）进行描述性定量汇总。结果显示六个主要且相互关联的主题：土壤侵蚀控制；土壤肥力与有机质管理；水资源保护与水文调节；综合土地利用与景观管理；农艺最佳方法实践；以及决策过程支持。定量综合表明，在所报告的各区域中，基于覆盖和工程性最佳管理实践（BMPs）使土壤流失减少50%–80%，有机改良剂使土壤有机碳（SOC）增加10%–35%，综合水资源管理使径流减少20%–60%。",null,"WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT","2026-10-05T00:00:00Z","论文",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,12,6,1,"系统综述量化了油棕园水土保持措施效果，对热带农业可持续管理有参考价值，但属细分领域研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"保护性耕作","土壤有机碳","决策支持","水土保持","油棕种植",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":72},[39,48,54,60,67],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":47},"H2",15,true,[44],"soil",[46,44],"土壤",[28],{"code":49,"score":20,"supported":42,"title":50,"abstract":52,"tags":53},"I2",[51],"decision support",[51],[29],{"code":55,"score":56,"supported":42,"title":57,"abstract":58,"tags":59},"H6",4,[],[30],[30],{"code":61,"score":62,"supported":42,"title":63,"abstract":64,"tags":66},"H3",3,[],[65],"水资源",[],{"code":68,"score":22,"supported":14,"title":69,"abstract":70,"tags":71},"H8",[],[],[27],"农业相关性需要复核",[74,75],"油棕种植园 水土保持","油棕 最佳管理实践","油棕种植园水土保持-4348",0,"10.37394\u002F232015.2026.22.93",{"doi":78,"openalex_id":80,"authors":81,"venue":10,"cited_by_count":77,"oa_url":87,"card":88,"direction":94,"ingested_from":95},"W7220407703",[82,85],{"name":83,"orcid":84},"Loso Judijanto","https:\u002F\u002Forcid.org\u002F0009-0007-7766-0647",{"name":86,"orcid":9},"Sri Nurhayati","https:\u002F\u002Fwseas.com\u002Fjournals\u002Fead\u002F2026\u002Fb885115-041(2026).pdf",{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"系统综述油棕园水土保持机制，归纳最佳管理实践与决策支持策略。","系统文献综述，检索Scopus 2020-2025年38篇文献，主题综合与定量汇","覆盖与工程措施减蚀50-80%，有机改良增碳10-35%，综合水管理减径流20-60%。","农业绿色发展与碳","可构建面向油棕园的水土保持决策支持系统，融合多源数据与模型，填补热带种植园智能管理空白。","智慧农业 \u002F 农业物联网","openalex","2026-10-07T23:30:11.425576Z",{"total":21,"page":22,"page_size":21,"items":98},[99,178,238,319,394,501],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":113,"tags":115,"primary_topic":120,"secondary_topics":126,"classification":127,"search_phrases":158,"slug":161,"view_count":77,"doi":162,"paper":163,"created_at":177},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","2026-10-21T00:00:00Z",61,{"impact":17,"substance":109,"depth":110,"authority":20,"freshness":111,"relevant":22,"comment":112},18,14,9,"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。",[114],{"name":105,"url":102},[116,117,118,119,30],"遥感","土地利用","土壤侵蚀","GIS",{"code":121,"slug":122,"name":123,"path":124,"parent_code":125},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":128,"locked":14,"version":35,"method":36,"evidence":129},"classified",{"candidates":130,"agricultural_evidence":42},[131,137,144,148,153],{"code":121,"score":13,"supported":42,"title":132,"abstract":133,"tags":136},[],[134,119,135],"地理信息","geographic information",[119],{"code":138,"score":139,"supported":42,"title":140,"abstract":141,"tags":143},"I3",7,[],[116,142],"remote sensing",[116],{"code":40,"score":21,"supported":42,"title":145,"abstract":146,"tags":147},[],[46,44],[],{"code":61,"score":62,"supported":42,"title":149,"abstract":150,"tags":152},[],[151],"灌溉",[],{"code":55,"score":154,"supported":14,"title":155,"abstract":156,"tags":157},2,[],[],[117,30],[159,160],"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":162,"openalex_id":164,"authors":165,"venue":105,"cited_by_count":77,"oa_url":9,"card":171,"direction":176,"ingested_from":95},"W7223117733",[166,168],{"name":167,"orcid":9},"MERİÇ KURT",{"name":169,"orcid":170},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":172,"method":173,"finding":174,"direction":92,"opportunity":175},"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。","农业遥感与作物表型","2026-10-22T11:20:39.475037Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":9,"content":183,"source_name":184,"source_url":9,"published_at":185,"category":186,"cover_url":9,"hotness":13,"is_selected":14,"score":187,"score_detail":188,"sources":191,"tags":193,"primary_topic":198,"secondary_topics":204,"classification":205,"search_phrases":233,"slug":236,"view_count":77,"doi":9,"paper":9,"created_at":237},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","报道",82,{"impact":189,"substance":189,"depth":19,"authority":41,"freshness":21,"relevant":22,"comment":190},22,"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。",[192],{"name":184,"url":181},[194,27,195,196,197],"黑土地保护","水肥一体化","玉米单产","抗旱技术",{"code":199,"slug":200,"name":201,"path":202,"parent_code":203},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[],{"status":128,"locked":14,"version":35,"method":36,"evidence":206},{"candidates":207,"agricultural_evidence":42},[208,213,219,224,229],{"code":199,"score":209,"supported":42,"title":210,"abstract":211,"tags":212},11,[201],[201],[],{"code":214,"score":62,"supported":42,"title":215,"abstract":216,"tags":218},"H7",[],[217],"干旱",[],{"code":220,"score":22,"supported":14,"title":221,"abstract":222,"tags":223},"F3",[],[],[195],{"code":225,"score":22,"supported":14,"title":226,"abstract":227,"tags":228},"H1",[],[],[194],{"code":68,"score":22,"supported":14,"title":230,"abstract":231,"tags":232},[],[],[27],[234,235],"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",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":9,"source_name":244,"source_url":241,"published_at":245,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":246,"score_detail":247,"sources":251,"tags":253,"primary_topic":254,"secondary_topics":258,"classification":259,"search_phrases":283,"slug":286,"view_count":77,"doi":287,"paper":288,"created_at":318},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.","2026-10-20T00:00:00Z",69,{"impact":20,"substance":18,"depth":248,"authority":249,"freshness":17,"relevant":22,"comment":250},16,13,"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。",[252],{"name":244,"url":241},[116,117,118,30],{"code":40,"slug":44,"name":255,"path":256,"parent_code":257},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":128,"locked":14,"version":35,"method":36,"evidence":260},{"candidates":261,"agricultural_evidence":42},[262,266,271,275,279],{"code":40,"score":110,"supported":42,"title":263,"abstract":264,"tags":265},[44],[46,44],[],{"code":55,"score":267,"supported":42,"title":268,"abstract":269,"tags":270},5,[],[30],[117,30],{"code":61,"score":62,"supported":42,"title":272,"abstract":273,"tags":274},[],[65],[],{"code":121,"score":62,"supported":42,"title":276,"abstract":277,"tags":278},[],[119],[],{"code":138,"score":22,"supported":14,"title":280,"abstract":281,"tags":282},[],[],[116],[284,285],"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":287,"openalex_id":289,"authors":290,"venue":244,"cited_by_count":77,"oa_url":312,"card":313,"direction":176,"ingested_from":95},"W7223058039",[291,294,297,300,303,306,309],{"name":292,"orcid":293},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":295,"orcid":296},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":298,"orcid":299},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":301,"orcid":302},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":304,"orcid":305},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":307,"orcid":308},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":310,"orcid":311},"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":314,"method":315,"finding":316,"direction":92,"opportunity":317},"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":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":9,"source_name":325,"source_url":322,"published_at":326,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":327,"sources":329,"tags":331,"primary_topic":334,"secondary_topics":338,"classification":339,"search_phrases":368,"slug":371,"view_count":77,"doi":372,"paper":373,"created_at":393},5166,"Application of the SCS–CN Method Integrated with GIS and Remote Sensing for Watershed Runoff Estimation: A Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i105735","Accurate estimation of runoff is crucial for watershed planning, soil and water conservation, flood mitigation, and the management of ungauged watersheds. The Soil Conservation Service Curve Number (SCS-CN) method has been extensively used because of its minimal input requirements and its ability to model the spatial influence of land use, soil properties, and hydrologic soil groups. This review compiles applications of the GIS-based SCS-CN method reported for watersheds in semi-arid and monsoon-affected countries such as India, Saudi Arabia, Afghanistan, China, Iraq, and Ethiopia. The reviewed literature employed rainfall data, land-use\u002Fland-cover data, soil maps, hydrologic soil groups, digital elevation models, and watershed morphometry to calculate curve numbers and direct runoff. The reported CN values varied substantially, from about 40 under dry conditions to more than 90 under urban, rocky, agricultural, or poorly infiltrating conditions. Runoff estimates ranged from relatively low values in arid catchments to more than 1,300 mm per year in high-rainfall watersheds. Several studies reported high R2 correlations between observed and estimated runoff, typically ranging from 0.73 to 0.98. Important limitations identified in the review include the sensitivity of results to the choice of curve number, antecedent moisture condition, initial abstraction assumption, land-use classification, soil-group assignment, rainfall representation, and the lack of observed discharge data. The overall design of the SCS-CN method and its integration with RS and GIS offer a practical approach to assessing spatial runoff and prioritising watersheds, but the method requires local calibration, uncertainty analysis, and validation against observed rainfall–runoff data.","径流的准确估算对于流域规划、水土保持、洪水缓解以及无资料流域的管理至关重要。土壤保持服务曲线数（SCS-CN）方法因其输入需求少、能够模拟土地利用、土壤性质和水文土壤组的空间影响而被广泛使用。本综述汇编了基于GIS的SCS-CN方法在半干旱和受季风影响国家（如印度、沙特阿拉伯、阿富汗、中国、伊拉克和埃塞俄比亚）流域的应用报道。所综述的文献利用降雨数据、土地利用\u002F土地覆盖数据、土壤图、水文土壤组、数字高程模型和流域形态计量来计算曲线数和直接径流。报道的CN值差异很大，从干旱条件下的约40到城市、岩石、农业或入渗不良条件下的90以上不等。径流估算值从干旱集水区的相对较低值到高降雨流域的年径流量超过1300 mm不等。多项研究报告了观测径流与估算径流之间的高R²相关性，通常在0.73至0.98之间。本综述识别出的重要局限性包括：结果对曲线数选择、前期土壤湿润条件、初始损失假设、土地利用分类、土壤组赋值、降雨代表性以及缺乏实测流量数据的敏感性。SCS-CN方法的总体设计及其与遥感和GIS的集成，为评估空间径流和流域优先排序提供了一种实用方法，但该方法需要本地校准、不确定性分析，并依据实测降雨—径流数据进行验证。","International Journal of Environment and Climate Change","2026-10-16T00:00:00Z",{"impact":17,"substance":109,"depth":248,"authority":20,"freshness":111,"relevant":22,"comment":328},"综述系统梳理了GIS与遥感集成的SCS-CN径流估算方法及其局限，对农业信息化中的流域水资源与水土保持管理有参考价值，但属方法综述、影响面有限，未达每日精选门槛。",[330],{"name":325,"url":322},[116,119,30,332,333],"流域管理","水文模型",{"code":125,"slug":335,"name":336,"path":337,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":340,"locked":14,"version":35,"method":36,"evidence":341},"partial",{"candidates":342,"agricultural_evidence":42,"reason":367},[343,347,351,355,359,363],{"code":138,"score":20,"supported":42,"title":344,"abstract":345,"tags":346},[142],[116],[116],{"code":121,"score":20,"supported":42,"title":348,"abstract":349,"tags":350},[119],[119],[119],{"code":40,"score":21,"supported":42,"title":352,"abstract":353,"tags":354},[],[46,44],[],{"code":55,"score":56,"supported":42,"title":356,"abstract":357,"tags":358},[],[30],[30],{"code":214,"score":62,"supported":42,"title":360,"abstract":361,"tags":362},[],[217],[],{"code":61,"score":22,"supported":14,"title":364,"abstract":365,"tags":366},[],[],[333],"一级领域明确，二级方向需要细分",[369,370],"SCS-CN GIS 遥感 径流估算","水土保持 水文模型 流域管理 遥感","SCS-CNGIS遥感径流估算-5166","10.9734\u002Fijecc\u002F2026\u002Fv16i105735",{"doi":372,"openalex_id":374,"authors":375,"venue":325,"cited_by_count":77,"oa_url":322,"card":388,"direction":176,"ingested_from":95},"W7222632567",[376,378,380,382,384,386],{"name":377,"orcid":9},"Sudhir Lad",{"name":379,"orcid":9},"R. D. Bansod",{"name":381,"orcid":9},"Bhaupatil Gavit",{"name":383,"orcid":9},"Sachin Nandgude",{"name":385,"orcid":9},"Shailendra Gadge",{"name":387,"orcid":9},"Mahesh Patil",{"tldr":389,"method":390,"finding":391,"direction":176,"opportunity":392},"综述GIS与遥感集成的SCS-CN法在半干旱与季风区流域径流估算的应用。","汇总印度、中国等国文献，用降雨、土地利用、土壤图、DEM与流域形态计算CN和径流","CN约40至90以上，年径流差异大，R²多在0.73至0.98，但结果对CN与前期湿度等敏感。","可研究CN本地化校准与不确定性量化，并结合实测径流验证，提升无资料流域径流估算可靠性。","2026-10-17T23:30:28.373754Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":9,"source_name":400,"source_url":397,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":402,"score_detail":403,"sources":405,"tags":407,"primary_topic":411,"secondary_topics":412,"classification":417,"search_phrases":453,"slug":456,"view_count":77,"doi":457,"paper":458,"created_at":500},5102,"Integrating Proximal and Remote Sensing to Improve Characterization and Mapping of Soil Organic Carbon Stocks under Contrasting Land Uses","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42729-026-03629-3","Abstract Soils are fundamental to support ecosystem functions. Therefore, understanding soil organic carbon stock (SCS) as a strategic environmental component for climate regulation, sustainable agricultural practices, and maximizing fertilizer application is essential. This study aimed to predict and map the SCS at Palmital Farm, Minas Gerais state, using land use, parent materials, soil classes, proximal and remote sensing data as predictor variables. A total of 76 soil samples were collected in the surface (A) and subsurface (B) horizons using a regular grid design (200 × 200 m). Carbon content was determined using the Walkley–Black method. Prediction models were created via Random Forest algorithm and validated through leave-one-out cross-validation. Environmental and anthropogenic influences on SCS were analyzed statistically and spatially. The highest mean SCS values were found at the surface horizon under native vegetation and pasture areas (~ 33 Mg ha⁻¹), due to continuous organic matter deposition and lack of disturbance. Charnockite-derived soils showed higher SCS (31.24 Mg ha⁻¹, surface). Among soil classes, Argissolos stood out reaching the highest SCS (35.86 Mg ha⁻¹). Remote sensing data and terrain attributes yielded the best SCS prediction (R²=0.43). The main factors influencing SCS were land use\u002Fmanagement and soil texture. Also, strong correlations were observed between vegetation indices, some terrain attributes and SCS. This approach, combining multiple variables, indicated a moderate potential to achieve accurate SCS predictions using these data, demonstrating the high variability and complexity of SCS spatial variation in detailed-scale mapping.","摘要 土壤是支撑生态系统功能的基础。因此，理解土壤有机碳储量（SCS）作为气候调节、可持续农业实践和最大化肥料施用的战略性环境组成部分至关重要。本研究旨在利用土地利用、成土母质、土壤类别、近端和遥感数据作为预测变量，对米纳斯吉拉斯州Palmital农场的SCS进行预测和制图。采用规则网格设计（200 × 200 m），在表层（A）和亚表层（B）共采集76个土壤样品。碳含量采用Walkley–Black法测定。通过随机森林算法建立预测模型，并采用留一交叉验证进行验证。对环境和人为因素对SCS的影响进行了统计和空间分析。表层中原生植被和牧草地利用方式下的SCS均值最高（约33 Mg ha⁻¹），这归因于有机质的持续沉积和缺乏扰动。源自紫苏花岗岩的土壤表现出较高的SCS（31.24 Mg ha⁻¹，表层）。在土壤类别中，Argissolos表现突出，达到最高SCS（35.86 Mg ha⁻¹）。遥感数据和地形属性获得了最佳的SCS预测效果（R²=0.43）。影响SCS的主要因素为土地利用\u002F管理和土壤质地。此外，植被指数、部分地形属性与SCS之间观察到较强的相关性。这种结合多变量的方法表明，利用这些数据实现准确SCS预测具有中等潜力，同时也揭示了详细尺度制图中SCS空间变异的高变异性和复杂性。","Journal of soil science and plant nutrition","2026-10-14T00:00:00Z",67,{"impact":17,"substance":18,"depth":19,"authority":249,"freshness":111,"relevant":22,"comment":404},"该研究结合近地与遥感数据及随机森林算法预测土壤有机碳储量，方法新颖、数据扎实，对农业信息化与精准农业有参考价值，但属区域性案例研究，影响范围有限。",[406],{"name":400,"url":397},[408,409,28,116,410],"数字农业","随机森林","土壤制图",{"code":40,"slug":44,"name":255,"path":256,"parent_code":257},[413],{"code":138,"slug":414,"name":415,"path":416,"parent_code":125},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":128,"locked":14,"version":35,"method":36,"evidence":418},{"candidates":419,"agricultural_evidence":42},[420,424,428,434,439,444,449],{"code":40,"score":248,"supported":42,"title":421,"abstract":422,"tags":423},[44],[46,44],[410,28],{"code":138,"score":41,"supported":42,"title":425,"abstract":426,"tags":427},[142],[116,142],[116],{"code":220,"score":21,"supported":42,"title":429,"abstract":430,"tags":433},[],[431,432],"肥料","fertilizer",[],{"code":68,"score":62,"supported":42,"title":435,"abstract":436,"tags":438},[],[437],"可持续农业",[],{"code":121,"score":62,"supported":42,"title":440,"abstract":441,"tags":443},[],[442],"空间分析",[],{"code":445,"score":22,"supported":14,"title":446,"abstract":447,"tags":448},"I1",[],[],[408],{"code":49,"score":22,"supported":14,"title":450,"abstract":451,"tags":452},[],[],[409],[454,455],"Palmital Farm 土壤有机碳","Minas Gerais 遥感 土壤碳","PalmitalFarm土壤有机碳-5102","10.1007\u002Fs42729-026-03629-3",{"doi":457,"openalex_id":459,"authors":460,"venue":400,"cited_by_count":77,"oa_url":494,"card":495,"direction":176,"ingested_from":95},"W7222335269",[461,464,467,470,473,476,479,482,485,488,491],{"name":462,"orcid":463},"Leonardo Augusto de Sousa","https:\u002F\u002Forcid.org\u002F0009-0003-1560-5600",{"name":465,"orcid":466},"Fernanda Almeida Bócoli","https:\u002F\u002Forcid.org\u002F0000-0001-5476-6886",{"name":468,"orcid":469},"Eduane José de Pádua","https:\u002F\u002Forcid.org\u002F0009-0003-1782-2756",{"name":471,"orcid":472},"Luiza Maria Pereira Pierangeli","https:\u002F\u002Forcid.org\u002F0000-0002-9218-3718",{"name":474,"orcid":475},"Junior César Avanzi","https:\u002F\u002Forcid.org\u002F0000-0003-2455-0325",{"name":477,"orcid":478},"Bruno Montoani Silva","https:\u002F\u002Forcid.org\u002F0000-0002-8240-8987",{"name":480,"orcid":481},"Carlos Victor Lima Albuquerque","https:\u002F\u002Forcid.org\u002F0009-0009-4597-4443",{"name":483,"orcid":484},"Luiz Roberto Guimarães Guilherme","https:\u002F\u002Forcid.org\u002F0000-0002-5387-6028",{"name":486,"orcid":487},"Marco Aurélio Carbone Carneiro","https:\u002F\u002Forcid.org\u002F0000-0003-4349-3071",{"name":489,"orcid":490},"Nilton Curi","https:\u002F\u002Forcid.org\u002F0000-0002-2604-0866",{"name":492,"orcid":493},"Sérgio Henrique Godinho Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2750-5976","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs42729-026-03629-3.pdf",{"tldr":496,"method":497,"finding":498,"direction":176,"opportunity":499},"结合近地与遥感数据及随机森林预测并制图巴西农场不同土地利用下的土壤有机碳储量。","76个样点、Walkley-Black法测碳，随机森林建模，留一交叉验证。","遥感与地形因子预测最佳（R²=0.43），土地利用和土壤质地主导碳储量空间变异。","可探索多源遥感与深度学习方法提升小尺度土壤碳制图精度，并耦合管理措施情景。","2026-10-16T23:30:31.433840Z",{"id":502,"title":503,"url":504,"summary":505,"summary_zh":9,"content":9,"source_name":506,"source_url":504,"published_at":507,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":77,"score_detail":508,"sources":510,"tags":512,"primary_topic":9,"secondary_topics":515,"classification":516,"search_phrases":523,"slug":526,"view_count":154,"doi":527,"paper":528,"created_at":538},4819,"Governance and operational management of public photovoltaic systems: A decision support framework for decentralized installations","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10705468\u002Fv1","Governance and operational management of public photovoltaic systems: A decision support framework for decentralized installations。Research Square","Research Square","2026-10-12T00:00:00Z",{"impact":77,"substance":77,"depth":77,"authority":77,"freshness":77,"relevant":77,"comment":509},"主题为公共光伏系统的治理与运营管理，与三农、农业信息化、智慧农业等无关，不予入选。",[511],{"name":506,"url":504},[29,513,514],"公共光伏","分布式能源",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":517},{"candidates":518,"agricultural_evidence":14,"reason":72},[519],{"code":49,"score":20,"supported":42,"title":520,"abstract":521,"tags":522},[51],[51],[29],[524,525],"公共光伏系统 分布式安装 决策支持","分布式能源 公共光伏 决策支持","公共光伏系统分布式安装决策支持-4819","10.21203\u002Frs.3.rs-10705468\u002Fv1",{"doi":527,"openalex_id":529,"authors":530,"venue":506,"cited_by_count":77,"oa_url":537,"card":9,"direction":94,"ingested_from":95},"W7221571739",[531,534],{"name":532,"orcid":533},"Manoel Henrique Alves","https:\u002F\u002Forcid.org\u002F0000-0002-1392-9607",{"name":535,"orcid":536},"Fabiano Perin Gasparin","https:\u002F\u002Forcid.org\u002F0000-0001-9422-6948","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10705468\u002Flatest.pdf","2026-10-13T23:30:16.091921Z"]