[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4943":3,"related-4943":80},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":74,"slug":77,"view_count":78,"doi":8,"paper":8,"created_at":79},4943,"江苏农科院资环所技术支撑南京耕地质量提升'四新'项目观摩会召开","https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fxww\u002Fgzdt\u002Fart\u002F2026\u002Fart_76bbacc4963f497da5785a2c05accb7e.html","10月10日,由江苏省农科院资环所技术支撑的南京市耕地质量提升'四新'项目观摩会召开。针对南京地区耕地普遍存在的土壤有机质消耗快、新增耕地有机质含量低、耕层浅薄等瓶颈,通过科学运筹稻麦周年有机物料投入的配比与季节施用量,实现稻麦周年土壤有机质快速提升6.64%,矿物结合态有机碳含量提升15.1%。利用土壤有机质含量变化数据结合多源环境协变量,借助机器学习构建有机质变化评估预测模型。",null,"资环所技术支撑的南京市耕地质量提升“四新”技术集成展示项目现场观摩会顺利召开\n\n文章来源：资环所 发布时间：2026-10-13 18:27 阅读：\n\n10月10日，由江苏省农科院资环所提供技术支撑的南京市耕地质量提升“新产品、新技术、新模式、新装备”集成展示项目（以下简称“四新”项目）现场观摩会顺利召开。全国农业技术推广服务中心研究员汤松、江苏省耕地质量与农业环境保护站副站长张莹、江苏省农业技术推广中心研究员邓建平、南京市农业农村局副局长张震宇、市农业农村局耕地质量处处长左洪林，以及各区农技推广人员、肥料企业代表等共计 60 人参会。会议由南京市耕地质量保护站站长刘胜环主持。\n\n观摩现场，项目技术负责人高岩研究员重点介绍了项目核心技术及其阶段性成效。针对南京地区耕地普遍存在的土壤有机质消耗速率快、新增耕地有机质含量低、耕层浅薄、物理结构差等瓶颈问题，通过科学运筹稻麦周年有机物料投入的配比与季节施用量，结合秸秆快速腐解-适深还田与易氧化有机质长效固持技术，实现稻麦周年土壤有机质快速提升6.64%，矿物结合态有机碳含量提升15.1%。同时，利用土壤有机质含量变化数据结合多源环境协变量，借助机器学习构建了有机质变化评估预测模型；应用光谱技术结合NDRE反演模型开展稻麦季氮肥精准调控，实现田块尺度减量施氮，氮肥偏生产力提升 6%-15%，作物产量同步提高 4%-13%。\n\n南京宁粮环保集团、江苏思农高科技、南京轩凯生物科技、江苏艾萨斯新型肥料工程技术有限公司等合作企业现场展示了高碳有机肥、生物有机肥、秸秆腐解剂、聚谷氨酸类水溶肥、聚合物包膜控释肥等新型肥料产品，为“四新”项目提供了有力支撑。\n\n与会专家充分肯定了项目在中低产田土壤改良方面取得的技术成效，建议总结形成便于农户操作的技术模式和实操手册，加快新技术、新产品推广应用。观摩结束后，会议继续开展技术培训，张震宇副局长部署耕地质量保护相关工作，刘胜环站长作开班动员，全国农业技术推广服务中心汤松研究员、省农业技术推广中心邓建平研究员分别作专题授课。\n\n[![Image 1: 1013.jpg](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002FS569a5bc91a1a4af09168bea31bb9cfc1-750.jpg)](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002F569a5bc91a1a4af09168bea31bb9cfc1.jpg)[](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fxww\u002Fgzdt\u002Fart\u002F2026\u002Fart_76bbacc4963f497da5785a2c05accb7e.html)\n\n[![Image 2: 101301.jpg](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002FS2226cc0a132d4a5088eaa9e3a4a01d41-750.jpg)](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002F2226cc0a132d4a5088eaa9e3a4a01d41.jpg)[](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fxww\u002Fgzdt\u002Fart\u002F2026\u002Fart_76bbacc4963f497da5785a2c05accb7e.html)\n\n[![Image 3: 101302.jpg](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002FSc84ed0ebddce4c9a93c9f8b6aa617296-750.jpg)](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002Fc84ed0ebddce4c9a93c9f8b6aa617296.jpg)[](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fxww\u002Fgzdt\u002Fart\u002F2026\u002Fart_76bbacc4963f497da5785a2c05accb7e.html)\n\n[![Image 4: 101303.jpg](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002FSfc8b07abf4c843c68cbc57aefe18234c-750.jpg)](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002Ffc8b07abf4c843c68cbc57aefe18234c.jpg)[](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fxww\u002Fgzdt\u002Fart\u002F2026\u002Fart_76bbacc4963f497da5785a2c05accb7e.html)","江苏省农业科学院农业资源与环境研究所 2026 年 10 月 13 日","2026-10-13T00:00:00Z","报道",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},14,20,15,12,8,1,"省级科研机构支撑的地市级耕地质量提升项目观摩会，含有机质提升6.64%、氮肥偏生产力提升6%-15%等实测数据与多方信源，专业增量较足，但属区域性技术推广动态，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"精准施肥","秸秆还田","耕地质量","新型肥料","土壤有机质",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,59,64,69],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},4,true,[],[50],"土壤",[29],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"I2",3,[],[57],"机器学习",[],{"code":60,"score":22,"supported":14,"title":61,"abstract":62,"tags":63},"F3",[],[],[27],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"H1",[],[],[29],{"code":70,"score":22,"supported":14,"title":71,"abstract":72,"tags":73},"H5",[],[],[28],[75,76],"南京 耕地质量提升 四新项目","江苏农科院 资环所 有机质提升","南京耕地质量提升四新项目-4943",0,"2026-10-15T00:06:02.313598Z",{"total":81,"page":22,"page_size":81,"items":82},6,[83,174,253,308,377,424],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":8,"source_name":89,"source_url":86,"published_at":90,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":92,"score_detail":93,"sources":97,"tags":99,"primary_topic":103,"secondary_topics":108,"classification":115,"search_phrases":146,"slug":149,"view_count":150,"doi":151,"paper":152,"created_at":173},3916,"Hyperspectral inversion of soil total nitrogen content using spectral and machine learning","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjb.v55i3.93850","The Agro-pastoral Transitional Ecotone of Northern Shaanxi is an ecologically fragile region. Soil nitrogen is a critical factor influencing crop productivity and regional ecological balance. Rapid and accurate monitoring of farmland soil total nitrogen (TN) content is therefore essential for implementing precision agriculture and ensuring sustainable land use in this area. This study focused on the cultivated soils of Dingbian County, a representative area within this ecotone. Field-collected soil samples underwent TN content determination and laboratory-based hyper spectral reflectance measurement. The original spectral reflectance (R) was first transformed using mathematical methods, including first-derivative (FDR), second-derivative (SDR), and logarithm of the reciprocal's first-derivative (LRFD) analysis, to enhance spectral information and suppress background noise. Subsequently, the successive projections algorithm (SPA) and correlation analysis (CA) were integrated to identify feature wavelengths highly correlated with soil TN from the transformed spectral data. Partial Least Squares Regression (PLSR), Decision Tree (DT), and Random Forest (RF) were developed to estimate soil TN content. Model performance was evaluated and compared using the coefficient of determination (R²), root mean square error (RMSE), and ratio of performance to deviation (RPD). The results indicated that: (1) Spectral derivative transformations effectively highlighted subtle information, with LRFD showing the greatest sensitivity to TN. Models built using LRFD-derived features consistently outperformed those based on original reflectance. (2) Among the machine learning algorithms, the Random Forest (RF) model demonstrated superior predictive performance on the validation set (R² = 0.85, RMSE = 0.11 g\u002Fkg, RPD = 2.15), indicating the highest stability and generalization capability. The PLSR model performed moderately well, while the DT model was prone to over fitting and yielded the lowest accuracy. (3) This study confirms that the integration of hyper spectral technology and machine learning algorithms enables efficient and precise estimation of soil TN content in farmland within the Northern Shaanxi agro-pastoral ecotone, providing a reliable technical framework for soil fertility assessment and precision nutrient management in the region. Bangladesh J. Bot. 55(3): 605-614, 2026 (September)","陕北农牧交错带是生态脆弱区，土壤氮素是影响作物生产力和区域生态平衡的关键因子。快速、准确地监测农田土壤全氮（TN）含量，对于该区实施精准农业和保障土地可持续利用至关重要。本研究以该交错带典型区域定边县的耕作土壤为研究对象，对野外采集的土壤样品进行TN含量测定和实验室高光谱反射率测量。首先采用数学方法对原始光谱反射率（R）进行变换，包括一阶微分（FDR）、二阶微分（SDR）和倒数对数一阶微分（LRFD）分析，以增强光谱信息并抑制背景噪声。随后，将连续投影算法（SPA）与相关分析（CA）相结合，从变换后的光谱数据中筛选出与土壤TN高度相关的特征波长。构建偏最小二乘回归（PLSR）、决策树（DT）和随机森林（RF）模型估算土壤TN含量。采用决定系数（R²）、均方根误差（RMSE）和相对分析误差（RPD）对模型性能进行评价与比较。结果表明：（1）光谱微分变换有效凸显了细微信息，其中LRFD对TN的敏感性最强，基于LRFD衍生特征构建的模型始终优于基于原始反射率的模型。（2）在机器学习算法中，随机森林（RF）模型在验证集上表现出优越的预测性能（R² = 0.85，RMSE = 0.11 g\u002Fkg，RPD = 2.15），具有最高的稳定性和泛化能力。PLSR模型表现中等，而DT模型易于过拟合，精度最低。（3）本研究证实，高光谱技术与机器学习算法相结合能够高效、精确地估算陕北农牧交错带农田土壤TN含量，为该区域土壤肥力评估和精准养分管理提供了可靠的技术框架。Bangladesh J. Bot. 55(3): 605-614, 2026 (September)","Bangladesh Journal of Botany","2026-09-30T00:00:00Z","论文",70,{"impact":20,"substance":18,"depth":94,"authority":95,"freshness":21,"relevant":22,"comment":96},17,13,"高光谱结合机器学习反演土壤全氮，方法对比扎实、结论可靠，对精准农业养分管理有参考价值，但属区域性田间研究，影响范围有限。",[98],{"name":89,"url":86},[100,27,57,101,102],"智慧农业","遥感","土壤氮素",{"code":53,"slug":104,"name":105,"path":106,"parent_code":107},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[109,110],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":111,"slug":112,"name":113,"path":114,"parent_code":107},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":116},{"candidates":117,"agricultural_evidence":47},[118,123,127,133,141],{"code":53,"score":19,"supported":47,"title":119,"abstract":121,"tags":122},[120],"machine learning",[57,120],[57],{"code":33,"score":17,"supported":47,"title":124,"abstract":125,"tags":126},[34],[50,34],[],{"code":111,"score":128,"supported":47,"title":129,"abstract":131,"tags":132},9,[130],"hyperspectral",[],[101],{"code":134,"score":135,"supported":47,"title":136,"abstract":137,"tags":140},"I1",7,[],[138,139],"精准农业","precision agriculture",[100],{"code":60,"score":46,"supported":47,"title":142,"abstract":143,"tags":145},[],[144],"养分管理",[27],[147,148],"定边县 土壤全氮 高光谱","陕北农牧交错带 土壤氮素 反演","定边县土壤全氮高光谱-3916",2,"10.3329\u002Fbjb.v55i3.93850",{"doi":151,"openalex_id":153,"authors":154,"venue":89,"cited_by_count":78,"oa_url":86,"card":165,"direction":171,"ingested_from":172},"W7214885868",[155,158,160,162],{"name":156,"orcid":157},"Zhenyu Dong","https:\u002F\u002Forcid.org\u002F0000-0003-0028-3293",{"name":159,"orcid":8},"Ni Wang",{"name":161,"orcid":8},"Jiancang Xie",{"name":163,"orcid":164},"Jungang Luo","https:\u002F\u002Forcid.org\u002F0009-0009-0285-4839",{"tldr":166,"method":167,"finding":168,"direction":169,"opportunity":170},"用高光谱与机器学习反演陕北农牧交错带农田土壤全氮含量。","野外采样测光谱，经FDR\u002FSDR\u002FLRFD变换，SPA+CA选特征波长，建PLS","LRFD变换最敏感，RF模型验证集R²=0.85、RPD=2.15，精度与泛化最优。","农业遥感与作物表型","可探索多源遥感与深度学习融合，提升生态脆弱区土壤氮素时空反演与制图能力。","农业人工智能与决策模型","openalex","2026-10-01T23:30:53.960449Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":8,"source_name":180,"source_url":177,"published_at":181,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":187,"tags":189,"primary_topic":193,"secondary_topics":194,"classification":196,"search_phrases":223,"slug":226,"view_count":150,"doi":227,"paper":228,"created_at":252},3805,"Machine Learning Prediction of Soil Nutrients and pH in Tibetan Alpine Croplands: A Comparison of Nine Algorithms","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18199875","Soils underpin terrestrial ecosystem functioning and exert strong control over farmland productivity, nutrient turnover, environmental quality, and agricultural sustainability. Reliable prediction of soil nutrients and pH is therefore important for precision fertilization, soil-quality assessment, and the sustainability of farmland management, particularly in fragile alpine agroecosystems. However, the comparative performance of candidate algorithms for estimating soil properties in alpine croplands of the Qinghai-Tibetan Plateau remains poorly characterized. Here, we assessed nine approaches, namely random forest (RF), generalized boosting regression (GBR), multiple linear regression (MLR), artificial neural network (ANN), generalized linear regression (GLR), conditional inference tree (CIT), extreme gradient boosting (eXGB), support vector machine (SVM), and recursive partitioning regression tree (RRT), for predicting soil carbon, nitrogen, phosphorus, potassium, and pH from annual mean temperature, annual precipitation, annual solar radiation, and maximum normalized difference vegetation index (NDVImax). Field observations were obtained from 103 sampling plots, with four individual soil samples collected per plot and depth, resulting in 412 observations for each depth layer. Clear contrasts in model performance emerged under alpine agricultural conditions. Across all soil variables and depths, eXGB and RF showed the best overall performance, with the lowest mean relative bias values of 1.35% and 1.55%, respectively, and the lowest mean RMSE values of 0.43 and 0.47. These results indicate that nonlinear ensemble algorithms better captured the heterogeneous relationships between environmental covariates and soil properties than linear models or single-tree approaches. Overall, RF and eXGB provide a practical methodological basis for precision fertilization, soil-quality assessment, and long-term sustainability in fragile alpine agroecosystems.","土壤是陆地生态系统功能的基础，并对农田生产力、养分周转、环境质量和农业可持续性具有强烈控制作用。因此，可靠预测土壤养分和pH对于精准施肥、土壤质量评估以及农田管理的可持续性十分重要，尤其是在脆弱的高寒农业生态系统中。然而，在青藏高原高寒农田中，用于估算土壤属性的候选算法的比较性能仍缺乏系统表征。本研究评估了九种方法，即随机森林（RF）、广义提升回归（GBR）、多元线性回归（MLR）、人工神经网络（ANN）、广义线性回归（GLR）、条件推断树（CIT）、极端梯度提升（eXGB）、支持向量机（SVM）和递归分割回归树（RRT），利用年平均气温、年降水量、年太阳辐射和最大归一化差异植被指数（NDVImax）预测土壤碳、氮、磷、钾和pH。野外观测来自103个采样样地，每个样地和每个深度采集4个独立土壤样品，每个深度层共获得412个观测值。在高寒农业条件下，模型性能表现出明显差异。在所有土壤变量和深度上，eXGB和RF总体表现最佳，平均相对偏差值最低，分别为1.35%和1.55%，平均RMSE值最低，分别为0.43和0.47。这些结果表明，与线性模型或单树方法相比，非线性集成算法更好地捕捉了环境协变量与土壤属性之间的异质性关系。总体而言，RF和eXGB为脆弱高寒农业生态系统中的精准施肥、土壤质量评估和长期可持续性提供了实用的方法学基础。","Sustainability","2026-09-27T00:00:00Z",73,{"impact":20,"substance":184,"depth":185,"authority":95,"freshness":21,"relevant":22,"comment":186},22,18,"基于103个样点412份观测对比九种算法预测高原农田土壤养分与pH，方法扎实、结论明确，对精准施肥有直接参考价值，但属区域性研究，产业影响有限。",[188],{"name":180,"url":177},[100,190,27,191,192],"农业人工智能","土壤养分","青藏高原",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[195],{"code":53,"slug":104,"name":105,"path":106,"parent_code":107},{"status":40,"locked":14,"version":41,"method":42,"evidence":197},{"candidates":198,"agricultural_evidence":47},[199,203,207,213,219],{"code":33,"score":19,"supported":47,"title":200,"abstract":201,"tags":202},[34],[50,34],[191],{"code":53,"score":128,"supported":47,"title":204,"abstract":205,"tags":206},[120],[],[190],{"code":60,"score":135,"supported":47,"title":208,"abstract":209,"tags":212},[],[210,211],"施肥","fertilization",[27],{"code":214,"score":54,"supported":47,"title":215,"abstract":216,"tags":218},"H6",[],[217],"农业生态",[],{"code":134,"score":22,"supported":14,"title":220,"abstract":221,"tags":222},[],[],[100],[224,225],"青藏高原 农田 土壤养分 机器学习","土壤pH 预测 随机森林 XGBoost","青藏高原农田土壤养分机器学习-3805","10.3390\u002Fsu18199875",{"doi":227,"openalex_id":229,"authors":230,"venue":180,"cited_by_count":78,"oa_url":177,"card":247,"direction":171,"ingested_from":172},"W7214680092",[231,233,235,238,240,243,245],{"name":232,"orcid":8},"Chenjun Zhao",{"name":234,"orcid":8},"Shaowei Li",{"name":236,"orcid":237},"Yuan Tian","https:\u002F\u002Forcid.org\u002F0000-0002-8758-1826",{"name":239,"orcid":8},"ChengQun Yu",{"name":241,"orcid":242},"Gang Fu","https:\u002F\u002Forcid.org\u002F0000-0002-6055-8730",{"name":244,"orcid":8},"Zhiming Zhong",{"name":246,"orcid":8},"Wei Sun",{"tldr":248,"method":249,"finding":250,"direction":171,"opportunity":251},"比较九种机器学习算法预测青藏高原高寒农田土壤养分与pH，发现集成算法最优。","基于103个样点412个观测，用气候与NDVI变量比较RF、eXGB等九种算法。","eXGB和RF表现最佳，平均相对偏差最低（1.35%和1.55%），优于线性与单树模型。","可引入多时相遥感与深度学习方法，提升高寒区土壤属性制图精度与泛化能力。","2026-09-29T23:30:57.794777Z",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":8,"content":258,"source_name":259,"source_url":8,"published_at":260,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":261,"score_detail":262,"sources":265,"tags":267,"primary_topic":272,"secondary_topics":277,"classification":279,"search_phrases":303,"slug":306,"view_count":150,"doi":8,"paper":8,"created_at":307},3738,"河南农科院举办土壤质量-粮食产量协同提升玉米观摩会","https:\u002F\u002Fwww.hnjjbs.com\u002Farticle\u002F2026-09\u002F179056750272011.html","9月22日至23日农业农村部作物栽培现代农业科技试验示范基地（新乡平原示范区）组织开展土壤质量-粮食产量协同提升技术集成模式应用效果（玉米季）现场观摩会。河南省农科院植物营养与资源环境研究所黄绍敏研究员等专家授课并现场测产。","9月22日-23日，农业农村部作物栽培现代农业科技试验示范基地(新乡平原示范区)，先后在河南现代农业研究开发基地、获嘉县位庄乡大位庄村，组织开展“土壤质量-粮食产量协同提升技术集成模式应用效果(玉米季)”现场观摩会。河南省农科院植物营养与资源环境研究所黄绍敏研究员、获嘉县农业农村局王庆安推广研究员、新乡市种业发展中心武志斌正高级农艺师出席活动，平原示范区种植大户、获嘉县各类新型农业经营主体代表共计100余人参会。\n\n观摩会前，黄绍敏、王庆安、武志斌三位专家分别围绕耕层深度改良、秸秆还田培肥地力、测土配方与精准水肥调控、绿色田间管理、科学合理密植等关键内容开展技术授课。培训结束后，与会人员走进示范展示田开展实地观摩，专家组开展玉米现场测产，同时向参会代表发放技术指导手册。黄绍敏表示，土壤质量\n\n粮食产量协同提升技术坚持用地与养地相结合，通过改良耕层、培肥地力，提升肥料利用效率，增强玉米抗逆能力，充分挖掘粮食增产潜力，实现耕地保育与粮食产能同步提高。\n\n此次观摩会既是示范样板效应的实践落地，也是对基地试验示范工作成效的集中检验。一是紧扣国家粮食安全战略，服务新一轮千亿斤粮食产能提升行动。立足区域生产实际，深挖粮食增产潜能，推动粮食单产提升相关举措落地见效，为稳定当地粮食生产、筑牢粮食安全根基提供科技支撑。二是紧盯一线生产需求，回应农户现实生产难题。基地坚持面向大田生产，聚焦种植主体实际诉求，以田间现场观摩直观展示优良品种及配套栽培技术，真正做到让农户看得见、学得会、用得上。三是突出示范引领带动，高效完成基地技术推广任务。依托观摩平台，充分发挥科研示范基地窗口样板作用，集中展示新品种、新技术集成示范成果，以点带面推动成熟技术模式复制扩散，扩大技术辐射覆盖面。","河南经济报","2026-09-28T03:49:00Z",59,{"impact":263,"substance":17,"depth":20,"authority":128,"freshness":21,"relevant":22,"comment":264},16,"省级科研机构主办的玉米季技术集成观摩会，有实地测产与专家授课，属细分领域实质进展，但层级与增量有限，可入精选备选。",[266],{"name":259,"url":256},[268,269,28,270,271],"粮食安全","玉米","土壤改良","科学施肥",{"code":273,"slug":274,"name":269,"path":275,"parent_code":276},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[278],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":280},{"candidates":281,"agricultural_evidence":47},[282,286,290,295,299],{"code":273,"score":20,"supported":47,"title":283,"abstract":284,"tags":285},[269],[269],[269],{"code":33,"score":20,"supported":47,"title":287,"abstract":288,"tags":289},[50],[50],[270],{"code":291,"score":22,"supported":14,"title":292,"abstract":293,"tags":294},"A8",[],[],[268],{"code":60,"score":22,"supported":14,"title":296,"abstract":297,"tags":298},[],[],[271],{"code":70,"score":22,"supported":14,"title":300,"abstract":301,"tags":302},[],[],[28],[304,305],"河南农科院 玉米 观摩会","土壤质量 粮食产量 协同提升","河南农科院玉米观摩会-3738","2026-09-29T00:08:24.697115Z",{"id":309,"title":310,"url":311,"summary":312,"summary_zh":313,"content":8,"source_name":314,"source_url":311,"published_at":315,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":316,"score_detail":317,"sources":319,"tags":321,"primary_topic":324,"secondary_topics":325,"classification":331,"search_phrases":359,"slug":362,"view_count":150,"doi":363,"paper":364,"created_at":376},3680,"AI-Driven Nutrient Optimization Systems for Precision Agriculture: A Data-Integrated Approach to Reducing Nutrient Runoff and Enhancing Soil Health in Agricultural Systems","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.85286","Nutrients play a fundamental role in plant growth, soil fertility, and overall agricultural productivity. However, inefficient fertilizer application in conventional farming systems often leads to nutrient imbalance, reduced soil quality, and increased environmental degradation. The emergence of precision agriculture and artificial intelligence (AI) has introduced new opportunities for improving nutrient management through data-driven decision-making. This study investigates an AI-driven nutrient optimization system designed to enhance fertilizer efficiency while reducing nutrient runoff and improving soil health. Findings indicate that AI-based systems significantly improve nutrient use efficiency by enabling site-specific fertilizer recommendations and adaptive management strategies. The integration of multi-source agricultural data enhances prediction accuracy for soil nutrient status and crop demand, while also supporting early identification of nutrient loss risks such as runoff and leaching. The study concludes that AI-driven nutrient optimization provides a sustainable and scalable approach for modern agriculture, supporting both productivity enhancement and environmental protection through intelligent, data-integrated farming systems.","养分在植物生长、土壤肥力和农业整体生产力中发挥着基础性作用。然而，传统农业系统中低效的施肥方式往往导致养分失衡、土壤质量下降以及环境退化加剧。精准农业与人工智能（AI）的出现，为通过数据驱动决策改善养分管理带来了新的机遇。本研究探讨了一种AI驱动的养分优化系统，旨在提高肥料利用效率，同时减少养分径流并改善土壤健康。研究结果表明，基于AI的系统通过实现特定地块的施肥建议和适应性管理策略，显著提高了养分利用效率。多源农业数据的整合提升了土壤养分状况和作物需求的预测精度，同时也有助于早期识别径流和淋溶等养分流失风险。研究得出结论，AI驱动的养分优化为现代农业提供了一种可持续且可扩展的方法，通过智能化、数据整合的农业系统，同时支持生产力提升和环境保护。","International Journal For Multidisciplinary Research","2026-09-26T00:00:00Z",61,{"impact":20,"substance":185,"depth":19,"authority":21,"freshness":21,"relevant":22,"comment":318},"论文提出AI驱动的养分优化系统，方法有新意但属学术探讨，产业落地影响有限，可作为智慧农业技术动态收录。",[320],{"name":314,"url":311},[100,190,27,322,323],"土壤健康","养分流失",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[326],{"code":60,"slug":327,"name":328,"path":329,"parent_code":330},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":40,"locked":14,"version":41,"method":42,"evidence":332},{"candidates":333,"agricultural_evidence":47},[334,338,342,348,353],{"code":33,"score":19,"supported":47,"title":335,"abstract":336,"tags":337},[34],[50,34],[322],{"code":134,"score":19,"supported":47,"title":339,"abstract":340,"tags":341},[139],[138,139],[100],{"code":60,"score":13,"supported":47,"title":343,"abstract":344,"tags":347},[],[210,345,144,346],"肥料","fertilizer",[27],{"code":53,"score":46,"supported":47,"title":349,"abstract":350,"tags":352},[],[351],"artificial intelligence",[190],{"code":354,"score":54,"supported":47,"title":355,"abstract":356,"tags":358},"I6",[],[357],"agricultural data",[],[360,361],"AI 养分优化 精准农业","养分流失 土壤健康","AI养分优化精准农业-3680","10.36948\u002Fijfmr.2026.v08i05.85286",{"doi":363,"openalex_id":365,"authors":366,"venue":314,"cited_by_count":78,"oa_url":311,"card":371,"direction":171,"ingested_from":172},"W7214553198",[367,369],{"name":368,"orcid":8},"Olufisayo Obebe",{"name":370,"orcid":8},"Ebenezer Akinola",{"tldr":372,"method":373,"finding":374,"direction":171,"opportunity":375},"研究AI驱动的养分优化系统，通过数据集成实现精准施肥，减少养分流失并改善土壤健康。","集成多源农业数据与AI算法，进行站点特异性施肥推荐和自适应管理。","AI系统显著提高养分利用效率，增强土壤养分与作物需求预测精度，并早期识别流失风险。","可探索多源数据融合的实时决策模型，以及在不同土壤气候条件下AI系统的可扩展性与长期环境效应评估。","2026-09-28T23:30:49.535963Z",{"id":378,"title":379,"url":380,"summary":381,"summary_zh":8,"content":382,"source_name":383,"source_url":8,"published_at":384,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":385,"score_detail":386,"sources":388,"tags":390,"primary_topic":395,"secondary_topics":396,"classification":397,"search_phrases":419,"slug":422,"view_count":54,"doi":8,"paper":8,"created_at":423},2819,"中国农科院组团全面入驻国家盐碱地中心:10支创新团队布局黄三角农高区","https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Fmtbd\u002F362cd263199245d7a9bc94847e372f4c.htm","9月11日,中国农业科学院在山东省黄三角农高区牵头召开盐碱地综合利用国家技术创新中心建设推进会议。国家盐碱地中心总部基地已全面建成投用,中国农科院10支创新团队入驻,常驻科研人员和研究生数量持续增加,打造轻度、中度、重度全类型盐碱地示范场景超1700亩。资划所所长吴文斌介绍,作为国家盐碱地中心和中国农科院盐碱地农业中心建设牵头单位,已初步形成优势互补、联动发展的良好创新格局。","[中国农科院](http:\u002F\u002Fwww.caas.cn\u002F)| 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[身边榜样](https:\u002F\u002Fiarrp.caas.cn\u002Fdqyd\u002Fdqcxwh\u002Fgrb\u002Findex.htm)\n\n        *   ### [荣誉称号](https:\u002F\u002Fiarrp.caas.cn\u002Fdqyd\u002Fdqcxwh\u002Frych\u002Findex.htm)\n\n        *   ### [文化展示](https:\u002F\u002Fiarrp.caas.cn\u002Fdqyd\u002Fdqcxwh\u002Fwhzs\u002Findex.htm)\n\n    *   ### [学习园地](https:\u002F\u002Fiarrp.caas.cn\u002Fdqyd\u002Fdqxxyd\u002Findex.htm)\n\n*   ### [学术期刊](https:\u002F\u002Fiarrp.caas.cn\u002Fxzqk\u002Findex.htm)\n\n您当前所在位置： [首页](https:\u002F\u002Fiarrp.caas.cn\u002Findex.htm)» [院所新闻](https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Findex.htm)» 媒体报道 \n# 【科技日报】中国农业科学院组团全面入驻国家盐碱地中心\n\n发布者：管理员 发布时间：2026-09-15 作者：马爱平 来源：科技日报 点击量：\n\n9月11日，中国农业科学院（以下简称“中国农科院”）在山东省黄三角农高区牵头召开盐碱地综合利用国家技术创新中心建设推进会议。\n\n![Image 6](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F14\u002F580580_5d055b35-6a23-46e6-bbfc-020ed463472dcopy.jpg)\n\n黄三角农高区党工委副书记、管委会常务副主任朱英坤表示，国家盐碱地中心总部基地已全面建成投用，中国农科院10支创新团队入驻，农高区设立科教产发展专项资金，布局“一基地四园区”产业载体，从根本上解决了“有地方、有人、有钱搞科研”的问题。农高区将全力当好科研工作“勤务员”，为科研团队营造最优创新创业环境，与中国农科院同心同向，共同抓好国家盐碱地中心考核评估、2026国际盐碱地农业大会等重点任务，为保障国家粮食安全、推进黄河流域生态保护和高质量发展作出新贡献。\n\n中国农科院副院长刘现武表示，国家盐碱地中心自2025年6月正式揭牌并实体化运行以来，10支院级创新团队陆续入驻，常驻科研人员和研究生数量持续增加，打造轻度、中度、重度全类型盐碱地示范场景超1700亩，各项工作取得实实在在的阶段性成效。各入驻团队要真驻实驻，进一步聚焦研究方向，加强团队建设，聚力核心攻关，共同打造国内领先、国际一流的盐碱地科技创新高地。\n\n中国农科院农业资源与农业区划研究所（以下简称“资划所”）所长吴文斌表示，作为国家盐碱地中心和中国农科院盐碱地农业中心建设牵头单位，资划所秉持开放合作、互利共赢的理念，与各共建单位开展了扎实有效的合作，初步形成了优势互补、联动发展的良好创新格局，为国家盐碱地中心常态化运行、高质量发展筑牢了坚实基础。下一步，资划所将继续与各兄弟单位同心携手，坚持“平台共建、资源共享、攻坚协同”，深耕共建共享、协同协作的创新发展模式，以规范化、制度化保障多方协作高效运转，全力将盐碱地中心打造成为国内领先、国际一流的盐碱地科技创新高地。\n\n![Image 7](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F14\u002F580580_56636f40-5fbb-41f3-a258-e9586bd6d0f1copy.jpg)\n\n中国农科院盐碱地农业中心和中国农科院10支创新团队在现场进行了情况介绍与成果推介。会上还举行了合作项目签约仪式，4支团队和合作企业围绕盐碱地综合治理、特色农产品加工等科技成果转化与产业培育达成合作协议。\n\n（资划所供图）\n\n  \n\n『[打印](https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Fmtbd\u002F362cd263199245d7a9bc94847e372f4c.htm#)』『[关闭](https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Fmtbd\u002F362cd263199245d7a9bc94847e372f4c.htm#)』\n\n[设为首页](http:\u002F\u002Fiarrp.caas.cn\u002F)∣[加入收藏](javascript:window.external.AddFavorite('http:\u002F\u002Fiarrp.caas.cn\u002F', '%E5%86%9C%E4%B8%9A%E8%B5%84%E6%BA%90%E4%B8%8E%E5%86%9C%E4%B8%9A%E5%8C%BA%E5%88%92%E7%A0%94%E7%A9%B6%E6%89%80'))∣[联系我们](https:\u002F\u002Fiarrp.caas.cn\u002Flxwm\u002Findex.htm)\n\n  \n Copyright©2012-2021 中国农业科学院农业资源与农业区划研究所版权所有  \n 地址：北京市海淀区中关村南大街12号 邮编：100081 电话：010-82109640  \niarrp.caas.cn ([京ICP备14003094号-1](https:\u002F\u002Fbeian.miit.gov.cn\u002F))京公网安备 11010802028641号 技术支持：中国农业科学院农业信息研究所","中国农业科学院农业资源与农业区划研究所 \u002F 科技日报","2026-09-15T00:00:00Z",76,{"impact":184,"substance":185,"depth":17,"authority":17,"freshness":21,"relevant":22,"comment":387},"国家级科研机构成建制入驻盐碱地国家技术创新中心，属盐碱地综合利用领域的实质性布局进展，具备聚合价值。",[389],{"name":383,"url":380},[391,392,393,394,29],"黄三角农高区","农业科技创新","院地合作","盐碱地治理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":398},{"candidates":399,"agricultural_evidence":47},[400,405,410,414],{"code":33,"score":95,"supported":47,"title":401,"abstract":403,"tags":404},[402],"盐碱地",[402],[394,29],{"code":406,"score":22,"supported":14,"title":407,"abstract":408,"tags":409},"G1",[],[],[393],{"code":65,"score":22,"supported":14,"title":411,"abstract":412,"tags":413},[],[],[29],{"code":415,"score":22,"supported":14,"title":416,"abstract":417,"tags":418},"J2",[],[],[392],[420,421],"农业科技创新 黄三角农高区 盐碱地治理 耕地质量","农业科技创新 黄三角农高区","农业科技创新黄三角农高区盐碱地治理耕地质量-2819","2026-09-18T00:03:26.848170Z",{"id":425,"title":426,"url":427,"summary":428,"summary_zh":8,"content":429,"source_name":430,"source_url":8,"published_at":431,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":92,"score_detail":432,"sources":434,"tags":436,"primary_topic":443,"secondary_topics":444,"classification":445,"search_phrases":465,"slug":468,"view_count":22,"doi":8,"paper":8,"created_at":469},1011,"妙手回春,酸瘦红壤变良田 浙江省农科院环土所为10万余亩田块'调理'体质 耕地质量显著改善","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7677744831895994886\u002F","浙江新一批耕地土壤改良效果评估新鲜出炉:经浙江省农科院环境资源与土壤肥料研究所'调理',近期有10万余亩田块的土壤'体质'明显增强,耕地质量显著改善。浙江人均耕地仅0.3亩,粮食产量已连续10年保持稳健向上增长,但据环土所调研,浙江部分耕地面临酸瘦、耕层浅薄、养分库容小等困境,其中丘陵盆地尤甚,中低产地占6成。近日,记者跟着环土所所长马军伟团队进入金衢盆地,从取土采样、田间诊脉到实验室检测,体验耕地'调理'全过程。改良后的土壤呈深褐色、酥松,形成水稳性团粒结构,保水保肥又透气;退化板结的对照田则像砖块、孔隙稀少、根系难以下扎。该改良技术为浙江深入实施大面积单产提升行动提供'良田'根基。","潮新闻客户端 记者 来逸晨 实习生 姜景心\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F14fed140422c4be994c4198c7c4c51e2~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788480426&x-signature=6FK%2Fz4gTqh3HISA7UI7AA6YfJUc%3D)\n马军伟（左）带着我们在兰溪香溪镇的稻田间采集土样。通讯员 王雨嫣 摄\n\n浙江新一批耕地土壤改良效果评估新鲜出炉：经省农科院环境资源与土壤肥料研究所（以下简称环土所）“调理”，近期有10万余亩田块的土壤“体质”明显增强，耕地质量显著改善。\n\n浙江人均耕地仅0.3亩，粮食产量已连续10年保持稳健向上增长。为进一步挖掘增产潜力，浙江正深入实施大面积单产提升行动。提单产不仅要良种、良法、良机，更需要良田作根基。然而，据环土所调研，浙江部分耕地面临酸瘦、耕层浅薄、养分库容小等困境，其中丘陵盆地尤甚，中低产地占6成。\n\n助农田“强身健体”有妙招。近日，我们跟着环土所所长马军伟团队进入金衢盆地，从取土采样、田间诊脉到实验室检测，体验耕地“调理”全过程。\n\n**插管入田，给耕地“抽血”取样**\n\n驱车至金华兰溪，车窗外，山坡上的裸土地时隐时现，黄中带红。马军伟告诉我们，这就是金衢盆地典型的“低丘红壤”，“体质”可用三个字形容：酸、瘦、黏。\n\n车停在香溪镇的千亩科技示范区，5年来，这里分两批开展了土壤改良。我们此行的目的，是采集土样、回访改良成效、为剩余未改良的零碎田块“查漏补缺”。\n\n沿着田埂往深处走，马军伟忽然停下脚步，让我们对比两处地块：未培肥的对照田在一处曾经被遗忘的转角，不足半亩，土表呈暗棕色，踩上去不塌陷；田埂另一侧则是连片改良后的示范田，土壤呈深褐色，酥松不少。\n\n“用手感受下。”马军伟招呼道。我们蹲下，手指拂过对照田的土壤，像碰到砂纸一样，硬邦邦的；而捏一把改良后的褐土，轻轻一攥，土粒聚团，手掌张开，它们又自然散落。“这就是水稳性团粒结构”，马军伟解释，优质耕地的土壤团粒就像米花糖，疏松多孔且有韧性，保水保肥又透气；退化板结的土壤则像砖块，孔隙稀少、根系难以下扎。\n\n对比完土质，马军伟给我们递来一套不锈钢取土器和橡胶锤：“分别取样。”\n\n除去杂草，他示意我们一人扶着取土器垂直立于田面，一人拿着橡胶锤瞄准顶端一下下敲击。\n\n5厘米、10厘米、15厘米……取土器连着的金属管像一根针管，慢慢扎入大地的“肌肤”。\n\n“停。”他一声令下，我们立刻停止敲击，合力将取土器垂直拔出。只见一截完整的“土柱”嵌在管内。\n\n“水田土壤采样一般取地表向下0至20厘米的耕作层，它是水稻根系分布最密集、物质交换最活跃的层位，也是土壤调理的目标区域。” 马军伟介绍。在示范田和对照田分别取了样，我们感叹：“整个过程，和人体抽血相似。”\n\n“这个比喻很贴切。”马军伟笑着点头。他带我们轻敲取土器外壁，将土样完整剥离，逐一装入透明样品袋中，标记、封存。和做“血常规”一样，它们将被送进实验室，开展多项检测，为土壤质量精准“画像”。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F3419c8d765cf5b5e34d88ad2f12a0bcd~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788480426&x-signature=BUE8t%2F1AidcPYWz23kOpXm4fhWo%3D)\n浙江省环土所的工作人员正在检测土壤。\n\n**辨证施治，三剂良方优“体质”**\n\n“马老师，来回访啦！”烈日下，一位皮肤黝黑的汉子自田间走来。他是这片耕地的生产负责人项力通。\n\n他告诉我们，这片土地近年经历了全域土地综合整治，不少区域由建设用地复垦，或是由弃耕失管的碎片化田块重整成片。头年试种，单季稻亩产仅200至300公斤。\n\n“多亏马老师团队‘妙手回春’，土壤改良的第一年，亩产大幅提升，第二年又涨了100公斤！”项力通竖起大拇指。\n\n我们暗自赞叹，同时发出疑问：“底子差的红壤，究竟是如何快速蜕变成良田的？”\n\n团队的王峰博士拎着3个麻袋前来揭秘。马军伟蹲下来，把手伸进1号袋子，掏出一把浅褐色的细颗粒，在掌心摊开：“这是土壤调理剂，内含碱性矿物质，能中和酸度，给土壤‘降火气’。”2号袋子里是深褐色颗粒，凑近能闻到淡淡的腐殖质气息。“这个专用肥能促成水稳性团粒结构，让黏重的土质变得疏松透气。”3号袋子里的颗粒颜色更深，褐中带黑。“这是生物有机肥，补充有机质的同时引入功能菌群，有益菌能分解有机质、释放作物可吸收的速效养分，还能调控土壤微生态、抑制土传病害。”\n\n三个“方子”，三种功效，组成了全套一体化改良方案。马军伟像一位“老中医”，为我们讲解他的开方法则：“土壤调理不是随便撒点石灰、施点肥，只求一时的表面改善。它就像给人调理身体，得先辨明病根，再搭配药方综合施策，以求长效提升。”\n\n随后，项力通带我们来到附近农户的一小块自留地。“我们测过，有机质含量不足2%，低于高标准农田2.6%的基准线。”他对马军伟说，农户也想试试土壤调理。\n\n“先用2号袋子里的专用肥，一周后再用3号袋子里的‘药’。”马军伟迅速“开方”，然后带我们背起撒肥机、手持喷管，将2号袋子里的深褐色颗粒撒向田间。项力通则喊来农户，启动微耕机跟在我们身后，翻动土层，将它们悉数“喂”入土里。\n\n不一会儿，我们就热得满头大汗。我们揩掉脸上的汗水，望着脚下的土地憧憬着：作物根系扎进这样的土里，定能吃得好、喝得饱、长得壮。\n\n**三年蜕变，“体检报告”更健康**\n\n一周后，我们来到位于杭州的省农科院农业装备检测中心环境资源检测室。从兰溪田里取来的土样，已经在这里风干了。\n\n进门后，检测人员首先递来一份“检测清单”——pH值、有机质、全氮、全钾、全磷、有效磷、速效钾、碱解氮。\n\n王峰为我们详解：有机质是土壤肥力的核心基础，决定着土壤的结构稳定性和保肥保水能力；全氮、全磷、全钾是土壤养分的“总储备库”；碱解氮、有效磷、速效钾则是当季作物可直接吸收利用的“速效养分”，对应土壤的即时供肥水平。\n\n走进风干室，只见层层架子上摆满了各式土样。白色托盘垫着牛皮纸，土样被均匀平铺其上，经过自然阴干后变得干燥松散。\n\n“这就是我们从兰溪取来的土样，只剩最后一项有机质含量待测定，一起见证结果吧。”王峰带着我们来到通风橱前。我们跟着他一道戴上口罩和手套，将风干的土块放在研钵里细细研磨一遍，再过两道筛：2毫米大孔径和0.25毫米小孔径筛。\n\n我们将研磨好的土样装入透明的样品袋后，实验人员精密称取0.5克制备好的土样倒入玻璃试管，在高温油浴环境下，用定量的硫酸氧化土壤中的有机碳，再通过一系列换算得出有机质含量。\n\n结果出来了：未改良土壤有机质含量仅13.4g\u002Fkg，改良后的土壤有机质含量达31g\u002Fkg。“经过3年回访，我们可以确定，兰溪香溪镇示范田块的土壤调理成功了。”马军伟欣喜地宣布。\n\n曾经，那里酸化面积占比超55%，有机质平均仅9.5g\u002Fkg，耕地质量等级7.5等（共10等，1等最优）；三年系统改良后，有机质稳定在30g\u002Fkg以上，等级提升至2等及以上，达到优质耕地水平。\n\n目前，环土所已在全省11个设区市累计成功改良了50万余亩田块的土质，这套土壤调理技术正在浙江多地示范推广，从先行示范的试验田走向更多亟待“强身健体”的中低产田。此外，这套技术还走出浙江，在贵州、云南、广西、四川等地得以应用。\n\n“转载请注明出处”","潮新闻 2026-08-25","2026-08-25T00:00:00Z",{"impact":185,"substance":18,"depth":19,"authority":13,"freshness":135,"relevant":22,"comment":433},"报道浙江省农科院土壤改良技术成效，涉及农业科技与耕地质量提升，有具体数据和案例，但属省级科研进展，影响范围有限。",[435],{"name":430,"url":427},[437,438,439,29,440,441,442],"浙江省农科院","红壤改良","环土所","金衢盆地","马军伟","水稳性团粒结构",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":446},{"candidates":447,"agricultural_evidence":47},[448,452,457,461],{"code":33,"score":46,"supported":47,"title":449,"abstract":450,"tags":451},[],[50],[29],{"code":291,"score":54,"supported":47,"title":453,"abstract":454,"tags":456},[],[455],"单产提升",[],{"code":60,"score":54,"supported":47,"title":458,"abstract":459,"tags":460},[],[345],[],{"code":65,"score":22,"supported":14,"title":462,"abstract":463,"tags":464},[],[],[29],[466,467],"水稳性团粒结构 浙江省农科院 红壤改良 耕地质量","水稳性团粒结构 浙江省农科院","水稳性团粒结构浙江省农科院红壤改良耕地质量-1011","2026-08-28T00:06:59.968752Z"]