[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-419":3,"related-419":63},{"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":57,"slug":60,"view_count":61,"doi":8,"paper":8,"created_at":62},419,"制度组态与保护性农业成效:基于中国黑土区梨树县生产单元的模糊集分析","https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566557.html","通过模糊集定性比较分析(fsQCA)方法,以吉林省梨树县26个生产单元为案例,考察标准实施强度、服务链协调、外包和金融支持四个条件对复合可持续性结果(涵盖土壤保护、粮食产量、产量稳定性及溢出效应,Cronbach's α = 0.76)的影响。研究发现不存在任何单一条件接近高绩效的必要条件;基础解识别出一条高覆盖度的协调路径和一条受金融条件制约的外包路径;在更严格的频数阈值下,解简化为仅保留协调路径,该路径在100%的阈值网格设定和96%的留一分析中保持稳定。分析揭示外包与金融能力之间存在条件互补性,政策含义在于推动县级政策工具从以面积扩张为指标向以治理质量门槛转变。",null,"[![Image 11: 榜言堂](https:\u002F\u002Fcss.bim99.cn\u002Frcgz\u002Flogo.jpg)![Image 12: 榜言堂](https:\u002F\u002Fcss.bim99.cn\u002Frcgz\u002Flogo.jpg)](https:\u002F\u002Fh.mffb.com.cn\u002F)\n\n*    农业信息 \n    *   [农业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F64 \"农业\")\n    *   [畜牧业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F65 \"畜牧业\")\n    *   [林业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F66 \"林业\")\n    *   [渔业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F67 \"渔业\")\n\n当前位置：[首页](https:\u002F\u002Fh.mffb.com.cn\u002F \"首页\")>[农业](https:\u002F\u002Fh.mffb.com.cn\u002Ff\u002F64 \"农业\")>【文献分享】制度组态与保护性农业成效:基于中国黑土区梨树县生产单元的模糊集分析 \n\n# [【文献分享】制度组态与保护性农业成效:基于中国黑土区梨树县生产单元的模糊集分析 * 2026-08-12 07:52:38 【文献分享】制度组态与保护性农业成效:基于中国黑土区梨树县生产单元的模糊集分析 ![Image 13](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-08-05\u002F5609_17858915834465.webp)**摘要** 中国黑土区的保护性农业由在县级统一政策框架下运作的服务型生产单元提供，但各单元的实施成效差异显著。本研究探究何种治理条件组态与高可持续绩效相关联。运用模糊集定性比较分析方法，以吉林省梨树县26个生产单元为案例，考察了标准实施强度、服务链协调、外包和金融支持四个条件对复合可持续性结果（涵盖土壤保护、粮食产量、产量稳定性及溢出效应，Cronbach's α = 0.76）的影响。不存在任何单一条件接近高绩效的必要条件。基础解识别出一条高覆盖度的协调路径和一条受金融条件制约的外包路径；在更严格的频数阈值下，解简化为仅保留协调路径，该路径在100%的阈值网格设定和96%的留一分析中保持稳定。分析揭示外包与金融能力之间存在一种条件互补性，这种互补性是一般性互补理论在次国家级农业治理层面所未预测到的。本文将研究发现定位为可供制度和生物物理条件相似情境参照的治理参考点，其政策含义在于推动县级政策工具从以面积扩张为指标的考核向以治理质量门槛为方向的转变。 **研究背景及意义** 中国东北黑土区是国家粮食安全和土壤碳储存的核心区域。在上一个政策周期内，保护性农业在《东北黑土地保护性耕作行动计划（2020—2025年）》推动下迅速扩展，然而中国农田土壤健康仍面临集约化耕作和土壤侵蚀的持续威胁。在最早实施保护性农业的梨树县，生产单元在相同的县级政策框架下运行，却呈现不同的可持续性表现。 这一模式引出了一个供给方问题：在相似的政策目标和行政监管下，为何一些单元的效果优于其他单元？现有保护性农业研究主要聚焦于农艺表现、农户采纳行为以及黑土区耕地生态系统服务价值，但对生产单元层面的服务-治理组态关注较少。这一空白之所以重要，是因为县级政府并非逐户推进保护性农业，而是依靠合作社、服务队和生产单元等中介组织。如果实施效果取决于治理条件的组合，那么单一维度的政策工具可能会在财政支持和质量控制方面出现偏差。 在本文中，可持续绩效是指保护性农业预期应实现的政策目标维度的综合达成，包括土壤保护、粮食产出维持、产量稳定性和公共产品溢出效应。之所以将结果视为多维度的，是因为政策任务本身具有多维性；单一产量指标会忽视土壤保护和扩散效应方面的实施质量。文中将解释如何将这些维度操作化为复合模糊集结果。 一个关键的制度特征塑造了分析任务。在中国，保护性农业的推广是行政驱动的：县级政府设定扩展目标，通过指定的生产单元分配补贴，并开展年度验收检查。因此，问题不在于生产单元是否采纳保护性实践（因为大多数已经参与其中），而在于在同一政策任务下，不同单元的实施质量为何存在差异。这将分析焦点从采纳决定因素转向治理组态诊断。 近期研究表明，土地政策绩效受制度匹配度、地方执行能力和治理结构的影响。有学者指出制度安排塑造了生态系统服务结果，但哪些具体治理组态在实施情境中最具影响仍有待明确。梨树案例提供了一个来自主要黑土区政策情境的微观证据，使这一问题得以检验。 基于上述空白，本文提出三个集合论研究问题： RQ1（必要性）：在梨树县生产单元中，何种治理条件（如有）接近高可持续绩效的集合论必要条件？ RQ2（充分性与等效性）：在基线和更严格真值表设定下，哪些治理条件组态足以产生高绩效，各等效路径在覆盖度和稳定性方面如何相互关联？ RQ3（稳健性）：哪些组态路径在方向性期望变化、留一估计以及替代性结果和条件构建中保持稳健？ 由于保护性农业成效可能取决于组合因素而非孤立驱动因素，且同一结果可能源于不同组合，本文采用模糊集定性比较分析方法（fsQCA），该方法识别充分和必要的组态，而非估计净边际效应。组态方法也已应用于可比的发展中国家背景下的土地治理结果，包括埃塞俄比亚的土地交易结果，这支持其在中小规模制度案例群体中的应用。 本文借鉴制度互补性理论，解释为何在采购、运营和产后处理需在狭窄农时窗口内协调的服务交付情境中，协调密集型治理结构可能产生绩效优势。本文识别了外包与金融能力之间的一种条件互补性，这一互补关系在该领域中未被现有互补性理论所预测，并将路径层级转化为与当前中国县级制度相容的操作性政策矩阵。 **研究亮点** 本研究对现有黑土地保护性农业、农业服务治理与制度层面相关研究的边际贡献主要体现在三大层面： 第一，理论层面拓宽了制度互补理论的适用场景，挖掘出农业服务领域特有的条件型制度互补逻辑，证明外包模式无法独立实现高可持续绩效，必须依托完善的金融风险缓冲配套才能发挥作用，弥补了传统制造业导向的互补理论难以解释县域农业托管分工机制的不足，同时运用 fsQCA 组态思维摆脱单一变量线性分析的局限，证实优质实施成效是多重治理条件组合作用的结果，丰富了土地治理领域的组态分析研究范式。 第二，实证层面借助梨树县统一政策、均质黑土地的天然准实验条件，把研究核心聚焦政策落地的生产经营主体而非普通农户，厘清了相同政策环境下实施效果出现明显分化的内在原因，经过多维度稳健检验确定全服务链协同是唯一稳定、适合大范围推广的治理路径，明确区分可规模化复制的主流模式与仅小范围适用的本地化适配路径，填补了生产单元微观治理组态相关实证研究的空白。 第三，政策层面直指现行保护性耕作考核只看重耕作面积、忽视治理质量的突出问题，设计出可依托现有行政台账落地的分层差异化财政激励机制，设置治理质量准入门槛、外包合约绑定补贴、治理薄弱主体限期整改等实操举措，推动地方政策从单纯追求扩面转向兼顾黑土保护、粮食稳产、技术外溢的多元可持续目标，为全国黑土粮食主产区完善保护性耕作管理体系提供切实可行的优化思路。 ![Image 14](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-08-05\u002F5609_17858915853551.webp)**研究结果** 1.必要性分析 没有任何单一条件的一致性达到 0.9 这一公认的必要性判定标准。在所有 条件的存在态中，服务链协同（COORDINATION）与金融支撑（FINANCE）的一致性数值最高，分别为 0.732、0.718；无外包（~OUTSOURCING）的一致性同样为 0.718，但必要性相关系数（RoN）仅 0.543，解释力不足。由此可见，不存在能够单独促成高可持续绩效的必要条件。 2.充分性分析结果 在基准参数设定（案例频次阈值 n.cut = 1）下，中间解包含三条充分组态：服务链协同、外包金融支撑、~ 标准化~ 外包 *~ 金融支撑。模型整体一致性 0.833、准唯一一致性（PRI）0.794、整体覆盖度 0.813。三条组态的原始覆盖度存在明显层级差异：服务链协同的解释力最强，原始覆盖度 0.732；外包搭配金融支撑次之，为 0.213；第三条组态覆盖度最低，原始覆盖度 0.159、唯一覆盖度仅 0.029。该组态仅包含 3 个样本案例，当案例频次阈值提升至 n.cut = 2 时便直接消失；本文保留该组态仅用于完整诊断，不具备实质政策参考价值。这一结果与 2.3 节第三条理论预期相符：标准化、外包、金融支撑均薄弱的本地化适配路径虽然在样本中客观存在，但覆盖样本少、阈值稳定性差，无法作为可规模化推广的治理参照模式。三条充分组态的原始覆盖度对比见图 4。 ![Image 15](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-08-05\u002F5609_17858915874380.webp) 采用更严苛的频次阈值（n.cut = 2）后，最终简化为仅服务链协同一条组态。该严苛阈值下的结果是政策制定的核心参考，代表样本中反复出现、稳定可靠的治理模式。整体路径层级可总结为：服务链协同是规模化推广的核心基准路径，外包搭配金融支撑是具备条件限制的补充路径。 3.稳健性检验结果 18 组不同阈值组合的检验中，所有参数方案均能输出有效组态，且服务链协同在 100% 的检验方案中均出现（详见附表 S2）。定向预期敏感性检验显示，放宽标准化实施强度、服务外包两个变量的正向预期后，基准组态结果没有发生改变。留一法全样本检验共开展 26 轮，全部能得到有效组态，其中 25 轮（96.2%）均包含服务链协同（详见附表 S3）；仅存在一组例外：剔除特定案例后，金融支撑会在一定程度上替代服务链协同的作用，该组输出组态为金融支撑 +~ 标准化~ 外包，一致性 0.849、覆盖度 0.790，依旧满足基准阈值标准。剔除第三条小众路径对应的 3 个样本后，组态回归为服务链协同 + 外包金融支撑；若同步开启严苛频次阈值 n.cut = 2，则再次简化为仅服务链协同。更换结果变量测算（剔除外溢效应分项）、更换金融支撑衡量方式（仅采用主观扶持感知指标）两种检验方案下，服务链协同始终是核心乃至唯一路径。全部参数组合的完整汇总结果见附表 S1，图 5 统计了各路径在所有有效稳健性检验中出现的频次。 ![Image 16](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-08-05\u002F5609_17858915881729.webp) 本研究存在一个典型偏离案例，编号 LS-SKS-HHJ-JSNH。该案例四项条件并非全部高隶属：标准化（0.972）、服务链协同（0.960）、金融支撑（0.901）均处于高隶属水平，未采用外包模式，但综合绩效隶属度仅 0.140，其原始绩效均值 0.646，低于 0.8 的交叉判定点。拆解五项绩效分项可见：黑土保护目标完全达成，粮食产量目标仅完成一半，外溢效应中等，产量下滑，产量稳定性数据缺失；整体表现为生态目标达标但生产产出薄弱，本文将其归因于短期突发减产冲击，而非治理体系本身存在系统性缺陷。该真值表行共包含 6 个样本，绩效隶属度分别为 0.809、0.970、0.273、0.970、0.140、0.760，同一组态下绩效分化明显，说明即便同时具备高水平协同与金融支撑，也不代表必然实现高绩效，局部运营瓶颈仍会阻碍这套治理组合转化为优质成效。剔除该偏离案例后，模型一致性由 0.833 提升至 0.872，但组态结构完全不变，证明核心研究结论不受该异常样本影响；基准分析仍保留此案例，避免人为抬高一致性指标。 唯一一组金融支撑替代协同的留一法检验，源于剔除样本 LS-BS-LST-TC。该结果仅具备分析参考意义，不会动摇核心结论。原始数据中协同与金融支撑相关系数 r=0.73，剔除这个低协同、高度依赖金融的边缘样本后，原本由协同解释的变异部分会转移至金融支撑变量。样本 LS-BS-LST-TC 是其所在真值表行（~ 标准化~ 协同~ 外包 * 金融支撑）的唯一观测案例（附表 S4），剔除该样本会直接删除整行组态，而非仅降低该行样本权重，因此替代型组态更多是真值表结构变化带来的结果，而非普遍存在的等效路径。本文仅将金融支撑替代协同这一现象作为受样本结构影响的诊断结论，不将其视作可大范围推广的政策模式。 4.实质性解读与跨样本整体规律 实证结果呈现稳定的关联规律：服务链一体化协同是实现高可持续绩效最可靠的路径，经营主体统一统筹农资采购、田间作业、产销时序全链条；若经营主体选择外包模式，只有配套完善的信贷、保险风险缓冲机制，稳定合约执行，外包才能实现高绩效。本研究为截面数据，仅能识别具备充分性的治理组态，无法判定变量间时间先后因果关系：究竟是一体化协同治理带来高绩效，还是高绩效主体更容易集聚资源搭建协同链条，有待基于面板数据的 QCA 研究进一步验证。 图 6 展示了全部样本在协同、综合绩效两个集合上的隶属分布，协同≥0.8 且绩效≥0.8 的高隶属象限包含 9 个样本，占总样本 34.6%，印证以协同为核心的主流路径。少数样本协同水平低但绩效优异，这类案例仅可作为问题诊断参考，不适合作为规模化推广模板。高绩效样本中同时存在外包与非外包两类主体，但治理特征差异明显：外包主体平均绩效隶属度 0.735、平均协同隶属度 0.543；非外包主体平均绩效 0.690、平均协同 0.657，说明外包模式依靠金融、合约保障实现模块化补偿，以此弥补一体化协同的缺失。 ![Image 17](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-08-05\u002F5609_17858915907467.webp)**研究展望** 除跨情境注意事项外，本研究存在几点局限性。第一，案例规模较小（26个案例，4个条件），导致了文中所报告的有限多样性。在16种逻辑上可能的组态中，11种被经验数据覆盖，5种为逻辑余项，这限制了反事实分析的空间，并增加了需采取保守解释的要求。第二，外包（OUTSOURCING）变量为二分变量，可能掩盖了外包强度的异质性。生物物理条件，包括土壤类型、排水等级、初始土壤有机碳和微地形，未被作为组态变量纳入模型。此分析选择基于三点理由：一是在梨树县典型黑土带内的单县设计使得生物物理禀赋相对于跨区域设计而言保持准恒定；二是绩效（PERFORMANCE）的校准主要基于相对和变化型指标，这部分吸收了地块尺度的生物物理变异；三是fsQCA识别充分和必要的组态而非估计净边际效应，因此较少暴露于经典回归式的遗漏变量偏差。这并未使分析免受未建模的生物物理异质性可能引起的范围误差影响，当前设计亦无法考察治理组态与土壤条件之间的交互作用。因此，本文将研究结论限定于在统一政策框架下、生物物理条件同质的黑土县内运营的生产单元；未来研究可在多县混合QCA设计中，将治理数据与遥感指标及地块尺度土壤检测记录相结合。 初审：徐娴雅 审核：徐彩瑶 排版编辑：周杨熠 文献推荐人：周杨熠 参考文献：Hongwei Yu, Yufeng Hou, Youchao Xing, Huixin Ran, Chuanling Xiang, Mingze Xia, Zijie Liu, Hu Zhou,Institutional configurations and conservation agriculture outcomes: A fuzzy-set analysis of production units in Lishu County, China's black-soil region,Land Use Policy,Volume 170,2026,108197,ISSN 0264-8377. 以上内容仅代表个人对文章的理解，详情请点击阅读原文。 [【绿色生态与绿色发展学术团队】Institutional configurations and conservation agriculture outcomes: A fuzzy-set analysis of production units in Lishu County, China's black-soil region.pdf](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566557.html) **推荐****阅****读** **[![Image 18](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-08-05\u002F5609_17858915916476.webp)](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566557.html)** **[![Image 19](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-08-05\u002F5609_17858915976145.webp)](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566557.html)** **[![Image 20](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-08-05\u002F5609_17858915981283.webp)](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566557.html)** ![Image 21](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-08-05\u002F5609_17858916015282.webp)![Image 22](https:\u002F\u002Fimg.bim99.cn\u002Fssd\u002Fssd1\u002F5609\u002F2026-08-05\u002F5609_17858916021209.webp)**数字生态与绿色发展学术团队** 探索基于自然的生态文明解决方案，助力实现“双碳”战略目标，协同推进人与自然和谐共生和全体人民共同富裕的中国式现代化建设，让我们一起加油！](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566557.html)\n\n 本文来自网友投稿或网络内容，如有侵犯您的权益请联系我们删除，联系邮箱：wyl860211@qq.com 。 \n\n### [农业银行|科技e贷|产品大纲(深圳)](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566556.html \"农业银行|科技e贷|产品大纲(深圳)\")\n\n### [《CEJ》安徽农业大学--饶相君老师团队:蓖麻油基水性聚氨酯纳米涂层提升白僵菌生长、抗逆、叶面附着及昆虫免疫逃逸能力](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566558.html \"《CEJ》安徽农业大学--饶相君老师团队:蓖麻油基水性聚氨酯纳米涂层提升白僵菌生长、抗逆、叶面附着及昆虫免疫逃逸能力\")\n\n### 最新文章\n\n*   [农业银行|科技e贷|产品大纲(深圳)](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566556.html \"农业银行|科技e贷|产品大纲(深圳)\")\n*   [四川省2027年农业科技成果转化项目申报工作开始](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566555.html \"四川省2027年农业科技成果转化项目申报工作开始\")\n*   [起荣贵丨改革开放的农业科技带头人](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566554.html \"起荣贵丨改革开放的农业科技带头人\")\n*   [广西百色农业学校兼职教师招聘公告](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566553.html \"广西百色农业学校兼职教师招聘公告\")\n*   [奋进十五五·书记谈党建|市农业农村局直属机关党委书记王璟:旗帜引领 服务三农](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566552.html \"奋进十五五·书记谈党建|市农业农村局直属机关党委书记王璟:旗帜引领  服务三农\")\n*   [澜沧现代农业发展集团有限责任公司招聘公告](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566551.html \"澜沧现代农业发展集团有限责任公司招聘公告\")\n*   [【意见】关于在农业农村基础设施建设领域积极推广以工代赈方式的意见20201103](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566550.html \"【意见】关于在农业农村基础设施建设领域积极推广以工代赈方式的意见20201103\")\n*   [2026福建建宁县农业农村局特聘渔技员招募1人公告](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566549.html \"2026福建建宁县农业农村局特聘渔技员招募1人公告\")\n*   [吉林农业大学主办国家中药材标准化与质量评估创新联盟第二届中药材生物育种研讨会](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566548.html \"吉林农业大学主办国家中药材标准化与质量评估创新联盟第二届中药材生物育种研讨会\")\n*   [中国农业发展银行招聘!综合工资20000+元\u002F月!](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566547.html \"中国农业发展银行招聘!综合工资20000+元\u002F月!\")\n\n### 热门文章\n\n*   [山东农业大学侯汉学教授团队发表论文: 多元酸通过“酸解-缔合平衡”效应调控木薯淀粉向热塑性淀粉转化过程中的结构、性能演变](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566546.html \"山东农业大学侯汉学教授团队发表论文: 多元酸通过“酸解-缔合平衡”效应调控木薯淀粉向热塑性淀粉转化过程中的结构、性能演变\")\n*   [什么叫真正的生态循环农业!看完你会为中国农民的智慧点赞](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566545.html \"什么叫真正的生态循环农业!看完你会为中国农民的智慧点赞\")\n*   [从中国到世界——全球农业合作的“朋友圈”正在扩大](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566544.html \"从中国到世界——全球农业合作的“朋友圈”正在扩大\")\n*   [2026年度中国农业发展银行山东省分行纪委办审查调查专业人才社会招聘公告](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566543.html \"2026年度中国农业发展银行山东省分行纪委办审查调查专业人才社会招聘公告\")\n*   [乡村振兴与农业农村重点工作推进会讲话稿(强农惠农)](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566542.html \"乡村振兴与农业农村重点工作推进会讲话稿(强农惠农)\")\n*   [【银行招聘】中国农业发展银行北京市分行纪委办公室审查调查专业人才社会2026招聘1人公告](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566541.html \"【银行招聘】中国农业发展银行北京市分行纪委办公室审查调查专业人才社会2026招聘1人公告\")\n*   [CIMMYT科学家访问吉林省农业科学院](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566540.html \"CIMMYT科学家访问吉林省农业科学院\")\n*   [农业轮胎 钢丝越野 防爆越野 加深人字](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566539.html \"农业轮胎 钢丝越野 防爆越野 加深人字\")\n*   [新消费场景:解锁未来农业产业跃迁的黄金密码](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566538.html \"新消费场景:解锁未来农业产业跃迁的黄金密码\")\n*   [全国供销合作社农业社会化服务培训班释放信号:蓝海盛和的路子走对了!](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566537.html \"全国供销合作社农业社会化服务培训班释放信号:蓝海盛和的路子走对了!\")\n\n### 随机文章\n\n*   [![Image 23: 10个月宝宝每天需要喝多少奶粉？是否有一个标准量？](https:\u002F\u002Fcss.bim99.cn\u002Frcgz\u002F60879a3334b9cd01e9edc5303a0145af-300-180-1.jpg)10个月宝宝每天需要喝多少奶粉？](https:\u002F\u002Fh.mffb.com.cn\u002F)\n*   [中国农业银行五一劳动奖章](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566536.html \"中国农业银行五一劳动奖章\")\n*   [New Phytol | 云南省农业科学院郑泗军团队揭示香蕉抗病品种响应TR4病原侵染的根际微生物组重塑机制](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566535.html \"New Phytol | 云南省农业科学院郑泗军团队揭示香蕉抗病品种响应TR4病原侵染的根际微生物组重塑机制\")\n*   [大疆:农业无人机行业白皮书2025\u002F2026](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566534.html \"大疆:农业无人机行业白皮书2025\u002F2026\")\n*   [农业税收九个坑,一篇帮你全捋清](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566533.html \"农业税收九个坑,一篇帮你全捋清\")\n*   [农业人工智能发展全面提速!](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566532.html \"农业人工智能发展全面提速!\")\n*   [第四次全国农业普查工作中收集的问题解答(二)](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566531.html \"第四次全国农业普查工作中收集的问题解答(二)\")\n*   [农业农村部公告:冠牧精准诊断猪伪狂犬病病毒(gB\u002FgE基因)双重荧光PCR检测试剂盒获农村部兽药产品注册批准](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566530.html \"农业农村部公告:冠牧精准诊断猪伪狂犬病病毒(gB\u002FgE基因)双重荧光PCR检测试剂盒获农村部兽药产品注册批准\")\n*   [华昇金玛农业植保系统课之氮磷钾](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566529.html \"华昇金玛农业植保系统课之氮磷钾\")\n*   [农业银行上海分行收到一则“重磅级”罚单!](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566528.html \"农业银行上海分行收到一则“重磅级”罚单!\")\n*   [省农业农村厅赴广州南沙调研强调 聚力科技创新 全力推进现代化海洋牧场建设](https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566527.html \"省农业农村厅赴广州南沙调研强调 聚力科技创新 全力推进现代化海洋牧场建设\")\n\n[琼ICP备2025051398号-21](https:\u002F\u002Fbeian.miit.gov.cn\u002F)\n\n基本 文件 流程 错误 SQL 调试\n\n1.   请求信息 : 2026-08-12 07:58:02 HTTP\u002F2.0 GET : https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F566557.html\n2.   运行时间 : 0.229763s [ 吞吐率：4.35req\u002Fs ] 内存消耗：4,482.61kb 文件加载：140\n3.   缓存信息 : 0 reads,0 writes\n4.   会话信息 : SESSION_ID=2ca0aea1e8bea20380051acbea593558\n\n1.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fpublic\u002Findex.php ( 0.79 KB )\n2.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fautoload.php ( 0.17 KB )\n3.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fcomposer\u002Fautoload_real.php ( 2.49 KB )\n4.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fcomposer\u002Fplatform_check.php ( 0.90 KB )\n5.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fcomposer\u002FClassLoader.php ( 14.03 KB )\n6.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fcomposer\u002Fautoload_static.php ( 4.90 KB )\n7.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-helper\u002Fsrc\u002Fhelper.php ( 8.34 KB )\n8.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-validate\u002Fsrc\u002Fhelper.php ( 2.19 KB )\n9.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-orm\u002Fsrc\u002Fhelper.php ( 1.47 KB )\n10.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-orm\u002Fstubs\u002Fload_stubs.php ( 0.16 KB )\n11.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FException.php ( 1.69 KB )\n12.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-container\u002Fsrc\u002FFacade.php ( 2.71 KB )\n13.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fsymfony\u002Fdeprecation-contracts\u002Ffunction.php ( 0.99 KB )\n14.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fsymfony\u002Fpolyfill-mbstring\u002Fbootstrap.php ( 8.26 KB )\n15.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fsymfony\u002Fpolyfill-mbstring\u002Fbootstrap80.php ( 9.78 KB )\n16.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fsymfony\u002Fvar-dumper\u002FResources\u002Ffunctions\u002Fdump.php ( 1.49 KB )\n17.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-dumper\u002Fsrc\u002Fhelper.php ( 0.18 KB )\n18.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fsymfony\u002Fvar-dumper\u002FVarDumper.php ( 4.30 KB )\n19.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FApp.php ( 15.30 KB )\n20.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-container\u002Fsrc\u002FContainer.php ( 15.76 KB )\n21.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fpsr\u002Fcontainer\u002Fsrc\u002FContainerInterface.php ( 1.02 KB )\n22.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fapp\u002Fprovider.php ( 0.19 KB )\n23.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FHttp.php ( 6.04 KB )\n24.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-helper\u002Fsrc\u002Fhelper\u002FStr.php ( 7.29 KB )\n25.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FEnv.php ( 4.68 KB )\n26.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fapp\u002Fcommon.php ( 0.03 KB )\n27.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fhelper.php ( 18.78 KB )\n28.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FConfig.php ( 5.54 KB )\n29.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Fapp.php ( 0.95 KB )\n30.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Fcache.php ( 0.78 KB )\n31.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Fconsole.php ( 0.23 KB )\n32.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Fcookie.php ( 0.56 KB )\n33.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Fdatabase.php ( 2.48 KB )\n34.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002Ffacade\u002FEnv.php ( 1.67 KB )\n35.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Ffilesystem.php ( 0.61 KB )\n36.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Flang.php ( 0.91 KB )\n37.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Flog.php ( 1.35 KB )\n38.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Fmiddleware.php ( 0.19 KB )\n39.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Froute.php ( 1.89 KB )\n40.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Fsession.php ( 0.57 KB )\n41.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Ftrace.php ( 0.34 KB )\n42.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fconfig\u002Fview.php ( 0.82 KB )\n43.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fapp\u002Fevent.php ( 0.25 KB )\n44.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FEvent.php ( 7.67 KB )\n45.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fapp\u002Fservice.php ( 0.13 KB )\n46.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fapp\u002FAppService.php ( 0.26 KB )\n47.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FService.php ( 1.64 KB )\n48.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FLang.php ( 7.35 KB )\n49.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Flang\u002Fzh-cn.php ( 13.70 KB )\n50.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002Finitializer\u002FError.php ( 3.31 KB )\n51.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002Finitializer\u002FRegisterService.php ( 1.33 KB )\n52.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fservices.php ( 0.14 KB )\n53.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002Fservice\u002FPaginatorService.php ( 1.52 KB )\n54.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002Fservice\u002FValidateService.php ( 0.99 KB )\n55.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002Fservice\u002FModelService.php ( 2.04 KB )\n56.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-trace\u002Fsrc\u002FService.php ( 0.77 KB )\n57.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FMiddleware.php ( 6.72 KB )\n58.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002Finitializer\u002FBootService.php ( 0.77 KB )\n59.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-orm\u002Fsrc\u002FPaginator.php ( 11.86 KB )\n60.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-validate\u002Fsrc\u002FValidate.php ( 63.20 KB )\n61.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-orm\u002Fsrc\u002FModel.php ( 23.55 KB )\n62.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-orm\u002Fsrc\u002Fmodel\u002Fconcern\u002FAttribute.php ( 21.05 KB )\n63.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-orm\u002Fsrc\u002Fmodel\u002Fconcern\u002FAutoWriteData.php ( 4.21 KB )\n64.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-orm\u002Fsrc\u002Fmodel\u002Fconcern\u002FConversion.php ( 6.44 KB )\n65.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-orm\u002Fsrc\u002Fmodel\u002Fconcern\u002FDbConnect.php ( 5.16 KB )\n66.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-orm\u002Fsrc\u002Fmodel\u002Fconcern\u002FModelEvent.php ( 2.33 KB )\n67.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-orm\u002Fsrc\u002Fmodel\u002Fconcern\u002FRelationShip.php ( 28.29 KB )\n68.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-helper\u002Fsrc\u002Fcontract\u002FArrayable.php ( 0.09 KB )\n69.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-helper\u002Fsrc\u002Fcontract\u002FJsonable.php ( 0.13 KB )\n70.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-orm\u002Fsrc\u002Fmodel\u002Fcontract\u002FModelable.php ( 0.09 KB )\n71.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FDb.php ( 2.88 KB )\n72.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-orm\u002Fsrc\u002FDbManager.php ( 8.52 KB )\n73.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FLog.php ( 6.28 KB )\n74.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FManager.php ( 3.92 KB )\n75.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fpsr\u002Flog\u002Fsrc\u002FLoggerTrait.php ( 2.69 KB )\n76.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fpsr\u002Flog\u002Fsrc\u002FLoggerInterface.php ( 2.71 KB )\n77.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FCache.php ( 4.92 KB )\n78.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Fpsr\u002Fsimple-cache\u002Fsrc\u002FCacheInterface.php ( 4.71 KB )\n79.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fthink-helper\u002Fsrc\u002Fhelper\u002FArr.php ( 16.63 KB )\n80.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002Fcache\u002Fdriver\u002FFile.php ( 7.84 KB )\n81.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002Fcache\u002FDriver.php ( 9.03 KB )\n82.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002Fcontract\u002FCacheHandlerInterface.php ( 1.99 KB )\n83.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fapp\u002FRequest.php ( 0.09 KB )\n84.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc\u002Fthink\u002FRequest.php ( 55.78 KB )\n85.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fapp\u002Fmiddleware.php ( 0.25 KB )\n86.   \u002Fyingpanguazai\u002Fssd\u002Fssd1\u002Fwww\u002Fh.mffb.com.cn\u002Fvendor\u002Ftopthink\u002Fframework\u002Fsrc","Frontiers in Sustainable Food Systems 2026-08-08","2026-08-08T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,12,4,1,"论文聚焦黑土区保护性农业治理组态，方法新颖，结论可靠，对政策优化有参考价值，但时效性稍差。",[25],{"name":10,"url":6},[27,28,29,30,31],"可持续发展","组态分析","保护性农业","黑土区","模糊集",{"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],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},3,true,[],[50],"土壤",[],{"code":53,"score":22,"supported":14,"title":54,"abstract":55,"tags":56},"H8",[],[],[29],[58,59],"保护性农业 可持续发展 组态分析 模糊集","保护性农业 可持续发展","保护性农业可持续发展组态分析模糊集-419",6,"2026-08-11T23:57:03.281748Z",{"total":61,"page":22,"page_size":61,"items":64},[65,179,249,326,392,468],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":70,"content":8,"source_name":71,"source_url":68,"published_at":72,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":73,"score_detail":74,"sources":79,"tags":81,"primary_topic":86,"secondary_topics":87,"classification":92,"search_phrases":135,"slug":138,"view_count":22,"doi":139,"paper":140,"created_at":178},3756,"Improving soil carbon stabilization within conservation agriculture: An adaptive framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104980","CONTEXT Conservation agriculture (CA) is widely promoted for improving soil ecosystem services, including erosion reduction, water conservation, and soil health. Its contribution to soil organic carbon (SOC) restoration, however, is variable, often modest and concentrated near the surface and strongly dependent on climate, soil, residue management and cropping system. OBJECTIVE To examine why SOC stabilization under CA is limited in some systems and to propose an adaptive framework that increases the probability that crop-residue carbon (CR-C) and root-derived C are converted into more stable SOC pools. METHODS We synthesize evidence on CR-C fate, root-derived C, SOC fractions, nutrient stoichiometry and soil-profile placement, under CA and organize this evidence into a diagnosis-based adaptive framework that links intervention choice with evidence strength, operational feasibility and trade-offs. RESULTS AND CONCLUSIONS A central limitation is the low but highly variable conversion of aboveground CR-C into stabilized SOC, governed by soil texture, mineralogy, climate, residue quality, nutrient availability, rooting depth and time since CA adoption. We propose an adaptive CA framework with six optional, evidence-graded interventions triggered by diagnosed SOC-stabilization bottlenecks: (1) residue retention thresholds to avoid a “problem of plenty”; (2) conditional strategic soil disturbance for compaction, surface stratification and C placement; (3) companion crops that improve residue amount, quality and root inputs; (4) breeding and selection for root traits for deeper C delivery and SOC stabilization; (5) fertilization strategies that better align decomposition with microbial stoichiometric needs while considering trade-offs; and (6) fungal consortia to accelerate lignocellulose breakdown where field conditions support residue turnover without increasing C losses. Together these interventions provide an adaptive extension of CA that can improve the likelihood of measurable and more durable SOC stabilization where specific bottlenecks are diagnosed. SIGNIFICANCE Options exist to strengthen the SOC-stabilization component of CA where site-specific bottlenecks are present, while explicitly considering feasibility, costs, trade-offs and the need for empirical validation through long-term monitoring of whole-profile SOC stocks and greenhouse-gas outcomes.","背景 保护性农业（conservation agriculture, CA）被广泛推广以改善土壤生态系统服务，包括减少侵蚀、保持水分和促进土壤健康。然而，其对土壤有机碳（soil organic carbon, SOC）恢复的贡献具有变异性，通常较为有限，且集中于表层，并高度依赖气候、土壤、秸秆管理和种植制度。目的 探讨保护性农业下SOC稳定化在某些系统中受限的原因，并提出一个适应性框架，以提高作物秸秆碳（crop-residue carbon, CR-C）和根系来源碳转化为更稳定SOC库的概率。方法 我们综合了保护性农业下关于作物秸秆碳去向、根系来源碳、SOC组分、养分化学计量学及土壤剖面分布的证据，并将这些证据组织为一个基于诊断的适应性框架，将干预措施选择与证据强度、操作可行性和权衡取舍相联系。结果与结论 一个核心局限在于地上作物秸秆碳向稳定化SOC的转化率低且高度可变，受土壤质地、矿物学、气候、秸秆质量、养分有效性、根系深度及保护性农业采用时间等因素调控。我们提出了一个适应性保护性农业框架，包含六项可选的、按证据分级排序的干预措施，由诊断出的SOC稳定化瓶颈所触发：（1）秸秆还田阈值以避免“过多的问题”；（2）针对压实、表层分层和碳 placement 的条件性战略土壤扰动；（3）改善秸秆数量、质量和根系输入的伴生作物；（4）选育和筛选根系性状以实现更深层的碳输送和SOC稳定化；（5）施肥策略，在考虑权衡取舍的同时更好地使分解过程与微生物化学计量需求相匹配；（6）在田间条件支持秸秆周转且不增加碳损失的情况下，利用真菌菌群加速木质纤维素分解。这些干预措施共同构成了保护性农业的适应性延伸，在诊断出特定瓶颈的情况下，可提高实现可测量且更持久SOC稳定化的可能性。意义 在存在特定地点瓶颈的情况下，现有方案可强化保护性农业的SOC稳定化功能，同时需明确考虑可行性、成本、权衡取舍，以及通过长期监测全剖面SOC储量和温室气体排放结果进行实证验证的必要性。","Agricultural Systems","2026-09-28T00:00:00Z",79,{"impact":75,"substance":18,"depth":17,"authority":76,"freshness":77,"relevant":22,"comment":78},16,14,9,"该研究提出基于诊断的保护性农业自适应框架，为提升土壤有机碳稳定提供可操作路径，专业深度与信息增量突出，值得进入每日精选。",[80],{"name":71,"url":68},[29,82,83,84,85],"土壤健康","农业碳汇","土壤固碳","根际性状",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[88],{"code":53,"slug":89,"name":90,"path":91,"parent_code":37},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":40,"locked":14,"version":41,"method":42,"evidence":93},{"candidates":94,"agricultural_evidence":47},[95,99,104,111,118,124,130],{"code":33,"score":75,"supported":47,"title":96,"abstract":97,"tags":98},[34],[50,34],[82,84],{"code":53,"score":20,"supported":47,"title":100,"abstract":102,"tags":103},[101],"conservation agriculture",[101],[29],{"code":105,"score":61,"supported":47,"title":106,"abstract":107,"tags":110},"B7",[],[108,109],"温室","greenhouse",[],{"code":112,"score":61,"supported":47,"title":113,"abstract":114,"tags":117},"F3",[],[115,116],"施肥","fertilization",[],{"code":119,"score":46,"supported":47,"title":120,"abstract":121,"tags":123},"H5",[],[122],"秸秆还田",[],{"code":125,"score":46,"supported":47,"title":126,"abstract":127,"tags":129},"H6",[],[128],"生态系统服务",[],{"code":131,"score":22,"supported":14,"title":132,"abstract":133,"tags":134},"H4",[],[],[83],[136,137],"保护性农业 土壤有机碳 固碳","保护性农业 根际性状 碳稳定","保护性农业土壤有机碳固碳-3756","10.1016\u002Fj.agsy.2026.104980",{"doi":139,"openalex_id":141,"authors":142,"venue":71,"cited_by_count":170,"oa_url":68,"card":171,"direction":175,"ingested_from":177},"W7214662811",[143,146,149,151,154,157,159,162,165,168],{"name":144,"orcid":145},"Anupam Das","https:\u002F\u002Forcid.org\u002F0000-0001-6548-215X",{"name":147,"orcid":148},"Ashim Datta","https:\u002F\u002Forcid.org\u002F0000-0002-1843-9981",{"name":150,"orcid":8},"Samrat Ghosh",{"name":152,"orcid":153},"Sahely Kanthal","https:\u002F\u002Forcid.org\u002F0000-0001-6550-5227",{"name":155,"orcid":156},"Anwesha Mandal","https:\u002F\u002Forcid.org\u002F0009-0003-0113-211X",{"name":158,"orcid":8},"Dibyendu Sarkar",{"name":160,"orcid":161},"Probir Kumar Ghosh","https:\u002F\u002Forcid.org\u002F0000-0003-1580-5883",{"name":163,"orcid":164},"Richard William Bell","https:\u002F\u002Forcid.org\u002F0000-0002-7756-3755",{"name":166,"orcid":167},"Debashis Chakraborty","https:\u002F\u002Forcid.org\u002F0000-0001-9664-8095",{"name":169,"orcid":8},"Biswapati Mandal",0,{"tldr":172,"method":173,"finding":174,"direction":175,"opportunity":176},"提出诊断式自适应保护性农业框架，提升秸秆与根系碳向稳定土壤有机碳的转化。","综合秸秆碳去向、根系碳、有机碳组分与养分化学计量证据，构建六项分级干预框架。","保护性农业固碳受质地、气候、残体质量等制约，需按诊断瓶颈选择干预以提升稳定性。","农业绿色发展与碳","可针对不同土壤-气候区量化各干预的固碳效率与权衡，并开展全剖面长期监测验证。","openalex","2026-09-29T23:30:04.622148Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":8,"content":8,"source_name":184,"source_url":8,"published_at":185,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":73,"score_detail":186,"sources":189,"tags":191,"primary_topic":196,"secondary_topics":201,"classification":203,"search_phrases":234,"slug":237,"view_count":238,"doi":8,"paper":239,"created_at":248},3121,"Optimization of Farmland Management Zoning in the Black Soil Region: A Climate Adaptability Assessment Considering Crop Growth Response and Topographic Characteristics（黑土区农田管理分区优化：考虑作物生长响应与地形特征的气候适应性评估）","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3260","吉林农业大学 Han Yongqi 等联合中科院东北地理与农业生态研究所、东北农业大学在《Remote Sensing》18(18): 3260 发表论文（2026-09-21 发表）。针对精准农业管理分区对单日期影像依赖难以捕捉年际作物环境变化问题，研究评估 29 个特征组合（融合 Sentinel-2 多光谱、PCA、NDVI 和 DEM 数据）在黑土区友谊农场干旱、湿润和融合场景下的气候适应性。实施异构空间注意力网络（HSAN）和 K-means 聚类，以变异系数（CV）评估稳定性与适应性。结果显示 HSAN 在多源融合下优于 K-means，CV 分别为 11.303-14.774% 与 14.823-16.011%；多期 NDVI 数据是主导因素，相对 CV 减少 34.850-53.701%；DEM 贡献有限；PCA 增强稳定性；多期融合在极端气候年份提升分区生态一致性与适用性。","MDPI Remote Sensing","2026-09-21T00:00:00Z",{"impact":187,"substance":18,"depth":17,"authority":76,"freshness":13,"relevant":22,"comment":188},15,"黑土区精准农业管理分区研究，方法新颖、数据扎实，对农业遥感应用有参考价值。",[190],{"name":184,"url":182},[192,193,30,194,195],"智慧农业","精准农业","遥感","管理分区",{"code":197,"slug":198,"name":199,"path":200,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[202],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":204},{"candidates":205,"agricultural_evidence":47},[206,212,217,223,229],{"code":197,"score":76,"supported":47,"title":207,"abstract":209,"tags":211},[208],"气候适应",[208,210],"干旱",[],{"code":33,"score":213,"supported":47,"title":214,"abstract":215,"tags":216},8,[34],[],[],{"code":218,"score":219,"supported":47,"title":220,"abstract":221,"tags":222},"I1",5,[],[193],[192,193],{"code":224,"score":21,"supported":47,"title":225,"abstract":226,"tags":228},"I3",[],[227],"remote sensing",[194],{"code":125,"score":46,"supported":47,"title":230,"abstract":231,"tags":233},[],[232],"农业生态",[],[235,236],"黑土区 管理分区 遥感","Sentinel-2 黑土区 气候适应性","黑土区管理分区遥感-3121",2,{"doi":8,"openalex_id":8,"authors":240,"venue":8,"cited_by_count":170,"oa_url":8,"card":241,"direction":245,"ingested_from":247},[],{"tldr":242,"method":243,"finding":244,"direction":245,"opportunity":246},"评估黑土区多源遥感特征组合在干旱湿润场景下的农田管理分区气候适应性。","融合Sentinel-2多光谱、NDVI、PCA与DEM，用HSAN和K-mea","HSAN优于K-means，多期NDVI主导稳定性提升，DEM贡献有限，PCA增强稳定性。","农业遥感与作物表型","可探索多期时序特征与深度聚类在极端气候下的跨区域迁移及分区决策落地。","agent","2026-09-22T00:05:38.189091Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":254,"content":8,"source_name":255,"source_url":252,"published_at":256,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":257,"score_detail":258,"sources":261,"tags":263,"primary_topic":268,"secondary_topics":269,"classification":270,"search_phrases":295,"slug":298,"view_count":22,"doi":299,"paper":300,"created_at":325},2791,"Integrating multi-source data and support vector machine to assess the spatio-temporal pattern of land degradation in the Eastern Cape of South Africa","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.indic.2026.101524","Land degradation remains a major environmental challenge, particularly in semi-arid and heterogeneous landscapes, where interactions between vegetation loss and soil exposure are complex and spatially dynamic. This study, therefore, seeks to evaluate the spatial extent of land degradation and drivers over time (2005 - 2025) using Landsat data series and support vector machine (SVM) in the Keiskamma Catchment of South Africa. Degraded land followed a non-monotonic trajectory: it declined from ∼197 km 2 in 2005 to ∼157 km 2 in 2015 (a temporary contraction of 20.3%, consistent with short-term restoration and land-use shifts), before rising sharply and unsustainably to ∼328 km 2 by 2025 (a 108.9% increase relative to 2015, and a net increase of 66.5% over the full two-decade period), largely at the expense of grassland and agricultural land. Furthermore, the findings show that soil-sensitive indicators, particularly BSI and SWIR spectral bands, play a crucial role in determining degraded land. In contrast, vegetation indices such as NDVI contribute less under degraded conditions because degraded areas were severely dominated by exposed soil rather than vegetation. Correlation matrix analysis further reveals a temporal shift from mixed soil–vegetation spectral relationships toward strong soil-dominated reflectance patterns by 2025, indicating advanced degradation stages. Overall, the integration of SVM classification with VIF and SHAP provides a transparent, reliable, and spatially explicit framework for monitoring land degradation. The findings support land degradation neutrality monitoring and provide critical insights for sustainable land-management planning in support of Sustainable Development Goal (SDG) 15.3.","土地退化仍然是一项重大环境挑战，尤其是在半干旱和异质性景观中，植被丧失与土壤裸露之间的相互作用复杂且具有空间动态性。因此，本研究旨在利用Landsat数据序列和支持向量机（SVM），评估南非Keiskamma集水区2005—2025年间土地退化的空间范围及其驱动因素。退化土地呈非单调变化轨迹：从2005年的约197 km²下降至2015年的约157 km²（暂时收缩20.3%，与短期恢复和土地利用变化相一致），随后急剧且不可持续地上升至2025年的约328 km²（较2015年增加108.9%，在整个二十年期间净增加66.5%），且主要以草地和农用地为代价。此外，研究结果表明，土壤敏感指标，尤其是BSI和SWIR光谱波段，在判定退化土地方面发挥着关键作用。相比之下，NDVI等植被指数在退化条件下贡献较小，因为退化区域严重以裸露土壤为主，而非植被。相关矩阵分析进一步揭示，到2025年，光谱关系由土壤—植被混合关系向强烈的土壤主导反射模式发生时间转变，表明退化已进入后期阶段。总体而言，将SVM分类与VIF和SHAP相结合，为监测土地退化提供了一个透明、可靠且具有空间显式性的框架。研究结果支持土地退化零增长监测，并为支持可持续发展目标（SDG）15.3的可持续土地管理规划提供了关键见解。","Environmental and Sustainability Indicators","2026-09-16T00:00:00Z",77,{"impact":187,"substance":18,"depth":17,"authority":259,"freshness":77,"relevant":22,"comment":260},13,"基于Landsat时序与SVM\u002FSHAP的南非土地退化监测研究，方法透明可复现，对农业遥感与土地退化中性监测有参考价值，但属区域案例、非国内三农直接政策信息。",[262],{"name":255,"url":252},[264,265,27,266,267],"农业遥感","机器学习","土地退化","遥感监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":271},{"candidates":272,"agricultural_evidence":47},[273,277,281,285,290],{"code":33,"score":61,"supported":47,"title":274,"abstract":275,"tags":276},[],[50,34],[],{"code":197,"score":46,"supported":47,"title":278,"abstract":279,"tags":280},[],[210],[],{"code":224,"score":238,"supported":14,"title":282,"abstract":283,"tags":284},[],[],[264,267],{"code":286,"score":22,"supported":14,"title":287,"abstract":288,"tags":289},"H1",[],[],[266],{"code":291,"score":22,"supported":14,"title":292,"abstract":293,"tags":294},"I2",[],[],[265],[296,297],"可持续发展 农业遥感 土地退化 机器学习","可持续发展 农业遥感","可持续发展农业遥感土地退化机器学习-2791","10.1016\u002Fj.indic.2026.101524",{"doi":299,"openalex_id":301,"authors":302,"venue":255,"cited_by_count":170,"oa_url":319,"card":320,"direction":245,"ingested_from":177},"W7213298151",[303,305,308,311,313,316],{"name":304,"orcid":8},"Mandisa Zameko",{"name":306,"orcid":307},"Kgabo Humphrey Thamaga","https:\u002F\u002Forcid.org\u002F0000-0002-2305-9975",{"name":309,"orcid":310},"Mthunzi Mndela","https:\u002F\u002Forcid.org\u002F0000-0002-2384-6856",{"name":312,"orcid":8},"Matthieu Tshanga",{"name":314,"orcid":315},"Nobert Tafadzwa Mukomberanwa","https:\u002F\u002Forcid.org\u002F0009-0003-1896-9813",{"name":317,"orcid":318},"Mohamed Zhran","https:\u002F\u002Forcid.org\u002F0000-0002-1112-387X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2665972726004137\u002Fpdf",{"tldr":321,"method":322,"finding":323,"direction":245,"opportunity":324},"用Landsat与SVM评估南非Keiskamma流域2005-2025年土地退化时空格局。","Landsat时序数据、SVM分类，结合VIF与SHAP做特征解释。","退化面积先降后升，2025年达328km²，土壤光谱指标比NDVI更关键。","可将该SVM-SHAP框架迁移到其他半干旱区，并耦合气候与土地利用驱动做退化预警。","2026-09-17T23:30:36.054539Z",{"id":327,"title":328,"url":329,"summary":330,"summary_zh":331,"content":8,"source_name":332,"source_url":329,"published_at":333,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":334,"score_detail":335,"sources":339,"tags":341,"primary_topic":346,"secondary_topics":347,"classification":353,"search_phrases":371,"slug":374,"view_count":170,"doi":375,"paper":376,"created_at":391},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":213,"substance":336,"depth":337,"authority":77,"freshness":13,"relevant":22,"comment":338},20,17,"该研究给出稀疏土壤样本下主动学习标注效率的理论边界与聚类设计规则，方法新颖、结论具体，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[340],{"name":332,"url":329},[192,342,343,344,345],"农业人工智能","主动学习","线虫检测","土壤采样",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[348],{"code":291,"slug":349,"name":350,"path":351,"parent_code":352},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":354},{"candidates":355,"agricultural_evidence":47},[356,360,365],{"code":33,"score":76,"supported":47,"title":357,"abstract":358,"tags":359},[34],[50,34],[],{"code":291,"score":77,"supported":47,"title":361,"abstract":363,"tags":364},[362],"artificial intelligence",[],[342],{"code":218,"score":366,"supported":47,"title":367,"abstract":368,"tags":370},7,[],[193,369],"precision agriculture",[192],[372,373],"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":375,"openalex_id":377,"authors":378,"venue":332,"cited_by_count":170,"oa_url":329,"card":385,"direction":389,"ingested_from":177},"W7223082195",[379,381,383],{"name":380,"orcid":8},"Amine Ben Salah",{"name":382,"orcid":8},"Ines Trabelsi",{"name":384,"orcid":8},"Mohamed Aziz Khelifi",{"tldr":386,"method":387,"finding":388,"direction":389,"opportunity":390},"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。","农业人工智能与决策模型","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":393,"title":394,"url":395,"summary":396,"summary_zh":397,"content":8,"source_name":398,"source_url":395,"published_at":399,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":73,"score_detail":400,"sources":402,"tags":404,"primary_topic":409,"secondary_topics":410,"classification":411,"search_phrases":430,"slug":433,"view_count":170,"doi":434,"paper":435,"created_at":467},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",{"impact":75,"substance":18,"depth":17,"authority":76,"freshness":77,"relevant":22,"comment":401},"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。",[403],{"name":398,"url":395},[267,405,406,407,408],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":412},{"candidates":413,"agricultural_evidence":47},[414,418,422,426],{"code":33,"score":76,"supported":47,"title":415,"abstract":416,"tags":417},[34],[50,34],[],{"code":224,"score":366,"supported":47,"title":419,"abstract":420,"tags":421},[],[194,227],[267],{"code":197,"score":61,"supported":47,"title":423,"abstract":424,"tags":425},[],[208,210],[],{"code":125,"score":22,"supported":14,"title":427,"abstract":428,"tags":429},[],[],[407],[431,432],"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":434,"openalex_id":436,"authors":437,"venue":398,"cited_by_count":170,"oa_url":395,"card":462,"direction":245,"ingested_from":177},"W7223069171",[438,440,442,444,447,449,452,455,457,459],{"name":439,"orcid":8},"Yue Li",{"name":441,"orcid":8},"Mengfei Shi",{"name":443,"orcid":8},"Fei Zhang",{"name":445,"orcid":446},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":448,"orcid":8},"Gowhar Meraj",{"name":450,"orcid":451},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":453,"orcid":454},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":456,"orcid":8},"Yuanfang Chai",{"name":458,"orcid":8},"Hongtao Xu",{"name":460,"orcid":461},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":463,"method":464,"finding":465,"direction":245,"opportunity":466},"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。","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":469,"title":470,"url":471,"summary":472,"summary_zh":473,"content":8,"source_name":474,"source_url":471,"published_at":333,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":257,"score_detail":475,"sources":478,"tags":480,"primary_topic":483,"secondary_topics":489,"classification":492,"search_phrases":526,"slug":529,"view_count":170,"doi":530,"paper":531,"created_at":549},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",{"impact":75,"substance":476,"depth":337,"authority":259,"freshness":13,"relevant":22,"comment":477},21,"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。",[479],{"name":474,"url":471},[192,265,267,481,482],"土壤墒情","冬小麦",{"code":484,"slug":485,"name":486,"path":487,"parent_code":488},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[490,491],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":291,"slug":349,"name":350,"path":351,"parent_code":352},{"status":40,"locked":14,"version":41,"method":42,"evidence":493},{"candidates":494,"agricultural_evidence":47},[495,501,505,510,515,522],{"code":484,"score":496,"supported":47,"title":497,"abstract":499,"tags":500},26,[498,485],"winter wheat",[486,498,485],[482],{"code":33,"score":187,"supported":47,"title":502,"abstract":503,"tags":504},[34],[50,34],[481],{"code":291,"score":187,"supported":47,"title":506,"abstract":508,"tags":509},[507],"machine learning",[265,507],[265],{"code":224,"score":13,"supported":47,"title":511,"abstract":512,"tags":514},[],[194,227,513],"multispectral",[267],{"code":516,"score":61,"supported":47,"title":517,"abstract":518,"tags":521},"H3",[],[519,520],"灌溉","irrigation",[],{"code":218,"score":21,"supported":47,"title":523,"abstract":524,"tags":525},[],[192],[192],[527,528],"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":530,"openalex_id":532,"authors":533,"venue":474,"cited_by_count":170,"oa_url":471,"card":543,"direction":548,"ingested_from":177},"W7223156204",[534,537,539,541],{"name":535,"orcid":536},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":538,"orcid":8},"Yi Zhang",{"name":540,"orcid":8},"WEI Lei",{"name":542,"orcid":8},"Chengcai Zhang",{"tldr":544,"method":545,"finding":546,"direction":245,"opportunity":547},"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。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z"]