为密苏里州农田定制10个土壤健康分区,提升精准管理效率。
Toward precision soil health: A regional framework for site-specific management across Missouri
- 用高分辨率土壤数据+变分自编码器与聚类算法划分土壤类型。
- 识别出10个管理区,关键差异来自根系深度和饱和导水率。
- 适合农业规划者和农艺师参考,优化资源利用。
有效的土壤健康管理对农业可持续、生态系统韧性及水质保护至关重要。然而,密苏里州地貌多样,现有广义管理建议效果有限。现有土壤分类系统分辨率不足,亟需基于数据的精细化、因地制宜干预方案。为此,本文构建了一套区域土壤聚类框架,支持全州范围内的精准土壤健康管理。方法上,利用高分辨率SSURGO数据集,整合0–30厘米根区土壤属性,结合变分自编码器与KMeans聚类进行多变量分析,形成具有空间一致性的土壤群组。通过轮廓系数等统计指标及与已有分类单元比对验证聚类有效性。结果划分出10个土壤健康管理区,该数量在捕捉土壤固有格局与实际应用可操作性间取得平衡。根系深度限制与饱和导水率是驱动土壤分异的主要因素。各分区由黏粒、有机质、pH值和有效持水能力的独特组合定义。该框架将复杂数据分析转化为可执行的本地化建议,助力保护规划者与农艺师优化实践,提升全州资源利用效率。
原文摘要 · Abstract (English)
Effective soil health management is crucial for sustaining agriculture, adopting ecosystem resilience, and preserving water quality. However, Missouri's diverse landscapes limit the effectiveness of broad generalized management recommendations. The lack of resolution in existing soil grouping systems necessitates data driven, site specific insights to guide tailored interventions. To address these critical challenges, a regional soil clustering framework designed to support precision soil health management strategies across the state. The methodology leveraged high resolution SSURGO dataset, explicitly processing soil properties aggregated across the 0 to 30 cm root zone. Multivariate analysis incorporating a variational autoencoder and KMeans clustering was used to group soils with similar properties. The derived clusters were validated using statistical metrics, including silhouette scores and checks against existing taxonomic units, to confirm their spatial coherence. This approach enabled us to delineate soil groups that capture textures, hydraulic properties, chemical fertility, and biological indicators unique to Missouri's diverse agroecological regions. The clustering map identified ten distinct soil health management zones. This alignment of 10 clusters was selected as optimal because it was sufficiently large to capture inherited soil patterns while remaining manageable for practical statewide application. Rooting depth limitation and saturated hydraulic conductivity emerged as principal variables driving soil differentiation. Each management zone is defined by a unique combination of clay, organic matter, pH, and available water capacity. This framework bridges sophisticated data analysis with actionable, site targeted recommendations, enabling conservation planners, and agronomists to optimize management practices and enhance resource efficiency statewide.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。