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.

翻译：有效的土壤健康管理对于维持农业可持续性、增强生态系统韧性及保护水质至关重要。然而，密苏里州多样化的地形地貌限制了广泛通用管理建议的有效性。现有土壤分组系统分辨率不足，亟需基于数据驱动的站点特异性分析来指导定制化干预措施。为应对这些关键挑战，本研究构建了一个区域性土壤聚类框架，旨在支持全州范围内的精准土壤健康管理策略。该方法利用高分辨率SSURGO数据集，明确处理0至30厘米根区范围内聚合的土壤属性。通过结合变分自编码器与KMeans聚类的多变量分析，对具有相似属性的土壤进行分组。所得聚类结果采用轮廓系数等统计指标，并与现有分类单元进行比对验证，以确认其空间一致性。该框架成功划分出能体现密苏里州多样化农业生态区特有质地、水力特性、化学肥力及生物指标的土壤组别。聚类图谱识别出十个不同的土壤健康管理区。选择十个聚类作为最优方案，因其既能充分捕捉遗传性土壤格局，又能在全州实际应用中保持可操作性。根系深度限制与饱和导水率成为驱动土壤分异的主要变量。每个管理区由黏粒含量、有机质、pH值和有效水容量的独特组合定义。该框架将复杂数据分析与可操作的站点定向建议相结合，使保育规划师和农艺师能够优化管理实践，提升全州资源利用效率。