Toward precision soil health: A regional framework for site-specific management across Missouri
Dipal Shah (1), Jordon Wade (2), Timothy Haithcoat (3), Robert Myers (4), Kelly Wilson (1) ((1) School of Natural Resources, University of Missouri, Columbia, MO, USA, (2) Crop Protection Research & Development, Syngenta Group, Basel, Switzerland, (3) Institute for Data Science

TL;DR
This paper develops a high-resolution, data-driven regional soil clustering framework for Missouri, enabling site-specific soil health management through advanced analysis of soil properties and validation of distinct management zones.
Contribution
It introduces a novel regional soil clustering method using high-resolution data and machine learning to identify ten distinct soil health management zones in Missouri.
Findings
Identified ten soil management zones with unique properties.
Rooting depth and hydraulic conductivity are key differentiators.
Framework supports targeted, efficient soil health interventions.
Abstract
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…
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Taxonomy
TopicsSoil Geostatistics and Mapping · Soil Carbon and Nitrogen Dynamics · Soil Moisture and Remote Sensing
