ESTIMATING SOIL CARBON CONTENT OF GRAZING LANDS IN ROCKBRIDGE COUNTY, VIRGINIA USING STATISTICAL AND MACHINE LEARNING TECHNIQUES

In recent years, organizations have explored various methods of quantifying soil carbon to document carbon flux or provide economic incentive to farmers utilizing management practices that sequester carbon in their soil. This study utilizes soil samples from three livestock farms in Rockbridge County, Virginia practicing either conventional or regenerative agricultural practices. Two adjacent farms graze carbonate residual soils and the third farm is on alluvial soils. We chose sampling locations using conditioned Latin Hypercube Sampling (cLHS) to replicate the distribution of soil, topographic, and remote sensing covariates in the feature space of the sampled points. These topographic and remote sensing variables represent our understanding of soil development and carbon sequestration at the field scale using widely available data. Applied covariates include management practice, seasonal maximum NDVI from Planet imagery, USDA gSSURGO soil series expressed as clay content, plus slope, aspect, and Saga Wetness Index from LIDAR-based 3-m-DEMs. Sampling density was minimized until distributions of the covariate input dataset diverged from those of the sampled points, as measured by the value of the cLHS objective function. At each selected sample point, we took a soil core up to 35 cm depth using a 2.5-cm hammer auger, split the core by pedologic features into up to three subsamples, and dried each sample for measurement of total carbon in an Elemental Analyzer and inorganic carbon with a pressure calcimeter. Carbon inventory is assessed using geographic regression and other machine learning models, with the goal of providing a suite of statistical sampling design and analytical techniques sufficient to document baseline and subsequent variability of soil carbon stocks in grazing lands in Rockbridge County, Virginia, and perhaps the wider the central Appalachian region.