Leveraging legacy data with targeted field sampling for low-cost mapping of soil organic carbon stocks on extensive rangeland properties

Accurately quantifying high-resolution field-scale soil organic carbon (SOC) stocks is challenging yet crucial for improving site-specific land management and carbon accounting. This challenge is even greater when the study units are large heterogenous ranches. This study utilizes a digital soil mapping (DSM) approach and a U.S. legacy soil dataset, combined with soil, climate, biotic, and topographic covariate datasets, to design a targeted soil sampling plan for acquiring local samples. The resulting local samples were then used in combination with the legacy data to build optimal ranch-scale SOC stock models. We provide an example of this approach using ranch units in the western U.S. as a case study. In our approach we first applied a clustering analysis to generate spatial clusters. This was followed by adopting a conditioned Latin hypercube sampling scheme within each cluster, to generate sets of strategically selected local sampling points. The local samples required for improved estimates of SOC stocks were determined to have a sample size of 15 and 40 soil cores, within the respective 13 and 36 km(2) parcels. While our modeling results for SOC concentrations at a relatively homogeneous site in eastern Montana showed a significant two-fold improvement in model fit when using individually selected calibration datasets for each point, as opposed to selecting the calibration dataset as a whole at the ranch level, the disparity between the pixel- and ranch-based models was inconsequential for the other two ranch sites in Colorado that were more spatially diverse in terms of land management and vegetation cover. Compared to SOC concentration models (R-2 between 0.3 and 0.7), the performance of models for bulk density (BD) (R-2 < 0.4) and SOC stocks (R-2 < 0.2) were relatively poor. Strategies including utilizing a subset of covariates, incorporating broader-scale national calibration datasets, and modeling by depths did not further improve BD and SOC stock models. Future work should explore whether the addition of temporally dynamic environmental covariates can improve SOC stock estimates, and whether the DSM-supported targeted field sampling strategy and high-resolution mapping approach can be successfully applied elsewhere.