Influence of land use and topographic factors on soil organic carbon stocks and their spatial and vertical distribution

Authors: BLACKBURN, KYLE - Wake Forest University; Libohova, Zamir; Adhikari, Kabindra; KOME, CHARLES - Natural Resources Conservation Service (NRCS, USDA); MANESS, XANDER - Wake Forest University; SILMAN, R - Wake Forest University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/5/2022
Publication Date: 6/14/2022
Citation: Blackburn, K., Libohova, Z., Adhikari, K., Kome, C., Maness, X., Silman, R.M. 2022. Influence of land use and topographic factors on soil organic carbon stocks and their spatial and vertical distribution. Remote Sensing. https://doi.org/10.3390/rs14122846.
DOI: https://doi.org/10.3390/rs14122846

Interpretive Summary: The accurate accounting of soil organic carbon (SOC) stocks is important for evaluating the potential of different vegetation and management practices on increasing the SOC stocks and develop carbon counting methods for farmers. SOC stocks were measured for three major vegetation types (prairie, managed lawn, and forest) in North Carolina southeast US. Scientist from the USDA-ARS, Dale Bumpers Small Farms Research Center in Booneville, Zamir Libohova, developed SOC stock maps at field level and for the extent of the Fairview soil in the region by combining results from this study with USDA-NRCS Soil Survey maps United States Geological Survey (USGS) land use land cover maps. SOC stocks were concentrated mostly on the upper 30 cm soil layer with summits and low areas having more SOC stock than sloping areas. Accurate accounting of SOC stocks at field/farm levels would help farmers get credit and extra income by participating in carbon trading markets. Also, expanding the SOC stock counting from small study sites and farms to larger areas like regions and states would help guide policy makers on targeting areas with highest potential for SOC stock storage and address climate challenges.

Technical Abstract: Soil organic carbon (SOC) and stocks play a critical role in major ecosystem processes, agriculture, and climate mitigation. Many factors such as climate, land use, topography, management, and soils determine the amount and distribution of SOC stocks. Accurate SOC predictions are challenging due to these factors as well as data sources, sampling design and modeling approaches. The goal of this study was to (i) understand the SOC stock distribution due to land use and local topography and (ii) asses the scalability of SOC stock predictions from the study site to the geographic ex-tension of the Fairview soil based on US Soil Survey Geographic (SSURGO). The study site is in North Carolina (Lat: 36° 7’N, Longitude: 80° 16’W), representing restored prairie grass (PG), lawn grass (LG), and forest (F). Overall, LG had the highest SOC stock (82 Mg ha-1) followed by PG (79 Mg ha-1) and Forest (73.1 Mg ha-1). SOC stock decreased with depth with LG and PG having about 60% concentrated on the surface horizon (0-23 cm) while the Forest with only 40%. At the PG site, topography had the strongest influence on SOC stock spatial distribution, but only for Bt horizons (adj-R2=0.6) compared to Ap and BC horizons. Most of the SOC stock was concentrated in the summits compared to toe slope and backslope. The differences between measured SOC stocks and those estimated by SSURGO and modeled based on land use for the Fairview series extent were comparable. However, subtracting maps of the uncertainty predictions based on the 90% confidence interval (CI) derived from the measured values and estimated SSURGO upper and lower values (an estimated CI) resulted in a range from -17 to 41 Mg ha-1. The range indicated that spa-tially SOC stock uncertainty for the Fairview soil extent (436,000 ha) was over or underestimated by SSURGO relative to the measured SOC stocks. When converted to $ Mg ha-1 stocks, the uncertainty value varied from $33 million to $824 million for the Fairview extent. In addition, the spatial differences from subtracting SSURGO estimated and measured uncertainty aligned with county administrative boundaries. The distribution of SOC stock was related to land use, topography and soil depth, while accuracy predictions were influenced also by data source.