Scaling of soil organic carbon in space and time in the Southern Coastal Plain, USA

Rajneesh Sharma; Matthew R Levi; Matthew C Ricker; Aaron Thompson; Elizabeth G King; Kevin Robertson

doi:10.1016/j.scitotenv.2024.173060

Scaling of soil organic carbon in space and time in the Southern Coastal Plain, USA

Sci Total Environ. 2024 Jul 10:933:173060. doi: 10.1016/j.scitotenv.2024.173060. Epub 2024 May 7.

Authors

Rajneesh Sharma¹, Matthew R Levi², Matthew C Ricker³, Aaron Thompson⁴, Elizabeth G King⁵, Kevin Robertson⁶

Affiliations

¹ Department of Crop and Soil Sciences, University of Georgia, Athens, GA 30602, USA; Now at the Department of Geography, University of Georgia, Athens, GA 30602, USA.
² Department of Crop and Soil Sciences, University of Georgia, Athens, GA 30602, USA. Electronic address: matthew.levi@uga.edu.
³ Department of Crop and Soil Sciences, NC State University, Box 7620, Raleigh, NC 27695-7620, USA.
⁴ Department of Crop and Soil Sciences, University of Georgia, Athens, GA 30602, USA.
⁵ Odum School of Ecology, University of Georgia, Athens, GA 30602, USA; Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA.
⁶ Tall Timbers Research Station, Tallahassee, FL, USA.

PMID: 38723962
DOI: 10.1016/j.scitotenv.2024.173060

Abstract

Soil organic carbon (SOC) is a dynamic soil property (DSP) that represents the largest portion of terrestrial carbon. Its relevance to carbon sequestration and the potential effects of land use on SOC storage, make it imperative to map across both space and time. Most regional-scale studies mapping SOC give static estimates and train different models for different periods with varying accuracies. We developed a flexible modeling approach called DSP-Scale to map SOC in both space and time. DSP-Scale uses ecological concepts and empirical data to predict DSP dynamics using inherent soil properties (static factors) and land cover changes (dynamic factors). We compiled SOC data for the 0-20 cm depth (SOC20) from 1441 points spanning a 25 million ha study area in the southeastern U.S. Coastal Plain, incorporating data from the Rapid Carbon Assessment, National Cooperative Soil Survey Soil Characterization database, and other regional studies. We developed a random forest model using climate, topography, soil survey, and land cover changes to predict SOC20 dynamics for five-year periods between 2001 and 2019. Our model explained 66 % and 59 % of the variation for the training and test data, respectively. Top predictors included mean annual precipitation, slope, and soil erosion class. Land cover 10 years before measurements of SOC20 was more important than current land cover for estimating SOC20. We estimated total SOC stocks of 207.1 and 208.3 Tg for 2001 and 2019, respectively. Highest gains of total SOC stock (0.9 Tg from 2001 to 2019) were associated with land cover change from mixed to evergreen forest. The greatest loss of total SOC stock (0.2 Tg) in the same period was associated with land cover change from pasture/hay to evergreen forest. We concluded that the DSP-Scale approach provides a flexible way to use dynamic and static factors affecting SOC stocks to predict changes in space and time at regional scales.

Keywords: Land cover; Modeling; Soil organic carbon; Soil survey.