AUTHOR=Xia Yushu , McSweeney Kevin , Wander Michelle M. 

TITLE=Digital Mapping of Agricultural Soil Organic Carbon Using Soil Forming Factors: A Review of Current Efforts at the Regional and National Scales

JOURNAL=Frontiers in Soil Science

VOLUME=2

YEAR=2022

URL=https://www.frontiersin.org/journals/soil-science/articles/10.3389/fsoil.2022.890437

DOI=10.3389/fsoil.2022.890437

ISSN=2673-8619

ABSTRACT=<p>To explore how well large spatial scale digital soil mapping can contribute to efforts to monitor soil organic carbon (SOC) stocks and changes, we reviewed regional and national studies quantifying SOC within lands dominated by agriculture using SCORPAN approaches that rely on soil (<italic>S</italic>), climate (<italic>C</italic>), organisms (<italic>O</italic>), relief (<italic>R</italic>), parent material (<italic>P</italic>), age (<italic>A</italic>), and space (<italic>N</italic>) covariates representing soil forming factors. After identifying 79 regional (&gt; 10,000 km<sup>2</sup>) and national studies that attempted to estimate SOC, we evaluated model performances with reference to soil sampling depth, number of predictors, grid-distance, and spatial extent. SCORPAN covariates were then investigated in terms of their frequency of use and data sources. Lastly, we used 67 studies encompassing a variety of spatial scales to determine which covariates most influenced SOC in agricultural lands using a subjective ranking system. Topography (used in 94% of the cases), climate (87%), and organisms (86%) covariates that were the most frequently used SCORPAN predictors, aligned with the factors (precipitation, temperature, elevation, slope, vegetation indices, and land use) currently identified to be most influential for model estimate at the large spatial extent. Models generally succeeded in estimating SOC with fits represented by R<sup>2</sup> with a median value of 0.47 but, performance varied widely (R<sup>2</sup> between 0.02 and 0.86) among studies. Predictive success declined significantly with increased soil sampling depth (p &lt; 0.001) and spatial extent (p &lt; 0.001) due to increased variability. While studies have extensively drawn on large-scale surveys and remote sensing databases to estimate environmental covariates, the absence of soils data needed to understand the influence of management or temporal change limits our ability to make useful inferences about changes in SOC stocks at this scale. This review suggests digital soil mapping efforts can be improved through greater use of data representing soil type and parent material and consideration of spatio-temporal dynamics of SOC occurring within different depths and land use or management systems.</p>