Soil Organic Carbon Across Mexico and the Conterminous United States (1991–2010). Guevara, M., Arroyo, C., Brunsell, N., Cruz, C. O., Domke, G., Equihua, J., Etchevers, J., Hayes, D., Hengl, T., Ibelles, A., Johnson, K., de Jong, B., Libohova, Z., Llamas, R., Nave, L., Ornelas, J. L., Paz, F., Ressl, R., Schwartz, A., Victoria, A., Wills, S., & Vargas, R. Global Biogeochemical Cycles, 34(3):no, March, 2020. Publisher: Blackwell Publishing Ltd
Soil Organic Carbon Across Mexico and the Conterminous United States (1991–2010) [link]Paper  doi  abstract   bibtex   
Soil organic carbon (SOC) information is fundamental for improving global carbon cycle modeling efforts, but discrepancies exist from country-to-global scales. We predicted the spatial distribution of SOC stocks (topsoil; 0–30 cm) and quantified modeling uncertainty across Mexico and the conterminous United States (CONUS). We used a multisource SOC dataset (\textgreater10 000 pedons, between 1991 and 2010) coupled with a simulated annealing regression framework that accounts for variable selection. Our model explained ${\}sim$50% of SOC spatial variability (across 250-m grids). We analyzed model variance, and the residual variance of six conventional pedotransfer functions for estimating bulk density to calculate SOC stocks. Two independent datasets confirmed that the SOC stock for both countries represents between 46 and 47 Pg with a total modeling variance of ±12 Pg. We report a residual variance of 10.4 ±5.1 Pg of SOC stocks calculated from six pedotransfer functions for soil bulk density. When reducing training data to define decades with relatively higher density of observations (1991–2000 and 2001–2010, respectively), model variance for predicted SOC stocks ranged between 41 and 55 Pg. We found nearly 42% of SOC across Mexico in forests and 24% in croplands, whereas 31% was found in forests and 28% in croplands across CONUS. Grasslands and shrublands stored 29 and 35% of SOC across Mexico and CONUS, respectively. We predicted SOC stocks \textgreater30% below recent global estimates that do not account for uncertainty and are based on legacy data. Our results provide insights for interpretation of estimates based on SOC legacy data and benchmarks for improving regional-to-global monitoring efforts.
@article{guevara_soil_2020,
	title = {Soil {Organic} {Carbon} {Across} {Mexico} and the {Conterminous} {United} {States} (1991–2010)},
	volume = {34},
	issn = {0886-6236},
	url = {https://onlinelibrary.wiley.com/doi/10.1029/2019GB006219},
	doi = {10.1029/2019GB006219},
	abstract = {Soil organic carbon (SOC) information is fundamental for improving global carbon cycle modeling efforts, but discrepancies exist from country-to-global scales. We predicted the spatial distribution of SOC stocks (topsoil; 0–30 cm) and quantified modeling uncertainty across Mexico and the conterminous United States (CONUS). We used a multisource SOC dataset ({\textgreater}10 000 pedons, between 1991 and 2010) coupled with a simulated annealing regression framework that accounts for variable selection. Our model explained \${\textbackslash}sim\$50\% of SOC spatial variability (across 250-m grids). We analyzed model variance, and the residual variance of six conventional pedotransfer functions for estimating bulk density to calculate SOC stocks. Two independent datasets confirmed that the SOC stock for both countries represents between 46 and 47 Pg with a total modeling variance of ±12 Pg. We report a residual variance of 10.4 ±5.1 Pg of SOC stocks calculated from six pedotransfer functions for soil bulk density. When reducing training data to define decades with relatively higher density of observations (1991–2000 and 2001–2010, respectively), model variance for predicted SOC stocks ranged between 41 and 55 Pg. We found nearly 42\% of SOC across Mexico in forests and 24\% in croplands, whereas 31\% was found in forests and 28\% in croplands across CONUS. Grasslands and shrublands stored 29 and 35\% of SOC across Mexico and CONUS, respectively. We predicted SOC stocks {\textgreater}30\% below recent global estimates that do not account for uncertainty and are based on legacy data. Our results provide insights for interpretation of estimates based on SOC legacy data and benchmarks for improving regional-to-global monitoring efforts.},
	number = {3},
	journal = {Global Biogeochemical Cycles},
	author = {Guevara, Mario and Arroyo, Carlos and Brunsell, Nathaniel and Cruz, Carlos O. and Domke, Grant and Equihua, Julian and Etchevers, Jorge and Hayes, Daniel and Hengl, Tomislav and Ibelles, Alejandro and Johnson, Kris and de Jong, Ben and Libohova, Zamir and Llamas, Ricardo and Nave, Lucas and Ornelas, Jose L. and Paz, Fernando and Ressl, Rainer and Schwartz, Anita and Victoria, Arturo and Wills, Skye and Vargas, Rodrigo},
	month = mar,
	year = {2020},
	note = {Publisher: Blackwell Publishing Ltd},
	keywords = {NALCMS},
	pages = {no},
}
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