This document summarizes a project to synthesize soil carbon datasets across the United States and Mexico to better understand soil carbon dynamics. The project aims to (1) harmonize existing soil carbon datasets from various US and Mexican agencies, (2) develop synthesis approaches to scale the information, and (3) identify knowledge gaps. Over 100,000 soil samples from the US and 30,000 from Mexico were compiled. Digital soil mapping was used to predict soil organic carbon levels based on environmental factors. Preliminary results estimate soil carbon stocks of 29.3 petagrams for the top 30 cm of US soils and 14.2 petagrams for the top 20 cm of Mexican soils. The study represents a baseline estimate of regional soil carbon variability and

1. Reducing uncertainty in carbon
cycle science of North America: a
synthesis program across United
States and Mexico
Rodrigo Vargas
Department of Plant and Soil Sciences
University of Delaware
CoPIs: Nathaniel Brunsell
University of Kansas
Daniel Hayes
University of Maine
Contact: rvargas@udel.edu
Agroclimatology PD meeting
December 16-18, 2016
San Francisco, CA

2. Interagency Carbon Cycle Science (FY 2014)
(2014-67003-22070)

3. • Synthesize new existing datasets and models across the
United States (U.S.) and Mexico in a consistent analysis
framework.
…directed towards improving our understanding of forest and
soil carbon dynamics, and the validation of terrestrial
ecosystem models.
The specific objectives:
a) Harmonize available datasets
b) Develop the synthesis approaches for scaling information
c) Develop a to identify knowledge gaps.
Objectives

4. Biederman, et al (2016) Global Change Biology 22:1867–1879.
Villarreal, et al (2016) Journal of Geophysical Research-Biogeosciences 121:494-508.
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McKinney et al (2015) IEEE 11th International Conference on e-Science: 108-117.
Programa de Investigación en Cambio Climático (PICC) (2015) Reporte Mexicano de Cambio
Climático. (Mexican Report on Climate Change. Group I: Scientific Bases, Models and
Modeling).
FAO and ITPS (2015) Status of the World’s Soil Resources (SWSR) – Main Report.
Vargas , et al (2015) EOS, 96. doi:10.1029/ 2015EO037893
Reimer, et al (2015) PLoS ONE. 10(4):e0125177
Cueva, et al (2015) Journal of Geophysical Research-Biogeosciences 120:737-751.
King, et al (2015) Biogeosciences 12:399-414
Milne, et al (2015) “Soil Carbon: science, management and policy for multiple benefits”. CABI.
10-25.
Banwart, et al (2014) Carbon Management 5:185-19
Hengl, et al. SoilGrids250m: global gridded soil information based on Machine Learning
(in review) PlosONE
Vargas R, et al. (in review) Enhancing interoperability to facilitate implementation of REDD+:
case study of Mexico. Carbon Management
Publications

7. Soil carbon across North America
- For decades the USA and Mexico have
collected soil organic carbon (SOC)
information.
- Can we describe the spatial variability of SOC
across North America?
- Can we relate observations with biophysical
information to predict SOC?

8. • Digital soil mapping (predictive soil mapping)
- Computer-assisted production of digital maps of soil
properties.
- Use of field and laboratory observational (data and
methods) with spatial and non-spatial inference
systems.
Digital soil mapping
+ many others

9. United States Mexico
International Soil Carbon Network Federal agencies
NRCS N=94778
1938-2010
INEGI Legacy Series 1 N=21153
1969-2001
USGS N=5623
1928-2006
INEGI Legacy Series 2 N=2805
1999-2009
Oak Ridge National Lab N=588
1992-2006
INEGI – National land degradation
project N=2472
2008-2012
Other institutions (e.g. Universities,
Long Term Ecological Research
sites) N=2330 1905-2009
CONAFOR – INFyS N=3061
2009-2011
TOTAL=103319 analyzed samples TOTAL=29491 analyzed samples
NRCS = Natural Resource Conservation Service
USGS = United States Geological Survey
INEGI = National Institute for Statistics and Geography
CONAFOR = National Forest Commission
SOC databases

10. SOC databases

11. 0-30cm
n=12,360
SOC database for USA (ISCN)

12. • Randomized sample from INEGI series 1 & 2
0-30cm
n=12,997
SOC database for Mexico (INEGI and CONAFOR)

13. SOC = f(S,C,O,R,P,A,N)+e
Soil- soil type maps
Climate, climatic properties
Organisms, land cover and natural vegetation
Relief, terrain parameters from DEM`s
Parent material, geological maps
Age, the time factor
N, space, relative position
e, autocorrelated random spatial variation
Dokuchayev 1883->Jenny 1941->McBratney et al., 2003,-> Grunwald et al., 2011
Conceptual model for SOC variability

14. Model
evaluation
(e.g. cross
valitacion,
AIC, BIC, Cp)
Variable
selection
(e.g. linear
model)
Prediction
to
new data
(e.g. random
forest
Cubist)
Uncertainty
Estimation
(e.g. different
models,
Global/local)
SOC = f (Soils, Climate, Organisms, Parent material, Age, Space) + error

15. Digital soil mapping
Guevara and Vargas (in preparation)

16. Hypothesis
driven
Machine
learning
*
Median
Statistical performance
(explained variance)

17. Unexplained variability

18. Model r raca RMSEraca r inegi RMSE inegi MX (Pg) US (Pg)
linear 5km 0.45 0.94 0.47 0.23 16.6 ± 2.16e-05 23.16 ± 2.96e-05
rf 5km 0.46 0.95 0.33 0.24 17.4 ±2.68-e05 21.53 ± 3.36e-05
SOC stocks across North America (PRELIMINARY)
29.3Pg for 0-30 cm depth (SSURGO; Bliss et al 2014) for CONUS
14.2 Pg for 0-20 cm depth (+- 3.9 Pg; Murray-Tortarolo et al 2015) for Mexico
RaCA = Rapid Assessment of US Soil Carbon (USDA)
INEGI = Series 1& 2
RaCA INEGI
Soil carbon density:
CONUS = 2.8 Mg km-2
Mexico = 8.5 Mg km-2

19. Towards a continental map of SOC for North America
SOC next steps

20. - - This approach represents a regional baseline
estimate of SOC (0-30cm) including variability
- - Useful in future soil sampling planning (i.e. for
inventory, SOC monitoring networks) aiming to
reduce areas dominated by high variability
- -This approach is reproducible (and semi automated)
and can be periodically updated with new data and
new covariates (Land use time 1, land use time2 and
so on)
Conclusions

21. Vargas et al (in review)
Stakeholder Scientists
Interoperability

22. Vargas et al (in review)
Interoperability
Interoperability is a collective effort with the ultimate goal of
sharing and using information to produce knowledge and
apply knowledge gained, by removing conceptual,
technological, organizational, and cultural barriers.

23. Vargas et al (in review)
Interoperability

24. Interagency Carbon Cycle Science (FY 2014)
(2014-67003-22070)
THANK YOU