This study aims to precisely quantify soil carbon stocks in nine farms in the Red River Valley using tools like precision agriculture, GIS, remote sensing, and deep learning. Over 4,000 soil samples were taken and analyzed for total carbon, inorganic carbon, organic carbon, and bulk density. Remote sensing data and yield data were also collected. Different machine learning models were tested to predict carbon levels from the data, with gradient boosting and histgradient boosting models achieving R2 values around 0.8. Traditional methods achieved lower accuracy. The research is 40% complete and aims to finish within three years to allow for better carbon sequestration contracting and investment targeting.

1. Soil carbon in the Red River Valley: towards
precision quantification and modeling
JOSE PABLO CASTRO, CALEY GASCH, PAULO FLORES,
PETER ODUOR, ERIK HANSON, MINGAO YUAN
NORTH DAKOTA STATE UNIVERSITY

4. CO2 CH4
CaCO3
CO

9. Short-mid
term
Long
term

12. Goal
To quantify in a precise and accurate way
the soil carbon stocks of nine farms located
in the Red River Valley using PrecisionAg
tools, GIS, remote sensing and deep
learning methods.

13. Electromagnetic
induction

14. Multispectral
photogrammetry

15. Annual
yield
datasets

16. Sampling
design

17. Sampling
design

18. Soil sampling
40 samples per farm
6 depths
2 repetitions
0-4 inches
4-8 inches
8-12 inches
12-24 inches
24-36 inches
36-48 inches
40 samples* 6 depths* 2 repetitions* 9 farms = 4320 samples

19. Yield sampling

20. Lab Analysis

21. Independent variables (X)
X1 = Band 1 Blue UAV (Average of four flights)
X2 = Band 2 Green UAV (Average of four flights)
X3 = Band 3 Red UAV (Average of four flights)
X4 = Band 4 NIR1 (Average of four flights)
X5 = Band 5 NIR2 (Average of four flights)
X6 = Band 6 NIR2 (Average of two flights)
X7 = Enhanced Natural Difference Vegetation Index UAV (Average of four flights)
X8 = Green Natural Difference Vegetation Index UAV (Average of four flights)
X9 = GSAVI Green Salinity Vegetation Index UAV (Average of four flights)
X10 = Natural Difference Reflectance Enhanced UAV (Average of four flights)
X11 = Natural Difference Vegetation Index UAV (Average of four flights)
X12 = Optimized Salinity Vegetation Index UAV (Average of two flights)
X13 = Salinity and Vegetation Index UAV (Average of four flights)
X14 = VSEC_D (Apparent electrical conductivity (0-30 cm depth)
X15 = VSEC_S (Apparent electrical conductivity (0-90 cm depth)
X16 = DEM RTK (Digital elevation model)
X17 = Relative yield 1
X18 = Relative yield 2
X19 = Depth
X20 = Longitude
X21 = Latitude
Dependent variables (Y)
• Y1 = Total carbon in soil
• Y2 = Inorganic carbon in soil
• Y3 = Organic carbon in soil
• Y4 = Bulk density
Database

23. Thiessen polygons Kriging

25. Target Training Size Test Size Best model AdjR2 R2 RMSE
Samples
for
training
Y1 = TC 0.8 0.2 GradientBoostingRegressor 0.38 0.5 0.48 32
Y1 = TC 0.6 0.4 HistGradientBoostingRegressor 0.4 0.5 0.5
24
Y1 = TC 0.4 0.6 HistGradientBoostingRegressor 0.37 0.4 0.52
16
Y1 = TC 0.2 0.8 RandomForestRegressor 0.15 0.2 0.59 8
Y1 = TC 0.01 0.99 LGBMRegressor -0.03 0 0.67 1
Y2 = IC 0.8 0.2 GradientBoostingRegressor 0.74 0.8 0.46 32
Y2 = IC 0.6 0.4 HistGradientBoostingRegressor 0.74 0.8 0.47
24
Y2 = IC 0.4 0.6 GradientBoostingRegressor 0.71 0.7 0.52 16
Y2 = IC 0.2 0.8 HistGradientBoostingRegressor 0.66 0.7 0.56
8
Y2 = IC 0.01 0.99 XGBRegressor 0.54 0.6 0.66 1
Y3 = OC 0.8 0.2 GradientBoostingRegressor 0.86 0.9 0.31 32
Y3 = OC 0.6 0.4 GradientBoostingRegressor 0.88 0.9 0.3 24
Y3 = OC 0.4 0.6 LGBMRegressor 0.88 0.9 0.29 16
Y3 = OC 0.2 0.8 HistGradientBoostingRegressor 0.83 0.8 0.35
8
Y3 = OC 0.01 0.99 AdaBoostRegressor 0.7 0.7 0.48 1

27. Conclusion
Soil organic and inorganic carbon stocks can be predicted using precision ag tools, GIS, remote
sensing, and deep learning models with determination’s coefficients around 0.8. Traditional
methods as Thiessen polygons or Kriging does not achieve more than 0.2-0.4.
An accuracy and precise quantification of carbon in the soil allows farmers to negotiate better
contracts for carbon sequestration and at the same time allows to the companies to have a
better control of where they are investing the money.
It research project is at 40% development. It is expected to be 100% complete in the next three
years.

28. Acknowledgment
This research is based upon work supported by the U.S. Department of Agriculture, Agricultural
Research Service, under agreement No. 58-6064-8-023.
Thanks to NDSU research specialist Joel Bell and undergraduate student Mike McKenna for their
hard work in the field of this project.

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