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United States Department of Agriculture

Agricultural Research Service

Mitigation of Climatic Change

Research on carbon sequestration, crop residue assessment, and greenhouse gas emissions.

Soil Carbon Sequestration:

Truck mounted L-band radiometer, Photo by Agricultural Research magazine

Chemist Barry Francis loads Brazilian soil samples into an autosampler for mid-infrared analysis of carbon content.

  • Rapid measurement of soil carbon using Infrared Spectroscopy: Our ability to accurately measure soil carbon stocks in agricultural landscapes is limited by accurate analytical methods that can rapidly measure soil carbon content. Both near infrared (NIR) and mid infrared (MIR) spectroscopy hold promise and comparison of these methods is needed to optimize measurement technologies. This study showed that diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) is superior to near-infrared diffuse reflectance spectroscopy for the analysis of carbon in soil samples for dried samples. Also demonstrated that mid-infrared outperforms remote sensing methods, but requires that samples be dried and be analyzed in a proximate mode. Rapid and economical methods for soil carbon are feasible and can be used to can be routinely used in soil carbon stock assessments. Significant publication:Reeves III, J.B., G.W. McCarty, F. Calderon, W.D. Hively. Advances in Spectroscopic Methods of Quantifying Soil Carbon. In: Liebig, M. Franzluebbers, A.J. and Follett R.F. (eds.) Managing Agricultural Greenhouse Gases pp. 345-366. Academic Press, Amsterdam. 2012.

    Portrait of soil moisture sampling team, Photo by Mike Cosh

    Soil scientist Gregory McCarty, chemist James Reeves, and LABEX coordinator Pedro Arraes discuss use of mid-infrared spectral data for soil analyses.

  • Mapping soil carbon using airborne hyperspectral data: Spatial assessment of soil properties is important for understanding the dynamics of agricultural ecosystems and often the spatial distribution of soil properties, such as organic carbon content, is unknown. The utility of hyperspectral imagery in conjunction with partial least squares regression models to develop detailed maps of soil properties was investigated. The aircraft-based hyperspectral data was shown to provide accurate maps of important soil properties such as carbon, aluminum, iron, and silt and sand. Application of this technology will greatly improve site specific management of agricultural lands and provide an accurate assessment of the spatial distribution of soil texture, fertility, and carbon storage within agricultural fields. Significant publication:Hively, W.D., McCarty, G.W., Reeves III, J.B., Lang, M.W., Oesterling, R.A., Delwiche, S.R. Use of airborne hyperspectral imagery to map soil properties in tilled agricultural fields. Applied and Environmental Soil Science. pp. 1-13. 2011.

  • Improved data mining approaches for measurement of soil properties by use of infrared spectroscopy: Near-infrared and mid infrared diffuse reflectance spectroscopy hold great promise for rapid measurement of soil properties such as organic carbon but ability to extract information from soil spectra is limited by data mining approaches . Mathematical treatment of spectral data can be an important determinant of success. Spectral pretreatments, partial least squares, least squares support vector machines, and locally weighted regression were tested for improvement in quantitative spectroscopic analysis of soil. The results showed that calibration models for soil are quite sensitive to the complexity of the model and the ability of locally weighted regression helped selection of appropriate calibration samples for development of robust calibrations. These findings will improve our ability to develop robust calibrations for soil properties such as organic carbon and should enable better assessment of soil carbon storage in agricultural ecosystems under management to sequester atmospheric carbon in soil. Significant publication:Igne, B., Reeves III, J.B., McCarty, G.W., Hively, W.D., Lund, E. and Hurburgh Jr., C.R. Evaluation of spectral pretreatments, partial least squares, least squares support vector machines and locally weighted regression for quantitative spectroscopic analysis of soils. Journal of Near Infrared Spectroscopy. 18(3):167-176. 2010.

  • Improving Capacity of Developing Countries to Quantify Soil Carbon in Production Systems: Improved ability for within country measurement of soil carbon in agricultural production systems can build capacity of developing countries to engage emerging carbon markets. Evaluation and validation of low cost technology approaches such as loss on ignition for measurement soil carbon has improved capacity of soil analytical laboratories in West Africa to monitor soil carbon in agricultural systems. Increased capacity for measurement of soil carbon in agricultural soils will strengthen capacity of developing countries to gain development aid for improved agricultural production and greater food security. Significant publication:McCarty, G.W., Reeves III, J.B., Yost, R., Doraiswamy, P.C., and Doumbia, M. Evaluation of methods for measuring soil organic carbon in West African soils. African Journal of Agricultural Research 5(16):2169-2177. 2010.

  • Improving Capacity of Developing Countries to Participate in Carbon Credit Markets: International aid and development funding for agricultural improvement in developing countries may be linked to verifiable credits for carbon sequestration in agricultural soils as influenced by better soil management. Soil carbon modeling efforts involving the Environmental Policy Integrated Climate (EPIC) model has reduced uncertainty concerning sequestration and storage of soil carbon in agricultural production systems in both West Africa and Central Asia. Improved ability to model soil carbon in agricultural soils will strengthen capacity of developing countries to gain development aid for improved agricultural production and greater food security. Significant publication:Causarano, H.J., Doraiswamy, P.C., Muratova, N., Pachikin, K., McCarty, G.W., Akhmedov, B., and Williams, J.R. Improved modeling of soil organic carbon in a semiarid region of central east Kazakhstan using EPIC. Agron. Sustain. Dev. DOI:10.1051/agro/2010028. 2010.

Greg McCarty,, 301-504-7401

Last Modified: 11/22/2016
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