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Estimating the spatial distribution of organic carbon density for the soils of Ohio, USA

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Abstract

Historical database of National Soil Survey Center containing 1424 geo-referenced soil profiles was used in this study for estimating the organic carbon (SOC) for the soils of Ohio, USA. Specific objective of the study was to estimate the spatial distribution of SOC density (C stock per unit area) to 1.0-m depth for soils of Ohio using geographically weighted regression (GWR), and compare the results with that obtained from multiple linear regression (MLR). About 80% of the analytical data were used for calibration and 20% for validation. A total of 20 variables including terrain attributes, climate data, bedrock geology, and land use data were used for mapping the SOC density. Results showed that the GWR provided better estimations with the lowest (3.81 kg m−2) root mean square error (RMSE) than MLR approach. Total estimated SOC pool for soils in Ohio ranged from 727 to 742 Tg. This study demonstrates that, the local spatial statistical technique, the GWR can perform better in capturing the spatial distribution of SOC across the study region as compared to other global spatial statistical techniques such as MLR. Thus, GWR enhances the accuracy for mapping SOC density.

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References

  • Adams W A, 1973. The effect of organic matter on the bulk and true densities of some uncultivated podzolic soils. Eur. J. Soil Sci., 24: 10–17.

    Article  Google Scholar 

  • Agterberg F P, 1984. Trend surface analysis. In: Gaile G L, Willmott C J eds. Spatial Statistics and Models. D. Reidel, Dordrecht, Holland, 147–171.

    Google Scholar 

  • Batjes N H, 1996. Total carbon and nitrogen in the soils of the world. European Journal of Soil Science, 47: 151–163.

    Article  Google Scholar 

  • Bellamy P H, Loveland P J, Bradley R I et al., 2005. Carbon losses from all soils across England and Wales 1978–2003. Nature, 437: 245–248.

    Article  Google Scholar 

  • Bouchard V, Cochran M, 2006. Wetland and carbon sequestration. In: Lal R ed. Encyclopedia of Soil Science, Vol. 2. Boca Raton, FL: CRC Press, 1887–1890.

    Google Scholar 

  • Calhoun F G, Smeck N E, Slater B K et al., 2001. Predicting bulk density of Ohio soils from morphology, genetic principles and laboratory characterization data. Soil Science Society of America Journal, 65: 811–819.

    Article  Google Scholar 

  • Cliff A D, Ord J K, 1973. Spatial Autocorrelation: Monographs in Spatial and Environmental Systems Analysis. London: Pion Limited.

    Google Scholar 

  • Consortium M-R L C, 2007. 2001 National land cover data.

  • Daly C, Taylor G H, Gibson W P et al., 2001. High quality spatial climate data sets for the United States and beyond. Transactions of the American Society of Agricultural Engineers, 43: 1957–1962.

    Google Scholar 

  • Eldeiry A A, Garcia L A, 2010. Comparison of ordinary kriging, regression kriging, and cokriging techniques to estimate soil salinity using Landsat images. Journal of Irrigation and Drainage Engineering-Asce, 136: 355–364.

    Article  Google Scholar 

  • Fotheringham A S, Brunsdon C, Charlton M, 2002. Geographically weighted regression: The Analysis of Spatially Varying Relationships. Chichester, UK: John Wiley & Sons Ltd.

    Google Scholar 

  • Ge V Y, Thomasson J A, Morgan C L et al., 2007. VNIR diffuse reflectance spectroscopy for agricultural soil property determination based on regression-kriging. Transactions of the ASABE, 50: 1081–1092.

    Google Scholar 

  • Grigal D F, McRoberts R E, Ohmann L F, 1991. Spatial variation in chemical properties of forest floor and surface mineral soil in the North Central United States. Soil Science, 151: 282–290.

    Article  Google Scholar 

  • Guo Y Y, Amundson R, Gong P et al., 2006. Quantity and spatial variability of soil carbon in the conterminous United States. Soil Science Society of America Journal, 70: 590–600.

    Article  Google Scholar 

  • Hengl T, Heuvelink G B M, Rossiter D G, 2007. About regression-kriging: From equations to case studies. Computers and Geosciences, 33: 1301–1315.

    Article  Google Scholar 

  • Hengl T, Heuvelink G B M, Stein A, 2004. A generic framework for spatial prediction of soil variables based on regression kriging. Geoderma, 120: 75–93.

    Article  Google Scholar 

  • Jenny H, 1941. Factors of Soil Formation, A System of Quantitative Pedology. New York: McGraw-Hill.

    Google Scholar 

  • Jones C, McConnell C, Coleman K et al., 2005. Global climate change and soil carbon stocks; predictions from two contrasting models for the turnover of organic carbon in soil. Global Change Biology, 11: 154–166.

    Article  Google Scholar 

  • Kern J S, 1994. Spatial patterns of soil organic carbon in the contiguous United States. Soil Science Society of America Journal, 58: 439–455.

    Article  Google Scholar 

  • Knowles T A, Singh B, 2003. Carbon storage in cotton soils in northern New South Wales. Australian Journal of Soil Research, 41: 889–903.

    Article  Google Scholar 

  • Lal R, 2004. Soil carbon sequestration impacts on global climate change and food security. Science, 304: 1623–1627.

    Article  Google Scholar 

  • Lal R, Kimble J, Follett R F et al., 1998. The Potential for U.S. Cropland to Sequester Carbon and Mitigate the Greenhouse Effect. Ann Arbor, MI: Sleeping Bear Press, 1–128.

    Google Scholar 

  • Lantz A, Lal R, Kimble J, 2001. Land use effects on soil carbon pools in two major land resource areas of Ohio, USA. In: Stott R H M D E, Steinhardt G C eds. Sustaining the Global Farm. Purdue Un iversity, 499–502.

  • Lloyd C D, 2010. Analysing population characteristics using geographically weighted principal components analysis: A case study of Northern Ireland in 2001. Computers Environment and Urban Systems 34: 389–399.

    Article  Google Scholar 

  • McBratney A B, Santos M L M, Minasny B, 2003. On digital soil mapping. Geoderma, 117: 3–52.

    Article  Google Scholar 

  • Mennis J, 2006. Mapping the results of geographically weighted regression. The Cartographic Journal, 43: 171–179.

    Article  Google Scholar 

  • Minasny B, McBratney A B, Mendonca-Santos M L et al., 2006. Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley. Australian Journal of Soil Research, 44: 223–244.

    Article  Google Scholar 

  • Moran P A P, 1973. Notes on continuous stochastic phenomena. Biometrika, 37: 17–23.

    Google Scholar 

  • Mueller T G, Pierce F J, 2003. Soil carbon maps: Enhancing spatial estimates with simple terrain attributes at multiple scales. Soil Science Society of America Journal, 67: 258–267.

    Article  Google Scholar 

  • Natural Resources Conservation Service (NRCS), 2006. Land resource regions and major land resource areas of the United States, the Caribbean, and the Pacific Basin. USDA Handbook.

  • Odeh I O A, McBratney A B, Chittleborough D J, 1994. Spatial prediction of soil properties from landform attributes derived from a digital elevation model. Geoderma, 63: 197–214.

    Article  Google Scholar 

  • Odeh I O A, McBratney A B, Chittleborough D J, 1995. Further results on prediction of soil properties from terrain attributes: Heterotopic cokriging and regression-kriging. Geoderma, 67: 215–226.

    Article  Google Scholar 

  • Páez A, 2004. Anisotropic variance functions in geographically weighted regression models. Geographical Analysis, 36: 299–314.

    Google Scholar 

  • Post W M, Peng T H, Emanuel W R et al., 1990. The global carbon cycle. American Scientist, 78: 310–326.

    Google Scholar 

  • Qualls R G, Richardson C J, 2008. Decomposition of litter and peat in the Everglades. Springer Science+Business Media, New York.

    Google Scholar 

  • Rafique R, Anex R, Hennessy D et al., 2012. What are the impacts of grazing and cutting events on the N2O dynamics in humid temperate grassland? Geoderma, 181/182: 36–44.

    Article  Google Scholar 

  • Saetre P, 1999. Spatial patterns of ground vegetation, soil microbial biomass and activity in a mixed spruce-birch stand. Ecography, 22: 183–192.

    Article  Google Scholar 

  • Spain A V, Isbell R F, Probert M E, 1983. Soil organic matter. Soils, an Australian Viewpoint. Melbourne, Australia: CSIRO/London, UK: Academic Press.

    Google Scholar 

  • Tan Z, Lal R, Smeck N E et al., 2004. Taxonomic and geographic distribution of soil organic carbon pools in Ohio. Soil Sci. Soc. Am. J., 68: 1896–1904.

    Article  Google Scholar 

  • Thompson J A, Kolka R K, 2005. Soil carbon storage estimation in a forested watershed using quantitative soil landscape modeling. Soil Science Society of America Journal, 69: 1086–1093.

    Article  Google Scholar 

  • Webster R, Oliver M A, 2007. Geostatistics for Environmental Scientists. Chichester: John Wiley & Sons.

    Book  Google Scholar 

  • Wheeler D C, 2009. Simultaneous coefficient penalization and model selection in geographically weighted regression: The geographically weighted lasso. Environmental and Planning A, 41: 722–742.

    Article  Google Scholar 

  • Wheeler D C, Páez A, 2010. Geographically Weighted Regression. Berlin & Heidelberg: Springer-Verlag Publ.

    Google Scholar 

  • Wills S A, Burras C L, Sandor J A, 2007. Prediction of soil organic carbon content using field and laboratory measurements of soil color. Soil Science Society of America Journal, 71: 380–388.

    Article  Google Scholar 

  • Wilson K B, Meyers T P, 2001. The spatial variability of energy and carbon dioxide fluxes at the floor of a deciduous forest. Boundary-Layer Meteorology, 98: 443–473.

    Article  Google Scholar 

  • Zinn Y L, Lal R, Resck D V S, 2005. Texture and organic carbon relations described by a profile pedotransfer function for Brazilian Cerrado soils. Geoderma, 127.

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Authors and Affiliations

  1. Department of Plant Science, Room 248C NPB, South Dakota State University, 1110 Rotunda Lane North, BOX 2140C, Brookings, SD, 57007, USA

    Sandeep Kumar

  2. Carbon Management and Sequestration Center, School of Environment and Natural Resources, The Ohio State University, 2021 Coffey Road, 414A Kottman Hall, Columbus, OH, 43210-1085, USA

    Rattan Lal

  3. Department of Geography and Department of Statistics, The Ohio State University, Columbus, OH, 43210, USA

    Desheng Liu

  4. Department of Microbiology & Plant Biology, University of Oklahoma, Norman, OK, 73019, USA

    Rashid Rafiq

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  1. Sandeep Kumar
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  2. Rattan Lal
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  3. Desheng Liu
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  4. Rashid Rafiq
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Correspondence to Sandeep Kumar.

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Kumar, S., Lal, R., Liu, D. et al. Estimating the spatial distribution of organic carbon density for the soils of Ohio, USA. J. Geogr. Sci. 23, 280–296 (2013). https://doi.org/10.1007/s11442-013-1010-1

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  • DOI: https://doi.org/10.1007/s11442-013-1010-1

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