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Development of Mid-Infrared Spectroscopic Feature-Based Indices to Quantify Soil Carbon Fractions

  • SOIL CHEMISTRY
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Abstract

Mid-infrared spectroscopy (MIRS) provides a rapid-throughput methodology in predicting many soil properties. The objective was to evaluate the performance of spectral feature-based indices to predict soil carbon fractions and compare it to partial least square regression (PLSR) model. We scanned 135 soil samples by MIRS and analyzed for total organic carbon (TOC), microbial biomass carbon (Cmic), hot-water extractable carbon (HWEC) and total inorganic carbon (TIC). Ninety samples were used for model calibration, while remaining 45 samples were further used for model validation. To develop spectral indices, peak area at specific absorbance feature related to labile organic carbon (PA 2930 cm–1) and carbonate (PA 2515 and 713 cm–1) was calculated and correlated to measured values of interest. PLSR resulted in successful calibrated models with R2 = 0.95, 0.99 for TOC, TIC, and moderate successful models with R2 = 0.85, 0.81 for Cmic and HWEC, respectively. The same results were also obtained for model validation. In terms of spectral indices, regression analyses between PA 2930 cm–1 and values of TOC, Cmic and HWEC was moderate successful with R2 values 0.85 to 0.88. Correlation between PA 2515 and 713 cm–1 and TIC resulted in a successful regression model with R2 = 0.98. As conclusion, precision level of spectral indices in predicting soil properties is almost equivalent to the PLSR models. However, correct large-scale application of the method requires excluding spectral mineral interference and model development by taking samples with a greater variability of soils and mineralogy.

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REFERENCES

  1. W. M. Post, R. C. Izaurralde, L. K. Mann, and N. Bliss, “Monitoring and verifying changes of organic carbon in soil,” Clim. Change 51, 73–99 (2001).

    Article  Google Scholar 

  2. J. T. Houghton, L. G. Meira Filho, B. A. Callander, N. Harris, A. Kattenberg, and K. Maskell, Climate Change 1995: The Science of Climate Change. Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, Cambridge, 1995)

    Google Scholar 

  3. N. Barros, L. D. Hansen, V. Piñeiro, C. Pérez-Cruzado, M. Villanueva, J. Proupín, and J. A. Rodríguez-Añón, “Factors influencing the calorespirometric ratios of soil microbial metabolism,” Soil Biol. Biochem. 92, 221–229 (2016).

    Article  Google Scholar 

  4. B. H. Ellert, H. H. Janzen, and B. G. McConkey, “Measuring and comparing soil carbon storage,” in Assessment Methods for Soil Carbon, Ed. by R. Lal, J. M., Kimble, R. F. Follett, and B. A. Stewart (Lewis, Boca Raton, FL, 2001), pp. 131–146

  5. J. A. Baldock, B. Hawke, J. Sanderman, and L. M. Macdonald, “Predicting contents of carbon and its component fractions in Australian soils from diffuse reflectance mid-infrared spectra,” Soil Res. 51, 577–595 (2013).

    Article  Google Scholar 

  6. R. Gehl and C. Rice, “Emerging technologies for in situ measurement of soil carbon,” Clim. Change 80, 43–54 (2007).

    Article  Google Scholar 

  7. D. M. Haaland and E. V. Thomas, “Partial least-squares methods for spectral analyses. 2. Application to simulated and glass spectral data,” Anal. Chem. 60, 1202–1208 (1988).

    Article  Google Scholar 

  8. L. J. Janik, R. H. Merry, and J. O. Skjemstad, “Can mid infrared diffuse reflectance analysis replace soil extractions?” Aust. J. Exp. Agric. 38, 681–696 (1998).

    Article  Google Scholar 

  9. R. A. Viscarra Rossel, D. J. J. Walvoort, A. B. McBratney, L. J. Janik, and J. O. Skjemstad, “Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties,” Geoderma 131, 59–75 (2006).

    Article  Google Scholar 

  10. M. L. McDowell, J. L. Bruland, J. L. Deenik, S. Grunwald, and N. M. Knox, “Soil total carbon analysis in Hawaiian soils with visible, near-infrared and mid-infrared diffuse reflectance spectroscopy,” Geoderma 189–190, 312–320 (2012).

    Article  Google Scholar 

  11. J. M. Soriano-Disla, L. J. Janik, R. A. Viscarra Rossel, L. M. Macdonald, and M. J. McLaughlin, “The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties,” Appl. Spectrosc. Rev. 49, 139–186 (2014).

    Article  Google Scholar 

  12. D. J. Brown, R. S. Bricklemyer, and R. R. Miller, “Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana,” Geoderma 129, 251–267 (2005).

    Article  Google Scholar 

  13. H. P. Hartmann and T. Appel, “Calibration of near infrared spectra for measuring decomposing cellulose and green manure in soils,” Soil Biol. Biochem. 38, 887–897 (2006).

    Article  Google Scholar 

  14. V. Bellon-Maurel and A. McBratney, “Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils - Critical review and research perspectives,” Soil Biol. Biochem. 43, 1398–1410 (2011).

    Article  Google Scholar 

  15. F. J. Calderon, M. M. Mikha, M. F. Vigil, D. C. Nielsen, J. B. Benjamin, and J. B. Reeves III, “Diffuse-reflectance mid-infrared spectral properties of soils under alternative crop rotations in a semi-arid climate,” Commun. Soil Sci. Plant Anal. 42, 2143–2159 (2011).

    Article  Google Scholar 

  16. M. S. Demyan, F. Rasche, E. Schulz, M. Breulmann, T. Müller, and G. Cadisch, “Use of specific peaks obtained by diffuse reflectance Fourier transform mid-infrared spectroscopy to study the composition of organic matter in a haplic chernozem,” Eur. J. Soil Sci. 63, 189–199 (2012).

    Article  Google Scholar 

  17. A. Bayer, M. Bachmann, A. Mueller, and H. Kaufmann, “A comparison of feature-based MLR and PLS regression techniques for the prediction of three soil constituents in a degraded South African ecosystem,” Appl. Environ. Soil Sci. 2012, 971252 (2012).

    Article  Google Scholar 

  18. C. Giacometti, M. S. Demyan, L. Cavani, C. Marzadori, C. Ciavatta, and E. Kandeler, “Chemical and microbiological soil quality indicators and their potential to differentiate fertilization regimes in temperate agroecosystems,” Appl. Soil Ecol. 64, 32–48 (2013).

    Article  Google Scholar 

  19. R. Mirzaeitalarposhti, M. S. Demyan, F. Rasche, G. Cadisch, and T. Müller, “Overcoming carbonate interference on labile soil organic matter peaks for midDRIFTS analysis,” Soil Biol. Biochem. 99, 150–157 (2016).

    Article  Google Scholar 

  20. Y. Kooch, F. Rostayee, and S. M. Hosseini, “Effects of tree species on topsoil properties and nitrogen cycling in natural forest and tree plantations of northern Iran,” Catena 144, 65–73 (2013).

    Article  Google Scholar 

  21. M. Soleimany Rahimabady, M. Akbarinia, and Y. Kooch, “The effect of land covers on soil quality properties in the Hyrcanian regions of Iran,” J. BioSci. Biotechnol. 4, 73–79 (2015).

    Google Scholar 

  22. M. Padilla, S. V. Stehman, R. Ramo, D. Corti, S. Hantson, P. Oliva, I. Alonso, A. Bradley, K. Tansey, B. Mota, J. M. Pereira, and E. Chuvieco, “Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation,” Remote Sens. Environ. 160, 114–121 (2015).

    Article  Google Scholar 

  23. ISO 10694: Soil Quality. Determination of Organic and Total Carbon after Dry Combustion (Elementary Analysis) (International Organization for Standardization, Geneva, 1995)

  24. ISO 10693: Soil Quality-Determination of Carbonate Content–Volumetric Method (International Organization for Standardization, Geneva, 1995)

  25. R. G. Joergensen and T. Mueller, “The fumigation-extraction method to estimate soil microbial biomass: calibration of the kEN value,” Soil Biol. Biochem. 28, 33–37 (1996).

    Article  Google Scholar 

  26. E. Schulz and M. Koerschens, “Characterization of the decomposable part of soil organic matter (SOM) and transformation processes by hot water extraction,” Eurasian Soil Sci. 31, 809–813 (1998).

    Google Scholar 

  27. G. J. Bouyoucos, “Hydrometer method improved for making particle size analysis of soils,” Agron. J. 54, 464–465 (1962).

    Article  Google Scholar 

  28. R. Mirzaeitalarposhti, M. S. Demyan, F. Rasche, M. Poltoradnev, G. Cadisch, and T. Müller, “MidDRIFTS-PLSR-based quantification of physicochemical soil properties across two agroecological zones in Southwest Germany: generic independent validation surpasses region specific cross-validation,” Nutr. Cycl. Agroecosyst. 102, 265–283 (2015).

    Article  Google Scholar 

  29. A. Pirie, B. Singh, and K. Islam, “Ultra-violet, visible, near-infrared, and mid-infrared diffuse reflectance spectroscopic techniques to predict several soil properties,” Aust. J. Soil Res. 43, 713–721 (2005)

    Article  Google Scholar 

  30. A.U. Baes and P.R. Bloom, “Diffuse reflectance and transmission Fourier transform infrared (DRIFT) spectroscopy of humic and fulvic acids,” Soil Sci. Soc. Am. J. 53, 695–700(1989).

    Article  Google Scholar 

  31. M. Tatzber, F. Mutsch, A. Mentler, E. Leitgeb, M. Englisch, and M. H. Gerzabek, “Determination of organic and inorganic carbon in forest soil samples by midinfrared spectroscopy and partial least squares regression,” Appl. Spectrosc. 64, 1167–1175 (2010).

    Article  Google Scholar 

  32. V. J. Bruckman and K. Wriessnig, “Improved soil carbonate determination by FT-IR and X-ray analysis,” Environ. Chem. Lett. 11, 65–70 (2013).

    Article  Google Scholar 

  33. World Reference Base for Soil Resources 2006: A Framework for International Classification, Correlation, and Communication (Food Agriculture Organisation, Rome, 2007).

  34. G. W. McCarty, J. B. Reeves, V. B. Reeves, R. F. Follett, and J. M. Kimble, “Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement,” Soil Sci. Soc. Am. J. 66, 640–646 (2002).

    Article  Google Scholar 

  35. F. Saiano, G. Oddo, R. Scalenghe, T. La Mantia, and F. Ajmone-Marsan, “DRIFTS sensor: Soil carbon validation at large scale (Pantelleria, Italy),” Sensors 13, 5603–5613 (2013).

    Article  Google Scholar 

  36. J. G. Cobo, G. Dercon, T. Yekeye, L. Chapungu, C. Kadzere, A. Murwira, R. Delve, and G. Cadisch, “Integration of mid-infrared spectroscopy and geostatistics in the assessment of soil spatial variability at landscape level,” Geoderma 158, 398–411 (2010).

    Article  Google Scholar 

  37. J. B. Reeves III, G. W. McCarty, and V. B. Reeves, “Mid-infrared diffuse reflectance spectroscopy for the quantitative analysis of agricultural soils,” J. Agricult. Food Chem. 49, 766–772 (2001).

    Article  Google Scholar 

  38. J. B. Reeves, B. A. Francis, and S. K. Hamilton, “Specular reflection and diffuse reflectance spectroscopy of soils,” Appl. Spectrosc. 59, 39–46 (2005).

    Article  Google Scholar 

  39. M. Vohland, M. Ludwig, S. Thiele-Bruhn, and B. Ludwig, “Determination of soil properties with visible to near- and mid-infrared spectroscopy: effects of spectral variable selection,” Geoderma 223–225, 88–96 (2014).

    Article  Google Scholar 

  40. B. Ludwig, R. Nitschke, T. Terhoeven-Urselmans, K. Michel, and H. Flessa, “Use of mid-infrared spectroscopy in the diffuse-reflectance mode for the prediction of the composition of organic matter in soil and litter,” J. Plant Nutr. Soil Sci. 171, 384–391 (2008).

    Article  Google Scholar 

  41. B. E. Madari, J. B. Reeves III, P. L. O. A. Machado, C. M. Guimarães, E. Torres, and G. W. McCarty, “Mid- and near-infrared spectroscopic assessment of soil compositional parameters and structural indices in two ferralsols,” Geoderma 136, 245–259 (2006).

    Article  Google Scholar 

  42. D. J. Brown, K. D. Shepherd, M. G. Walsh, M. M. Dewayne, and T. G. Reinsch, “Global soil characterization with VNIR diffuse reflectance spectroscopy,” Geoderma 132, 273–290 (2006).

    Article  Google Scholar 

  43. J. B. Reeves III, G. W. McCarty, F. Calderon, and W. D. Hively, “Advances in spectroscopic methods for quantifying soil carbon,” in Managing Agricultural Greenhouse Gases, Ed. by A. J. Franzluebbers and R. F. Follett (Academic, San Diego, CA, 2012), pp. 345–366.

    Google Scholar 

  44. J. B. Reeves III, “Mid-infrared spectral interpretation of soils: Is it practical or accurate?” Geoderma 189–190, 508–513 (2012).

    Article  Google Scholar 

  45. M. A. Legodi, D. de Waal D, J. H. Potgieter, and S. S. Potgieter, “Technical note rapid determination of CaCO3 in mixtures utilizing FT-IR spectroscopy,” Miner Eng. 14, 1107–1111 (2001).

    Article  Google Scholar 

  46. D. M. Smith, J. J. Griffin, and E. D. Goldberg, “Spectrometric method for the quantitative determination of elemental carbon,” Anal. Chem. 47, 233–238 (1975).

    Article  Google Scholar 

  47. R. D. Hewson, T. J. Cudahy, M. Jones, and T. Matilda, “Investigations into soil composition and texture using infrared spectroscopy (2–14 μm),” Appli. Environ. Soil Sci. 2012, 535646 (2012).

    Google Scholar 

  48. A. Prechtel, M. Lützow, B. U. Schneider, O. Bens, C. G. Bannick, I. Kögel-Knabner, and R. F. Hüttl, “Organic carbon in soils of Germany: status quo and the need for new data to evaluate potentials and trends of soil carbon sequestration,” J. Plant Nutr. Soil Sci. 172, 601–614 (2009).

    Article  Google Scholar 

  49. Q. Wang and S. Wang, “Response of labile soil organic matter to changes in forest vegetation in subtropical regions,” Appl. Soil Ecol. 47, 210–216 (2011).

    Article  Google Scholar 

  50. F. J. Stevenson, Humus Chemistry: Genesis, Composition, Reactions (Wiley, New York, 1982)

    Google Scholar 

  51. F. Calderon, G. McCarty, and J. Revees, “Pyrolisis-MS and FT-IR analysis of fresh and decomposed dairy manure,” J. Anal. Appl. Pyrol. 76, 14–23 (2006).

    Article  Google Scholar 

  52. J. F. Ji, Y. Ge, W. Balsam, J. E. Damuth, and J. Chen, “Rapid identification of dolomite using a Fourier transform infrared spectrophotometer (FTIR): a fast method for identifying Heinrich events in IODP Site U1308,” Mar. Geol. 258, 60–68 (2009).

    Article  Google Scholar 

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ACKNOWLEDGMENTS

The authors deeply acknowledged M.S. Demyan for editing and comments on this manuscript.

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  1. Department of Agroecology, Shahid Behehsti University, Tehran, Iran

    R. Mirzaeitalarposht & J. Kambouzia

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Mirzaeitalarposht, R., Kambouzia, J. Development of Mid-Infrared Spectroscopic Feature-Based Indices to Quantify Soil Carbon Fractions. Eurasian Soil Sc. 53, 73–81 (2020). https://doi.org/10.1134/S1064229320010111

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