Estimating organic carbon content of soil in Papua New Guinea using infrared spectroscopy
Ryan Orr A B , Anna V. McBeath A , Wouter I. J. Dieleman A , Michael I. Bird A and Paul N. Nelson A
+ Author Affiliations
- Author Affiliations
A James Cook University, College of Science and Engineering, Building A2 Room 227, 14-88 McGregor Rd, Smithfield, Qld 4878, Australia.
B Corresponding author. Email: ryan.orr@jcu.edu.au
Soil Research 55(8) 735-742 https://doi.org/10.1071/SR16227
Submitted: 26 August 2016 Accepted: 3 April 2017 Published: 3 May 2017
Abstract
Quantification of soil organic carbon (SOC) content is important for sustainable agricultural management and accurate carbon accounting. Infrared (IR) absorbance can be used to estimate SOC content, but the relationship differs between regions due to matrix effects. We developed an IR-based model specific for SOC in Papua New Guinean soils. A total of 437 samples from 0.0–0.3 m depth were analysed for SOC using Dumas combustion. IR absorption spectra were collected from the same samples, and a predictive regression model was developed using the 6000–1030 cm–1 spectral range. Using a validation set, predicted SOC values resulting from the IR-based model compared well with values from Dumas combustion (R2 = 0.905; ratio of performance-to-deviation = 5.64). Constraining wavelengths to positively correlated regions of the spectra was also explored and showed improved model performance (R2 = 0.932). Overall, IR analysis provides a robust method for estimating SOC content for a range of Papua New Guinean soils.
Additional keywords: infrared spectroscopy, partial least-squares regression, positively correlated regression coefficient, soil organic carbon prediction.
References
Araújo SR, Wetterlind J, Demattê JAM, Stenberg B (2014) Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques. European Journal of Soil Science 65, 718–729.| Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques.Crossref | GoogleScholarGoogle Scholar |
Baldock JA, Hawke B, Sanderman J, Macdonald LM (2013) Predicting contents of carbon and its component fractions in Australian soils from diffuse reflectance mid-infrared spectra. Soil Research 51, 577–595.
| Predicting contents of carbon and its component fractions in Australian soils from diffuse reflectance mid-infrared spectra.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhvF2ktbjF&md5=94118d0fd41b5b4e649615ae05dbfcd0CAS |
Bartholomeus HM, Schaepman ME, Kooistra L, Stevens A, Hoogmoed WB, Spaargaren OSP (2008) Spectral reflectance based indices for soil organic carbon quantification. Geoderma 145, 28–36.
| Spectral reflectance based indices for soil organic carbon quantification.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXmtlCmurg%3D&md5=9311b4eaf6c210f3f5bdc388444df025CAS |
Bleeker P (1983) ‘Soils of Papua New Guinea/Pieter Bleeker.’ (Commonwealth Scientific and Industrial Research Organization, Australia in association with Australian National University Press: Canberra)
Brown DJ, Shepherd KD, Walsh MG, Dewayne Mays M, Reinsch TG (2006) Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 132, 273–290.
| Global soil characterization with VNIR diffuse reflectance spectroscopy.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XksFaqsbw%3D&md5=7fbc80622a7a652bba0c895df4ca506aCAS |
Cambule AH, Rossiter DG, Stoorvogel JJ, Smaling EMA (2012) Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique. Geoderma 183–184, 41–48.
| Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique.Crossref | GoogleScholarGoogle Scholar |
Chang C-W, Laird DA, Mausbach MJ, Hurburgh CR (2001) Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Soil Science Society of America Journal 65, 480–490.
| Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38Xpt1Om&md5=56434baca1529feba4b03668c377db6bCAS |
Dieleman WIJ, Venter M, Ramachandra A, Krockenberger AK, Bird MI (2013) Soil carbon stocks vary predictably with altitude in tropical forests: implications for soil carbon storage. Geoderma 204–205, 59–67.
| Soil carbon stocks vary predictably with altitude in tropical forests: implications for soil carbon storage.Crossref | GoogleScholarGoogle Scholar |
Esbensen KH (2012) ‘Multivariate data analysis – in practice.’ 5th edn. (Camo Process AS: Esbjerg, Denmark)
Goni M, Nittrouer C, Ogston A, Kurtz A, Aalto R, Lauer J, Aufdenkampe A, Dietrich W (2013) Organic carbon sequestration in soils and sediments along the land-ocean continuum of the Fly River, Papua New Guinea. In ‘AGU Fall Meeting Abstracts’, 9–13 December 2013, San Francisco. p. 0889. (American Geophysical Union)
Guerrero C, Stenberg B, Wetterlind J, Viscarra Rossel RA, Maestre FT, Mouazen AM, Zornoza R, Ruiz-Sinoga JD, Kuang B (2014) Assessment of soil organic carbon at local scale with spiked NIR calibrations: effects of selection and extra-weighting on the spiking subset. European Journal of Soil Science 65, 248–263.
| Assessment of soil organic carbon at local scale with spiked NIR calibrations: effects of selection and extra-weighting on the spiking subset.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXktlKlt7c%3D&md5=c08029d6be6a61cd2ba348de22066fb4CAS |
Hedley C, Roudier P, Maddi L (2015) VNIR Soil spectroscopy for field soil analysis. Communications in Soil Science and Plant Analysis 46, 104–121.
Janik LJ, Skjemstad JO, Shepherd KD, Spouncer LR (2007) The prediction of soil carbon fractions using mid-infrared-partial least square analysis. Soil Research 45, 73–81.
| The prediction of soil carbon fractions using mid-infrared-partial least square analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXjsFygurk%3D&md5=15fcb8f95c1482f08998d5b8d5e6de45CAS |
Jobbágy EG, Jackson RB (2000) The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecological Applications 10, 423–436.
| The vertical distribution of soil organic carbon and its relation to climate and vegetation.Crossref | GoogleScholarGoogle Scholar |
McDowell ML, Bruland GL, Deenik JL, Grunwald S, Knox NM (2012) Soil total carbon analysis in Hawaiian soils with visible, near-infrared and mid-infrared diffuse reflectance spectroscopy. Geoderma 189–190, 312–320.
| Soil total carbon analysis in Hawaiian soils with visible, near-infrared and mid-infrared diffuse reflectance spectroscopy.Crossref | GoogleScholarGoogle Scholar |
Minasny B, McBratney A (2013) Why you don’t need to use RPD. Pedometron 33, 14–15. Available at http://www.pedometrics.org/Pedometron/Pedometron33.pdf [verified 13 April 2017].
Nake S (2010) Potassium fixation and release in alluvial clay soils of Milne Bay, Papua New Guinea: effects of management under oil palm. MSc Thesis, James Cook University.
Nelson PN, Webb MJ, Berthelsen S, Curry G, Yinil D, Fidelis C (2011) ‘Nutritional status of cocoa in Papua New Guinea.’ (Australian Centre for International Agricultural Research: Canberra)
Nelson PN, Webb MJ, Berthelsen S, Curry G, Yinil D, Fidelis C (2012) Nutrient contents and associated parameters for cocoa tree leaves, ripe cocoa pods and soil from 63 cocoa farms in Papua New Guinea in 2007. James Cook University.
Nocita M, Stevens A, van Wesemael B, Aitkenhead M, Bachmann M, Barthès B, Ben Dor E, Brown DJ, Clairotte M, Csorba A, Dardenne P, Demattė JAM, Genot V, Guerrero C, Knadel M, Montanarella L, Noon C, Ramirez-Lopez L, Robertson J, Sakai H, Soriano-Disla JM, Shepherd KD, Stenberg B, Towett EK, Vargas R, Wetterlind J (2015) Soil spectroscopy: an alternative to wet chemistry for soil monitoring. In ‘Advances in agronomy. Vol. 132’. (Ed. LS Donald) Chapter 4, pp. 139–159. (Academic Press)
O’Rourke SM, Holden NM (2011) Optical sensing and chemometric analysis of soil organic carbon – a cost effective alternative to conventional laboratory methods? Soil Use and Management 27, 143–155.
| Optical sensing and chemometric analysis of soil organic carbon – a cost effective alternative to conventional laboratory methods?Crossref | GoogleScholarGoogle Scholar |
Persson M, Henders S, Kastner T (2014) Trading forests: quantifying the contribution of Global Commodity Markets to emissions from tropical deforestation-Working Paper 384.
Rabenarivo M, Chapuis-Lardy L, Brunet D, Chotte J-L, Rabeharisoa L, Barthès B (2013) Comparing near and mid-infrared reflectance spectroscopy for determining properties of Malagasy soils, using global or LOCAL calibration. Journal of Near Infrared Spectroscopy 21, 495–509.
| Comparing near and mid-infrared reflectance spectroscopy for determining properties of Malagasy soils, using global or LOCAL calibration.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXisFarsbY%3D&md5=b8161824d600aac7fb185911b5592ca2CAS |
Shepherd KD, Walsh MG (2002) Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society of America Journal 66, 988–998.
| Development of reflectance spectral libraries for characterization of soil properties.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38XlslOru7c%3D&md5=76b836d0c3571dfaba70b18d3d0ebfefCAS |
Soriano-Disla JM, Janik LJ, Viscarra Rossel RA, Macdonald LM, McLaughlin MJ (2014) The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Applied Spectroscopy Reviews 49, 139–186.
| The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhtVCrtLfF&md5=d2093ee66e1dcd33f6e0dcff8dac3196CAS |
Stenberg B, Viscarra Rossel RA, Mouazen AM, Wetterlind J (2010) Visible and near infrared spectroscopy in soil science. In ‘Advances in Agronomy. Vol. 107’. (Ed. LS Donald) Chapter 5, pp. 163–215. (Academic Press)
Stevens A, Nocita M, Tóth G, Montanarella L, van Wesemael B (2013) Prediction of soil organic carbon at the European scale by visible and near infrared reflectance spectroscopy. PLoS One 8, e66409
| Prediction of soil organic carbon at the European scale by visible and near infrared reflectance spectroscopy.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhtVGqtLnO&md5=a7afd1ba7f47738d051202e69dc0ee3cCAS |
Stumpe B, Weihermüller L, Marschner B (2011) Sample preparation and selection for qualitative and quantitative analyses of soil organic carbon with mid-infrared reflectance spectroscopy. European Journal of Soil Science 62, 849–862.
| Sample preparation and selection for qualitative and quantitative analyses of soil organic carbon with mid-infrared reflectance spectroscopy.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xmt1answ%3D%3D&md5=e0d677a933a89b97b50ff09459c6b0ebCAS |
Terhoeven-Urselmans T, Vagen T-G, Spaargaren O, Shepherd KD (2010) Prediction of soil fertility properties from a globally distributed soil mid-infrared spectral library. Soil Science Society of America Journal 74, 1792–1799.
| Prediction of soil fertility properties from a globally distributed soil mid-infrared spectral library.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXht1CmsrvO&md5=92d6b07c807c7fefbf01ed6efb985e20CAS |
Vågen T, Shepherd K, Walsh M, Winowiecki L, Desta LT, Tondoh J (2010) ‘AfSIS technical specifications: soil health surveillance.’ (Africa Soil Information Service (AfSIS). Nairobi, Kenya: World Agroforestry Centre)
Vaudour E, Gilliot JM, Bel L, Lefevre J, Chehdi K (2016) Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra. International Journal of Applied Earth Observation and Geoinformation 49, 24–38.
| Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra.Crossref | GoogleScholarGoogle Scholar |
Veraverbeke S, Gitas I, Katagis T, Polychronaki A, Somers B, Goossens R (2012) Assessing post-fire vegetation recovery using red–near infrared vegetation indices: accounting for background and vegetation variability. ISPRS Journal of Photogrammetry and Remote Sensing 68, 28–39.
| Assessing post-fire vegetation recovery using red–near infrared vegetation indices: accounting for background and vegetation variability.Crossref | GoogleScholarGoogle Scholar |
Viscarra Rossel RA (2009) The Soil Spectroscopy Group and the development of a global soil spectral library. In ‘EGU General Assembly Conference Abstracts’, 19–24 April 2009, Vienna, Austria. p. 14021. (EGU General Assembly: Vienna, Austria)
Viscarra Rossel RA, Hicks WS (2015) Soil organic carbon and its fractions estimated by visible–near infrared transfer functions. European Journal of Soil Science 66, 438–450.
| Soil organic carbon and its fractions estimated by visible–near infrared transfer functions.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXptVSjs7g%3D&md5=af3f8f4ac95c9d7e685fbe4d02a1c9beCAS |
Viscarra Rossel RA, Behrens T, Ben-Dor E, Brown DJ, Demattė JAM, Shepherd KD, Shi Z, Stenberg B, Stevens A, Adamchuk V, Aïchi H, Barthès BG, Batholomeus HM, Bayer AD, Bernoux M, Böttcher K, Brodsky L, Du CW, Chappell A, Fouad Y, Genot V, Gomez C, Grunwald S, Gubler A, Guerrero C, Hedley CB, Knadel M, Morrás HJM, Nocita M, Ramirez-Lopez L, Roudier P, Campos EMR, Sanborn P, Sellito VM, Sudduth KA, Rawlins BG, Walter C, Winowiecki LA, Hong SY, Ji W (2016) A global spectral library to characterize the world’s soil. Earth-Science Reviews 155, 198–230.
| A global spectral library to characterize the world’s soil.Crossref | GoogleScholarGoogle Scholar |
