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RESEARCH ARTICLE

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.


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