Estimating organic carbon content of soil in Papua New Guinea using infrared spectroscopy

Orr, Ryan, McBeath, Anna V., Dieleman, Wouter I.J., Bird, Michael I., and Nelson, Paul N. (2017) Estimating organic carbon content of soil in Papua New Guinea using infrared spectroscopy. Soil Research. pp. 1-8. (In Press)

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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.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.

Item ID: 49015
Item Type: Article (Refereed Research - C1)
Keywords: infrared spectroscopy, partial least-squares regression, positively correlated regression coefficient, soil organic carbon prediction
ISSN: 1838-6768
Funders: Australian Centre for International Agricultural Research (ACIAR)
Projects and Grants: ACIAR SMCN-2006-031
Date Deposited: 06 Jul 2017 02:57
FoR Codes: 05 ENVIRONMENTAL SCIENCES > 0503 Soil Sciences > 050301 Carbon Sequestration Science @ 20%
05 ENVIRONMENTAL SCIENCES > 0503 Soil Sciences > 050304 Soil Chemistry (excl Carbon Sequestration Science) @ 60%
03 CHEMICAL SCIENCES > 0301 Analytical Chemistry > 030101 Analytical Spectrometry @ 20%
SEO Codes: 96 ENVIRONMENT > 9614 Soils > 961403 Forest and Woodlands Soils @ 50%
96 ENVIRONMENT > 9614 Soils > 961402 Farmland, Arable Cropland and Permanent Cropland Soils @ 50%
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