Near Infrared Reflectance Spectroscopy for Characterization of Plant Litter Quality: Towards a Simpler Way of Predicting Carbon Turnover in Peatlands?
The ability of near infrared reflectance spectroscopy (NIRS) for the rapid determination of several chemical properties of plant litters was investigated. The chemical properties included fractions that potentially affect decomposition dynamics in peatlands: total carbon, total nitrogen, extractable (soluble) substances, holocellulose (sum of cellulose and hemicelluloses), sulfuric acid (H2SO4) insoluble lignin (Klason lignin), copper oxide (CuO) oxidation products of lignin: vanillin (V1), vanillic acid (V2), acetovanillone (V3), 4-hydroxybenzaldehyde (P1), 4-hydroxyacetophenone (P2), 4-hydroxybenzoic acid (P3), and ferulic acid (C2), as well as carbon (C) to nitrogen (N), and N to lignin ratios. To maximize variability, the samples included litters of nine plant species, representing five groups of plant litter: graminoid, deciduous foliage, conifer foliage, wood, and moss. For each quality parameter we compared (1) the model calibrated for all the various litter types (combined dataset) and (2) the model calibrated only for pine branch litter (branch dataset). Relationships were found between the chemical properties and near infrared (NIR) spectra using partial least squares (PLS) regression. Using both the combined and the branch datasets, very good NIR calibrations were possible for total C and N, ash content, nonpolar (dichlormethane) extractives (NPE), ethanol extractives (EE), and total extractives (TE) (sum of all soluble substances), holocellulose, C:N, and N:lignin ratios based on r 2 that varied from 0.80 to 0.99. Using only the combined dataset very good calibrations were also possible for acetone extractives (AE), water extractives (WE), lignin, P2, P3, C2, V1, V2, and V3, with r 2 from 0.81 to 0.97. The models combining all the different litter types performed better than the models constructed for the pine branch litter only, based on r 2 and residual prediction deviation (RPD), the ratio of the Y-variable standard deviation to the root mean square error of prediction (RMSEP). However, RMSEP was generally smaller when a branch litter property was predicted by the branch model compared to the prediction using the combined model, indicating the potential of improving the NIR calibrations by constructing specific models for different groups of litter. NIRS proved to be an accurate and fast method for the determination of plant litter quality that might be highly relevant for decomposition and C dynamics in peatlands.
KeywordsDecomposition litter quality NIRS peatlands
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