Predicting the decomposability of arctic tundra soil organic matter with mid infrared spectroscopy. (February 2019)

• Record Type: Journal Article
• Authors: Matamala, Roser; Jastrow, Julie D.; Calderón, Francisco J.; Liang, Chao; Fan, Zhaosheng; Michaelson, Gary J.; Ping, Chien-Lu
• Is Part Of: Soil biology and biochemistry. Volume 129(2019)
• Page Start: 1
• Page End: 12
• Publication Date: 2019-02
• Subjects: MIR spectroscopy -- Tundra soil incubations -- Mineralization of SOM -- PLSR predictive models; Soil biochemistry -- Periodicals; Soil biology -- Periodicals; Sols -- Biochimie -- Périodiques; Sols -- Biologie -- Périodiques; Sols -- Microbiologie -- Périodiques; Bodembiologie; Biochemie; 631.46
• DOI: 10.1016/j.soilbio.2018.10.014
• Languages: English
• ISSNs: 0038-0717

Abstract

Abstract: Vast amounts of soil organic matter (SOM) have been preserved in arctic soils over millennia time scales due to the limiting effects of cold and wet environments on decomposer activity. With the increase in high latitude warming due to climate change, the potential decomposability of this SOM needs to be assessed. In this study, we investigated the capability of mid infrared (MIR) spectroscopy to quickly predict soil carbon and nitrogen concentrations and carbon (C) mineralized during short-term incubations of tundra soils. Active layer and upper permafrost soils collected from four tundra sites on the North Slope of Alaska were incubated at 1, 4, 8 and 16 °C for 60 days. All incubated soils were scanned to obtain the MIR spectra and analyzed for total organic carbon (TOC) and total nitrogen (TN) concentrations, and salt-extractable organic matter carbon (SEOM). Partial least square regression (PLSR) models, constructed using the MIR spectral data for all soils, were excellent predictors of soil TOC and TN concentrations and good predictors of mineralized C for these tundra soils. We explored whether we could improve the prediction of mineralized C by splitting the soils into the groups defined by the influential factors and thresholds identified in a principal components analysis: (1) TOC >10%, (2) TOC < 10%, (3) TN < 0.6%, (4) TN > 0.6%, (5) acidic tundra, and (6) non-acidic tundra. The best PLSR mineralization models were found for soils with TOC < 10% and TN < 0.6%. Analysis of the PLSR loadings and beta coefficients from these models indicated a small number of influential spectral bands. These bands were associated with clay content, phenolics, aliphatics, silicates, carboxylic acids, and amides. Our results suggest that MIR could serve as a useful tool for quickly and reasonably estimating the initial decomposability of tundra soils, particularly for mineral soils and the mixed organic-mineral horizons of cryoturbated soils. Highlights: Mid infrared spectroscopy can predict carbon mineralization potential of tundra soil. MIR was most effective at predicting active-layer mineral and upper permafrost soils. MIR showed SOM associations with clay, amount of phenolics and polysaccharides. MIR calibrations will reduce the need for incubation studies in tundra soils. Development of a MIR database would improve upscaling studies and benchmark models.