DRIFTS band areas as measured pool size proxy to reduce parameter uncertainty in soil organic matter models

Abstract

Abstract. Soil organic matter (SOM) turnover models predict changes in SOM due to management and environmental factors. Their initialization remains challenging as partitioning of SOM into different hypothetical pools is intrinsically linked to model assumptions. Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) provides information on SOM quality and could yield a measurable pool-partitioning proxy for SOM. This study tested DRIFTS-derived SOM pool partitioning using the Daisy model. The DRIFTS stability index (DSI) of bulk soil samples was defined as the ratio of the area below the aliphatic absorption band (2930 cm−1) to the area below the aromatic–carboxylate absorption band (1620 cm−1). For pool partitioning, the DSI (2930 cm−1 ∕ 1620 cm−1) was set equal to the ratio of fast-cycling ∕ slow-cycling SOM. Performance was tested by simulating long-term bare fallow plots from the Bad Lauchstädt extreme farmyard manure experiment in Germany (Chernozem, 25 years), the Ultuna continuous soil organic matter field experiment in Sweden (Cambisol, 50 years), and 7 year duration bare fallow plots from the Kraichgau and Swabian Jura regions in southwest Germany (Luvisols). All experiments were at sites that were agricultural fields for centuries before fallow establishment, so classical theory would suggest that a steady state can be assumed for initializing SOM pools. Hence, steady-state and DSI initializations were compared, using two published parameter sets that differed in turnover rates and humification efficiency. Initialization using the DSI significantly reduced Daisy model error for total soil organic carbon and microbial carbon in cases where assuming a steady state had poor model performance. This was irrespective of the parameter set, but faster turnover performed better for all sites except for Bad Lauchstädt. These results suggest that soils, although under long-term agricultural use, were not necessarily at a steady state. In a next step, Bayesian-calibration-inferred best-fitting turnover rates for Daisy using the DSI were evaluated for each individual site or for all sites combined. Two approaches significantly reduced parameter uncertainty and equifinality in Bayesian calibrations: (1) adding physicochemical meaning with the DSI (for humification efficiency and slow SOM turnover) and (2) combining all sites (for all parameters). Individual-site-derived turnover rates were strongly site specific. The Bayesian calibration combining all sites suggested a potential for rapid SOM loss with 95 % credibility intervals for the slow SOM pools' half-life being 278 to 1095 years (highest probability density at 426 years). The credibility intervals of this study were consistent with several recently published Bayesian calibrations of similar two-pool SOM models, i.e., with turnover rates being faster than earlier model calibrations suggested; hence they likely underestimated potential SOM losses.

Mineral and Climatic Controls Over Soil Organic Matter Stability Across the Tibetan Alpine Permafrost Region

“…The disk was then scanned with the FTIR spectroscopy (Tensor‐27, Bruker, Germany) as an average of 50 scans over the spectral range of 4,000–400 cm −1 at 4 cm −1 resolution (wavenumber). Based on the FTIR relative absorption spectra (Figure S7 in Supporting Information S1), we chose the peak at ∼1,620 cm −1 attributed to the stretching vibration of C=C from aromatics and C=O from carboxylate and amides, and the peaks at ∼2,850 and ∼2,920 cm −1 corresponding to the stretching vibration of C‐H from aliphatic ‐CH 3 and ‐CH 2 ‐ groups (Demyan et al., 2012; Flanagan et al., 2020; Laub et al., 2020; Zaccone et al., 2007). Given that C=C from aromatics and C=O from carboxylate and amides are more recalcitrant than C‐H from aliphatic ‐CH 3 and ‐CH 2 ‐ groups (Demyan et al., 2012; Laub et al., 2020), we calculated the ratio of peak value at 1,620 cm −1 to the sum of peak values at 2,850 cm −1 and 2,920 cm −1 (abbreviated as r1620/(2850 + 2920)) to characterize substrate quality.…”

Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India

“…Maximum absorbance were selected for specific peaks in the wavelength ranges of 2930-2850 cm −1 (aliphatic compounds), 1660-1600 cm −1 (aromatic compounds), 1510-1500 cm −1 (lignin), 1260-1210 cm −1 (cellulose) and 893-852 cm −1 (aromatic compounds) according to ranges identified previously (e.g. Chatterjee et al, 2012;Laub et al, 2020;Yeasmin et al, 2017). Two ratios of aliphatic/aromatic (using the aromatic peaks at 1630 and 880 cm −1 respectively) and one of cellulose/lignin were calculated to assess qualitatively SOC dynamics with depth.…”

“…The SOC quality, here referring to its compound-specific composition (i. e. aliphatic, aromatic or cellulose-and lignin-like compounds) and decomposition state, is a good proxy for vegetation and SOC dynamics. It can be assessed by mid-infrared spectroscopy that is a semiquantitative, but highly efficient approach (Laub et al, 2020;Ramirez et al, 2021) in combination with organic carbon analyses ( 13 C, C:N).…”

Soil geochemistry as a driver of soil organic matter composition: insights from a soil chronosequence

“…Hence, SOM composition (the prevalence of certain biochemical compounds) may serve as a proxy for ecosystem properties and soil functioning, as it affects C cycling by the amount of energy provided to soil microorganisms (Nunan et al, 2015;Barré et al, 2016). Furthermore, the peak areas related to biochemical SOM compounds have been successfully used to parameterize fast-and slow-cycling SOM pools (Todd-Brown et al, 2013;Bailey et al, 2018;Laub et al, 2020;Baldock et al, 2021).…”