ALBERT

Description: The initialization of soil organic matter (SOM) turnover models has been a challenge for decades. Instead of using laborious and error prone size-density fractionation SOM pool partitioning, we propose the inexpensive, rapid, and non destructive Diffuse reflectance mid infrared Fourier transform spectroscopy (DRIFTS) technique on bulk soil samples to gain information on SOM pool partitioning from the spectra. Specifically, the DRIFTS stability index, defined as the ratio of aliphatic C-H (2930 cm−1) to aromatic C=C (1620 cm−1) stretching vibrations, was used to divide SOM into fast and slow cycling pools in the soil organic module of the DAISY model. Long-term bare fallow plots from Bad Lauchstädt (Chernozem, 25 years) and the Ultuna frame trial in Sweden (Cambisol, 50 years) were combined with bare fallow plots of 7 years duration in the Kraichgau and Swabian Jura region in Southwest Germany (Luvisols). All fields had been in agricultural use for centuries before fallow establishment, so classical theory would suggest an initial steady state of SOM, which was hence used to compare the performance of DAISY initializations against the newly established DRIFTS stability index. The test was done using two different published parameter sets (2.7 × 10−6 d−1, 1.4 × 10−4 d−1, 0.1 compared to 4.3 × 10−5 d−1, 1.4 × 10−4 d−1, 0.3 for the turnover rates of slow pool, fast pool and humification efficiency, respectively). The DRIFTS initialization of SOM pools significantly reduced model errors of poor performing model runs assuming steady state, irrespective of the turnover rates used, but the faster turnover parameter set fit better to all sites except Bad Lauchstädt. This suggests that soils under long-term agricultural use were not necessarily at steady state. A Bayesian calibration was applied in a next step to identify the best-fitting turnover rates for the DRIFTS stability index in DAISY, both for each site individually and for a combination of all sites. The two approaches which significantly reduced parameter uncertainty and equifinality were: (1) the addition of the physico-chemically based DRIFTS stability index, and (2) combining several sites into one Bayesian calibration, as derived turnover rates can be strongly site specific. The combination of all four sites showed that SOM is likely lost at relatively fast turnover rates with the 95 % credibility intervals of the slow SOM pools half life ranging from 278 to 1095 years, with 426 years as a value of highest probability density. The credibility intervals of this study were consistent with several recently published Bayesian calibrations of similar SOM models, all turnover rates were considerably faster than earlier models suggested. It is therefore likely that published turnover rates understimate the potential loss of SOM.

Description: 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.