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. Soil organic matter (SOM) turnover models predict changesin SOM due to management and environmental factors. Their initializationremains challenging as partitioning of SOM into different hypothetical poolsis intrinsically linked to model assumptions. Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) provides information on SOMquality and could yield a measurable pool-partitioning proxy for SOM. Thisstudy tested DRIFTS-derived SOM pool partitioning using the Daisy model. TheDRIFTS stability index (DSI) of bulk soil samples was defined as the ratioof the area below the aliphatic absorption band (2930 cm −1 ) to thearea below the aromatic–carboxylate absorption band (1620 cm −1 ). Forpool partitioning, the DSI (2930 cm −1 ∕  1620 cm −1 ) was setequal to the ratio of fast-cycling  ∕  slow-cycling SOM. Performance was tested by simulating long-term bare fallow plots from the Bad Lauchstadt extremefarmyard manure experiment in Germany (Chernozem, 25 years), the Ultunacontinuous soil organic matter field experiment in Sweden (Cambisol, 50 years), and 7 year duration bare fallow plots from the Kraichgau and SwabianJura regions in southwest Germany (Luvisols). All experiments wereat sites that were agricultural fields for centuries before fallow establishment, so classicaltheory would suggest that a steady state can be assumed for initializing SOMpools. Hence, steady-state and DSI initializations were compared, using twopublished parameter sets that differed in turnover rates and humificationefficiency. Initialization using the DSI significantly reduced Daisy model errorfor total soil organic carbon and microbial carbon in cases where assuminga steady state had poor model performance. This was irrespective of theparameter set, but faster turnover performed better for all sites except forBad Lauchstadt. These results suggest that soils, although underlong-term agricultural use, were not necessarily at a steady state. In a nextstep, Bayesian-calibration-inferred best-fitting turnover rates for Daisyusing the DSI were evaluated for each individual site or for all sitescombined. Two approaches significantly reduced parameter uncertainty andequifinality in Bayesian calibrations: (1) adding physicochemical meaningwith the DSI (for humification efficiency and slow SOM turnover) and (2) combining all sites (for all parameters). Individual-site-derived turnoverrates were strongly site specific. The Bayesian calibration combining allsites suggested a potential for rapid SOM loss with 95 % credibilityintervals for the slow SOM pools' half-life being 278 to 1095 years (highestprobability density at 426 years). The credibility intervals of this studywere consistent with several recently published Bayesian calibrations ofsimilar two-pool SOM models, i.e., with turnover rates being faster thanearlier model calibrations suggested; hence they likely underestimatedpotential SOM losses.

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