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

• Published in: Biogeosciences 2020-03, Vol.17 (6), p.1393-1413
• Main Authors: Laub, Moritz, Demyan, Michael Scott, Nkwain, Yvonne Funkuin, Blagodatsky, Sergey, Kätterer, Thomas, Piepho, Hans-Peter, Cadisch, Georg
• Format: Article
• Language: English
• Subjects: Absorption; Absorption bands; Absorption spectra; Agricultural land; Agricultural management; Aliphatic compounds; Analysis; Analytical methods; Aromatic compounds; Bayesian analysis; Calibration; Carbon; Chemical plants; Computer simulation; Cycles; Employee turnover; Environmental factors; Environmental management; Experiments; Fourier transforms; Humification; Infrared spectroscopy; Intervals; Manures; Markvetenskap; Mathematical models; Microorganisms; Organic carbon; Organic matter; Organic soils; Parameter uncertainty; Partitioning; Probability theory; Proxy; Reflectance; Soil; Soil carbon; Soil microbiology; Soil organic matter; Soil Science; Soil stability; Soils; Spectroscopy; Spectrum analysis; Steady state; Uncertainty
• ISSN: 1726-4189; 1726-4170; 1726-4189
• DOI: 10.5194/bg-17-1393-2020

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