Abstract

Interactions between model parameters and low spatiotemporal resolution of available data mean that conventional soil organic carbon (SOC) models are often affected by equifinality, with consequent uncertainty in SOC forecasts. Estimation of belowground C inputs is another major source of uncertainty in SOC modelling. Models are usually calibrated on SOC stocks and fluxes from long-term experiments (LTEs), whereas other point data are not used for constraining the model parameters. We used data from an agricultural long-term (>65 years) fertilization experiment to test a multi-objective parameter estimation approach on the RothC model, combining SOC data from different fertilization treatments with microbial biomass, basal respiration and Zimmermann's fractions data. We also compared two methods to estimate the belowground C inputs: a conventional scaling of belowground biomass from crop harvest yield and an alternative approach based on constant belowground C for cereals measured experimentally in the field. The resulting posterior parameter distributions still suffered from some equifinality; the most stable C pool kinetic constants and composition of exogenous organic matter were the most sensitive parameters. The use of fixed belowground C inputs for cereals improved the model performance, reducing the importance of treatment-specific parameters and processes. The introduction of microbial biomass and basal respiration data was effective for increasing determination of the calibration, but also suggested a change in the model structure: the microbial biomass pool, which is proportional to the C inputs in the traditional models, could be represented by different microbial physiology functions. HighlightsMulti-objective calibration with SOC and microbial or soil fractionation data can reduce model uncertainty. We compared different methods to estimate belowground C inputs on a long-term trial in Switzerland. Fixed belowground C inputs measured in the field gave the best model performance. Microbial data can improve model calibration, but a change in model structure is suggested.

Keywords

basal respiration; belowground C inputs; microbial biomass; SOM dynamics; Zimmermann's fractions

Published in

European Journal of Soil Science
2019, Volume: 70, number: 4, pages: 819-832
Publisher: WILEY

SLU Authors

• Menichetti, Lorenzo

  • Department of Ecology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Soil Science


Publication identifier

DOI: https://doi.org/10.1111/ejss.12802

Permanent link to this page (URI)

https://res.slu.se/id/publ/101126