Multi‐objective calibration of RothC using measured carbon stocks and auxiliary data of a long‐term experiment in Switzerland

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. Highlights Multi‐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.

Simulating soil organic C dynamics in managed grasslands under humid temperate climatic conditions

“…In particular, the estimation of below-ground C inputs is another major source of uncertainty for SOC modelling (Keel et al, 2017). Indeed, belowground C inputs depend on multiple factors, including site-specific agronomic practices and the response of plant genotypes to them, and direct measurements of belowground C inputs is a challenging issue (Cagnarini et al, 2019). Moreover, the use of pedotransfer equations for initialising SOC pools, as an alternative for soil physical fractionation, may represent another source of uncertainty (Van Looy et al, 2017).…”

“…Studies using RothC for grassland ecosystems implied specific initialization (Liu et al, 2011;Nemo et al, 2017) using information from long term grassland experiments (Cagnarini et al, 2019). Despite the number of possible interactions in grassland systems, e.g.…”

Estimating soil organic carbon changes in managed temperate moist grasslands with RothC

“…In particular, the estimation of below-ground C inputs is another major source of uncertainty for SOC modelling [97]. Indeed, belowground C inputs depend on multiple factors, including site-specific agronomic practices and the response of plant genotypes to them, and direct measurements of belowground C inputs is a challenging issue [34]. For instance, if we estimate R:S ratio according to Eq (5) with the measured values in Oensingen site, we found close values of 1.9 and 1.5, respectively.…”

“…In general, RothC showed a good performance under grassland ecosystems [30,31]. Studies using RothC for grassland ecosystems have required specific initialization [32,33] using information from long term grassland experiments [34]. On the other hand, there are also several limitations to RothC particularly under managed moist grasslands.…”

Editorial for soil organic matter 2019 special issue

“…The final three papers focus on important aspects of SOM models and their development. Cagnarini et al () used data from a long‐term experiment to test a multi‐objective parameter approach for the RothC model, based on microbial biomass, respiration and SOM fractionation data. Estimation of belowground inputs of C was recognized as a major challenge to SOM models.…”