Long term plot scale variability to explore Soil Carbon turnover modeling uncertainties: a C-TOOL implementation

C-TOOL is a simple flexible SOM turnover model competitive with other alternatives that often demands more input information. As most SOC turnover models, C-TOOL rely on plant C inputs derived from measured agricultural yields using simple allometric equations that establish the relation between C inputs and crop yields. The main sources of uncertainty in C turnover models rely on C input calculations and parametrization of initial pool distribution. Since the main output is the temporal dynamic SOC stock, we should refer to long-term data that can be challenging to obtain to appreciate meaningful changes. Nevertheless, in Denmark an experiment from 1981 to 2019 examined the effect of annual additions of different levels of C inputs on SOC storage. This experiment produced a robust validation platform for soil C modelling, permitting to account not only the temporal variability but also the variability beneath each treatment. In this work we implemented the C-TOOL model at a plot level, based on this precise data from spring barley straw disposal at plot level in order to explore SOC turnover modeling uncertainties and validate it performance. We performed a variance-based sensitivity analysis to evaluate how sensitive are C inputs calculations to allometric parametrization using the distributional information on spring barley allometrics (grain yield, harvest index and root biomass and root exudates). After exploring alternatives on the model parametrization related to allometric, initial soil C conditions and initialization period, we arranged a simulation design to test all the possible combinations to assess how accurate is the model in predicting the temporal plot variability of SOC. To be able to perform the numerous scenarios we worked on a feasible computational implementation trough R. Finally, we studied how dependent is the lack of fit to the alternative parametrization and the spatiotemporal variability of field conditions trough a variance component analysis. From the C input variance-based sensitivity analysis we conclude that root exudates and root biomass are the most sensitive parameters. Validation results show that C-TOOL was able to accurately describe the temporal dynamic of SOC in the topsoil due to non-significant differences between simulated and observed data. All the alternative parametrizations register a prediction error below 15 % related to the mean showing differences between observed and predicted between 3.60 and 6.46 Mg C/ha in the top 20 cm depth. This lack of fit was mainly explained by the spatiotemporal (year and block) variability rather than the parametrization alternatives tested. Nevertheless, we conclude that is relevant to focus on the initial soil C condition parametrization but not the in-situ measurements of harvest index. Besides, using a fixed amount of root biomass for spring barley presented better than using the standard allometrics. Further studies dive into a global sensitivity analysis on the multivariate variability distribution of all the inputs involved to get to a robust uncertainty estimation.