Prospects and challenges in the use of models to estimate the influence of crop residue input on soil organic carbon in long-term experiments in Canada

• Published in: Geoderma Regional Vol. 30; p. e00534
• Main Authors: Thiagarajan, Arumugam, Liang, Chang, MacDonald, J. Douglas, Smith, Ward, VandenBygaart, A.J., Grant, Brian, Krobel, Roland, Janzen, Henry, Zhang, Tiequan, McConkey, Brian, Ma, Baoluo, Bremer, Eric, Yang, Xueming, Cerkowniak, Darrel, Fan, Jianling
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2022
• Subjects: Chernozems; Crop residues; Ensemble models; Gleysols; Luvisols; Model comparison; SOC model; Gleysols; Luvisols; Crop residues; Chernozems; Ensemble models; Model comparison; SOC model
• ISSN: 2352-0094; 2352-0094
• DOI: 10.1016/j.geodrs.2022.e00534

Crop residue input plays a central role in regulating soil organic carbon (SOC) storage. Ten long-term field experiments were used to ascertain the changes in SOC in response to differing rates of crop residues. The amount of C input from crop residues varied significantly between and within sites due to soil-environmental conditions, management and cropping systems. Initial SOC stocks ranged from 45 to 165 t C ha−1 in the 0–30 cm soil depth. Four soil C models, Campbell, ICBM, IPCC Tier 2 steady state (IPCC) and RothC with their default parameterization were used to simulate the SOC. Two model ensemble approaches, a mean (Ens_Avg) and a weighted mean (Ens_Weighted) of the model predictions were also included in the evaluation. Individual model performance was evaluated on the model's ability to capture the SOC stock and change in SOC (ΔSOC). Across sites, the Ens_Weighted approach had the lowest overall RMSE for total SOC (5.2 t C ha−1) and ΔSOC (5.5 t C ha−1) followed by Ens_Avg. Ensemble models which had the lowest bias (PE) and model prediction error (d-index). Within individual models, Campbell and IPCC performed better than other models with the lowest RMSE of 5.8 and 6.3 (t C ha−1) for SOC stocks. In general, ICBM and Campbell underestimated, and RothC overestimated the ΔSOC per unit of C input to the soil with the IPCC model close to the average observations. The use of weighted and average ensemble approaches reduced estimation errors. Nonetheless, our results indicate that regional calibration and validation is warranted for the effective quantification of regional SOC dynamics. •Changes in SOC in 10 experimental sites were simulated with 4 models.•Addition of crop residues increased the SOC or slowed down the loss in SOC.•Campbell and IPCC Tier 2 steady state models performed better than RothC and ICBM with default parameterization.•Ensemble approach outperformed the individual models.