Soil carbon simulation confounded by different pool initialisation

Abstract

Process-based models of soil organic carbon (C) define soil organic matter by conceptual C pools. In the Roth C model these pools are represented by the particulate, humus and resistant organic C fractions (POC, HOC, and ROC). Here, we used three different sets of estimates of the C fractions to initialise Roth C and assessed their effect on the model predictions of the 0–0.3 m C stocks in a 30-year simulation. We estimated the stocks of POC, HOC and ROC with (1) pedotransfer functions (PTF), (2) proximal sensing, and (3) digital soil mapping with data from sensing. We conducted experiments in three C estimation areas of a cattle grazing farm in Australia. We found that in all cases, the model predicted increasing total organic C (TOC) stocks by up to 15.2 Mg C ha−1. Using the PTF estimates, the uncertainty in the model predictions ranged from 4.9 to 7.1 Mg C ha−1. Using proximal sensing and digital soil mapping, the uncertainty decreased to 2.3–3.6 Mg C ha−1 and 3.2–5.4 Mg C ha−1, respectively. Our results show that model initialisation with proximal sensing and digital soil maps well represent the spatial variation of TOC and the C pools, so that simulation of changes in C stocks were more reliable. Using the PTF estimates resulted in biased predictions that underestimated the TOC stocks by 2.0–4.5 Mg C ha−1 over the simulation period. Informing a soil C model with measurements is key to building confidence in model-predictions of TOC stocks, providing useful feedback to management practices.