Abstract. Changes in soil organic carbon (SOC) stocks are a major
source of uncertainty for the evolution of atmospheric CO2
concentration during the 21st century. They are usually simulated by
models dividing SOC into conceptual pools with contrasted turnover times.
The lack of reliable methods to initialize these models, by correctly
distributing soil carbon amongst their kinetic pools, strongly limits the
accuracy of their simulations. Here, we demonstrate that PARTYSOC, a
machine-learning model based on Rock-Eval® thermal analysis,
optimally partitions the active- and stable-SOC pools of AMG, a simple and well-validated SOC dynamics model, accounting for effects of soil management
history. Furthermore, we found that initializing the SOC pool sizes of AMG
using machine learning strongly improves its accuracy when reproducing the
observed SOC dynamics in nine independent French long-term agricultural
experiments. Our results indicate that multi-compartmental models of SOC
dynamics combined with a robust initialization can simulate observed SOC
stock changes with excellent precision. We recommend exploring their
potential before a new generation of models of greater complexity becomes
operational. The approach proposed here can be easily implemented on soil
monitoring networks, paving the way towards precise predictions of SOC stock
changes over the next decades.