Abstract
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.
Highlights
Soil organic carbon (SOC) plays an important role in sustaining soil functions and associated soil ecosystem services worldwide (IPCC, 2019)
A slight discrepancy was observed for higher stable-soil organic carbon (SOC) proportion values, the results validate our hypothesis showing that the centennially stable-SOC proportion determined by Rock-Eval® thermal analysis and the PARTYSOC machine-learning model built on fully independent data provides a good estimate of the optimal stable-SOC proportion of the AMG model for unrelated French agricultural soils
The method appears to be reliable, since additional Rock-Eval® measurements on topsoil samples from intermediate and final dates of the longterm experiments (LTEs) showed that the PARTYSOC predictions of the centennially stable-SOC content remained remarkably constant during the experimental period at most sites (Supplement Fig. S5)
Summary
Soil organic carbon (SOC) plays an important role in sustaining soil functions and associated soil ecosystem services worldwide (IPCC, 2019). 10 Pg C yr−1) may double the global annual anthropogenic CO2 emissions, while an equivalent increase may compensate them (Balesdent and Arrouays, 1999) This is the concept behind the 4 per 1000 initiative (Rumpel et al, 2018) that aims at increasing SOC stocks to fight global warming while ensuring food security, two Sustainable Development Goals of the United Nations (UN General Assembly, 2015). This initiative and other political headway have put the question of managing SOC stocks and assessing the global SOC sequestration potential at the top of political and scientific agendas (Vermeulen et al, 2019; FAO, 2019; Amelung et al, 2020). The prediction of SOC stock changes remains very uncertain, which makes soils a major source of uncertainty for the evolution of atmospheric CO2 concentration (Todd-Brown et al, 2014; He et al, 2016; Shi et al, 2018)
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