Publication | Open Access
Modelling soil carbon stocks following reduced tillage intensity: A framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe
28
Citations
36
References
2022
Year
North-west EuropeEngineeringLand UseAgricultural EconomicsSoil Carbon ChangeEarth ScienceSocial SciencesReduced Tillage IntensitySoil Carbon StocksSoil Carbon DynamicsTillage ToolCarbon SequestrationBiogeochemistryGeographySoil Biogeochemical CyclingSoil Carbon PoolsSoil Carbon CycleSoil ModelingSoil Carbon Sequestration
Simulating cropland soil carbon changes following a reduction in tillage intensity is necessary to determine the utility of this management practice in climate change mitigation. In instances where reduced or no tillage increases soil carbon stocks, this is typically due to reduced decomposition rates of plant residues. Although some soil carbon models contain a priori decomposition rate modifiers to account for tillage regime, these are typically not calibrated to specific climatic regions, and none are currently available for the Rothamsted Carbon Model (RothC). Here, we present a modelling framework to estimate a tillage rate modifier (TRM) for the decomposition rate constants in RothC-26.3 which determine decay between soil carbon pools. We demonstrate this for north-west Europe, using published data assembled through a recent systematic review with propagation of error from input parameters throughout the framework. The small magnitude of soil carbon change following a reduction in tillage intensity in this region is reflected in our TRM estimates for no-till of 0.95, with 95% Credible Intervals [0.91, 1.00], and reduced tillage of 0.93 [0.90, 0.97], relative to conventional high-intensity tillage with a default TRM of 1. These TRMs facilitate realistic simulation of soil carbon dynamics following a reduction of tillage intensity using RothC, and our simple, transparent, and repeatable modelling framework is suitable for application in other climatic regions using input data generalisable to the context of interest.
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