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

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 rat...

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Bibliographic Details
Main Authors: Jordon, MW, Smith, P
Format: Journal article
Language:English
Published: Elsevier 2022
Description
Summary: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.