CQUESST: A dynamical stochastic framework for predicting soil-carbon sequestration

A statistical framework we call CQUESST (Carbon Quantification and Uncertainty from Evolutionary Soil STochastics), which models carbon sequestration and cycling in soils, is applied to a long-running agricultural experiment that controls for crop type, tillage, and season. The experiment, known as the Millenium Tillage Trial (MTT), ran on 42 field-plots for 10 years from 2000–2010; here CQUESST is used to model soil carbon dynamically in six pools, in each of the 42 agricultural plots, and on a monthly time step for a decade. We show how CQUESST can be used to estimate soil-carbon cycling rates under different treatments. Our methods provide much-needed statistical tools for quantitatively inferring the effectiveness of different experimental treatments on soil-carbon sequestration. The decade-long data are of multiple observation types, and these interacting time series are ingested into a fully Bayesian model that has a dynamic stochastic model of multiple pools of soil carbon at its core. CQUESST’s stochastic model is motivated by the deterministic RothC soil-carbon model based on nonlinear difference equations. We demonstrate how CQUESST can estimate soil-carbon fluxes for different experimental treatments while acknowledging uncertainties in soil-carbon dynamics, in physical parameters, and in observations. An important outcome of our modeling is the inference of cropping-specific decay rates, with evidence suggesting that soil carbon decay-rates vary as a function of land management practices. CQUESST is implemented efficiently in the probabilistic programming language Stan using its MapReduce parallelization, and it scales well for large numbers of field-plots, using software libraries that allow for computation to be shared over multiple nodes of high-performance computing clusters.