Estimating soil carbon sequestration potential and approximating optimal management policies

Abstract

The impact of a management intervention on the soil organic carbon (SOC) stored in a given volume of soil is moderated by features that determine that soil's sequestration potential under that intervention. To maximize total SOC sequestration cost efficiently, interventions should be targeted to soils with the highest responses and lowest intervention costs. We present a framework for estimating SOC sequestration potentials and approximating efficient management policies. We review relevant sources of measurement uncertainty and formalize policy choice using potential outcomes. An optimal sequestration policy can be approximated by modeling SOC measurements as functions of covariates within each treatment group, using the fitted models to estimate SOC sequestration potential for each plot, and finding the policy that maximizes the average of those estimates. The modeling can use linear regression or other algorithms to learn relationships between features and SOC sequestration potential. We demonstrate this method using data from a study of compost amendments applied to California rangelands. We find that the plots exhibit treatment effects moderated by baseline SOC -- so targeting amendments to plots with lower baseline SOC would increase overall SOC sequestration rates. We evaluate these methods further in simulated field experiments. Refined policy estimates sequestered more SOC than uniform application of the single management policy estimated to have the largest average treatment effect, especially when SOC sequestration potential could be predicted from observed features. We conclude by discussing baseline SOC moderation, observational studies, inference, cost models, and broader policy uncertainties.