Sequestering carbon into the soil can mitigate the atmospheric concentration of greenhouse gases, improving crop productivity and yield financial gains for farmers through the sale of carbon credits. In this work, we develop and evaluate advanced Bayesian methods for modelling soil carbon sequestration and quantifying uncertainty around predictions that are needed to fit more complex soil carbon models, such as multiple-pool soil carbon dynamic models. This paper demonstrates efficient computational methods using a one-pool model of the soil carbon dynamics previously used to predict soil carbon stock change under different agricultural practices applied at Tarlee, South Australia. We focus on methods that can improve the speed of computation when estimating parameters and model state variables in a statistically defensible way. This paper also serves as a tutorial on advanced Bayesian methods for fitting complex state-space models, which will be of interest to soil scientists and other environmental scientists more generally.

将碳固存到土壤中可以减轻大气中温室气体的浓度，提高农作物的生产力，并通过出售碳信用额为农民带来经济收益。在这项工作中，我们开发和评估先进的贝叶斯方法，以模拟土壤碳固存和量化不确定性周围的预测，以适应更复杂的土壤碳模型，例如多池土壤碳动力学模型。本文演示了一种有效的计算方法，该方法使用的是土壤碳动力学的单池模型，该模型先前用于预测在南澳大利亚塔里应用的不同农业实践下的土壤碳储量变化。我们专注于可以以统计上合理的方式估计参数和模型状态变量时可以提高计算速度的方法。