Abstract Agroforestry is a form of productive land management that combines trees or bushes with annual crops or pasture, and it can bring benefits in terms of food security and increased carbon (C) sequestration compared with conventional agriculture. But agroforestry as a structured form of agronomic management is relatively new compared with well-established and widespread agronomic systems. Consequently, there is a lack of data and few models of soil organic carbon (SOC) have been developed specifically for agroforestry systems. Also, agroforestry SOC sequestration data measured in field experiments are often reported only as average linear sequestration rates over the study period. This approach, equivalent to zero-order kinetics, makes it difficult to compare results since, in reality, SOC sequestration rates are variable over time and change depending on the duration of measurements. Sequestration rates are also strongly dependent on former C stocks in the soil, further hampering comparisons between agroforestry systems established on different former land uses. To describe the SOC stocks variation over time, researchers often employ models considering at least first-order kinetics. This approach can take care of the two above mentioned issues, considering both the variation of the sequestration over time and the effect of previous land use. However, the variability of agroforestry systems makes applying these models more challenging compared to simpler agricultural systems. To deal with this problem we propose to use detailed uncertainty estimation methods, based on stochastic calibrations that can deal with broad probability distributions. To do so, we adapted a first-order compartmental SOC model to agroforestry systems. It was calibrated within a Bayesian framework on global agroforestry data. Compared to linear coefficients, the model (ICBMAgroforestry) estimates equilibrium SOC stocks of different agroforestry systems probabilistically and is providing uncertainty bounds. These values are independent of initial land use and time duration of the experiments. ICBMAgroforestry can be used for rapid assessment and comparison of the maximum potential SOC stocks for different agroforestry systems and climatic zones. In this study, we could use our approach to estimate the global maximum C that can be sequestered by agroforestry systems at equilibrium, which ranged between 156 and 263 Mg C ha−1 on average, above but comparable with similar estimates for simpler agricultural systems.

中文翻译：

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用于估计农林复合系统中平衡土壤有机碳固存的贝叶斯建模框架

摘要 农林业是一种将树木或灌木与一年生作物或牧场相结合的生产性土地管理形式，与传统农业相比，它可以在粮食安全和增加碳 (C) 封存方面带来好处。但与成熟和广泛的农艺系统相比，农林业作为一种结构化的农艺管理形式相对较新。因此，缺乏数据，并且专门为农林业系统开发的土壤有机碳 (SOC) 模型很少。此外，在田间试验中测量的农林业 SOC 封存数据通常仅报告为研究期间的平均线性封存率。这种方法相当于零级动力学，因此很难比较结果，因为实际上，SOC 封存率随时间变化并根据测量持续时间而变化。封存率也强烈依赖于土壤中以前的碳库，进一步阻碍了在不同的以前土地用途上建立的农林业系统之间的比较。为了描述 SOC 储量随时间的变化，研究人员通常采用至少考虑一阶动力学的模型。这种方法可以解决上述两个问题，同时考虑到封存随时间的变化和先前土地利用的影响。然而，与更简单的农业系统相比，农林业系统的可变性使得应用这些模型更具挑战性。为了解决这个问题，我们建议使用详细的不确定性估计方法，基于可以处理广泛概率分布的随机校准。为此，我们将一阶区室 SOC 模型应用于农林业系统。它在全球农林业数据的贝叶斯框架内进行了校准。与线性系数相比，该模型 (ICBMAgroforestry) 以概率方式估计不同农林复合系统的平衡 SOC 储量，并提供不确定性界限。这些值与初始土地利用和实验持续时间无关。ICBMAgroforestry 可用于快速评估和比较不同农林业系统和气候区的最大潜在 SOC 储量。在这项研究中，我们可以使用我们的方法来估计平衡状态下农林业系统可以隔离的全球最大 C，