Insights into the controlling factors of soil organic carbon (SOC) stock variation are necessary both for our scientific understanding of the terrestrial carbon balance and to support policies that intend to promote carbon storage in soils to mitigate climate change. In recent years, complex statistical and algorithmic tools from the field of machine learning have become popular for modelling and mapping SOC stocks over large areas. In this paper, we report on the development of a statistical method for interpreting complex models, which we implemented for the study of SOC stock variation. We fitted a random forest machine learning model with 2206 measurements of SOC stocks for the 0–50 cm depth interval from mainland France and used a set of environmental covariates as explanatory variables. We introduce Shapley values, a method from coalitional game theory, and use them to understand how environmental factors influence SOC stock prediction: what is the functional form of the association in the model between SOC stocks and environmental covariates, and how does the covariate importance vary locally from one location to another and between carbon-landscape zones? Results were validated both in light of the existing and well-described soil processes mediating soil carbon storage and with regards to previous studies in the same area. We found that vegetation and topography were overall the most important drivers of SOC stock variation in mainland France but that the set of most important covariates varied greatly among locations and carbon-landscape zones. In two spatial locations with equivalent SOC stocks, there was nearly an opposite pattern in the individual covariate contribution that yielded the prediction – in one case climate variables contributed positively, whereas in the second case climate variables contributed negatively – and this effect was mitigated by land use. We demonstrate that Shapley values are a methodological development that yield useful insights into the importance of factors controlling SOC stock variation in space. This may provide valuable information to understand whether complex empirical models are predicting a property of interest for the right reasons and to formulate hypotheses on the mechanisms driving the carbon sequestration potential of a soil.

中文翻译：

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Shapley 值揭示了土壤有机碳储量预测的驱动因素

深入了解土壤有机碳 (SOC) 储量变化的控制因素对于我们对陆地碳平衡的科学理解以及支持旨在促进土壤碳储存以减缓气候变化的政策都是必要的。近年来，机器学习领域的复杂统计和算法工具在大面积 SOC 库存建模和映射方面变得流行起来。在本文中，我们报告了用于解释复杂模型的统计方法的开发，我们为研究 SOC 库存变化而实施了该方法。我们用法国大陆 0-50 厘米深度间隔的 2206 个 SOC 库存测量值拟合了一个随机森林机器学习模型，并使用一组环境协变量作为解释变量。我们介绍 Shapley 值，一种来自联合博弈论的方法，并使用它们来了解环境因素如何影响 SOC 储量预测：模型中 SOC 储量与环境协变量之间关联的函数形式是什么，以及协变量重要性如何从一个位置到另一个位置局部变化另一个和碳景观区之间？根据介导土壤碳储存的现有和充分描述的土壤过程以及同一地区先前的研究，对结果进行了验证。我们发现植被和地形总体上是法国大陆 SOC 库变化的最重要驱动因素，但最重要的协变量集在不同地点和碳景观带之间差异很大。在具有等效 SOC 库存的两个空间位置，在产生预测的个体协变量贡献中几乎存在相反的模式——在一种情况下，气候变量贡献积极，而在第二种情况下，气候变量贡献消极——土地利用减轻了这种影响。我们证明了 Shapley 值是一种方法论的发展，它对控制空间 SOC 库存变化的因素的重要性产生了有用的见解。这可能提供有价值的信息，以了解复杂的经验模型是否出于正确的原因预测感兴趣的特性，并就驱动土壤固碳潜力的机制提出假设。而在第二种情况下，气候变量产生了负面影响——土地利用减轻了这种影响。我们证明了 Shapley 值是一种方法论的发展，它对控制空间 SOC 库存变化的因素的重要性产生了有用的见解。这可能提供有价值的信息，以了解复杂的经验模型是否出于正确的原因预测感兴趣的特性，并就驱动土壤固碳潜力的机制提出假设。而在第二种情况下，气候变量产生了负面影响——土地利用减轻了这种影响。我们证明了 Shapley 值是一种方法论的发展，它对控制空间 SOC 库存变化的因素的重要性产生了有用的见解。这可能提供有价值的信息，以了解复杂的经验模型是否出于正确的原因预测感兴趣的特性，并就驱动土壤固碳潜力的机制提出假设。