Abstract. Partitioning soil organic carbon (SOC) in fractions with different biogeochemical stability is useful to better understand and predict SOC dynamics and provide information related to soil health. Multiple SOC partition schemes exist, but few of them can be implemented on large sample sets and therefore be considered relevant options for soil monitoring. The well-established particulate organic carbon (POC) vs. mineral-associated organic carbon (MAOC) physical fractionation scheme is one of them. Introduced more recently, Rock-Eval® thermal analysis coupled with the PARTYSOC machine learning model can also fractionate SOC into active (Ca) and stable SOC (Cs). A debate is emerging as to which of these methods should be recommended for soil monitoring. To investigate the complementarity or redundancy of these two fractionation schemes, we compared the quantity and environmental drivers of SOC fractions obtained on an unprecedented dataset from mainland France. About 2000 topsoil samples were recovered all over the country, presenting contrasting land cover and pedoclimatic characteristics, and analysed. We found that the environmental drivers of the fractions were clearly different, the more stable MAOC and Cs fractions being mainly driven by soil characteristics, whereas land cover and climate had a greater influence on more labile POC and Ca fractions. The stable and labile SOC fractions provided by the two methods strongly differed in quantity (MAOC/Cs=1.88 ± 0.46 and POC/Ca=0.36 ± 0.17; n=843) and drivers, suggesting that they correspond to fractions with different biogeochemical stability. We argue that, at this stage, both methods can be seen as complementary and potentially relevant for soil monitoring. As future developments, we recommend comparing how they relate to indicators of soil health such as nutrient availability or soil structural stability and how their measurements can improve the accuracy of SOC dynamics models.

中文翻译：

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研究土壤热碳组分和物理有机碳组分的互补性


摘要。将土壤有机碳 （SOC） 划分为具有不同生物地球化学稳定性的组分有助于更好地理解和预测 SOC 动态并提供与土壤健康相关的信息。存在多种 SOC 分配方案，但其中很少有可以在大型样本集上实施，因此被视为土壤监测的相关选项。成熟的颗粒有机碳 （POC） 与矿物伴生有机碳 （MAOC） 物理分馏方案就是其中之一。最近推出的 Rock-Eval® 热分析与 PARTYSOC 机器学习模型相结合，还可以将 SOC 分为活性 （Ca） 和稳定 SOC （Cs）。关于应该推荐这些方法中的哪一种进行土壤监测，一场争论正在出现。为了研究这两种分馏方案的互补性或冗余性，我们比较了在法国大陆前所未有的数据集上获得的 SOC 馏分的数量和环境驱动因素。在全国各地回收了大约 2000 个表层土壤样本，呈现出对比鲜明的土地覆盖和土壤气候特征，并进行了分析。我们发现馏分的环境驱动因素明显不同，更稳定的 MAOC 和 Cs 馏分主要由土壤特性驱动，而土地覆盖和气候对更不稳定的 POC 和 Ca 馏分的影响更大。两种方法提供的稳定和不稳定的 SOC 组分在数量 （MAOC/Cs=1.88 ± 0.46 和 POC/Ca=0.36 ± 0.17;n=843） 和驱动因素上差异很大，表明它们对应于具有不同生物地球化学稳定性的组分。我们认为，在这个阶段，这两种方法可以被视为互补的，并且可能与土壤监测相关。 作为未来的发展，我们建议比较它们与土壤健康指标（如养分可用性或土壤结构稳定性）的关系，以及它们的测量如何提高 SOC 动力学模型的准确性。