生物介导的衰减率对模拟农业环境中土壤碳封存的影响
The effect of biologically mediated decay rates on modelling soil carbon sequestration in agricultural settings
摘要：
微生物生物量碳（MBC）是土壤有机碳中最活跃的组分，是土壤活性碳的重要组成部分，对陆地生态系统的生物地球化学过程起着重要的调控作用。文献中的一些研究忽略了微生物种群增长对碳分解速率的影响。实际上，我们可以预期分解率应该与土壤中的微生物数量有关，并且与微生物生物量库的大小呈正相关。在这项研究中，我们探讨了微生物种群的增长对模拟土壤固碳的准确性的影响，通过开发和比较两个土壤碳模型，考虑承载能力和限制微生物库的增长。我们将我们的模型应用于三个数据集，两个小数据集和一个大数据集，并通过两种模型选择方法选择具有最佳预测性能的最佳模型。通过这种分析，我们揭示了常用的复杂土壤碳模型在存在小型和大型时间序列数据集的情况下可能会过度拟合，而我们更简单的模型可以产生更准确的预测。我们的结论是，在土壤碳模型中考虑微生物种群的增长，提高了模型在大数据集存在下的准确性。
英文摘要：
Microbial biomass carbon (MBC), a crucial soil labile carbon fraction, is the most active component of the soil organic carbon (SOC) that regulates bio-geochemical processes in terrestrial ecosystems. Some studies in the literature ignore the effect of microbial population growth on carbon decomposition rates. In reality, we might expect that the decomposition rate should be related to the population of microbes in the soil and have a positive relationship with the size of the microbial biomass pool. In this study, we explore the effect of microbial population growth on the accuracy of modelling soil carbon sequestration by developing and comparing two soil carbon models that consider a carrying capacity and limit to the growth of the microbial pool. We apply our models to three datasets, two small and one large datasets, and we select the best model in terms of having the best predictive performance through two model selection methods. Through this analysis we reveal that commonly used complex soil carbon models can over-fit in the presence of both small and large time-series datasets, and our simpler model can produce more accurate predictions. We conclude that considering the microbial population growth in a soil carbon model improves the accuracy of a model in the presence of a large dataset.
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