arXiv subjects

Daniel Pagendam

Publications and source records attributed to Daniel Pagendam.

2 recordsLinked to original sources

arXiv2022

The effect of biologically mediated decay rates on modelling soil carbon sequestration in agricultural settings

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.

stat.AP↗

arXiv2021

Innovative Approaches in Soil Carbon Sequestration Modelling for Better Prediction with Limited Data

Soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the Paris and Kyoto protocol agreements. Land managers typically rely on computationally complex models fit using sparse datasets to make these accounts and predictions. The model complexity and sparsity of the data can lead to over-fitting, leading to inaccurate results when making predictions with new data. Modellers address over-fitting by simplifying their models and reducing the number of parameters, and in the current context this could involve neglecting some soil organic carbon (SOC) components. In this study, we introduce two novel SOC models and a new RothC-like model and investigate how the SOC components and complexity of the SOC models affect the SOC prediction in the presence of small and sparse time series data. We develop model selection methods that can identify the soil carbon model with the best predictive performance, in light of the available data. Through this analysis we reveal that commonly used complex soil carbon models can over-fit in the presence of sparse time series data, and our simpler models can produce more accurate predictions. The published version of this study is available in Scientific Reports (https://www.nature.com/articles/s41598-024-53516-z/<10.1038/s41598-024-53516-z>)

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