Can inert pool models improve predictions of biochar long-term persistence in soils?

• Published in: Geoderma 2024-12, Vol.452, p.117093, Article 117093
• Main Authors: Li, Haichao, Azzi, Elias S., Sundberg, Cecilia, Karltun, Erik, Cederlund, Harald
• Format: Article
• Language: English
• Subjects: Biochar carbon modeling; Biochar persistence; Carbon sequestration; Environmental Sciences; Inert pool model; Markvetenskap; Miljövetenskap; Soil Science
• ISSN: 0016-7061; 1872-6259; 1872-6259
• DOI: 10.1016/j.geoderma.2024.117093

•The evaluated inert pool models overestimated biochar persistence in soil.•Commonly used exponential models underestimate biochar persistence in soil.•The power model appears to offer more reliable predictions for biochar persistence. The long-term persistence of biochar in soil is often predicted by extrapolating mineralization data from short-term laboratory incubations. Single first-order, double first-order, triple first-order and power models have been employed for this purpose, all of which have an inherent assumption that biochar is biodegradable. However, recent insights challenge this assumption by suggesting that a large fraction of biochar is inert. If so, it would make sense to reflect this in the models used, by incorporating an inert carbon (C) pool. We hypothesized that such inert pool models would fit better to incubation data than existing models and give more reliable long-term predictions. We evaluated this by fitting the models to data from a recently compiled extensive dataset of biochar incubations. The inclusion of an inert pool enhanced the model fits over first-order models in most cases. However, inert pool models overestimated biochar persistence compared to the measured outcomes. By contrast, the double first-order model, which has been the most widely used to date, underestimated biochar persistence even in the short term. The power model in general outperformed all other models and gave the most reliable predictions, although it was sensitive to increasing or fluctuating mineralization rates in the datasets.