Reevaluating multi-pool first-order kinetic models for fitting soil incubation data
Soil incubation experiments are frequently conducted to investigate soil carbon (C) cycling and its response to environmental changes. Multi-pool first-order models, which apply constant kinetic rate parameters for each pool, are widely used for fitting these datasets due to their simplicity. Howeve...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Geoderma |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0016706125000564 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Soil incubation experiments are frequently conducted to investigate soil carbon (C) cycling and its response to environmental changes. Multi-pool first-order models, which apply constant kinetic rate parameters for each pool, are widely used for fitting these datasets due to their simplicity. However, their ability to accurately represent instantaneous C effluxes is often overlooked, as cumulative effluxes are typically prioritized. Here, we calibrated a three-pool first-order model using a 384-day incubation dataset with fluctuating CO2 and CH4 effluxes. Despite constantly good performance for cumulative C effluxes (R2 = 0.99–1.00), the R2 values vary greatly with experimental conditions for instantaneous effluxes (R2 = 0.36–0.93), depending on the frequency of aerobic-anaerobic shifts. The results are regardless of the objective function used for model fitting (i.e., maximizing R2 for instantaneous or cumulative C effluxes). Compared to cumulative effluxes, we propose emphasizing instantaneous effluxes and calibrating additional variables in soil C modeling. This strategy can offer more insights for improving predictions and enhancing understanding of soil C-climate feedbacks under dynamic environmental conditions. |
|---|---|
| ISSN: | 1872-6259 |