Reactive transport modelling of greenhouse gas cycling and emissions from macroporous agricultural soils
Bibliographic record
Abstract
Agricultural lands are important anthropogenic sources of greenhouse gases (GHG) and play a vital role in affecting global climate change. GHGs (i.e., CO₂, N₂O and CH₄) are all produced (or consumed) as a result of microbial processes; however, the size of the concentrations and fluxes depends heavily on soil structure characteristics. Macroporous agricultural soils have a multitude of pore domains (e.g., macropores, soil matrix, soil aggregates) leading to varying degrees of fluid flow, solute and gas transport. Consequently, a spectrum of biogeochemical C-N processes may occur in macroporous soils that collectively, and coupled with flow and mass transport, determine GHG cycling. In this work, various modeling approaches were developed to assess the mechanisms of GHG production, consumption, transport and emissions, and interpret the simulation results in the context of observations at a field site in Ontario, Canada, featuring macroporous agricultural soil. The modeling approaches include a classical uniform reactive transport model, a dual-permeability reactive transport model with an emphasis on non-equilibrium gas transport and exchange, a discrete macropore model, and a more complex hybrid multi-domain model. Simulation results from all modeling approaches suggest that spatial distributions (e.g., hotspots) of GHG emissions can be attributed to differences in soil characteristics, soil nutrient supply, and organic C availability, whereas short- (hot moments) and long-term (seasonal) temporal variations are strongly affected by environmental factors including seasonal temperature and in-season acute precipitation events. In addition, results showed that all modeling approaches were successful in reproducing observed spatial and long-term temporal variations in pore gas GHG concentrations and fluxes; however, the hybrid multi-domain model clearly improved the simulation of hot moment N₂O emissions related to acute precipitation events compared to other approaches, which highlights the potential of improving the simulation of N₂O hot moments through accounting for three interconnected subdomains. The strengths and weaknesses of modeling approaches for simulating GHG cycling and emissions from macroporous agricultural soil were compared and evaluated. All modeling approaches showed promise for future studies with the aim to develop a more complete understanding of how complex soil structure affects the spatiotemporal variations of GHG cycling from macroporous agricultural soils.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".