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Record W4408427965 · doi:10.5194/egusphere-egu25-12649

Challenges and Insights for Simulating Nitrous Oxide Emissions in Eastern Canada: Evaluating an Agroecosystem Model Ensemble

2025· preprint· en· W4408427965 on OpenAlexaffabout
Ward Smith, Brian Grant, Budong Qian, Guillaume Jégo, Marianne Crépeau, Stephen J. Del Grosso, Stephen M. Ogle, David E. Pelster

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAgroecosystemNitrous oxideEnvironmental scienceGreenhouse gasGeographyEcologyAgricultureArchaeologyBiology

Abstract

fetched live from OpenAlex

Biogeochemical models continue to be improved in their ability to account for the impacts of agricultural management, soil characteristics, and climate on crop productivity and greenhouse gas emissions. Depending on the model, limitations still exist including the ability to characterize a limited range of management practices, the oversimplifications of crop physiology, and an inadequate simulation of soil microbial environments. This study uses a long-term 22-year field experiment in eastern Canada to calibrate and evaluate several agroecosystem models, including DayCent, DNDC, DSSAT, and STICS, for their ability to simulate crop productivity and nitrous oxide (N2O) emissions. Model performance was assessed against near-continuous N2O measurements using flux towers. Corn, wheat, soybean, and canola were grown over the 22 years for several treatments including manure versus inorganic fertilizer, fertilizer rate, timing of fertilizer applications, early and late planting, and use urease and nitrification inhibitors. Findings suggest that the ensemble of models could accurately predict corn, wheat and soybean yields in contrast to the general overprediction of canola yields. Growing season N2O emissions are generally well-simulated at the Ottawa site with weekly performance statistics showing Wilmot d values of 0.7 for conventional management and 0.75 for BMP management. However, challenges persist in accurately capturing daily emission patterns and estimating emissions during the spring-thaw period. The DSSAT and STICS models, which do not have explicit soil mechanisms related to spring thaw, simulated low N2O emissions and thus it is recommended that these mechanisms be incorporated in the future. Difficulties in modeling the timing of denitrification events highlighted limitations in the representation of microsite-level pedoclimatic conditions, diffusion processes, and the simulation of microbial activity. The model ensemble simulated an acceptable level of annual N2O emissions for most treatments with 5.8% overprediction across 22 years, with the overestimation mainly from the manure and dual inhibitor treatments. Comparing model strengths and weaknesses across different locations provides valuable insights for future model improvements.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.068
GPT teacher head0.299
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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