Corn Yield Simulation under Different Nitrogen Loading and Climate Change Scenarios
Bibliographic record
Abstract
Climate change in recent years has been affecting agriculture and especially crop production worldwide. This study analyzes the effect of two different climate change scenarios on crop production of an experimental site in southern Québec, Canada. The DSSAT model, which was calibrated for years 2008 and 2009, was used to simulate corn growth with 30 years of synthetic data for climate scenarios baseline (1961–1990), A2 (2040–2069), and B1 (2040–2069). In comparison with the baseline scenario, the A2 and B1 scenarios projected a decrease in grain and biomass, an increase in crop ET and evaporation, and an early crop emergence and maturity dates. Reduction in grain yield of up to 40% for A2 and 24% for B1 scenarios was observed, which could be attributed to water-deficit conditions resulting from decreased rainfall and increase in temperature during the growing season. Because drought indices were found to be significantly correlated with grain yield and crop water stress, it could be used to define the variability of grain yield and water stress at the field scale. This study indicates that climate change might have a negative effect in terms of corn crop production under the given study area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".