Corn Yield Simulation Using the STICS Model under Varying Nitrogen Management and Climate-Change Scenarios
Why this work is in the frame
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Bibliographic record
Abstract
This study evaluated the performance of the simulateur multidisciplinaire pour les cultures standard (STICS) crop model for predicting grain yield and dry biomass of corn under three nitrogen (N) treatments—low, medium, and high N levels—applied on a conventional drainage field in eastern Canada over a 2-year period. The impacts of climate change on simulated grain corn and biomass yield in eastern Canada under tile-drained conditions was also evaluated over a 30-year future period (2040–2069). The 2008 data set was selected for calibration, whereas the 2009 data set was used for validation of the model. Corn grain yield was underestimated by 1.5–2.6 Mg ha−1 for the 2 years of measurement. Total dry biomass was also underestimated by 0.9–2.6 Mg ha−1. Tukey’s studentized range (HSD) test of corn grain yield indicated that yields at high and low N and high and medium N were different at the 95% confidence level. Grain and biomass production from 2040–2069 under B1 emission scenarios responded differently (P<0.05) for the three N treatments. A Mann–Kendall, nonparametric test performed on simulated corn grain and biomass yields attributable to climate change under B1 emission scenarios showed neither an increasing nor a decreasing trend at a 95% confidence level.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it