Predictions of soybean harvest index evolution and evapotranspiration using STICS crop model
Bibliographic record
Abstract
Abstract In the last century, soybean [ Glycine max (L.) Merr.] genetic improvements have resulted in increased yield partly due to an increase in harvest index (HI). To account for these genetic improvements, an update of the soybean calibration of the STICS soil‐crop model was carried out. The model was calibrated and evaluated for two sets of soybean plant parameters using datasets from the Ottawa region (ON, Canada); a low HI cultivar calibrated using datasets (1993–2008) of cultivars of maturity groups (MGs) 00 and 0 and a high HI cultivar calibrated with more recent datasets (2016–2017) of cultivars of MGs 0 and I. The model succeeded in reproducing the HI increase. Leaf area index (LAI), shoot biomass, and yield were also well predicted for the high HI cultivars with a normalized root mean square error (NRMSE) of 34, 10, and 14%, respectively, which was a great improvement compared to the default parametrization proposed in STICS for soybean. Under rainfed conditions, accurate simulation of evapotranspiration is a critical point to achieve good model performance. A comparison of the two crop evapotranspiration approaches available in STICS was also carried out. It showed that the resistive approach (NRMSE of 36%) was more efficient than the crop coefficient approach (NRMSE of 67%). This good performance of the model in predicting evapotranspiration allowed the model to perform equally well under water stress and non‐water stressed conditions. The model could therefore be used in future studies to simulate the impact of water stress on soybean growth in eastern Canada.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".