Evaluation of the CSM‐CROPGRO‐Canola Model for Simulating Canola Growth and Yield at West Nipissing in Eastern Canada
Bibliographic record
Abstract
With increasing demands for renewable energy and dietary vegetable oils, the production of canola has become widespread in recent years. Modeling canola growth and yield is a helpful approach to predict canola responses to various environments, especially under climate change. However, few studies have been performed for predicting growth and yield of canola in Canada. In this study, we evaluated the CSM‐CROPGRO‐Canola model in Decision Support System for Agrotechnology Transfer v4.6 for simulating spring canola at West Nipissing in Eastern Canada. The model was evaluated using plant and soil data collected from field experiments over three growing seasons (2012–2014). The model could predict the observed crop development and successfully mimic the characteristics of canola regarding light absorption and utilization using combinations of leaves and pods. The accumulations of aboveground biomass were satisfactorily simulated in the life cycle under different nitrogen (N) fertilizer application rates, with a normalized RMSE of 19%. The seed yields were successfully predicted with different N application rates except for an underestimation under zero N application. The underestimation of yield under low N rates was possibly related to the deficiency in the simulated N mineralization that could also be associated with inaccurate input soil data. A better simulation of seed yields under low N application was achieved when the soil organic matter module based on the CENTURY model was used in DSSAT v4.6. The calibrated model simulated soil moisture and inorganic N contents satisfactorily, showing a good performance of the CSM‐CROPGRO‐Canola model for the study region.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".