Assessing the Options to Improve Regional Wheat Yield in Eastern Canada Using the CSM–CERES–Wheat Model
Bibliographic record
Abstract
Core Ideas Wheat yield at both field and regional scales was successfully simulated using CSM–CERES–Wheat. There is a considerable room to improve spring wheat yield in eastern Ontario. Average yield in eastern Ontario can reach 3600 kg ha−1 with fertilization at 100 kg N ha−1. Crop models may need to include lodging—often related to high N rates in eastern Canada. Wheat (Triticum aestivum L.) yield is relatively low in eastern Canada. This study aimed to assess fertilizer N management options to improve the regional yield of wheat using the CSM–CERES–Wheat model. The model was adapted to simulate winter wheat by replacing air temperatures with estimated temperatures under snow cover, and then the model was evaluated for simulating winter wheat using experimental data collected at two sites and spring wheat at three sites in eastern Canada. Across all the experimental years and sites, the normalized root mean squared error (nRMSE) between simulated and measured yields was 14%. Regional yield under rainfed conditions in the Eastern Ontario Region (a Census of Agriculture unit as a case study) was simulated with 0, 1, 1.5, and 2 times the recommended N rate (around 50 kg N ha−1) and unlimited N for the calibrated cultivars of spring wheat from 1981 to 1999. The simulated average regional yield (in dry matter) with the recommended N rate ranged from 2180 kg ha−1 for cultivar Hoffman to 2502 kg ha−1 for AC Brio. Both were close to the reported yield of 2440 kg ha−1, with nRMSE values ranging between 20.3 and 16.6%. The simulated regional yields with unlimited N were two times that with the recommended N rate, showing a considerable yield gap. Our simulations indicate that regional yield could increase to 3600 kg ha−1 in the Eastern Ontario Region if the N rate was increased to around 100 kg N ha−1, although a slight decrease in N use efficiency would occur. In addition, with such increases in the N fertilization rate, other abiotic factors such as lodging should be evaluated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".