DRAINMOD simulation of drain spacing impact on canola yield in heavy clay soils in the Canadian prairies
Bibliographic record
Abstract
Abstract Excess moisture within the root zone due to the shallow water table is a leading cause of crop loss in Manitoba. In this study, the ability of the DRAINMOD model to predict water table depth (WTD) in clayey soil was evaluated using measured field data from the 2019 and 2020 canola‐growing seasons in Arborg, Manitoba, Canada. Statistical analysis and graphical plots showed close agreement between the measured and simulated WTD with an overall coefficient of determination (R2), root mean square error (RMSE), mean average error (MAE) and mean bias error (MBE) of 0.93, 9.84 cm, 7.06 cm and −0.13 cm, respectively. Since the model simulation was deemed satisfactory, the model was run with 30‐year historical climate data to assess the impacts of different drain spacing on canola yield. Simulation results showed that the average surface runoff increased while average drainage and relative canola yield decreased as drain spacing increased. The simulation results suggest that long‐term average yield would be maximized by close drain spacing ≤ 15 m. Economic analysis showed that 10 m drain spacing would maximize the return on investment. The need for long‐term simulations to develop appropriate site‐specific water management strategies is demonstrated.
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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 | 0.001 | 0.000 |
| 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".