Canola yield and quality under tile drainage in the Canadian Prairies
Bibliographic record
Abstract
For areas with seasonally shallow water tables and poorly drained soils, subsurface drainage systems are ideal for removing excess water from the root zone and improving soil workability, trafficability, and timeliness of field operations. With increased interest in tile drainage in southern Manitoba, the objective of this study was to evaluate the impacts of drainage on canola yield and canola oil qualities over three growing seasons (2019-2021) in Winkler, Manitoba. The study was carried out on replicated field plots with three different drainage treatments: controlled drainage (CD), free drainage (FD), and no drainage (ND). Subsurface drain tiles were installed at a depth of 0.9 m. The drains were spaced at 8 m for CD and 15 m for FD. Compared to FD plots (3.02 Mg/ha), the CD plots (3.51 Mg/ha) had significantly higher yields in 2019 with good rainfall. With low rainfall in 2020 and 2021, the impact of drainage, especially CD, diminished, with no significant differences between the treatments. In 2020, the average yields were 3.12, 2.52, and 2.97 Mg/ha for ND, CD, and FD, respectively. Similarly, in 2021, there was no significant difference between CD (1.14 Mg/ha), FD (1.52 Mg/ha), and ND (1.07 Mg/ha). The impact of CD under drought conditions was not significant. This could be related to the narrower drain spacing, which tends to remove water rapidly within the soil profile during short periods of high-intensity rainfall. The canola quality assessments (oil, protein, glucosinolate and fatty acid profile) showed no significant differences between ND, CD, and FD in each of the years. This suggests that environmental variables (mainly temperature and precipitation) may have masked drainage impacts on canola quality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".