Corn Yield Response to Drainage and Subirrigation in the Canadian Prairies
Bibliographic record
Abstract
Subsurface drains are commonly used in humid regions to deal with high water tables. However, corn (Zea mays L.) could benefit from subsurface drainage even under semi-arid conditions where high-intensity rainfall causes the water table to rise within the root zone for short periods. In southern Manitoba, seasonally high water tables with high salinity have led to salinization of the root zone, making subsurface drainage an attractive option to increase yields. The objective of this research was to evaluate agronomic performance of corn under water table management using subirrigation and tile drainage. Four treatments were tested in this experiment: (1) controlled drainage with subirrigation (CDSI), (2)no drainage with overhead irrigation (NDIR), (3) free drainage with overhead irrigation (FDIR), and (4) no drainage with no irrigation (NDNI) as control. The impacts of these treatments on crop performance, measured by yield, kernel quality, plant biomass, and plant height, were evaluated over two growing seasons. In the first year, which was 57% wetter than the 30-year average, yields were 8.48 (NDNI), 10.36 (NDIR), 10.10 (FDIR), and 9.22(CDSI) Mg ha-1 with only the mean yield difference for the NDIR and the CDSI treatments being statistically significant (p = 0.014). In the second year, which was 16% drier than normal, yields were 9.25 (NDNI), 10.47 (NDIR), 11.28 (FDIR), and 9.49 (CDSI) Mg ha-1 with no statistically significant differences in yield.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".