Agronomic and environmental impacts of corn production under different water management strategies in the Canadian Prairies
Bibliographic record
Abstract
A major challenge facing agriculture is to improve water use and minimize environmental impact while increasing productivity levels. This study, carried out in Winkler, Manitoba, tested four water management treatments: no drainage and no irrigation (NDNI as control), no drainage with overhead irrigation (NDIR), free drainage with overhead irrigation (FDIR), and controlled drainage with subirrigation (CDSI). Each treatment was replicated in three plots during two growing seasons in 2010 and 2011. The monitored variables included soil moisture content, water table depth variation, drainage outflow volume and quality, weather parameters, and agronomic indices. In 2010, 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 2011, 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. In 2010, the exports of NO3-N (138 kg ha-1), PO4-P (0.6 kg ha-1) and salts (2.34 Mg ha-1) from the FDIR treatment were significantly larger (p <0.05) than exports from CDSI, which were 0.07 kg ha-1, 0.08 kg ha-1, and 0.41 Mg ha-1, respectively. In 2011, the exports of NO3-N (36 kg ha-1), PO4-P (0.27 kg ha-1), and salts (1.1 Mg ha-1) from FDIR were significantly larger (p < 0.05) than the exports from CDSI which were 10 kg ha-1, 0.08 kg ha-1, and 0.39 Mg ha-1, respectively. These results indicate that irrigation was the main factor driving corn yields under the conditions prevailing in the Canadian Prairies, while subsurface drainage had a beneficial impact when the beginning of the season was wet. Also, this study showed the advantage of controlled drainage over free drainage in reducing the nutrients and salt exports.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".