Main factors affecting nutrient and water use efficiencies in spring canola in North America: a review of literature and analysis
Bibliographic record
Abstract
Improving nutrient and water use efficiencies by optimizing field management practices are important strategies to increase economic and environmental sustainability of canola production in North America. The objective of this study was to review recent research publications and quantitatively assess the impact of field management practices on the efficiency of water and selected macronutrients [nitrogen (N) and sulfur (S)] in canola and to identify the most effective cultural practices for improved efficiencies. The results showed that, overall, the addition of N and S inputs in studies across North America increased yield but had a negative impact on nitrogen use efficiency (NUE) and sulfur use efficiency (SUE) compared with corresponding controls. Split-applied N in spring can improve NUE, but these improvements are mostly dependent on the soil moisture content. SUE is improved when N is supplied to complement the S application. Sulfate forms of S are more readily available and should be applied early in the season, whereas elemental S must be applied in the fall to improve SUE. Maintenance of adequate soil moisture conditions during the reproductive phase of the canola crop improves water use efficiency (WUE). Supplementary irrigation improves SUE, but most canola crops are grown under rain-fed conditions in North America. Maintaining tall stubble until spring and then incorporating it with N improved WUE in canola. In summary, our analyses suggest that further research is required on the integration of canola genotypes with improved nutrient and water use efficiencies and effective management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".