Nitrogen fertilization and soil mineral nitrogen dynamics to optimize canola yield and nutrition in Québec
Bibliographic record
Abstract
Canola is an ideal feedstock for biodiesel production because of its high oil and low saturated fat concentrations. There is interest in producing more canola in Québec, but producers lack fertilization guidelines to optimize high oilseed yield and quality in canola. Nitrogen (N) is the most important determinate of oilseed yield and quality and N fertilization is important for biomass accumulation during the early vegetative stage and for oil synthesis during the reproductive stage. The first objective of this study was to monitor soil mineral N (NO3-N + NH4-N) dynamics and canola straw nutrition in response to N fertilization. Two fertilization methods - a pre-plant and split application of fertilizer N were studied at the Emile A. Lods Agronomy Research Centre on the Macdonald Campus of McGill University at Ste-Anne-de-Bellevue, Québec, using a fractional factorial experimental design. The second objective was to evaluate N use efficiency (NUE) and harvest index (HI) of canola grown in pots containing soils from Ste-Anne-de-Bellevue, St-Augustin-de-Desmaures and Ottawa using a completely randomized design. Split application of a sidedressed N fertilizer did not increase the post-harvest soil mineral N concentration or increase straw nutrition compared with the pre-plant N application. There was considerable spatio-temporal heterogeneity in soil mineral N dynamics, so additional field trials are warranted. The pot study showed inconsistent correlations between straw N concentration and yield in canola grown in the soils collected from Ste-Anne-de-Bellevue (not related), St-Augustin-de-Desmaures (negative), and Ottawa (positive). Straw N concentrations were related to low straw and oilseed yield, indicating there is an optimal straw N concentration to achieve target yields. Seeding in late May and disease occurrence close to the end of flowering stage reduced the oilseed yield more than straw yield. Future research on the pattern of N translocation (e.g.: from leaf to pod, then to oilseed) under Québec climatic conditions will contribute to the development of an N fertilization guideline. Since some soils in Québec have an appreciable soil N supply, knowledge of how much soil N is used to meet canola N requirement will keep N fertilizer costs low while optimizing oilseed yield and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".