Nitrogen fertilization and nutrition of canola in eastern Canada
Bibliographic record
Abstract
Canola (B. napus) is an ideal feedstock for biodiesel production due to its high oil and low saturated fatty acid concentration. In recent years, there is a growing interest to expand canola production in Eastern Canada. Canola producers in this region lack fertilization guidelines and need appropriate N fertilizer recommendations to achieve high yields and N fertilizer use efficiency. Nitrogen (N) is a limiting nutrient in canola and plays a determinant role in improving oilseed yield and quality. The objective of this study was to evaluate the status of canola N in relation to soil N mineralization and nitrification and microbial biomass N (MBN) at three sites in Eastern Canada. Experimental sites were located in Ste. Anne-de-Bellevue (Quebec), St. Augustin-de-Desmaures (Quebec) and Ottawa (Ontario). During 2012, the experiment was designed as a randomized complete block with four pre-plant N fertilizer treatments (0, 50, 100 and 150 kg N ha-1 from urea), replicated four times. Canola biomass and N concentration were assessed at four growth stages namely rosette, 20% flowering, 80% pod formation and 90% maturity. Soil N pools (NH4, NO3, MBN, net N mineralization rate and net nitrification rates) were also determined at stages. Canola N concentration was greater with 100 kg N ha-1 than at 0 kg N ha-1 at the rosette stage (P<0.001) in Ste. Anne-de-Bellevue and flowering stage (P<0.005) in St. Augustin-de-Desmaures, but by maturity there was no difference among N fertilizer treatments. Net N mineralization and nitrification rates as well as MBN concentration varied significantly (P<0.001) with canola growth stage, but this was not affected by N fertilization, suggesting that the soil N supply was derived from decomposition of organic residues by the activity of a relatively stable microbial population. At the end of the growing season, the NO3 concentration was elevated in plots that received 150 kg N ha-1 indicating that canola did not utilize all of the N in the soil and so did not benefit from fertilization. This residual soil NO3 represents economic inefficiency and pose environmental risk. Future research on seed yield and harvest index (HI) under the climatic conditions of Eastern Canada will contribute to the development of a precise N fertilization guideline.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".