Opportunities for improved fertilizer nitrogen management in production of arable crops in eastern Canada: A review
Bibliographic record
Abstract
There is increasing public pressure to reduce the environmental impacts of agricultural production. Therefore, one key challenge to producers is to manage their crop production systems in order to minimize losses of nitrogen to air or water, while achieving crop yield and quality goals. Many strategies have been developed in recent years to meet this challenge. These include: development of new tools to measure crop N status in order to refine in-season fertilizer N management, development of new soil N tests to improve prediction of soil N supply, development of new fertilizer N products with release patterns more closely matched to crop N uptake patterns, and development of site-specific N management strategies. We review the opportunities and limitations to these new strategies within different arable crop production systems under the humid and sub-humid soil moisture regimes present in eastern Canada. Future research opportunities to improve the efficiency of fertilizer N utilization include development of practical methods to predict the magnitude of soil N mineralization; refinement of decision-making processes which take into consideration the crop N status and soil properties as a basis for variable rate fertilizer N application; development of affordable controlled-release fertilizer N products with improved N release characteristics; development of practical methods for capturing and recycling nutrient-laden drainage water; development of gene expression profiling based techniques to identify crop N stress; and application of crop genomics and molecular breeding techniques to accelerate the development of new cultivars with increased N use efficiency. Key words: Soil N tests, plant N tests, nitrogen fertilizers, nitrogen cycling
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".