Advanced 4R nitrogen management options for corn in sandy soils of Manitoba, Canada
Bibliographic record
Abstract
Pillars of 4R nutrient management - Right Source, Right Rate, Right Time, Right Place, have guided the development of best management practices in various soil-climatic conditions in recent decades. Nitrogen (N) management options that reduce environmental losses and/or improve N use efficiency must be investigated in various soil-climatic contexts to ensure food security and a more sustainable future. 4R research that investigates various measures of sustainability for multiple management practices remains in short supply. The objectives of this research were to compare growing season N2O emissions and N use efficiency measures between 1) at-planting sources of N (Urea, ESN/Urea, SuperU); 2) in-season sources of N (UAN, Agrotain, AgrotainPlus); 3) in-season placement depths of UAN (Surface dribble, Shallow, Deep) applied to corn in sandy soils of Manitoba, Canada. At three sites with similar soil and management characteristics, treatments were replicated four times in a randomized complete block design. Higher soil-moisture in spring of 2020 resulted in greater magnitudes of N2O emissions and a greater potential for N loss. Across three site-years, ESN/Urea had the lowest mean area-scaled emissions among at-planting sources, and agronomic benefits were apparent during the year with high soil-moisture at-planting. Among in-season sources of N, area-scaled emissions of AgrotainPlus were significantly lower than UAN Surface; UAN Deep consistently had the lowest area-scaled emissions among in-season placements. In summary, relatively dry conditions and timely rains after fertilization likely impeded the potential for enhanced efficiency fertilizers and placement depth to substantially reduce N losses and subsequently show agronomic benefit(s). However, enhanced efficiency fertilizers and shallow/deep placement tended to have greater residual N after corn harvest at each N rate compared to conventional urea and UAN. In dry conditions, reduced N losses may not translate into yield or N uptake benefits, however, may give agronomic benefits during the following growing season(s).
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".