Synchronizing Nitrogen Application with Uptake Using Urease and Nitrification Inhibitors to Maximize Nitrogen Use in Forage Seed Stands in Northeastern Saskatchewan
Bibliographic record
Abstract
Management of nitrogen inputs in forage seed production systems continues to be a significant\nchallenge for forage seed producers. Since the decline in availability of ammonium nitrate\nin western Canada, urea has become a widely used source of fertilizer nitrogen (N). However, the\nnitrogen use efficiency (NUE) of surface applied of urea can be low due to the lack of incorporation.\nRecently, so-called enhanced efficiency, or stabilized urea products incorporating urease\nand nitrification inhibitors have shown promise in reducing gaseous N losses and enhancing NUE.\nThus, this study was initiated to examine the use of stabilized N products in forage seed production\nsystems in northeastern Saskatchewan. Three stabilized urea products and two application\nstrategies (fall vs. spring) were evaluated and compared to untreated urea. All treatments were\napplied at a rate of 100 kg N ha-1. The study evaluated two forage species: hybrid bromegrass\n(Bromus riparius Rehm. X Bromus inermis Leyss.) and timothy (Phleum pratense L.). The study\nwas undertaken in four established commercial fields (two each of bromegrass and timothy) and\nwas initiated in fall of 2012. Rates of N transformation, seed yield, biomass production, biomass\nquality, NUE, 1000-seed weight, and economic returns were evaluated over the 2013 growing season.\nThe use of controlled release N products delayed fall N transformation in the bromegrass\nfields and increased seed yields by 14 to 22%. Spring applied treatments also proved effective at\ndelaying N transformation, though their influence was less pronounced. Moreover, spring applied\nN treatments were generally associated with a yield reduction of 11 to 19%. In general, biomass\nquantity and quality, NUE, number of seed bearing tillers, 1000-seed weight and economic returns\nwere not significantly influenced by the use of controlled release N products. In timothy, seed yield\nand biomass production were greatest when the N was spring applied; however, there were no significant\ndifferences between the stabilized and untreated urea products. Similar results also were\nobserved for forage quality, NUE, number of seed bearing tillers, 1000-seed weight and economic\nreturn. The absence of consistent yield trends suggests that unless environmental conditions that\npromote high N loss are present, the utility of stabilized N products may be limited to that of a risk\nmanagement tool.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".