Yield and quality of oat in response to varying rates of swine slurry
Bibliographic record
Abstract
An experiment was conducted at two locations in southern Manitoba in 2001 and 2002 to assess the effect of multiple rates of spring-applied swine slurry on seed yield, kernel quality, dry matter accumulation, protein concentration and lodging response of three adapted oat (Avena sativa L.) cultivars: AC Medallion, AC Ronald and AC Assiniboia. Treatments included three rates of swine slurry, an unfertilized check and an inorganic fertilizer treatment at the recommended N rate. In spite of the nutrient variability in swine slurry, oat grain and dry matter yield remained largely unresponsive to increases in slurry rate except when residual soil nutrients were very low. Thousand kernel weight and percentage of plump kernels appeared to be affected more by environment and cultivar than by slurry rate. High rates of swine slurry did not result in high crude protein concentrations in grain or dry matter and may, under some environmental conditions, decrease protein concentrations. The data suggest that few differences exist between oat cultivars in response to the use of swine slurry as a fertilizer. The inconsistent response to slurry application indicates that oat may not be the ideal crop to use in the year of slurry application but may respond well to residual nutrients from nutrients applied in the prior year.Key words: Oat, swine slurry, grain yield, biomass yield, crop 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".