Effect of preceding crop and nitrogen application on malting barley quality
Bibliographic record
Abstract
As legume crops fix nitrogen (N) from the atmosphere, there is concern that soil residual N from legumes grown in rotation with malting barley may result in unacceptably high protein content and have negative effects on quality. However, little research has been conducted to investigate this. Field pea, lentil, faba bean [as seed or as a green manure (GM) crop], canola, and wheat were grown in 2009, canola in 2010, and malting barley in 2011. The objective was to determine the effects of crops grown in 2009 on the quality of barley grown in 2011. Crops were direct-seeded at Lacombe (Alberta), Swift Current (Saskatchewan), and Brandon (Manitoba). Fertilizer N (urea) was applied in 2010 and 2011 at 0, 30, 60, 90, and 120 kg ha−1. The legumes had few negative effects on barley quality compared with canola or wheat. Exceptions occurred at Lacombe where the lentil and faba bean GM crops increased protein and decreased kernel plumpness. This was not evident at other locations. Increasing N fertilizer rate negatively affected almost all malt quality parameters at all locations. The results indicate that growing legume crops prior to malting barley is less likely to reduce malting barley quality than applying fertilizer N.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".