Effect of crop residue, nitrogen rate and fungicide application on malting barley productivity, quality, and foliar disease severity
Bibliographic record
Abstract
Turkington, T. K., O'Donovan, J. T., Edney, M. J., Juskiw, P. E., McKenzie, R. H., Harker, K. N., Clayton, G. W., Xi, K., Lafond, G. P., Irvine, R. B., Brandt, S., Johnson, E. N., May, W. E. and Smith, E. 2012. Effect of crop residue, nitrogen rate and fungicide application on malting barley productivity, quality, and foliar disease severity. Can. J. Plant Sci. 92: 577–588. The productivity and quality of the malting barley cultivar AC Metcalfe and leaf disease severity were evaluated under three residue types [barley (Hordeum vulgare L.), canola (Brassica napus L.), field pea (Pisum sativum L.)], two nitrogen (N) fertilizer rates (50 or 100% of soil test recommendation for N), and two fungicide treatments (no fungicide or fungicide applied) at seven sites across western Canada from 2006 to 2009. Residue type had a significant effect on leaf disease severity, which was increased when barley was the previous crop compared with canola and field peas. In general, emergence, head counts, grain yield, kernel weight, test weight, kernel plumpness were lowest for barley grown on barley residue compared with canola and field pea residue. Fungicide application reduced leaf disease severity and increased yield, kernel weight, test weight, and kernel plumpness, while decreasing dockage and thins. However, the magnitude of the impact of fungicide on one or more of these parameters was lower compared with planting barley on field pea or canola residue. Overall, increasing the N rate from 50 to 100% had no effect on leaf disease levels and only increased yields slightly compared with not planting barley on barley residue. However, the 100% rate of N did significantly increase grain protein levels. In contrast, planting barley on field pea residue did not result in a consistent increase in grain protein.
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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".