Bread wheat performance, fusarium head blight incidence and weed infestation response to low-input conservation tillage systems in eastern Canada
Bibliographic record
Abstract
Munger, H., Vanasse, A., Rioux, S. and Légère, A. 2014. Bread wheat performance, fusarium head blight incidence and weed infestation response to low-input conservation tillage systems in eastern Canada. Can. J. Plant Sci. 94: 193-201. Bread wheat performance, the incidence of diseases like fusarium head blight (FHB) and weed infestations may be affected by low-input systems and conservation tillage practices. This 2-yr study assessed the effects of three 24-yr-old tillage treatments (MP: moldboard plow; CP: chisel plow; NT: no-till) and two cropping systems (high-input: herbicide and mineral fertilizer; low-input: mechanical weed control and organic fertilizer) on wheat productivity, deoxynivalenol (DON) content, Fusarium graminearum inoculum production, and weed infestation in hard red spring wheat. In 2009, low-input CP and NT yields were 13 and 31% lower, respectively, than low-input MP yield, which was comparable with all of the high-input treatment yields. In 2010, yields were 23% lower in CP and NT compared with MP, and 32% lower in low-input than in high-input systems. Optimum wheat yield in low-input systems appeared conditional to adequate weed control, which was achieved with MP and CP tillage. Protein content, test weight, and 1000-kernel weight were higher in the high-input system than in the low-input system, except for test weight in 2009. DON content was not affected by tillage in either year, and was lower in low-input system than in high-input system in 2009. Fusarium graminearum inoculum measured in 2009 was similar across tillage treatments in the high-input system, whereas in the low-input system, the inoculum was lower in NT than in MP. In 2010, DON content was not affected by any treatment. Hot and dry conditions were not conducive to pathogen development, and may explain the low level of DON and the lack of treatment effect.
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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) | 0.001 | 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".