Studies on breeding of maize for resistance to ear rots caused by Fusarium spp. and on the occurrence of viruses in maize in eastern Canada
Bibliographic record
Abstract
Responses from pedigree selection for resistance to gibberella ear rot were assessed in four maize (Zea mays L.) populations, two selected after inoculation of Fusarium graminearum (Schwabe) macroconidia into the silk channel and two selected after inoculation into developing kernels. Responses were significant in both populations selected for silk resistance and in one of the populations selected for kernel resistance. Selection was more effective in later generations and genetic gains were associated with among-family selection but not with within-family selection. Results obtained here indicate that responses to selection could be more efficiently obtained by applying high selection intensities in advanced generations, by managing earlier generations as bulks and by reducing the number of plants per family. In another experiment, a wide sample of Argentine maize germplasm was evaluated for silk and kernel resistance to gibberella ear rot and to fusarium ear rot (caused by F. verticillioides (Saccardo) Nirenberg [=F. moniliforme (Sheldon)]. Several entries exhibited disease resistance in comparison with local check hybrids, particularly for fusarium ear rot, the most prevalent ear rot in Argentina. Results obtained in this study suggested the presence of general mechanisms controlling silk and kernel resistance to both diseases. In a supplementary study, viral diseases were surveyed in maize fields from the provinces of Ontario and Quebec in 1999 and 2000. Barley yellow dwarf was found in 1999. Sugarcane mosaic, maize dwarf mosaic and wheat streak mosaic were found in 2000. These diseases were not important for grain-maize planted in May, the most prevalent kind of maize crop in these provinces. Some of these diseases, such as sugarcane maize mosaic and maize dwarf mosaic were found important only in maize fields planted during or after the month of June, and this is of commercial relevance only for sweet corn.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".