Breeding for resistance to ear rots caused by <i>Fusarium</i> spp. in maize – a review
Bibliographic record
Abstract
With 2 tables Abstract Ear rots caused by different Fusarium spp. are one of the most dangerous food and feed safety challenges in maize production. At present, the majority of the inbreds and hybrids are susceptible. Gibberella and Fusarium ear rots (caused by Fusarium graminearum and Fusarium verticillioides , respectively) are the two main diseases, but more than 10 further Fusarium spp. cause ear rots. Natural infection is initiated by a mixture of the local Fusarium spp., but usually one species predominates. Many maize breeders rely on natural infection to create sufficient levels of disease severity for selection‐resistant genotypes; however, there are few locations where the natural infection is sufficiently uniform to make this selection efficient and successful. Thus, an artificial inoculation method normally performed with one fungal species is now used by more breeders. Most published papers on breeding for ear rot resistance are focused on either F. graminearum or F. verticillioides , and reports involving both or more Fusarium spp. are rare. Several reports support the hypothesis that resistance to multiple species especially F. graminearum, F. culmorum and F. verticillioides may be common. Significant differences in genotypic resistance after inoculation exist. Resistance to the two major modes of fungal entry into the ear, via the silk or through kernel wounds, is not correlated in all genotypes. The reason is not clear. When silk channel resistance was assessed, the data from natural and artificial inoculation trials correlated well. Analogous data relating to kernel resistance have not been published. Both native and exotic sources of resistance are important, but surprisingly little information is available. Few papers report on the use of artificial inoculation during inbred development. Most of the publications on inoculation are concerned with testing at later stages when combining ability is tested. Inbreds differ in general and specific combining ability for ear rot resistance. The expression of resistance to disease severity and resistance to toxins is often used as synonyms, but in fact they are not. Higher resistance to visual disease severities mostly results in lower toxin contamination, and the resistance level seems to be the most important factor regulating the toxin content. The mode of inheritance of resistance appears to differ: additive, possibly non‐additive effects, digenic (dominant) and polygenic patterns have been identified. Improved phenotyping methods that take into account the influence of stalk rot and the use of several independent isolates are available. The QTLs mostly exhibit small effects and some are validated; however, marker‐assisted selection in breeding cannot yet be foreseen. As the severity of natural infections tends to correlate with the artificial inoculation results, the incorporation of artificial inoculation methods in breeding programmes is now the most important task. As genotypic resistance differences between hybrids are high, the registration of hybrids should consider the use of the inoculation tests to choose most resistant hybrids for commercial production. This is the most rapid way to increase feed safety.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".