Breeding for resistance to ear rots caused by <i>Fusarium</i> spp. in maize – a review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».