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Breeding for resistance to ear rots caused by <i>Fusarium</i> spp. in maize – a review

2011· review· en· W2145460047 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePlant Breeding · 2011
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsFusariumBiologyInoculationGibberellaResistance (ecology)Fusarium culmorumHybridGenotypeMycotoxinFungi imperfectiAgronomyHorticultureVeterinary medicineBotanyGeneticsGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.636
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.085
GPT teacher head0.271
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it