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Enregistrement W1969243558 · doi:10.1071/ar04311

Incidence and distribution of viruses infecting cucurbit crops in the Northern Territory and Western Australia

2005· article· en· W1969243558 sur OpenAlexaff
B. A. Coutts, Richard A. Jones

Notice bibliographique

RevueAustralian Journal of Agricultural Research · 2005
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiquePlant Virus Research Studies
Établissements canadiensDepartment of Environment and Conservation
Organismes subventionnairesnon disponible
Mots-clésBiologyZucchini yellow mosaic virusCropSquashCucumber mosaic virusVirusVeterinary medicinePlant virusCultivarHorticultureVirologyAgronomy

Résumé

récupéré en direct d'OpenAlex

During 2003–04, a survey was done to determine the incidence and distribution of virus diseases infecting cucurbit crops growing in the field at Kununurra, Broome, and Carnarvon in north-western Australia, Perth in south-western Australia, and Darwin and Katherine in the Northern Territory. Overall, 43 cucurbit-growing farms and 172 crops of susceptible cultivars were sampled. From each crop, shoot samples were collected from plants chosen at random and from symptomatic plants. Shoot samples were sometimes also collected from potential alternative virus hosts (cucurbit volunteer plants and weeds). All samples were tested by enzyme-linked immunosorbent assay (ELISA) using antibodies to Cucumber mosaic virus (CMV), Papaya ringspot virus-cucurbit strain (PRSV), Squash mosaic virus (SqMV), Watermelon mosaic virus (WMV), and Zucchini yellow mosaic virus (ZYMV). Samples from one-third of the crops were also tested by tissue blot immunosorbent assay (TBIA) using generic luteovirus antibodies. Overall, 72% of farms and 56% of crops sampled were virus-infected. The growing areas with the highest incidences of virus infection were Darwin and Carnarvon, and those with the lowest incidences were Katherine and Perth. For WA, overall 78% of farms and 56% of crops were virus-infected, and in the NT the corresponding figures were 55% of farms and 54% of crops. Overall virus incidences in individual crops sometimes reached 100% infection. Crops of cucumber, melon, pumpkin, squash, and zucchini were all infected, with squash and zucchini being the most severely affected. The most prevalent viruses were ZYMV and PRSV, each being detected in 5 and 4 of 6 cucurbit-growing areas, respectively, with infected crop incidences of <1–100%. SqMV was detected in 2 cucurbit-growing areas, sometimes reaching high incidences (<1–60%). WMV and CMV were found in 3 and 4 of 6 cucurbit-growing areas, respectively, but generally at low incidences in infected crops (<1–8%). Infection with luteovirus was found in 3 growing areas but only occurred in 16% of crops. Beet western yellows virus was detected once but at least one other luteovirus was also present. Infection of individual crops by more than 1 virus was common, with up to 4 viruses found within the same crop. Virus-resistant pumpkin cultivars (6 crops) had little infection when adjacent virus-susceptible cucurbit crops had high virus incidences. Viruses were detected in cucurbit volunteer plants and weeds, suggesting that they may act as important reservoirs for spread to nearby cucurbit crops. In general, established cucurbit-growing farms in close proximity to others and with poor crop hygiene suffered most from virus epidemics, whereas isolated farms with large-sized crops or that had only recently started growing cucurbits had less infection. The extent of infection revealed in this survey, and the financial losses to growers resulting from virus-induced yield losses and high fruit rejection rates, are cause for concern for the Australian cucurbit industry.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,334
Score d'incertitude au seuil0,825

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,133
Tête enseignante GPT0,375
Écart entre enseignants0,242 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations44
Publié2005
Routes d'admission1
Résumé présentoui

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