Towards measures of the eradicability of rain-splashed crop diseases
Notice bibliographique
Résumé
Controlling rain-splashed crop diseases is an extremely difficult task.Their spread is a complicated process and large-scale field surveys to determine the extent of an incursion over a large area are often economically intractable.A failed attempt at control or eradication of a pathogen can be very costly.In 1996 there was a major incursion of lupin anthracnose in Western Australia, which crippled the albus lupin industry.At the time of the outbreak a wide-spread survey was undertaken to estimate the extent of the incursion.A containment protocol involving broad-scale crop destruction was put into place with the view of eradicating the disease.This eradication attempt subsequently failed due to wild lupins acting as a reservoir for the disease from road verges and non-arable land outside the cropping area.There was also evidence of long distance dispersal vectors such as native budworm.Had all the relevant information related to spread and spatial habitat suitability been collected and taken into account, the decision to destroy the crops may not have been made and significant economic losses to growers may have been avoided.Estimates of the current extent of an incursion based solely on incomplete empirical data are likely to be inaccurate, as are predictions of the future trajectory of an incursion that do not take into account all available information.Therefore any control or eradication attempt based on these estimates and predictions may be ineffective.Simulation modelling is an important method for making the best use of all available empirical data and integrating all available knowledge to predict the spread of rain-splashed crop diseases.With this prediction, an evaluation of the potential success of control or eradication measures may be estimated.This study describes a model that was built to simulate a situation analogous to that of the 1996 lupin anthracnose incursion in Western Australia, for the purpose of identifying general indicators of the eradicability of rainsplashed crop diseases.We extended the spatiotemporal model AnthracnoseTracer to simulate the spread of lupin anthracnose in a heterogeneous paddock environment analogous to the 1996 conditions.Three control methods aimed at eradication were investigated.A simple detection model was assumed, where the probability of detecting the disease is dependent on the level of passive surveillance and the detectability of the disease.As part of the preliminary analysis contained in this paper we investigated two scenarios to identify potential indicators of eradicability, based on the time taken to detect the disease.Our preliminary results indicate that rain-splashed pathogens are extremely difficult to eradicate and the chance of successful eradication appears strongly dependent on the level of surveillance of the susceptible areas and the detectability of the disease.The level of surveillance and detectability of the disease may both serve as general indicators of eradicability for rain-splashed crop diseases.We discuss further modelling analyses to be carried out to refine these indicators.
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 | 0,023 | 0,028 |
| 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 | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».