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A Bayesian model for control strategy selection against Plasmopara viticola infections

Plant pathogens pose a persistent threat to grape production, causing significant economic losses if disease management strategies are not carefully planned and implemented. Simulation models are one approach to address this challenge because they provide short-term and field-scale disease predictio...

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Autores principales: Valleggi, Lorenzo, Carella, Giuseppe, Perria, Rita, Mugnai, Laura, Stefanini, Federico Mattia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399454/
https://www.ncbi.nlm.nih.gov/pubmed/37546263
http://dx.doi.org/10.3389/fpls.2023.1117498
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author Valleggi, Lorenzo
Carella, Giuseppe
Perria, Rita
Mugnai, Laura
Stefanini, Federico Mattia
author_facet Valleggi, Lorenzo
Carella, Giuseppe
Perria, Rita
Mugnai, Laura
Stefanini, Federico Mattia
author_sort Valleggi, Lorenzo
collection PubMed
description Plant pathogens pose a persistent threat to grape production, causing significant economic losses if disease management strategies are not carefully planned and implemented. Simulation models are one approach to address this challenge because they provide short-term and field-scale disease prediction by incorporating the biological mechanisms of the disease process and the different phenological stages of the vines. In this study, we developed a Bayesian model to predict the probability of Plasmopara viticola infection in grapevines, considering various disease management approaches. To aid decision-making, we introduced a multi-attribute utility function that incorporated a sustainability index for each strategy. The data used in this study were derived from trials conducted during the production years 2018-2020, involving the application of five disease management strategies: conventional Integrated Pest Management (IPM), conventional organic, IPM with substantial fungicide reduction combined with host-defense inducing biostimulants, organic management with biostimulants, and the use of biostimulants only. Two scenarios were considered, one with medium pathogen pressure (Average) and another with high pathogen pressure (Severe). The results indicated that when sustainability indexes were not considered, the conventional IPM strategy provided the most effective disease management in the Average scenario. However, when sustainability indexes were included, the utility values of conventional strategies approached those of reduced fungicide strategies due to their lower environmental impact. In the Severe scenario, the application of biostimulants alone emerged as the most effective strategy. These results suggest that in situations of high disease pressure, the use of conventional strategies effectively combats the disease but at the expense of a greater environmental impact. In contrast to mechanistic-deterministic approaches recently published in the literature, the proposed Bayesian model takes into account the main sources of heterogeneity through the two group-level effects, providing accurate predictions, although precise estimates of random effects may require larger samples than usual. Moreover, the proposed Bayesian model assists the agronomist in selecting the most effective crop protection strategy while accounting for induced environmental side effects through customizable utility functions.
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spelling pubmed-103994542023-08-04 A Bayesian model for control strategy selection against Plasmopara viticola infections Valleggi, Lorenzo Carella, Giuseppe Perria, Rita Mugnai, Laura Stefanini, Federico Mattia Front Plant Sci Plant Science Plant pathogens pose a persistent threat to grape production, causing significant economic losses if disease management strategies are not carefully planned and implemented. Simulation models are one approach to address this challenge because they provide short-term and field-scale disease prediction by incorporating the biological mechanisms of the disease process and the different phenological stages of the vines. In this study, we developed a Bayesian model to predict the probability of Plasmopara viticola infection in grapevines, considering various disease management approaches. To aid decision-making, we introduced a multi-attribute utility function that incorporated a sustainability index for each strategy. The data used in this study were derived from trials conducted during the production years 2018-2020, involving the application of five disease management strategies: conventional Integrated Pest Management (IPM), conventional organic, IPM with substantial fungicide reduction combined with host-defense inducing biostimulants, organic management with biostimulants, and the use of biostimulants only. Two scenarios were considered, one with medium pathogen pressure (Average) and another with high pathogen pressure (Severe). The results indicated that when sustainability indexes were not considered, the conventional IPM strategy provided the most effective disease management in the Average scenario. However, when sustainability indexes were included, the utility values of conventional strategies approached those of reduced fungicide strategies due to their lower environmental impact. In the Severe scenario, the application of biostimulants alone emerged as the most effective strategy. These results suggest that in situations of high disease pressure, the use of conventional strategies effectively combats the disease but at the expense of a greater environmental impact. In contrast to mechanistic-deterministic approaches recently published in the literature, the proposed Bayesian model takes into account the main sources of heterogeneity through the two group-level effects, providing accurate predictions, although precise estimates of random effects may require larger samples than usual. Moreover, the proposed Bayesian model assists the agronomist in selecting the most effective crop protection strategy while accounting for induced environmental side effects through customizable utility functions. Frontiers Media S.A. 2023-07-20 /pmc/articles/PMC10399454/ /pubmed/37546263 http://dx.doi.org/10.3389/fpls.2023.1117498 Text en Copyright © 2023 Valleggi, Carella, Perria, Mugnai and Stefanini https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Valleggi, Lorenzo
Carella, Giuseppe
Perria, Rita
Mugnai, Laura
Stefanini, Federico Mattia
A Bayesian model for control strategy selection against Plasmopara viticola infections
title A Bayesian model for control strategy selection against Plasmopara viticola infections
title_full A Bayesian model for control strategy selection against Plasmopara viticola infections
title_fullStr A Bayesian model for control strategy selection against Plasmopara viticola infections
title_full_unstemmed A Bayesian model for control strategy selection against Plasmopara viticola infections
title_short A Bayesian model for control strategy selection against Plasmopara viticola infections
title_sort bayesian model for control strategy selection against plasmopara viticola infections
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399454/
https://www.ncbi.nlm.nih.gov/pubmed/37546263
http://dx.doi.org/10.3389/fpls.2023.1117498
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