Bayesian correlated models for assessing the prevalence of viruses in organic and non-organic agroecosystems
Virus diseases constitute one of the most important limiting factors in horticultural production. Cultivation of horticultural species under organic management has increased in importance in recent years. However, the sustainability of this new production method needs to be supported by scientifi...
| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
2018
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| Acceso en línea: | http://hdl.handle.net/20.500.11939/6057 https://www.idescat.cat/serveis/biblioteca/docs/bib/publicacions/r00262017v411.pdf |
| _version_ | 1855032359338901504 |
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| author | Lázaro, Elena Armero, Carmen Rubio, Luis |
| author_browse | Armero, Carmen Lázaro, Elena Rubio, Luis |
| author_facet | Lázaro, Elena Armero, Carmen Rubio, Luis |
| author_sort | Lázaro, Elena |
| collection | ReDivia |
| description | Virus diseases constitute one of the most important limiting factors in horticultural production.
Cultivation of horticultural species under organic management has increased in importance in
recent years. However, the sustainability of this new production method needs to be supported
by scientific research, especially in the field of virology. We studied the prevalence of three im-
portant virus diseases in agroecosystems with regard to its management system: organic versus
non-organic, with and without greenhouse. Prevalence was assessed by means of a Bayesian
correlated binary model which connects the risk of infection of each virus within the same plot and
was defined in terms of a logit generalized linear mixed model (GLMM). Model robustness was
checked through a sensitivity analysis based on different hyperprior scenarios. Inferential results
were examined in terms of changes in the marginal posterior distributions, both for fixed and for
random effects, through the Hellinger distance and a derived measure of sensitivity. Statistical re-
sults suggested that organic systems show lower or similar prevalence than non-organic ones in
both single and multiple infections as well as the relevance of the prior specification of the random
effects in the inferential process. |
| format | article |
| id | ReDivia6057 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| record_format | dspace |
| spelling | ReDivia60572025-04-25T14:46:04Z Bayesian correlated models for assessing the prevalence of viruses in organic and non-organic agroecosystems Lázaro, Elena Armero, Carmen Rubio, Luis Virus diseases constitute one of the most important limiting factors in horticultural production. Cultivation of horticultural species under organic management has increased in importance in recent years. However, the sustainability of this new production method needs to be supported by scientific research, especially in the field of virology. We studied the prevalence of three im- portant virus diseases in agroecosystems with regard to its management system: organic versus non-organic, with and without greenhouse. Prevalence was assessed by means of a Bayesian correlated binary model which connects the risk of infection of each virus within the same plot and was defined in terms of a logit generalized linear mixed model (GLMM). Model robustness was checked through a sensitivity analysis based on different hyperprior scenarios. Inferential results were examined in terms of changes in the marginal posterior distributions, both for fixed and for random effects, through the Hellinger distance and a derived measure of sensitivity. Statistical re- sults suggested that organic systems show lower or similar prevalence than non-organic ones in both single and multiple infections as well as the relevance of the prior specification of the random effects in the inferential process. 2018-05-09T16:30:59Z 2018-05-09T16:30:59Z 2017 article publishedVersion Lazaro, E., Armero, C., Rubio, L. (2017a). Bayesian correlated models for assessing the prevalence of viruses in organic and non-organic agroecosystems. SORT-Statistics and Operations Research Transactions, 1(1), 93-116. 1696-2281 http://hdl.handle.net/20.500.11939/6057 10.2436/20.8080.02.52 https://www.idescat.cat/serveis/biblioteca/docs/bib/publicacions/r00262017v411.pdf en Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ electronico |
| spellingShingle | Lázaro, Elena Armero, Carmen Rubio, Luis Bayesian correlated models for assessing the prevalence of viruses in organic and non-organic agroecosystems |
| title | Bayesian correlated models for assessing the prevalence of viruses in organic and non-organic agroecosystems |
| title_full | Bayesian correlated models for assessing the prevalence of viruses in organic and non-organic agroecosystems |
| title_fullStr | Bayesian correlated models for assessing the prevalence of viruses in organic and non-organic agroecosystems |
| title_full_unstemmed | Bayesian correlated models for assessing the prevalence of viruses in organic and non-organic agroecosystems |
| title_short | Bayesian correlated models for assessing the prevalence of viruses in organic and non-organic agroecosystems |
| title_sort | bayesian correlated models for assessing the prevalence of viruses in organic and non organic agroecosystems |
| url | http://hdl.handle.net/20.500.11939/6057 https://www.idescat.cat/serveis/biblioteca/docs/bib/publicacions/r00262017v411.pdf |
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