Weather-based predictive models for Diaporthe helianthi ascospore release in Uruguay
Stem canker (SC), caused by Diaporthe helianthi, is the most serious sunflower disease in Uruguay. Yield losses have been estimated up to 75%. Chemical control is one of the strategies used to manage this disease, but fungicide application should be done before symptoms are visible. Ascospores are t...
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| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
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Springer
2019
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| Online Access: | https://link.springer.com/content/pdf/10.1007%2Fs13313-019-00655-x.pdf http://hdl.handle.net/20.500.12123/6117 https://doi.org/10.1007/s13313-019-00655-x |
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| author | Moschini, Ricardo Carlos Rodríguez, M.J Martinez, Malvina Irene Stewart, S. |
| author_browse | Martinez, Malvina Irene Moschini, Ricardo Carlos Rodríguez, M.J Stewart, S. |
| author_facet | Moschini, Ricardo Carlos Rodríguez, M.J Martinez, Malvina Irene Stewart, S. |
| author_sort | Moschini, Ricardo Carlos |
| collection | INTA Digital |
| description | Stem canker (SC), caused by Diaporthe helianthi, is the most serious sunflower disease in Uruguay. Yield losses have been estimated up to 75%. Chemical control is one of the strategies used to manage this disease, but fungicide application should be done before symptoms are visible. Ascospores are the primary source of inoculum, they are produced in perithecia which develop in infected stubble and are dispersed by wind to infect plants. As in other monocyclic diseases, quantifying primary inoculum is
essential to predict an epidemic. In this study, ascospores were trapped on microscope slides with solid petroleum jelly which were placed on top of flat open cages filled with natural infected stubble. Cages were placed outdoors, slides where replaced twice a week and stained ascospores were counted under the microscope. Our objective was to develop weather-based models to predict ascospore release levels of D. helianthi from infected stubble. Explanatory weather variables were calculated during the seven-day periods prior to each field weekly ascospore count using daily weather station data from La Estanzuela, Uruguay.
Then, logistic models were fit to estimate probabilities of having severe or moderate to light levels of ascospore counts. The best models included variables associated to the precipitation and dew-induced wetness frequency, combinated with the simultaneous occurrence of high relative humidity or low thermal amplitude records. Estimating the evolution of ascospore release through the weather-based models might help to guide preventive fungicide applications to control stem canker in Uruguay |
| format | info:ar-repo/semantics/artículo |
| id | INTA6117 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | INTA61172019-10-15T18:34:31Z Weather-based predictive models for Diaporthe helianthi ascospore release in Uruguay Moschini, Ricardo Carlos Rodríguez, M.J Martinez, Malvina Irene Stewart, S. Logit Analysis Weather Helianthus Annuus Diaporthe Helianthi Uruguay Stem Canker Sunflower Logistic Models Stem canker (SC), caused by Diaporthe helianthi, is the most serious sunflower disease in Uruguay. Yield losses have been estimated up to 75%. Chemical control is one of the strategies used to manage this disease, but fungicide application should be done before symptoms are visible. Ascospores are the primary source of inoculum, they are produced in perithecia which develop in infected stubble and are dispersed by wind to infect plants. As in other monocyclic diseases, quantifying primary inoculum is essential to predict an epidemic. In this study, ascospores were trapped on microscope slides with solid petroleum jelly which were placed on top of flat open cages filled with natural infected stubble. Cages were placed outdoors, slides where replaced twice a week and stained ascospores were counted under the microscope. Our objective was to develop weather-based models to predict ascospore release levels of D. helianthi from infected stubble. Explanatory weather variables were calculated during the seven-day periods prior to each field weekly ascospore count using daily weather station data from La Estanzuela, Uruguay. Then, logistic models were fit to estimate probabilities of having severe or moderate to light levels of ascospore counts. The best models included variables associated to the precipitation and dew-induced wetness frequency, combinated with the simultaneous occurrence of high relative humidity or low thermal amplitude records. Estimating the evolution of ascospore release through the weather-based models might help to guide preventive fungicide applications to control stem canker in Uruguay Fil: Moschini, Ricardo Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Rodríguez, M.J. Instituto Nacional de Investigaciones Agropecuarias (INIA), La Estanzuela. Sección Protección Vegetal; Uruguay Fil: Martinez, Malvina Irene. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Stewart, S. Instituto Nacional de Investigaciones Agropecuarias (INIA), La Estanzuela. Sección Protección Vegetal; Uruguay 2019-10-15T18:17:46Z 2019-10-15T18:17:46Z 2019-08-01 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://link.springer.com/content/pdf/10.1007%2Fs13313-019-00655-x.pdf http://hdl.handle.net/20.500.12123/6117 1448-6032 https://doi.org/10.1007/s13313-019-00655-x eng info:eu-repo/semantics/restrictedAccess application/pdf Uruguay (nation) Springer Austrasian plant pathology 48 (5) : 519-527.(September 2019) |
| spellingShingle | Logit Analysis Weather Helianthus Annuus Diaporthe Helianthi Uruguay Stem Canker Sunflower Logistic Models Moschini, Ricardo Carlos Rodríguez, M.J Martinez, Malvina Irene Stewart, S. Weather-based predictive models for Diaporthe helianthi ascospore release in Uruguay |
| title | Weather-based predictive models for Diaporthe helianthi ascospore release in Uruguay |
| title_full | Weather-based predictive models for Diaporthe helianthi ascospore release in Uruguay |
| title_fullStr | Weather-based predictive models for Diaporthe helianthi ascospore release in Uruguay |
| title_full_unstemmed | Weather-based predictive models for Diaporthe helianthi ascospore release in Uruguay |
| title_short | Weather-based predictive models for Diaporthe helianthi ascospore release in Uruguay |
| title_sort | weather based predictive models for diaporthe helianthi ascospore release in uruguay |
| topic | Logit Analysis Weather Helianthus Annuus Diaporthe Helianthi Uruguay Stem Canker Sunflower Logistic Models |
| url | https://link.springer.com/content/pdf/10.1007%2Fs13313-019-00655-x.pdf http://hdl.handle.net/20.500.12123/6117 https://doi.org/10.1007/s13313-019-00655-x |
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