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Resumen
The effects of meteorological conditions around silking (Si) and physiological maturity (PM) on production of fumonisins (FB=FB1 + FB2) in maize kernels were analyzed. Kernel FB contents were determined on kernel samples collected from an experimental susceptible hybrid (n = 35) and a Bt commercial hybrid (n = 23) planted in several growing seasons/sites from the Argentinean Pampas. Considering the effect of genetic divergence of the two maize hybrids, [ver mas...]
dc.contributor.authorMoschini, Ricardo
dc.contributor.authorBorsarelli, M.
dc.contributor.authorMartinez, Malvina Irene
dc.contributor.authorPresello, Daniel Alberto
dc.contributor.authorFerraguti, Facundo Javier
dc.contributor.authorCristos, Diego Sebastian
dc.contributor.authorRojas, Dante Emanuel
dc.date.accessioned2021-04-14T11:12:51Z
dc.date.available2021-04-14T11:12:51Z
dc.date.issued2020-09-25
dc.identifier.issn1833-928X
dc.identifier.otherhttps://doi.org/10.1007/s13313-020-00742-4
dc.identifier.urihttp://hdl.handle.net/20.500.12123/9083
dc.identifier.urihttps://link.springer.com/article/10.1007%2Fs13313-020-00742-4
dc.description.abstractThe effects of meteorological conditions around silking (Si) and physiological maturity (PM) on production of fumonisins (FB=FB1 + FB2) in maize kernels were analyzed. Kernel FB contents were determined on kernel samples collected from an experimental susceptible hybrid (n = 35) and a Bt commercial hybrid (n = 23) planted in several growing seasons/sites from the Argentinean Pampas. Considering the effect of genetic divergence of the two maize hybrids, total kernel FB concentrations (n = 52) binary coded, were predicted appropriately by weather-based logistic regression models but underestimated in some samples severely contaminated with FB. After removing thesemisclassified cases that registered maximum values ofa drought-heat stress index (DI) calculated over 30 days around Si, and weather conditions (assessed by weather interactive components) conducive to infection/production of FB in PM, new logistic models were fitted. These new models improved their predictive ability indices. It was remarkable a model including the discrete genetic variable and the weather variable associated with the simultaneous occurrence of precipitation, temperature between 19.5–33 °C and relative humidity >70%, required for fungal infection in Si. Models that also included weather variables calculated in PM and associated with the kernel drying rate and fungal infection, did not result in better prediction outcomes. Opposite to the general trend, the occurrence of both severe heat-drought stress around Si and favorable weather around PM led to high kernel FB contents. We hypothesize that husk shortening by stress at silking might expose ears, promoting Fusarium verticillioides colonization/FB synthesis in late stages of kernel development and maturity, whenever favorable environmental conditions for both processes prevail.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherSpringeres_AR
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_AR
dc.sourceAustralasian Plant Pathology 49 (6) : 665-677 (20209es_AR
dc.subjectFusariumes_AR
dc.subjectMycotoxinseng
dc.subjectMicotoxinases_AR
dc.subjectWeather Variableseng
dc.subjectVariables Meteorológicases_AR
dc.subjectMaíz
dc.subjectMaizeeng
dc.subjectHíbridos
dc.subjectHybridseng
dc.subjectFumonisinas
dc.subjectFumonisinseng
dc.subject.otherLogistic Regression Modelseng
dc.subject.otherModelos de Regresión Logísticaes_AR
dc.titleAnalysis of preharvest meteorological conditions in relation to concentration of fumonisins in kernels of two genetically different maize hybridses_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.description.filFil: Moschini, Ricardo Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentinaes_AR
dc.description.filFil: Borsarelli. M. Bayer Argentina; Argentinaes_AR
dc.description.filFil: Martinez, Malvina Irene. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentinaes_AR
dc.description.filFil: Presello, Daniel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Departamento de Maíz; Argentinaes_AR
dc.description.filFil: Ferraguti, Facundo Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros; Argentina.es_AR
dc.description.filFil: Cristos, Diego Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Tecnología de Alimentos; Argentinaes_AR
dc.description.filFil: Rojas, Dante Emanuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Tecnología de Alimentos; Argentinaes_AR
dc.subtypecientifico


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