Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1096580
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Campo DCValorIdioma
dc.contributor.authorBOGGIONE, I. M.
dc.contributor.authorANDRADE, C. de L. T. de
dc.contributor.authorBORGES JÚNIOR, J. C. F.
dc.contributor.authorVIANA, J. H. M.
dc.date.accessioned2018-09-29T00:36:38Z-
dc.date.available2018-09-29T00:36:38Z-
dc.date.created2018-09-28
dc.date.issued2018
dc.identifier.citationRevista Brasileira de Milho e Sorgo, Sete Lagoas, v. 17, n. 2, p. 201-215, 2018.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1096580-
dc.descriptionIn Brazil, the rainfed maize crop may undergo yield breaks due to uncertainties in the rainfall distribution. Irrigation can be a management alternative that, however, requires evaluation and planning to be helpful. The objective of this work was to analyze the simulated yield data of irrigated maize in counties of Minas Gerais state, Brazil. The CSM-CERES-Maize model was used to simulated weekly sowings of maize considering optimum agronomic conditions. A sprinkler irrigation scheme with 80% efficiency was used with automatic applications when the crop withdrew 50% of the soil available water. The harvest was scheduled to happen automatically when the crop had reached physiological maturity. The results were statistically analyzed for each county, based on goodness of fit test, ANOVA, Tukey?s test and risk analysis (stochastic dominance). The most promising sowing period was from January 16 to March 27 for all locations, except for Janaúba, for which the best sowing window was from November 14 to January 2. The treatments of highest average simulated maize yield stochastically dominated the other treatments evaluated. The CSM-CERES-Maize model proved to be a useful tool to help making decision in irrigated maize crop systems.
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectCSM-CERES-Maize
dc.subjectDSSAT
dc.subjectModelagem
dc.titleModeling applied to sowing date of irrigated maize.
dc.typeArtigo de periódico
dc.date.updated2019-03-15T11:11:11Zpt_BR
dc.subject.thesagroAnálise de Risco
dc.subject.thesagroIrrigação
dc.subject.thesagroModelo de Simulação
riaa.ainfo.id1096580
riaa.ainfo.lastupdate2019-03-15 -03:00:00
dc.contributor.institutionIvaldo Martins Boggione, Emater-MG; CAMILO DE LELIS TEIXEIRA DE ANDRADE, CNPMS; João Carlos Ferreira Borges Júnior, Universidade Federal de São João del-Rei; JOAO HERBERT MOREIRA VIANA, CNPMS.
Aparece nas coleções:Artigo em periódico indexado (CNPMS)

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