Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/963264
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dc.contributor.authorBERNARDES, T.pt_BR
dc.contributor.authorMOREIRA, M. A.pt_BR
dc.contributor.authorVERONA, J. D.pt_BR
dc.contributor.authorSHIMABUKURO, Y. E.pt_BR
dc.contributor.authorLUIZ, A. J. B.pt_BR
dc.date.accessioned2013-08-02T11:11:11Zpt_BR
dc.date.available2013-08-02T11:11:11Zpt_BR
dc.date.created2013-08-02pt_BR
dc.date.issued2013pt_BR
dc.identifier.citationIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 16., 2013, Foz do Iguaçu. Anais... Foz do Iguaçu: INPE, 2013. p. 0720-0727.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/963264pt_BR
dc.descriptionAbstract. Coffee fields present a specific pattern of productivity resulting in high and low production in alternated years. Branches grown the first phenological year will produce coffee beans the second phenological year. In high-production years a plant works mostly to grain-filling to the detriment of new branches which will be responsible for production the following year. In low-production years the plant works rather to grow new branches which will produce beans the subsequent year. This feature can be related to the foliar biomass, which can be estimated through remote sensing derived vegetation indices. Several studies report this feature must be incorporated in modeling coffee yield coupled with agrometeorogical models. In this paper we derived Landsat vegetation indices related to coffee plots in order to obtain relationships to yield of the same coffee plots. Biophisical variables and yield data were colected in interviews with farmers from four locations in the whole largest Brazilian coffee-exporting province. Vegetation indices and biophysical variables were selected through stepwise regression in order to obtain the best regression models to estimate coffee yield. Outcomes showed that general models and specific models obtained for Mundo Novo variety presented Pearson's correlation coeficients (r) from 0,64 to 0,71 while models for Catuaí variety showed better results (r = 0,85). Although coffee yield cannot be estimated exclusively from these models, they can be usefull coupled with agrometeorogical models for estimating coffee yield.pt_BR
dc.language.isoporpt_BR
dc.rightsopenAccesspt_BR
dc.subjectCoffee yieldpt_BR
dc.subjectBiophysics variablespt_BR
dc.subjectVegetation indicespt_BR
dc.subjectRadiometric correctionpt_BR
dc.subjectStepwise regressionpt_BR
dc.subjectCoffeept_BR
dc.titleVariáveis e modelos para estimativa da produtividade do cafeeiro a partir de índices de vegetação derivados de imagens Landsat.pt_BR
dc.typeArtigo em anais e proceedingspt_BR
dc.date.updated2013-08-05T11:11:11Zpt_BR
dc.subject.thesagroCafépt_BR
dc.subject.thesagroProdutividadept_BR
dc.subject.thesagroSensoriamento remotopt_BR
dc.subject.nalthesaurusGrain yieldpt_BR
dc.subject.nalthesaurusAgricultural management modelspt_BR
dc.subject.nalthesaurusStatistical modelspt_BR
riaa.ainfo.id963264pt_BR
riaa.ainfo.lastupdate2013-08-05pt_BR
dc.contributor.institutionTIAGO BERNARDES, CEMADEN; MAURÍCIO ALVES MOREIRA, INPE; JANE DELANE VERONA, INPE; YOSIO EDEMIR SHIMABUKURO, INPE; ALFREDO JOSE BARRETO LUIZ, CNPMA.pt_BR
Aparece nas coleções:Artigo em anais de congresso (CNPMA)

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