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dc.contributor.authorOliveira Aparecido, Lucas Eduardo de
dc.contributor.authorSilva Cabral de Moraes, Jose Reinaldo da
dc.contributor.authorRolim, Glauco de Souza [UNESP]
dc.contributor.authorMartorano, Lucieta Guerreiro
dc.contributor.authorSoares, Sabrina dos Santos
dc.contributor.authorMeneses, Kamila Cunha de [UNESP]
dc.contributor.authorSilva Costa, Cicero Teixeira
dc.contributor.authorMesquita, Daniel Zimmermann
dc.contributor.authorSilva Barbosa, Aline Michelle da [UNESP]
dc.contributor.authorAmaral, Eufran Ferreira do
dc.contributor.authorBardales, Nilson Gomes
dc.date.accessioned2019-10-04T12:30:57Z
dc.date.available2019-10-04T12:30:57Z
dc.date.issued2018-11-01
dc.identifierhttp://dx.doi.org/10.1007/s00484-018-1596-1
dc.identifier.citationInternational Journal Of Biometeorology. New York: Springer, v. 62, n. 11, p. 1955-1962, 2018.
dc.identifier.issn0020-7128
dc.identifier.urihttp://hdl.handle.net/11449/184891
dc.description.abstractBamboo has an important role in international commerce due to its diverse uses, but few studies have been conducted to evaluate its climatic adaptability. Thus, the objective of this study was to construct an agricultural zoning for climate risk (ZARC) for bamboo using meteorological elements spatialized by neural networks. Climate data included air temperature (T-AIR, degrees C) and rainfall (P) from 4947 meteorological stations in Brazil from the years 1950 to 2016. Regions were considered climatically apt for bamboo cultivation when T-AIR varied between 18 and 35 degrees C, and P was between 500 and 2800 mm year(-1), or P-WINTER was between 90 and 180 mm year(-1). The remainder of the areas was considered marginal or inapt for bamboo cultivation. A multilayer perceptron (MLP) neural network with a multilayered backpropagation training algorithm was used to spatialize the territorial variability of each climatic element for the whole area of Brazil. Using the overlapping of the T-AIR, P, and P-WINTER maps prepared by MLP, and the established climatic criteria of bamboo, we established the agricultural zoning for bamboo. Brazil demonstrates high seasonal climatic variability with T-AIR varying between 14 and 30 degrees C, and P varying between < 400 and 4000 mm year(-1). The ZARC showed that 87% of Brazil is climatically apt for bamboo cultivation. The states that were classified as apt in 100% of their territories were Mato Grosso do Sul, Goias, Tocantins, Rio de Janeiro, Espirito Santo, Sergipe, Alagoas, Ceara, Piaui, MaranhAo, Rondonia, and Acre. The regions that have restrictions due to low T-AIR represent just 11% of Brazilian territory. This agroclimatic zoning allowed for the classification of regions based on aptitude of climate for bamboo cultivation and showed that 71% of the total national territory is considered to be apt for bamboo cultivation. The regions that have restrictions are part of southern Brazil due to low values of T-AIR and portions of the northern region that have high levels of P which is favorable for the development of diseases.en
dc.format.extent1955-1962
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofInternational Journal Of Biometeorology
dc.sourceWeb of Science
dc.subjectModeling
dc.subjectCrop zoning
dc.subjectMultilayer perceptron
dc.subjectTraining algorithm
dc.subjectClimatology
dc.titleNeural networks in spatialization of meteorological elements and their application in the climatic agricultural zoning of bambooen
dc.typeArtigo
dcterms.licensehttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dcterms.rightsHolderSpringer
dc.contributor.institutionFed Inst Educ Sci & Technol Mato Grosso Sul
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
dc.description.affiliationFed Inst Educ Sci & Technol Mato Grosso Sul, Campus Navirai, Navirai, MS, Brazil
dc.description.affiliationSao Paulo State Univ, Dept Exact Sci, BR-14884900 Jaboticabal, SP, Brazil
dc.description.affiliationEmbrapa Eastern Amazon, Trav Dr Eneas Pinheiro S-N, Belem, Para, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Exact Sci, BR-14884900 Jaboticabal, SP, Brazil
dc.identifier.doi10.1007/s00484-018-1596-1
dc.identifier.wosWOS:000446176400003
dc.rights.accessRightsAcesso aberto
unesp.author.orcid0000-0002-8567-4893[2]
unesp.author.orcid0000-0001-9200-5260[6]
unesp.author.orcid0000-0001-8336-2645[9]
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