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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Acre; Embrapa Amazônia Oriental. |
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Data corrente: |
31/10/2018 |
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Data da última atualização: |
10/01/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
APARECIDO, L. E. de O.; MORAES, J. R. da S. C. de; ROLIM, G. de S.; MARTORANO, L. G.; SOARES, S. dos S.; MENESES, K. C. de; COSTA, C. T. S.; MESQUITA, D. Z.; BARBOSA, A. M. da S.; AMARAL, E. F. do; BARDALES, N. G. |
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Afiliação: |
Lucas Eduardo de Oliveira Aparecido, Federal Institute of Education, Science and Technology of Mato Grosso do Sul; José Reinaldo da Silva Cabral de Moraes, Federal Institute of Education, Science and Technology of Mato Grosso do Sul; Glauco de Souza Rolim, São Paulo State University; LUCIETA GUERREIRO MARTORANO, CPATU; Sabrina dos Santos Soares, Federal Institute of Education, Science and Technology of Mato Grosso do Sul; Kamila Cunha de Meneses, São Paulo State University; Cicero Teixeira Silva Costa, Federal Institute of Education, Science and Technology of Mato Grosso do Sul; Daniel Zimmermann Mesquita, Federal Institute of Education, Science and Technology of Mato Grosso do Sul; Aline Michelle da Silva Barbosa, São Paulo State University; EUFRAN FERREIRA DO AMARAL, CPAF-AC; Nilson Gomes Bardales, EMBRAPA. |
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Título: |
Neural networks in spatialization of meteorological elements and their application in the climatic agricultural zoning of bamboo. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
International Journal of Biometeorology, v. 62, n. 11, p. 1955-1962, Nov. 2018. |
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DOI: |
10.1007/s00484-018-1596-1 |
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Idioma: |
Inglês |
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Conteúdo: |
Bamboo has an important role in international commerce due to its diverse uses, but fewstudies 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 usingmeteorological elements spatialized byneural networks.Climatedata includedair temperature (TAIR, °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 TAIR varied between 18 and 35 °C, and P was between 500 and 2800 mm year−1, or PWINTER 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 amultilayered Bbackpropagation^ training algorithmwas used to spatialize the territorial variability of eachclimatic element for thewhole area ofBrazil.Usingtheoverlappingof theTAIR,P, andPWINTERmaps preparedbyMLP, and the established climatic criteria of bamboo, we established the agricultural zoning for bamboo. Brazil demonstrates high seasonal climatic variabilitywith TAIR varying between 14 and 30°C, andPvarying between< 400 and 4000mmyear−1.TheZARCshowed 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, Goiás, Tocantins, Rio de Janeiro, Espírito Santo, Sergipe, Alagoas, Ceará, Piauí, Maranhão, Rondônia, and Acre. The regions that have restrictions due to lowTAIR 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 TAIR and portions of the northern region that have high levels of P which is favorable for the development of diseases. MenosBamboo has an important role in international commerce due to its diverse uses, but fewstudies 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 usingmeteorological elements spatialized byneural networks.Climatedata includedair temperature (TAIR, °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 TAIR varied between 18 and 35 °C, and P was between 500 and 2800 mm year−1, or PWINTER 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 amultilayered Bbackpropagation^ training algorithmwas used to spatialize the territorial variability of eachclimatic element for thewhole area ofBrazil.Usingtheoverlappingof theTAIR,P, andPWINTERmaps preparedbyMLP, and the established climatic criteria of bamboo, we established the agricultural zoning for bamboo. Brazil demonstrates high seasonal climatic variabilitywith TAIR varying between 14 and 30°C, andPvarying between< 400 and 4000mmyear−1.TheZARCshowed 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, Goiás, Tocantins, Rio de Janeiro, Espírito Santo, Sergipe, Alagoas, Ceará, Piau... Mostrar Tudo |
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Palavras-Chave: |
Aclimatación; Climate risk; Crop zoning; Modeling; Multilayer perceptron; Redes neuronales; Training algorithm; Zonificación agrícola. |
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Thesagro: |
Aclimatação; Bambu; Bambusa Vulgaris; Climatologia; Modelo Matemático; Risco Climático; Zoneamento Agrícola. |
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Thesaurus NAL: |
Acclimation; Agricultural zoning; Bamboos; Climatology; Mathematical models; Neural networks. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 03571naa a2200505 a 4500
001 2098629
005 2019-01-10
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1007/s00484-018-1596-1$2DOI
100 1 $aAPARECIDO, L. E. de O.
245 $aNeural networks in spatialization of meteorological elements and their application in the climatic agricultural zoning of bamboo.$h[electronic resource]
260 $c2018
520 $aBamboo has an important role in international commerce due to its diverse uses, but fewstudies 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 usingmeteorological elements spatialized byneural networks.Climatedata includedair temperature (TAIR, °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 TAIR varied between 18 and 35 °C, and P was between 500 and 2800 mm year−1, or PWINTER 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 amultilayered Bbackpropagation^ training algorithmwas used to spatialize the territorial variability of eachclimatic element for thewhole area ofBrazil.Usingtheoverlappingof theTAIR,P, andPWINTERmaps preparedbyMLP, and the established climatic criteria of bamboo, we established the agricultural zoning for bamboo. Brazil demonstrates high seasonal climatic variabilitywith TAIR varying between 14 and 30°C, andPvarying between< 400 and 4000mmyear−1.TheZARCshowed 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, Goiás, Tocantins, Rio de Janeiro, Espírito Santo, Sergipe, Alagoas, Ceará, Piauí, Maranhão, Rondônia, and Acre. The regions that have restrictions due to lowTAIR 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 TAIR and portions of the northern region that have high levels of P which is favorable for the development of diseases.
650 $aAcclimation
650 $aAgricultural zoning
650 $aBamboos
650 $aClimatology
650 $aMathematical models
650 $aNeural networks
650 $aAclimatação
650 $aBambu
650 $aBambusa Vulgaris
650 $aClimatologia
650 $aModelo Matemático
650 $aRisco Climático
650 $aZoneamento Agrícola
653 $aAclimatación
653 $aClimate risk
653 $aCrop zoning
653 $aModeling
653 $aMultilayer perceptron
653 $aRedes neuronales
653 $aTraining algorithm
653 $aZonificación agrícola
700 1 $aMORAES, J. R. da S. C. de
700 1 $aROLIM, G. de S.
700 1 $aMARTORANO, L. G.
700 1 $aSOARES, S. dos S.
700 1 $aMENESES, K. C. de
700 1 $aCOSTA, C. T. S.
700 1 $aMESQUITA, D. Z.
700 1 $aBARBOSA, A. M. da S.
700 1 $aAMARAL, E. F. do
700 1 $aBARDALES, N. G.
773 $tInternational Journal of Biometeorology$gv. 62, n. 11, p. 1955-1962, Nov. 2018.
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