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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Efici?ncia nutricional, ?rea foliar e produtividade de planta??es de eucalipto em diferentes espa?amentos estimados com redes neurais artificiais.

Lafet?, Bruno Oliveira 29 February 2012 (has links)
Submitted by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2015-01-23T14:01:52Z No. of bitstreams: 5 11.pdf: 1601673 bytes, checksum: 39d0ae1d4c83f9f75b192f272b66de11 (MD5) license_url: 52 bytes, checksum: 3d480ae6c91e310daba2020f8787d6f9 (MD5) license_text: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) license.txt: 2109 bytes, checksum: aa477231e840f304454a16eb85a9235f (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2015-02-10T11:12:29Z (GMT) No. of bitstreams: 5 license_url: 52 bytes, checksum: 3d480ae6c91e310daba2020f8787d6f9 (MD5) license_text: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) license.txt: 2109 bytes, checksum: aa477231e840f304454a16eb85a9235f (MD5) 11.pdf: 1601673 bytes, checksum: 39d0ae1d4c83f9f75b192f272b66de11 (MD5) / Made available in DSpace on 2015-02-10T11:12:29Z (GMT). No. of bitstreams: 5 license_url: 52 bytes, checksum: 3d480ae6c91e310daba2020f8787d6f9 (MD5) license_text: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) license.txt: 2109 bytes, checksum: aa477231e840f304454a16eb85a9235f (MD5) 11.pdf: 1601673 bytes, checksum: 39d0ae1d4c83f9f75b192f272b66de11 (MD5) Previous issue date: 2012 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / O emprego de pr?ticas silviculturais apropriadas associado ao uso de m?todos de avalia??o nutricional e t?cnicas estat?sticas avan?adas pode ser uma alternativa vi?vel no estabelecimento de crit?rios pr?ticos de caracteriza??o e classifica??o nutricional, al?m de permitir obter informa??es sobre a din?mica de crescimento dentro de povoamentos florestais, enriquecendo estudos sobre a sustentabilidade e produ??o de um ecossistema florestal. O presente trabalho foi dividido em tr?s cap?tulos. Os objetivos foram avaliar os coeficientes de utiliza??o biol?gicos (CUB?s) dos nutrientes pelo eucalipto, a efici?ncia e possibilidade de utiliza??o das redes neurais artificiais (RNA) para obter os CUB?s e estimativas para a biomassa de tronco sob diferentes espa?amentos. O experimento foi instalado em dezembro de 2002 utilizando-se um h?brido de Eucalyptus grandis W. Hill ex Maiden x Eucalyptus camaldulensis Dehnh sobre Latossolo Vermelho-Amarelo em relevo plano a 1097 m altitude. Adotou-se o delineamento em blocos ao acaso sendo estudado, em tr?s blocos, o efeito de cinco espa?amentos de plantio: T1 - 3,0 x 0,5 m; T2 - 3,0 x 1,0 m; T3 - 3,0 x 1,5 m; T4 - 3,0 x 2,0 m e T5 - 3,0 x 3,0 m. Realizou-se o invent?rio florestal cont?nuo nas idades de 48, 61, 73, 85 e 101 meses. Em cada ?rvore-amostra por unidade experimental na ?ltima idade foram: quantificada a biomassa; mensurada a ?rea e per?metro foliar, ?rea foliar espec?fica e realizada a an?lise qu?mica de N, P, K, Ca, Mg e S para amostras composta (ao longo do fuste) e simples (regi?o do DAP) dos componentes. Os resultados foram submetidos ? ANOVA, regress?o e a aplica??o das RNA. A modelagem por redes neurais artificiais demonstrou-se adequada para estimar a produ??o de biomassa de tronco em fun??o da idade sob diferentes espa?amentos, utilizando o DAP e per?metro foliar como vari?veis preditoras. N?o houve grande varia??o da efici?ncia de uso dos nutrientes entre os espa?amentos, principalmente para o tronco. A rede neural artificial foi eficiente em estimar a efici?ncia de uso dos nutrientes. A modelagem por redes neurais artificiais utilizando-se apenas amostra da casca na regi?o do DAP demonstrou ser adequada para a estimativa do coeficiente de utiliza??o biol?gico do tronco. / Disserta??o (Mestrado) ? Programa de P?s-Gradua??o em Ci?ncia Florestal, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2012. / ABSTRACT The use of appropriate silvicultural practices associated with the use of methods of nutritional assessment and advanced statistical techniques can be a viable alternative to establish practical criteria characterization and classification of nutritional status, and allows information on the dynamics of growth in forest stands, enriching studies on the sustainability and production of a forest ecosystem. This work was divided into three chapters. The objectives were assess the coefficient of biological use (CUB's) of nutrients by eucalyptus trees, the efficiency and possible use of artificial neural networks (RNA) for the CUB's and biomass trunk under different spacings. The research plots was installed in december 2002 using a hybrid of Eucalyptus grandis W. Hill ex Maiden X Eucalyptus camaldulensis Dehnh. The statistical design was randomized blocks being studied, in three blocks, the effect of different planting spacings: T1???3,0?x?0,5?m, T2???3,0?x?1,0?m, T3???3,0?x?1,5?m, T4???3,0?x?2,0?m e T5???3,0?x?3,0?m. Data collection was carried out at ages 48, 61, 73, 85 and 101 months. In each sample-tree per experimental unit in the last age were: quantified biomass; measured leaf area, leaf perimeter, specific leaf area and chemical analysis of N, P, K, Ca, Mg and S for composite samples (along the stem) and simple samples (the region of the DAP) of components. Statistical analysis of the data consisted of ANOVA, regression and application of RNA. The modeling by artificial neural networks demonstred to be adequate to estimate the biomass of the trunk in relation to age at different spacings, using the DBH and perimeter leaf as predictors variables. There wasn't wide variation in efficiency use nutrient among spacings, especially for the trunk. The artificial neural network was effective in estimating the efficiency of nutrient use. The modeling by artificial neural networks using only sample in the DAP region proved to be adequate for estimating the coefficient of biological use of stem.

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