simulação estocástica de atributos do clima e da produtividade potencial de milho utilizando-se distribuição triangular stochastic simulation of climate parameters and potential productivity of maize using triangular distribution

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ID: 145478
2006
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Ranked #70 of 193 articles by views in proc - the fourth ieee workshop on software technol for future embedded and ubiquitous syst, seus 2006 andthe second int workshop on collaborative comput, integr, and assur, wccia 2006

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Abstract
O objetivo deste trabalho foi simular a produtividade potencial da cultura de milho, pelo método de Monte Carlo, utilizando um modelo agrometeorológico estocástico. O experimento foi conduzido em Piracicaba, SP, a 22º42'30''S, 47º38'30''W, e altitude de 546 m, o clima da região é do tipo Cwa (tropical úmido). Foram utilizados os valores médios diários de temperatura (de 1917 a 2002) e radiação solar global (de 1978 a 2002). Para comparar os dados reais com os simulados, foram utilizados índices de desempenho estatístico. Observou-se que os modelos probabilísticos, desenvolvidos para a simulação de dados médios diários de temperatura e de radiação solar global, geraram valores semelhantes aos observados por meio da distribuição triangular, a qual pode ser utilizada em modelo estocástico, para previsão da produtividade potencial de milho, nas diferentes épocas de semeadura.
The objective of this work was to simulate the potential productivity of maize, through Monte Carlo method, using an agrometeorological stochastic model. The experiment was conduct in Piracicaba, State of São Paulo, Brazil, at 22º42'30''S, 47º38'30''W, and 546 m altitude, the climate of the region is Cwa type, tropical humid. Daily average values of temperature (from 1917 to 2002) and global solar radiation (from 1978 to 2002) were used. Statistical performance index was used to compare observed and simulated data. The probabilistic models developed to simulate temperature and solar radiation averages produced similar values to observed data through triangular distribution, and it can be used in stochastic models to predict maize potential productivity in different dates of sowing.
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Authors ;Janilson Pinheiro de Assis;Durval Dourado Neto;Luciano Lourenço Nass;Paulo Augusto Manfron;Reinaldo Antonio Garcia Bonnecarrère;Thomas Newton Martin
Journal proc - the fourth ieee workshop on software technol for future embedded and ubiquitous syst, seus 2006 andthe second int workshop on collaborative comput, integr, and assur, wccia 2006
Year 2006
DOI
10.1590/S0100-204X2006000300024
URL
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2006000300024
Keywords
zea mays probability distribution

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