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Selecting statistical models to study the relationship between soybean yield and soil physical properties Rev. Bras. Ciênc. Solo
Oliveira,Marcio Paulo de; Tavares,Maria Hermínia Ferreira; Uribe-Opazo,Miguel Angel; Timm,Luis Carlos.
Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Autocorrelation; Cross correlation; Linear regression; State-space model; Soil and plant properties.
Ano: 2011 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832011000100009
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