Data/hora: 02/06/2024 00:41:02

Provedor de dados: 	90	
País: 	Brazil	
Título: 	Selecting statistical models to study the relationship between soybean yield and soil physical properties	
Autores: 	Oliveira,Marcio Paulo de; Tavares,Maria Hermínia Ferreira; Uribe-Opazo,Miguel Angel; Timm,Luis Carlos.	
Data: 	2011-02-01	
Ano: 	2011	
Palavras-chave: 	Autocorrelation; Cross correlation; Linear regression; State-space model; Soil and plant properties.	
Resumo: 	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 transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832011000100009	
Editor: 	Sociedade Brasileira de Ciência do Solo	
Relação: 	10.1590/S0100-06832011000100009	
Formato: 	text/html	
Fonte: 	Revista Brasileira de Ciência do Solo  v.35 n.1 2011	
Fonte: 	info:eu-repo/semantics/openAccess