Selecting statistical models to study the relationship between soybean yield and soil physical properties

• Published in: Revista Brasileira de Ciência do Solo Vol. 35; no. 1; pp. 97 - 104
• Main Authors: Marcio Paulo de Oliveira, Maria Hermínia Ferreira Tavares, Miguel Angel Uribe-Opazo, Luis Carlos Timm
• Format: Journal Article
• Language: English
• Published: Sociedade Brasileira de Ciência do Solo 01-02-2011
• Subjects: atributos do solo e da planta; autocorrelação; correlação cruzada; modelo de espaço de estados; regressão linear
• ISSN: 1806-9657
• DOI: 10.1590/S0100-06832011000100009

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.