<b>Regression models for prediction of corn yield in the state of Paraná (Brazil) from 2012 to 2014

• Main Authors: Rodolfo Seffrin, Everton Coimbra De Araújo, Claudio Leones Bazzi
• Format: Article
• Language: English
• Published: Eduem (Editora da Universidade Estadual de Maringá) 2018-03-01
• Series: Acta Scientiarum: Agronomy
• Subjects: autoregressive spatial model; moran’s index; spatial autocorrelation; spatial error model; spatial regression.
• Online Access: http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/36494

This study aimed to identify areas that showed spatial autocorrelation for corn yield and its predictive variables (i.e., average air temperature, rainfall, solar radiation, soil agricultural potential and altitude) and to determine the most appropriate spatial regression model to explain this culture. The study was conducted using data from the municipalities of the state of Paraná relating to the summer harvests in 2011/2012, 2012/2013, and 2013/2014. The statistical diagnostic of the OLS (Ordinary Least Square regression model) was employed to determine the most suitable regression model to predict corn yield. The SAR (Spatial Lag Model) was recommended for all crop years; however, the Spatial Error Model (CAR) was recommended only for the 2013/2014 crop year. The SAR and CAR spatial regressions chosen to predict corn yield in the various years had better results when compared to a regression model that does not incorporate data spatial autocorrelation (OLS). The coefficient of determination (R²), the Bayesian information criteria (BIC) and the maximum value of the logarithm of likelihood function proved to be better for the estimation of corn yield when SAR and CAR were used.