Statistical Forecasting Model for Maize Yield in the Semiarid Region of Córdoba Based on Areal Rainfall Data
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Abstract
An statistical model for corn yield forecasting is developed to estimate the crop productivity in the semiarid region of the Córdoba province. Monthly precipitation data were used to calculate areal precipitation, which was utilized as an independent variable set. The methodology consisted of a multivariate analysis with a stepwise program. The predictor variables were included or eliminated from the model one at a time, considering the F value used to evaluate the significance of the relationships. The model considered pluviometric, technology, and geographic terms. The error tests were 17 and 18% compared with the control data. Significant differences were detected in the Río Cuarto department. October and November precipitation are the variables more related to district corn yield.
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