Application of Spatial Modeling for Upland Cotton Yield in the Semi-Arid of Paraíba State, Brazil

Abstract

This study aimed to evaluate the spatial dependence between agrometeorological variables and upland cotton yield the microregion of Cariri Oriental, Paraíba state (Brazil), using weighted spatial modeling system. In this study, we used the historical agricultural production data from the Brazilian Institute of Geography and Statistics (IBGE), observed rainfall data from the Executive Water Management Agency of Paraíba State (AESA), and air temperature averages estimated by the Estima_T software. Spatial regression models (classical -CR, autoregressive -SARM, and spatial error -SEM) were used to correlate the dependent variable (upland cotton yield) with covariates (agrometeorological variables). Fitted model parameters were estimated using the Maximum Likelihood method. Model performance was evaluated based on coefficient of determination (R 2 ), maximum likelihood function logarithm (AIC), and spatial residue analysis. Moreover, an exploratory spatial analysis allowed us to verify spatial autocorrelation between upland cotton yield and agrometeorological elements, using statistical tools such as Moran's index I.