Exploring spatial dependence of cowpea-beans yield using global and local autocorrelation statistics in the Eastern Cariri region of Paraíba

Abstract

This study evaluated the variability and characterizedthe spatial dependence between some soil attributes in the Eastern Cariri microregion of Paraíba,and analyzed the spatial correlations in order to identify the interactions between such attributes in cowpea bean(Vigna unguiculata L. Walp)production. Harvest data of the agricultural years of 2000-2017 in the Eastern Cariri microregion of Paraíba were analyzed. Parameters of the fitted models wereestimated using the Maximum Likelihood method and the performance of the models was evaluated based on coefficients of determination(R2), maximum log-likelihood function, and Schwarz’s Bayesian information criterion (BIC). Correlation and spatial autocorrelation between the cowpea productivity and agrometeorological elements was detected through the spatial analysis, using techniques such as the Moran’s index I. The study showed that, according to the performance indicators used, the spatial error model offered better results in relation to the classical multiple regression models and the self-regressive spatial models, indicating that the inclusion of spatial dependence in the models improves the estimate of productivity of cowpea in the microregion of Cariri Oriental da Paraíba.

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

“…The spatial weights are usually defined by spatial nuclear functions such as the Gaussian (Fotheringham et al, 2015), in which weights are related to the closest observations. Several studies have explored GWR models (Di Leo et al, 2016;Luo & Peng, 2016;Zhao et al, 2018;Andrade et al, 2021, and. Among them, many have used one or more methods using biophysical variables as independent variables to explain the spatial variability of various physical phenomena of nature.…”