Estatística espacial da precipitação pluviométrica associada à produtividade de milho segunda safra no Paraná utilizando o modelo WAVE - Gamero, Paulo (2019)
Geostatistics has been used in several areas of research and consequently also in agriculture. It is an important ally for decision-making and a reliable support to the producer, who, using geostatistics, can improve the cultivation, increasing crop productivity, thereby favoring the agricultural sector. Precipitation is a natural phenomenon that has high spatial and temporal variation, and the study of its behavior is important since it is a phenomenon that directly affects crops, such as corn, which is a crop that presents high hydric dependence during all its physiological evolution, mostly between the flowerage and the grain formation. Due to this dependence, the present study aims to analyze the spatial behavior of precipitation in Paraná state in the period from 1996 to 2015, using data from the stations of the HidroWeb database of the Brazilian National Water Agency on an accumulated 10-day scale, as well as the influence of such phenomenon over the second crop of the corn cultivation (“safrinha”), specifically over the maturation stage (the stage in which the plant needs the greater water presence) until harvest. Statistical and geostatistical methods were employed in the research, starting with the imputation of the precipitation data by the Inverse Distance Weighted (IDW) method. Thereafter, the spatial behavior of the precipitation was analyzed, considering several periods using the Wave geostatistical model, estimating the parameters by the maximum likelihood method and interpolation by ordinary kriging. Finally, the spatial correlation of the decendial data with those of second-crop corn was studied, using the Moran bivariate and codispersion coefficient indexes, obtaining important results. Therefore, it was concluded that precipitation shows heterogeneous behavior, regarding both space and time, and presents spatial correlation with second-crop corn.