Método Bootstrap na agricultura de precisão

Abstract

One issue in precision agriculture studies concerns about the statistical methods applied in inferential analysis, since they have required assumptions that, sometimes, cannot be assumed. A possibility to traditional methods is to use the bootstrap method, which consists in resampling and replacing the original data set to carry out inferences. The bootstrap methodology can be applied to independent sample data as well as in cases of dependence, such as in spatial statistics. However, adjustments are required during the resampling process in order to use the bootstrap method in spatial data. Thus, this trial aimed at applying the bootstrap method in precision agriculture studies, whose result was the preparation of three scientific papers. Soybean yield and soil attributes datasets formed with few samples were used in the first paper to determine a multiple linear regression model. Bootstrap methods were chosen to select variables, identify influential points and determine confidence intervals of the model parameters. The results showed that the bootstrap methods allowed selecting significant attributes to design a model, to build confidence intervals of the studied parameters and finally to indentify the influential points on the estimated parameters. Besides, spatial dependence of soybean yield data and soil attributes were studied in the second paper by bootstrap method in geostatistical analysis. The spatial bootstrap method was used to quantify the uncertainties associated with the spatial dependence structure, the fitted model parameter estimators, kriging predicted values and multivariate normality assumption of data. Thus, it was possible to quantify the uncertainties in all phases of geostatistical analysis. A spatial linear model was used to analyze soybean yield considering the soil attributes in the third paper. Spatial bootstrap methods were used to determine point and interval estimators associated with the studied model parameters. Hypothesis tests were carried out on the model parameters and probability plots were developed to identify data normality. These methods allowed to quantify the uncertainties associated to the structure of spatial dependence, as well as to evaluate the individual significance of the parameters associated with the average of the spatial linear model and to verify data multivariate normality assumption. Finally, it is concluded that bootstrap method is an effective alternative to make statistical inferences in precision agriculture studies.