Data and script for "A Novel Methodological Framework for Predicting and Mapping Agriculture-Related Soil Attributes Using Euclidean Distance, Regular Grids, and Machine Learning Algorithms"

Article data and scripts: "A Novel Methodological Framework for Predicting and Mapping Agriculture-Related Soil Attributes Using Euclidean Distance, Regular Grids, and Machine Learning Algorithms" First Unzip the Necessary_folders.zip filesOpen the Dead_Kriging.Rproj file in R or Rstudio software The scripts should be to be run in a described sequence:00_Create_covariate01_create_data_frame_analysis02_predict_xy02_predict_2x2y_center02_predict_xy_2502_predict_xy_4902_predict_xy_10002_predict_IDW02_predict_Kriging Scripts numbered 02 can be run in any order; however, to obtain accurate time measurements, it is always recommended to restart the computer and run the process on a PC that is not being used by other software. It is necessary to install the library packages "dplyr", "caret" "sp", "sf", "doParallel", "terra", "randomForest", "Cubist","earth","kernlab", "e1071", "automap","hydroGOF","raster" and "gstat" The program ran successfully on R version 4.4.2. The data are free to use and please cite also the article:Veloso, G. V., de Mello, D. C., Silva, L. V., Fernandes-Filho, E. I., Rosas, J. T. F., de Oliveira Mello, F. A., ... & Demattê, J. A. (2026). Strategies for predictive digital soil mapping by geophysical, remote sensing and machine learning approaches. Catena, 264, 109822.