In this study, soil samples were collected from two locations: Samawa and Rumetha in southern Iraq. The samples from each location were split into two datasets: calibration set and validation set. A representative soil sample for each location was chosen for application of 5 levels of potassium phosphate fertilizer in 3 replications. Vis-NIR reflectance (350-2500 nm) and GIS-Kriging were used in combination with Partial Least Square (PLS) to predict soil available P. According to the results of this study, three wavelength regions were reported as a main sensitive bands for soil available P. The best prediction ability was achieved for Rumetha location at 1400-1600 nm with an R² of 0.85, lowest RMSE of 1.405, and lowest standard deviation of 1.577 and for Samawa location at 900-1000 nm with an R² of 0.81, RMSE of 2.666 and lowest standard deviation of 2.879. The capability of the Vis-NIRS- based and GIS-Kriging prediction models were evaluated by using cross-validation values Q² and R² between measured and predicted soil available P of each model. The selection principle parameters showed best prediction by NIRS-models with an R² of 0.79 for Rumetha and 0.75 for Samawa location. While the prediction ability of GIS-Kriging models were in worst with an Q² of 0.17 for Samawa location and reasonable with an Q² of 0.58 for Rumetha location. These empirically result is an evident of the superiority of NIRS-based models for prediction soil available P over the GIS-Kriging models.