Spatial interpolation is frequently used to predict values across a landscape enabling the spatial variation and patterns of a property to be quantified. Inverse distance weighting (IDW), ordinary kriging (OK), regression kriging (RK), and partial least squares (PLS) regression are interpolation techniques typically used where the region of interest's spatial extent is relatively small and observations are numerous and regularly spaced. In the current era of data ‘mining’ and utilisation of sparse data, the above criteria are not always fully met, increasing model uncertainties. Furthermore, regression modelling and kriging techniques require good judgement, experience, and expertise by the practitioner compared with IDW with its more rudimentary approach. In this study we compared spatial predictions derived from IDW, PLS, RK, and OK for Pinus radiata volume mean annual increment (referred to as 300 Index) and mean top height at age twenty (referred to as Site Index) across New Zealand using cross-validation techniques. Validation statistics (RMSE, ME, and R2) show that RK, OK, and IDW provided predictions that were less biased and of greater accuracy than PLS predictions. Standard deviation of rank (SDR) and mean rank (MR) validation statistics showed similar results with OK the most consistent (SDR) predictor, whereas RK had the lowest mean rank (MR), closely followed by IDW. However, the mean performance rankings for validation observations classified according to their distance to the nearest model data point indicate that although PLS provided the poorest predictions at relatively close separation distances (<2 km), in the medium range ( 4–8 km) performance was of similar ranking to that of the other techniques, and at greater separation distances PLS outperformed the other techniques. Maps illustrating the spatial variation of P. radiata forest productivity are provided.