The aim of this work was to study the variability and spatial dependence of soil properties and their effects on lime recommendation with three simulated sampling intensities. In an area of 725.9 ha, 154 samples were collected at a distance of 225 m in a regular grid. Samples were discarded within the spatial dependence range and two other data sets were created, with 76 and 38 samples each. Geostatistical analyses were performed for the variables, cation exchange capacity and sum of bases. After kriging interpolation of the variables lime recommendation maps were elaborated for the three data sets. In comparison with the traditional recommendation method, on average, there was no reduction on lime requirement. Differences among the three recommendation rates were noted, specially on spatial distribution of doses. On the other hand, assuming as correct recommendation with higher number of samples, the use of a lower intensity sampling strategy is viable due to the cost reduction and acceptable difference among recommendations.
spatial variability; geostatistics; precision agriculture
