Drilling and sampling are responsible for most of the costs during mineral exploration, especially in multi-elements and polymetallic deposits, such as iron, manganese and bauxite deposits. On these deposits, often it is necessary to estimate multiple attributes at several granulometric fractions, ie distinct particle sizes. This paper investigates the effect of sampling at a close spacing grid only the head grades for the multiple chemical species in iron ore deposits. The remaining required data at the various grain sizes are sampled at a sparse grid. The derived geological model is checked for precision and accuracy comparing the output obtained against a model obtained using a complete isotopic data set including head grades and chemical analysis at each granulometric fraction analysed at all sampled positions. Among the several tested alternatives, the best procedure for dealing with the posed problem involved utilising the correlation coefficients between the grades at distinct fractions to obtain an isotopic database prior to interpolation. Cokriging was the method used, based on the intrinsic coregionalisation model. Estimates were obtained with the quality needed for the proposal of the study. Results showed that at least 50 per cent of the blocks were estimated with errors less than ten per cent relative to the grades obtained at the reference model, using only 15 per cent of the granulochemical data employed to obtain the reference model. The methodology also preserved the correlations between the attributes in their several granulometric fractions on the estimated model.