While several potato (Solanum tuberosum L.) cultivars of different maturity groups (e.g. early, mid-season, late) are being selected each year as a result of successful breeding for disease resistance and market requirements, their nutrient management is based on past experience and few experiments. Nutrient profiles from leaf analysis can guide fertilization and liming programs of potato cultivars. Since leaf analytical data are strictly positive and compositional, nutrient profiling using raw data is spoiled by non normal distribution, resonance and spurious correlations. Compositional data analysis provides log ratio transformations that avoid such problems. Our objective was to derive nutrient profiles from tissue analysis using isometric log ratio (ilr) coordinates and meta-analysis for classification of cultivars into uniform nutrient management groups. The dataset comprised 678 potato fields producing more than 28.5 Mg marketable tuber ha-1, i.e. above Quebec average, of the early-, mid-, and late-season cultivars. The first mature leaf from top was sampled at the beginning of flowering for N, P, K, Ca, and Mg analysis. Anionic (N, P) and cationic (K, Ca, Mg) nutrients were arranged into binary partitions representing positive and negative nutrient interactions. Groups of cultivars were compared to ‘Superior’ using ilr mean and standard deviation in the mixed model of meta-analysis. We minimized the within-group heterogeneity (I2 value) by allocating cultivars iteratively between ilr groups. We derived group-specific ilr norms to compute the Aitchison distance. The critical value for nutrient imbalance was 0.38. To guide correcting nutrient deficiencies with appropriate nutrient management techniques, nutrient composition can be altered numerically by a perturbation vector on nutrients that lead to the largest and most negative ilr differences from ilr norms until the Aitchison distance falls below critical value