Maize is one of the most nutrient demanding staple crops. Tissue nutrient diagnosis of maize is currently conducted using critical nutrient concentration or dual ratio ranges, but such diagnoses are pathological as biased by data redundancy, sub-compositional incoherence and non-normal distribution. The use of orthogonal balances, a compositional data analysis technique, avoids such biases. Our objective was to develop foliar nutrient balance standards for maize. We collected 758 grain yields (15.5% moisture content) and foliar samples at silk stage in maize fields of southern Quebec, Canada, and analyzed ten nutrients in tissues (N, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe). Nutrients were arranged into ad hoc balances and computed as isometric log ratios (ilr). An optimized binary classification performed by a customized receiver operating characteristic procedure showed that a critical Mahalanobis distance of 4.21 separated balanced from imbalanced specimens about yield cut-off of 11.83 Mg grain·ha^-1 with test performance of 86%. Quebec maize balance standards differed from published standards computed from DRIS norms collected in other agroecosystems. The Redfield N/P ratio in maize leaves was found to be the least variable balance across regions of the world. The DRIS dual ratios and raw concentration values were found to be geometrically inadequate for conducting diagnosis. The unbiased nutrient balance diagnosis combined the critical Mahalanobis distance and a mobile representation of nutrient balances with ilr means of true negative (TN) specimens centered at fulcrums and back-transformed ilr values of TN specimens into raw concentrations loading the buckets below. Nutrients can be appreciated as relative shortage, adequacy or excess in the concentration domain following statistical analysis and diagnosis in the unbiased balance domain.