Tiered maize and wheat nutrient removal coefficients estimated from available data

Abstract Estimates of cropland nutrient budgets at national to global scale generally rely on regional or global mean coefficients for quantifying nutrients removed in crop yield and by-products. Use of such mean values masks the variability in these coefficients. Using maize and wheat as examples, we assessed variation in nutrient removal coefficients, namely harvest index (HI), nitrogen (N), phosphorus (P) and potassium (K) concentrations of crop products (Grain N, Grain P and Grain K respectively) and N, P and K concentrations of crop residues (Residue N, Residue P, and Residue K respectively). Variation in these coefficients was assessed by three categories (Tiers) of estimation. Statistical (mixed-effects) and machine learning (random forest regression) models (Tier 3) were used to predict the coefficients using generally available predictor variables at a global level. Mean prediction accuracies (R 2 ) of the mixed-effects and random forest models were 0.32 for maize coefficients and 0.45 for wheat coefficients when based on a random sub-selection of mainly replicated field experiment data. When predictions were applied to on-farm data only, prediction accuracies were lower (mean R 2 values of 0.08 and 0.36 for maize and wheat respectively). Variation in, and dearth of on-farm data for the coefficients contributed to these poor prediction accuracies. Until the limitations of on-farm data are overcome, it is recommended to use Tier 2 (regional) coefficient estimates in country and global cropland nutrient balance and nutrient use efficiency estimates. Where Tier 2 values are not available, then global average (Tier 1) coefficients can be used.