NIRS fingerprints alone were able to discriminate feed ingredient samples according to their country of origin with high prediction accuracy (Table and Fig. , balanced accuracy = 0.94). These predictions were successfully linked to ingredient-and country-specific environmental footprints, i.e., land stress and climate change (Figs. ). NIRS fingerprints could also predict nutritional variations among and within ingredient groups with generally high accuracy (Fig. , root mean squared error = 1.7-5.5 g/kg), with varying performances depending on the ingredient and nutrient analyzed and the sample size (Figs. ). These predictions served as the basis for thousands of simulations that captured the nutritional variability among and within feed ingredients during feed optimization.