Abstract: The present disclosure relates generally to agronomic modeling, and more specifically to determining uncertainty associated with agronomic predictions (e.g., agricultural yield of a field). An exemplary method comprises: receiving information associated with a location; providing the information to one or more trained machine-learning models; determining, based on the trained machine-learning models: a probabilistic distribution of the predicted crop yield of the location, wherein the probabilistic distribution is defined by a plurality of parameters; and an uncertainty measure associated with a moment of the probabilistic distribution of the predicted crop yield.