Abstract: In one embodiment, a computer-implemented method includes receiving digital field data from an agricultural field representing one or more parameters of the field, soil, or crops in the field; retrieving historical data for the same field from one or more field databases; training and/or applying machine learning models to the field data and the historical data to derive representations of causality of one or more agronomic processes pertaining to the field; receiving user input specifying an anomaly to address via treatment, application or experiment; automatically adjusting the treatment, application or experiment to create a modified treatment, application or experiment that is most likely to generate result data that is usable to train machine learning models in an optimal manner.