Accurate interpretation is the key to getting value from OFEs—good interpretation helps farmers learn more from each OFE, and manage with greater certainty as a result. Sadras and co-authors [Making Science More Effective for Agriculture: Advances in Agronomy, 163:153—77] call for an expanded role for agronomic logic to solve global crop production challenges. Yet many OFEs generate insights of complex and variable crop behaviour that call for stronger engagement of agronomy with these farmer-driven operations. In fact, some data scientists believe analysis can proceed without theory—an approach Taguchi adopted for dealing with complex systems. As we suggested in the early days of OFE  [(Cook, Adams, and Corner 1999)], this seems a pragmatic but inefficient alternative to understanding what is driving variation. Surely we can do better. Can we combine the power of data sciences with agronomy to drive advances in both? How can we frame these insights more effectively using decision sciences? We are looking to form a group of scientists interested in OFE to develop thinking in this area. If you are interested please send a message to the OFE-C leadership.