Digitization is influencing commodity trading and agricultural markets and as they transition towards extreme liquidity, agribusiness risk exposures increase, and traditional competitive advantages diminish. In commodity origination, logistics and destination basis comprise the most volatile and determinant influences of margin. To capture a consistently higher margin and represent narrowed interior basis, agribusiness firms must manage these risks by optimizing transformations. To accomplish this, ex-ante decision-making is often necessary as forward price clairvoyance is not always prevalent, is risky, or contains premium. Modeling this spatial equilibrium is difficult through traditional reductionist and essentialist application as the overdetermined and convoluted system presents bidirectional and simultaneous price discoveries. Developments in neurobiology, technology, and Artificial Intelligence expand capabilities to represent brain behavior and unconscious inference in computational modeling. The use of Recurrent Deep Machine Learning could improve ex-ante decision accuracy within commodity trading through its nonlinear, nonlocal, nonstationary, and sequential capabilities.