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Accurate representation of crop responses to climate is critically important to understand impacts of climate change and variability in food systems. We use Random Forest (RF), a diagnostic machine learning tool, to explore the dependence of yield on climate and technology for maize, sorghum and soybean in the US plains. We analyze the period from 1980 to 2016 and use a panel of county yields and climate variables for the crop-specific developmental phases: establishment, critical window (yield potential definition) and grain filling. The RF models accounted for between 71% to 86% of the yield variance. Technology, evaluated through the time variable, accounted for approximately 20% of the yield variance and indicates that yields have steadily increased. Responses to climate confirm prior findings revealing threshold-like responses to high temperature (yield decrease sharply when maximum temperature …