  The emerge of new technologies to synthesize and analyze big data with
high-performance computing, has increased our capacity to more accurately
predict crop yields. Recent research has shown that Machine learning (ML) can
provide reasonable predictions, faster, and with higher flexibility compared to
simulation crop modeling. The earlier the prediction during the growing season
the better, but this has not been thoroughly investigated as previous studies
considered all data available to predict yields. This paper provides a machine
learning based framework to forecast corn yields in three US Corn Belt states
(Illinois, Indiana, and Iowa) considering complete and partial in-season
weather knowledge. Several ensemble models are designed using blocked
sequential procedure to generate out-of-bag predictions. The forecasts are made
in county-level scale and aggregated for agricultural district, and state level
scales. Results show that ensemble models based on weighted average of the base
learners outperform individual models. Specifically, the proposed ensemble
model could achieve best prediction accuracy (RRMSE of 7.8%) and least mean
bias error (-6.06 bu/acre) compared to other developed models. Comparing our
proposed model forecasts with the literature demonstrates the superiority of
forecasts made by our proposed ensemble model. Results from the scenario of
having partial in-season weather knowledge reveal that decent yield forecasts
can be made as early as June 1st. To find the marginal effect of each input
feature on the forecasts made by the proposed ensemble model, a methodology is
suggested that is the basis for finding feature importance for the ensemble
model. The findings suggest that weather features corresponding to weather in
weeks 18-24 (May 1st to June 1st) are the most important input features.
