Machine learning is increasingly used in data analytics and is gaining popularity in agronomy as a new way of inferring and interpreting data to forecast and predict yields. This study evaluated the importance of multiple variables in corn yield predictions using regression analysis on five years historical yield data from multiple fields located in Canada using hybrid machine learning techniques such as Random Forest and TreeNet gradient boosting algorithms as an additional method for rating variable importance in factors predicting yields. Predictor variables that were used in the analysis were harvest locations recorded by the grain harvester, nitrogen applied at planting, top dressing and the total nitrogen applied, topdressing, remote sensing derived indices such as NDVI, SAVI, NSI and apparent electrical conductivity at shallow and deep levels recorded for the 2013 growing season. Our analysis revealed improved coefficient of determination, when we changed the total trees in the RF model from 200 to 1000, from a R2 value of 0.749 to 0.834 with mean absolute deviation improving from 0.7 t/ha to 0.5 t/ha. The model reported top dressing, X,Y locations, SAVI, NDVI, total nitrogen applied, NSI and nitrogen applied at planting as the order of variable importance. We analysed the model using the top six predictors reported by the hybrid model to see if the model outperforms itself with fewer variables for the yield predictions but we did not observed any significant improvement in the model output. We report analysis for the past six years data between 2011-2016.
