Title: Maize Yield and Nitrate Loss Prediction with Optimal Machine Learning Model Ensembles
Abstract: Pre-season prediction of crop production outcomes such as grain yields and N losses can provide insights to stakeholders when making decisions. Simulation models can assist in scenario planning, but their use is limited because of data requirements and long run times. In this talk, machine learning prediction models will be discussed and evaluated. In addition, ensemble learning models will be introduced to improve prediction accuracy. An optimization model that aims to combine predictions of base learners by finding the optimal weights for aggregation will be discussed.