This study looks at location evaluation methods for corn hybrids in the northern Midwest. This research attempts to discover new methods of location evaluation combining old harvest traits and new traits taken with machines during the growing season.  Data from 2014 thru 2022 was evaluated with harvest traits, machine traits taken within the growing season, precipitation information, text data taken by humans during the growing season, and agronomic practice data. This data was analyzed with correlation methods, regression methods, and an XGboost Machine learning method that produces a decision tree. The use of a decision tree for location evaluation was also demonstrated to be a valuable tool to discern different needs in environments according to changing variable interaction levels. Finally, the data was gathered to create a prototype dashboard for location selection or evaluation.