Automated Machine Learning for Ex-ante Life Cycle Assessment of Barley Production

In this paper, we discuss the use of Automated Machine Learning for the first time applied to an Ex-ante Life Cycle Assessment. This kind of analysis has been conducted for a particular crop production, i.e. barley. The aim is to assess the impact (in terms of carbon dioxide emissions and yield) of different production strategies. The data used in this study comes from a two-year measurement campaign involving five countries. The results are compared against the state-of-the-art technique, showing the good performance of the approach.