On crop yield modelling, predicting, and forecasting and addressing the common issues in published studies

HIGHLIGHTS

• What: Moving forward it is essential that clear definitions and guidelines for data-driven yield modelling and validation are outlined so that there is a greater connection between the goal of the study and the actual study outputs/outcomes. This study showcases how different model calibration and validation approaches clearly impact prediction quality and therefore how they should be interpreted in data-driven crop yield modelling studies. While crop yield modelling can also refer to mechanistic, simulation, or process-based models, for the purpose of this paper it specifically refers to statistical or data-driven models. The . . .
  

   

  

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