  Explainability in yield prediction helps us fully explore the potential of
machine learning models that are already able to achieve high accuracy for a
variety of yield prediction scenarios. The data included for the prediction of
yields are intricate and the models are often difficult to understand. However,
understanding the models can be simplified by using natural groupings of the
input features. Grouping can be achieved, for example, by the time the features
are captured or by the sensor used to do so. The state-of-the-art for
interpreting machine learning models is currently defined by the game-theoretic
approach of Shapley values. To handle groups of features, the calculated
Shapley values are typically added together, ignoring the theoretical
limitations of this approach. We explain the concept of Shapley values directly
computed for predefined groups of features and introduce an algorithm to
compute them efficiently on tree structures. We provide a blueprint for
designing swarm plots that combine many local explanations for global
understanding. Extensive evaluation of two different yield prediction problems
shows the worth of our approach and demonstrates how we can enable a better
understanding of yield prediction models in the future, ultimately leading to
mutual enrichment of research and application.
