Handling Climate Change Using Counterfactuals: Using Counterfactuals in
  Data Augmentation to Predict Crop Growth in an Uncertain Climate Future
  Climate change poses a major challenge to humanity, especially in its impact
on agriculture, a challenge that a responsible AI should meet. In this paper,
we examine a CBR system (PBI-CBR) designed to aid sustainable dairy farming by
supporting grassland management, through accurate crop growth prediction. As
climate changes, PBI-CBRs historical cases become less useful in predicting
future grass growth. Hence, we extend PBI-CBR using data augmentation, to
specifically handle disruptive climate events, using a counterfactual method
(from XAI). Study 1 shows that historical, extreme climate-events (climate
outlier cases) tend to be used by PBI-CBR to predict grass growth during
climate disrupted periods. Study 2 shows that synthetic outliers, generated as
counterfactuals on a outlier-boundary, improve the predictive accuracy of
PBICBR, during the drought of 2018. This study also shows that an
instance-based counterfactual method does better than a benchmark,
constraint-guided method.
