Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future

TL;DR

This paper enhances a case-based reasoning system for predicting crop growth under climate change by incorporating counterfactual data augmentation, improving accuracy during extreme climate events like droughts.

Contribution

It introduces a counterfactual data augmentation approach to improve crop growth prediction in climate-affected scenarios, outperforming existing methods.

Findings

Counterfactual augmentation improves prediction accuracy during droughts.

Synthetic outliers enhance model robustness to climate disruptions.

Instance-based counterfactuals outperform benchmark methods.

Abstract

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.