Causal machine learning for sustainable agroecosystems
This addresses the problem of evidence-based decision-making for stakeholders like farmers and policymakers in sustainable agriculture, representing a novel integration rather than an incremental improvement.
The paper tackles the challenge of understanding complex interactions in sustainable agriculture by proposing causal machine learning to move beyond descriptive predictions to prescriptive insights, showcasing its application across eight diverse agri-food scenarios.
In a changing climate, sustainable agriculture is essential for food security and environmental health. However, it is challenging to understand the complex interactions among its biophysical, social, and economic components. Predictive machine learning (ML), with its capacity to learn from data, is leveraged in sustainable agriculture for applications like yield prediction and weather forecasting. Nevertheless, it cannot explain causal mechanisms and remains descriptive rather than prescriptive. To address this gap, we propose causal ML, which merges ML's data processing with causality's ability to reason about change. This facilitates quantifying intervention impacts for evidence-based decision-making and enhances predictive model robustness. We showcase causal ML through eight diverse applications that benefit stakeholders across the agri-food chain, including farmers, policymakers, and researchers.