Causal machine learning for sustainable agroecosystems
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929471557206016 |
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| author | Sitokonstantinou, Vasileios Porras, Emiliano Díaz Salas Bautista, Jordi Cerdà Piles, Maria Athanasiadis, Ioannis Kerner, Hannah Martini, Giulia Sweet, Lily-belle Tsoumas, Ilias Zscheischler, Jakob Camps-Valls, Gustau |
| author_facet | Sitokonstantinou, Vasileios Porras, Emiliano Díaz Salas Bautista, Jordi Cerdà Piles, Maria Athanasiadis, Ioannis Kerner, Hannah Martini, Giulia Sweet, Lily-belle Tsoumas, Ilias Zscheischler, Jakob Camps-Valls, Gustau |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13155 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causal machine learning for sustainable agroecosystems Sitokonstantinou, Vasileios Porras, Emiliano Díaz Salas Bautista, Jordi Cerdà Piles, Maria Athanasiadis, Ioannis Kerner, Hannah Martini, Giulia Sweet, Lily-belle Tsoumas, Ilias Zscheischler, Jakob Camps-Valls, Gustau Machine Learning Artificial Intelligence Computers and Society 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. |
| title | Causal machine learning for sustainable agroecosystems |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2408.13155 |