[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120145-en":3,"doc-seo-120145-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120145,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Causal Machine Learning for Sustainable Agroecosystems - Abstract and Introduction","Sustainable agriculture is vital for food security and environmental health under climate change, yet it remains difficult to interpret complex biophysical, social, and economic interactions. Predictive machine learning can learn from data for tasks such as yield prediction and weather forecasting, but it does not explain causal mechanisms and therefore stays descriptive rather than prescriptive. The work proposes causal machine learning to combine data-driven learning with causal reasoning, enabling quantification of intervention impacts, improved robustness, and evidence-based decisionmaking across the agri-food chain.","arXiv :2408 . 13155v1 [ cs .LG] 23 Aug 2024  \nCausal Machine Learning for Sustainable Agroecosystems  \nVasileios Sitokonstantinou 1*, Emiliano D´ıaz Salas-Porras 1 , Jordi Cerd`a-Bautista 1 , Maria Piles 1 , Ioannis Athanasiadis2 , Hannah Kerner4 , Giulia Martini3 , Lily-belle Sweet5 , Ilias Tsoumas2,6 , Jakob Zscheischler5 and Gustau Camps-Valls 1  \n1* Image Processing Laboratory, Universitat de Val`encia, Spain.  \n2 Artificial Intelligence, Wageningen University and Research, The Netherlands.  \n3World Food Program, UN, Rome, Italy.  \n4 Arizona State University, USA.  \n5 Department of Compound Environmental Risks, Helmholtz Centre for Environmental Research  \n– UFZ, Leipzig, Germany.  \n6 National Observatory of Athens, Greece.  \n*Corresponding author(s) . E-mail(s): [Vasileios.Sitokonstantinou@uv.es](Vasileios.Sitokonstantinou@uv.es);  \nAbstract  \nSustainable agriculture is essential for food security and environmental health in a changing climate. 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 decisionmaking 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.  \n1 Introduction  \nThe perception of agriculture is evolving. Nowadays, policymakers, researchers, farmers, and consumers recognize farms as integral components of larger, interconnected agroecosystems that include biological, physical, social, and  \n2 Causal ML for Agriculture and Food  \neconomic dimensions [1] . However, current agricultural practices often fail to balance these elements, relying heavily on agricultural land expansion, external inputs like synthetic fertilizers and pesticides, and resource extraction. These practices contribute to environmental impacts like biodiversity loss and soil degradation that pose significant threats to future generations [2] .  \nSustainably achieving food security targets is a complex task. Production must be intensified to meet the growing food demand [3, 4], even on lands already degraded [5], while simultaneously reducing greenhouse gases emissions [6] . This challenge is compounded by slow technology adoption [7], inefficient policies [8], and unforeseen crises like pest outbreaks, pandemics, and financial disruptions [9] . Beyond achieving intensification without harming the environment and adding to climate change, agriculture has the potential to mitigate these problems by sequestering carbon in soils and enhancing biodiversity. Thus, agriculture can be both a contributor to and a solution for climate change [10] . To harness agriculture’s potential for positive impact, we need a comprehensive framework that evaluates strategies based on their contributions to sustainability across the entire agroecosystem. Improved understanding of sustainability across all dimensions of agriculture can help prioritize the most impactful actions [11] .  \nIn this context, where agricultural decisions have far-reaching impacts, traditional approaches to modeling and decision-making may struggle to capture the complexity of agroecosystems. Machine learning (ML) methods have emerged as powerful tools for finding patterns within large datasets and making predictions based on historical data [12] . However, while ML excels at predicting outcomes, it cannot explain the underlying causality, which limits its effectiveness in performing robustly in new, changi","cbCaifehkcvn9a2Y","https://ap.wps.com/l/cbCaifehkcvn9a2Y","pdf",5846889,1,18,"English","en",105,"# Abstract\n# Introduction\n# Causal ML for Agriculture and Food\n## Key differences between predictive ML and causal ML","[{\"question\":\"What limitation of predictive machine learning motivates causal machine learning in agriculture?\",\"answer\":\"Predictive ML can learn statistical associations and make forecasts, but it cannot explain causal mechanisms, making it descriptive rather than prescriptive and limiting evaluation of intervention effects.\"},{\"question\":\"How does causal machine learning improve decision-making in sustainable agroecosystems?\",\"answer\":\"By combining machine learning with causal reasoning, it supports estimating the impact of interventions and enables evidence-based decisions that target sustainability outcomes.\"},{\"question\":\"What are the key differences between predictive ML and causal ML (as summarized in Box 1)?\",\"answer\":\"Predictive ML focuses on statistical associations for prediction, while causal ML synergizes causal inference and machine learning, either improving causality with ML or improving ML with causality to answer causal questions such as “what happens if.”\"}]","Causal Machine Learning for Sustainable Agroecosystems - 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