mlsanityData to 14 Oct 2026

Towards assessing agricultural land suitability with causal machine learning

arXiv 2204.12956 ↗First posted 27 Apr 2022cs.LG cs.AI cs.CVcited by 11

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Abstract (arXiv)

Understanding the suitability of agricultural land for applying specific management practices is of great importance for sustainable and resilient agriculture against climate change. Recent developments in the field of causal machine learning enable the estimation of intervention impacts on an outcome of interest, for samples described by a set of observed characteristics. We introduce an extensible data-driven framework that leverages earth observations and frames agricultural land suitability as a geospatial impact assessment problem, where the estimated effects of agricultural practices on agroecosystems serve as a land suitability score and guide decision making. We formulate this as a causal machine learning task and discuss how this approach can be used for agricultural planning in a changing climate. Specifically, we extract the agricultural management practices of "crop rotation" and "landscape crop diversity" from crop type maps, account for climate and land use data, and use double machine learning to estimate their heterogeneous effect on Net Primary Productivity (NPP), within the Flanders region of Belgium from 2010 to 2020. We find that the effect of crop rotation was insignificant, while landscape crop diversity had a small negative effect on NPP. Finally, we observe considerable effect heterogeneity in space for both practices and analyze it.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 8 of its 72 references that are in the map. First posted on arXiv 27 Apr 2022.

Builds on

8 of 72 references.

References · 8

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  2. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  3. 2016Generalized Random Forestscited by 312
  4. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 255
  5. 2015Doubly Robust Policy Evaluation and Optimizationcited by 158
  6. 2020CausalML: Python Package for Causal Machine Learningcited by 74
  7. 2021Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confoundingcited by 50
  8. 2021Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacificcited by 6

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Cited by year

Citing papers per year: 2022: 3; 2023: 5; 2024: 3.

11 papers.

  1. 2023COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspectivecited by 22
  2. 2022Evaluating Digital Agriculture Recommendations with Causal Inferencecited by 7
  3. 2024A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Povertycited by 5
  4. 2022Personalizing Sustainable Agriculture with Causal Machine Learningcited by 4
  5. 2022Evaluating Digital Tools for Sustainable Agriculture using Causal Inferencecited by 2
  6. 2023Understanding the impacts of crop diversification in the context of climate change: a machine learning approachcited by 2
  7. 2023Assessing the Causal Impact of Humanitarian Aid on Food Securitycited by 1
  8. 2023Discovering Effective Policies for Land-Use Planning with Neuroevolutioncited by 1
  9. 2023Causality and Explainability for Trustworthy Integrated Pest Managementcited by 1
  10. 2024Distilling interpretable causal trees from causal forestscited by 0
  11. 2024Causal machine learning for sustainable agroecosystemscited by 0