# Towards assessing agricultural land suitability with causal machine learning

arXiv: https://arxiv.org/abs/2204.12956 · HTML: https://mlsanity.com/paper/2204.12956/

## 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 (most cited first, 8 of 8)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Doubly Robust Policy Evaluation and Optimization](https://mlsanity.com/paper/1503.02834/) (2015)
- [CausalML: Python Package for Causal Machine Learning](https://mlsanity.com/paper/2002.11631/) (2020)
- [Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding](https://mlsanity.com/paper/2103.04850/) (2021)
- [Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific](https://mlsanity.com/paper/2110.15084/) (2021)

## Built on by (most cited first, 11 of 11)

- [COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective](https://mlsanity.com/paper/2305.05191/) (2023)
- [Evaluating Digital Agriculture Recommendations with Causal Inference](https://mlsanity.com/paper/2211.16938/) (2022)
- [A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty](https://mlsanity.com/paper/2406.02584/) (2024)
- [Personalizing Sustainable Agriculture with Causal Machine Learning](https://mlsanity.com/paper/2211.03179/) (2022)
- [Evaluating Digital Tools for Sustainable Agriculture using Causal Inference](https://mlsanity.com/paper/2211.03195/) (2022)
- [Understanding the impacts of crop diversification in the context of climate change: a machine learning approach](https://mlsanity.com/paper/2307.08617/) (2023)
- [Assessing the Causal Impact of Humanitarian Aid on Food Security](https://mlsanity.com/paper/2310.11287/) (2023)
- [Discovering Effective Policies for Land-Use Planning with Neuroevolution](https://mlsanity.com/paper/2311.12304/) (2023)
- [Causality and Explainability for Trustworthy Integrated Pest Management](https://mlsanity.com/paper/2312.04343/) (2023)
- [Distilling interpretable causal trees from causal forests](https://mlsanity.com/paper/2408.01023/) (2024)
- [Causal machine learning for sustainable agroecosystems](https://mlsanity.com/paper/2408.13155/) (2024)

## 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.