# Personalizing Sustainable Agriculture with Causal Machine Learning

arXiv: https://arxiv.org/abs/2211.03179 · HTML: https://mlsanity.com/paper/2211.03179/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 6 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Towards assessing agricultural land suitability with causal machine learning](https://mlsanity.com/paper/2204.12956/) (2022)

## Built on by (most cited first, 4 of 4)

- [Evaluating Digital Agriculture Recommendations with Causal Inference](https://mlsanity.com/paper/2211.16938/) (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)
- [Causal machine learning for sustainable agroecosystems](https://mlsanity.com/paper/2408.13155/) (2024)

## Abstract (arXiv)

To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the "sustainable intensification" of agriculture, transforming it from carbon emitter to carbon sink is a priority, and understanding the environmental impact of agricultural management practices is a fundamental prerequisite to that. At the same time, the global agricultural landscape is deeply heterogeneous, with differences in climate, soil, and land use inducing variations in how agricultural systems respond to farmer actions. The "personalization" of sustainable agriculture with the provision of locally adapted management advice is thus a necessary condition for the efficient uplift of green metrics, and an integral development in imminent policies. Here, we formulate personalized sustainable agriculture as a Conditional Average Treatment Effect estimation task and use Causal Machine Learning for tackling it. Leveraging climate data, land use information and employing Double Machine Learning, we estimate the heterogeneous effect of sustainable practices on the field-level Soil Organic Carbon content in Lithuania. We thus provide a data-driven perspective for targeting sustainable practices and effectively expanding the global carbon sink.