# Understanding the impacts of crop diversification in the context of climate change: a machine learning approach

arXiv: https://arxiv.org/abs/2307.08617 · HTML: https://mlsanity.com/paper/2307.08617/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [CausalML: Python Package for Causal Machine Learning](https://mlsanity.com/paper/2002.11631/) (2020)
- [Towards assessing agricultural land suitability with causal machine learning](https://mlsanity.com/paper/2204.12956/) (2022)
- [Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities](https://mlsanity.com/paper/2301.12985/) (2023)
- [Evaluating Digital Agriculture Recommendations with Causal Inference](https://mlsanity.com/paper/2211.16938/) (2022)
- [Fuzzy clustering for the within-season estimation of cotton phenology](https://mlsanity.com/paper/2211.14099/) (2022)
- [Pest presence prediction using interpretable machine learning](https://mlsanity.com/paper/2205.07723/) (2022)
- [Personalizing Sustainable Agriculture with Causal Machine Learning](https://mlsanity.com/paper/2211.03179/) (2022)
- [Towards Space-to-Ground Data Availability for Agriculture Monitoring](https://mlsanity.com/paper/2205.07721/) (2022)
- [A Data Cube of Big Satellite Image Time-Series for Agriculture Monitoring](https://mlsanity.com/paper/2205.07752/) (2022)

## Built on by (most cited first, 2 of 2)

- [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)
- [Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs](https://mlsanity.com/paper/2411.02134/) (2024)

## Abstract (arXiv)

The concept of sustainable intensification in agriculture necessitates the implementation of management practices that prioritize sustainability without compromising productivity. However, the effects of such practices are known to depend on environmental conditions, and are therefore expected to change as a result of a changing climate. We study the impact of crop diversification on productivity in the context of climate change. We leverage heterogeneous Earth Observation data and contribute a data-driven approach based on causal machine learning for understanding how crop diversification impacts may change in the future. We apply this method to the country of Cyprus throughout a 4-year period. We find that, on average, crop diversification significantly benefited the net primary productivity of crops, increasing it by 2.8%. The effect generally synergized well with higher maximum temperatures and lower soil moistures. In a warmer and more drought-prone climate, we conclude that crop diversification exhibits promising adaptation potential and is thus a sensible policy choice with regards to agricultural productivity for present and future.