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

TL;DR

This study uses causal machine learning on Earth Observation data to analyze how crop diversification affects productivity under climate change, finding it enhances productivity especially in warmer, drier conditions.

Contribution

It introduces a data-driven causal machine learning approach to assess crop diversification impacts in climate change contexts, applied to Cyprus data.

Findings

Crop diversification increases net primary productivity by 2.8%.

Effects are amplified under higher temperatures and lower soil moisture.

Crop diversification shows potential as an adaptation strategy in changing climates.

Abstract

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.