Understanding the impacts of crop diversification in the context of climate change: a machine learning approach
This addresses the need for sustainable agricultural policies in the face of climate change, though it is incremental as it applies an existing causal machine learning method to new data.
The study tackled the problem of how crop diversification affects agricultural productivity under climate change, finding that it increased net primary productivity by 2.8% on average and synergized with higher temperatures and lower soil moisture.
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.