Understanding the impacts of crop diversification in the context of
  climate change: a machine learning approach
Georgios Giannarakis Ilias Tsoumas Stelios Neophytides Christiana Papoutsa Charalampos Kontoes Diofantos Hadjimitsis
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.