Drought impact-based forecasting of crop yield in Sweden through a machine-learning framework

Drought forecasting is a key component of agricultural risk management, yet important gaps still remain in linking drought hazard indicators to measurable impacts on crop yields. To translate hydro-climatic drought information into actionable insights for agricultural decision-making, a systematic investigation of relationships between hazard variables and impact indicators is needed to support process understanding and predictive modelling.

In this study, we focus on selected crop yield anomalies in Sweden as key agricultural impact indicators, and characterise the timing, magnitude, and persistence of drought-related yield reductions. Then, we identify their links to drought hazard indicators, e.g.  a set of meteorological, soil moisture, and hydrological drought indicators across relevant spatial and temporal scales, and explore their explanatory and predictive power. Building on the Framework for Index-based Drought Analysis (FRIDA), we leverage Machine Learning algorithms to elucidate the non-linear relationships between drought hazard indicators and crop yield impacts. Our results contribute to advancing impact-based drought early warning in Sweden and supports the development of more actionable drought information for agricultural stakeholders.