Quantifying agricultural resilience under climate variability: a data-driven climate resilience index for European cereal systems

Summary

This study proposes a data-driven Climate Resilience Index to quantify the capacity of European cereal systems to withstand and recover from climate variability. Using annual yield time series decomposed into trend and anomaly components, the authors trained multiple machine learning architectures (random forest, CatBoost, CNN, LSTM, TCN, and TCN-LSTM hybrid) to predict resilience dynamics. Results indicate that temporal deep learning models, particularly TCN-LSTM, outperform traditional ensemble methods in capturing multi-year recovery mechanisms, with lagged yield anomalies and rolling volatility emerging as dominant resilience predictors across European countries.

Regional applicability

This study directly addresses European cereal systems and provides country-level findings showing substantial variability across the continent, making the CRI framework directly applicable to United Kingdom cereal operations. The methodology and resilience metrics could inform UK agricultural policy and farm-level climate adaptation strategies, though site-specific recalibration would be needed to account for UK-specific agro-climatic conditions and cropping practices.

Key measures

Climate Resilience Index (CRI) components: climate exposure, yield variability, and recovery dynamics; model R² values (TCN-LSTM achieved 0.8347 R²); principal component analysis variance explained (68.4%); feature importance rankings

Outcomes reported

The study developed a Climate Resilience Index (CRI) to assess resilience of European cereal systems under climate stress, decomposing yield time series into trend and anomaly components and evaluating multiple machine learning models to predict climate-induced yield fluctuations.

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