Quantifying agricultural resilience under climate variability: a data-driven climate resilience index for European cereal systems
Anil Utku, Magdalena Radulescu, Abdulkadir Barut, Hind Alofaysan · 2026
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Description
Anil Utku and three colleagues built in 2026 a data-driven index for judging how well European cereal systems withstand climate stress. Most earlier work tracked average yield responses, leaving resilience itself poorly measured. Their index separates long-term yield trends from short-term climate-driven swings.
Annual yield series were split into trend and anomaly parts, and several machine and deep learning models tested which climate signals predicted them. A combined TCN-LSTM model predicted best, and a principal-component framework captured most climate variation in one factor. Lagged yield anomalies and rolling volatility led the predictors. Country results varied widely, and rankings held up under different weights. The index gives planners a consistent way to compare resilience across countries.
Catalog record
| CREATOR | Anil Utku, Magdalena Radulescu, Abdulkadir Barut, Hind Alofaysan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 72 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |