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

Anil Utku, Magdalena Radulescu, Abdulkadir Barut, Hind Alofaysan

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In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Anil UtkuComputer Engineering Department, Faculty of Engineering, Munzur University, Tunceli, 62000, Turkey.

Magdalena RadulescuNational University of Science and Technology Politehnica Bucharest, Bucharest, Romania.

Abdulkadir BarutHarran University, Şanlıurfa, Turkey. kadirbarut@harran.edu.tr.

Hind AlofaysanDepartment of Economics, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Climate variability and extreme weather events pose an increasingly serious threat to agricultural productivity in Europe. Although many studies have focused on the mean yield response to climate change, relatively few have attempted to offer a comprehensive, multi-dimensional view of agricultural resilience, separating long-term productivity trends from short-term climate-induced fluctuations. In this paper, a data-driven Climate Resilience Index is proposed to assess the resilience of European cereal systems under climate stress. Annual yield time series are decomposed into trend and anomaly components to identify interannual deviations driven by climate variability. Machine learning and deep learning algorithms, including RF, CatBoost, CNN, LSTM, TCN, and a TCN-LSTM hybrid model, are used to assess the predictive validity of climate extreme indices and persistence patterns. Among the evaluated models, the TCN-LSTM architecture achieved the highest predictive performance with 0.8347 R

Indexed as

Agricultural sustainabilityCereal productionClimate resilienceDeep learningPrincipal component analysisYield anomalies

Identifiers

PMID42236573

DOI10.1038/s41598-026-54921-2

PMCPMC13473689

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.