Drought response strategies of Arbequina, Arbosana and Koroneiki olive cultivars revealed by ring growth, wood anatomy and δ¹³C dynamics
Bibliographic record
Abstract
L.), a key crop in Mediterranean climates, is increasingly affected by climate variability. Over the last several decades, the Umbria region of central Italy, with its long-standing olive-growing tradition, has experienced a rise in extreme summer droughts, severely impacting water availability. This makes it an ideal case study for investigating olive tree responses to climatic stress. In this study, we examined the adaptive strategies of three economically important cultivars - Arbequina, Arbosana, and Koroneiki - grown as mature trees (7 years old) between 2020 and 2023. We combined dendrochronological techniques, wood anatomical analyses, and intra-seasonal δ¹³C profiling to assess growth dynamics, structural adjustments, and eco-physiological responses across four growing seasons. Our results revealed distinct cultivar-specific strategies in response to climate variation. In Arbequina and Arbosana, δ¹³C values showed significant correlations with current-year spring and summer climate conditions, as well as with conditions during the preceding winter, reflecting a more isohydric behavior. In contrast, Koroneiki exhibited a more anisohydric strategy: its δ¹³C values were primarily influenced by precipitation from the previous winter, indicating a reliance on stored carbon reserves to support early-season growth. Wood anatomical traits further supported these differences. Koroneiki exhibited higher vessel density and a greater proportion of lumen area, traits that enhance water transport efficiency. It also achieved the highest stem basal area and fruit production among the three cultivars, reaching 10.2 kg/tree in 2023. These characteristics highlight Koroneiki's potential as a drought-resilient cultivar suited for future orchard designs in Mediterranean regions increasingly affected by heat and water stress.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".