1. Introduction
The olive tree (Olea europaea L.) is the most important woody fruit crop in the Mediterranean Basin, covering more than 10 million hectares. Olive farming supports thousands of small farms, protects UNESCO-recognized landscapes, and encompasses over 2,000 traditional varieties developed over thousands of years.
Most olive orchards are still cultivated under rainfed conditions, making them entirely dependent on increasingly unpredictable precipitation patterns and other abiotic and biotic stresses. This lack of varietal innovation makes the olive sector vulnerable to environmental pressure, especially since the Mediterranean is a major climate change hotspot. Nevertheless, the olive tree is an excellent candidate for accelerated breeding due to its economic value, wide distribution, and large genetic pool.
The mobilisation of olive (Olea europaea L.) genetic resources through pre-breeding activities constitutes a critical strategy to address the emerging agronomic challenges posed by climate change and severe biotic threats. While international germplasm banks safeguard a massive genetic diversity, translating this potential into active breeding pipelines requires the comprehensive and systematic phenotypic characterisation of hundreds of accessions across multiple environments (Rallo, Barranco, et al., 2018a; Rallo, Díez, et al., 2018b).
While numerous scientific methodologies exist to evaluate specific physiological traits, their practical implementation across diverse institutions is frequently hindered by technical complexity and equipment availability. For instance, abiotic stress evaluations, such as frost or drought tolerance, can be quantified through advanced, resource-intensive laboratory techniques (e.g., thermal differential analysis, continuous gas exchange monitoring, or hormonal expression) or through empirical field observations. Consequently, research facilities often adopt different methods based on their specific technical and financial capabilities. This methodological divergence not only restricts replicability but also generates inherently incompatible datasets, as phenotypic results derived from fundamentally different analytical approaches cannot be directly compared or integrated into a unified database (Ćwiek-Kupczyńska et al., 2016; Krajewski et al., 2015; Papoutsoglou et al., 2020). Moreover, the significant genotype-by-environment (GxE) interactions observed in olive phenology, morphology, and stress resilience require uniform evaluation protocols. Without such standardization, it is impossible to compare results across different environments or quantify the true impact of these interactions (Lamoumni et al., 2025; Miho et al., 2021; Yılmaz-Düzyaman et al., 2025).
To evaluate GxE interactions and ensure robust data inter-comparability, multi-environment phenotyping inherently relies on the application of standard protocols along with the systematic use of shared benchmark reference cultivars. These reference genotypes function as universal biological standards that allow researchers to calibrate evaluations, detect seasonal bias, and normalise raw trait values relative to the benchmark performance at each specific site (Li et al., 2025; Mousavi et al., 2019).
To overcome the historical lack of use of standard methods in olive research, the European Horizon 2020 GEN4OLIVE consortium initiated a large-scale international pre-breeding evaluation encompassing over 500 olive varieties. This massive phenotypical characterisation required the establishment of a robust, highly replicable, and unified evaluation framework. Consequently, five participating germplasm banks—the University of Cordoba (UCO, Spain), the Institute of Olive Tree and Subtropical Plants (ELGO DIMITRA, Greece), the Council for Agricultural Research and Economics (CREA, Italy), the Olive Research Institute (ORI, Turkiye), and the Institut National de la Recherche Agronomique (INRA, Morocco)— collaborated to establish a consensus methodology for the accurate evaluation of key agronomic traits in olive germplasm (Web:
GEN4OLIVE Project - H2020, 2024).
This Methods Article describes the GEN4OLIVE compendium, which integrates 14 protocols, including 12 adapted and two de novo designs, for fast and accurate phenotyping. By utilizing shared benchmark cultivars, this standardized methodology generated the largest public olive dataset to date and establishes a new global standard for the scientific community.
2. Methods: Framework harmonisation and setup
2.1 The consensus process and protocol selection
The GEN4OLIVE consortium implemented a rigorous consensus process to standardize the evaluation of over 500 olive accessions, ensuring that all resulting data are fully comparable. Led by the University of Cordoba (UCO), the consortium organised specific working groups comprising the most reputable international experts in each corresponding research area.
During this process, the experts evaluated multiple pre-existing scientific methodologies to quantify each target trait. To definitively select and adapt the final consensus methods and protocols, the consortium strictly applied three primary criteria:
• Large-scale feasibility: The methodologies had to be robust enough to support high-throughput evaluations of massive germplasm collections (>500 varieties), effectively accommodating traits that required continuous monitoring over three consecutive years, such as reproductive phenology.
• Ease of implementation: The experimental procedures needed to be sufficiently straightforward to ensure accurate and reproducible execution by field and laboratory personnel, even those without prior specialised experience in that specific topic. To this end, the protocols were backed up by strict templates and pre-defined spreadsheets to prevent errors in data collection and processing (information available in the Extended Data section of this paper).
• Equipment accessibility: To ensure universal applicability, the protocols strictly avoided the necessity for highly sophisticated, complex, or expensive equipments, acknowledging that not all participating research centres possessed equivalent technical infrastructure or financial resources.
Applying these strict criteria, the designated lead research group for each specific topic prepared an initial methodological draft. Subsequently, all involved consortium members meticulously reviewed, commented on, and refined these initial drafts. This iterative internal review process continued until all partners reached a unanimous agreement, resulting in the establishment of 14 homogeneous and fully harmonised evaluation protocols.
These 14 consensus protocols were strategically grouped into six fundamental research dimensions: I) Phenological and Agronomic Evaluation; II) Abiotic Stress Tolerance Evaluation; III) Biotic Stress Tolerance Evaluation; IV) Determination of Oil Yield and Quality; V) Table Olive Quality Assessment; and VI) Standardised Photographing and Morphological Characterisation.
A comprehensive methodological handbook containing the detailed step-by-step procedures, visual assessment scales, experimental designs, and practical implementation guides for all 14 protocols is provided as Extended Data (Miho et al., 2026).
2.2 Methods origin and adaptation for effective phenotyping
As highlighted previously, physiological and chemical evaluations frequently rely on technically complex or highly resource-intensive methodologies. For instance, frost tolerance is traditionally quantified through advanced laboratory techniques such as Differential Thermal Analysis (DTA) or Electrolyte Leakage (EL) tests (Malyshev et al., 2020; Thalhammer et al., 2020), while robust drought tolerance assessment often demands water potential measurements via Scholander pressure chambers (Baquedano & Castillo, 2007; Scholander et al., 1965). To overcome these operational barriers and facilitate simultaneous multi-site evaluations across five countries, we explicitly prioritised the selection of methods based on their ease of implementation, cost-effectiveness, and strict independence from highly sophisticated or expensive equipments.
Most of the protocols included in this framework were carefully adapted and optimized from previously published scientific literature to meet the specific requirements of effective basic phenotyping in large scale implementation. For example, in the case of drought tolerance evaluation, rather than utilising complex and time demanding equipment, we optimized the minimum leaf conductance protocol to evaluate using a simple precision balance and a digital scanner (Moriana et al., 2002). Similarly, we standardised visual rating scales (0 to 10) for olive aerial diseases to highly accelerate field evaluations without requiring advanced and costly molecular quantification (Estudillo et al., 2025; Moral et al., 2017).
Conversely, specific methodologies lacking scalable precedents in the literature were developed de novo. These novel methodologies include the standardised ex situ frost tolerance assessment utilising detached leaves exposed to strictly controlled freezing—which successfully bypasses the need for complex EL or DTA equipment (Valverde et al., 2024)—and the specific defined artistic and technical photography protocol designed to generate homogenous databases for GenBank’s management. A comprehensive summary of the 14 harmonised protocols, their base references, and specific adaptations is presented in
Table 1.
Table 1. Summary of the 14 harmonised evaluation protocols on olive large scale phenotyping, and base reference methodologies.
| Evaluation dimension |
Specific protocol | Target trait/methodology | Adaptation for multi-environment trials | Base reference(s) |
|---|
| I. Phenology & Agronomy
| 1. Reproductive phenology | Budburst, flowering, and ripening tracking (BBCH & Ferreira scales). | Implementation of a simplified phenological triangle diagram (Fleckinger) to standardise observation dates. | (Fleckinger, 1954; Navero et al., 2017; Sanz-Cortés et al., 2002) |
| 2. Chilling & thermal units | Mathematical computation of dormancy release and thermal time. | Standardised daily weather data inputs mathematically mapped to unified baseline constants for olive trees. | (De Melo-Abreu et al., 2004) |
| 3. Agronomic traits | Tree vigour, growth habit, canopy density, flowering/fruiting intensity, and fruit detachment force. | Adoption of 0–5 visual scales for rapid canopy assessment and harmonised hand-held dynamometer application for detachment force evaluation. | (Castillo Ruiz et al., 2018; Ramírez-Santa Pau et al., 2002; UPOV, 2011). |
| II. Abiotic Stress Tolerance
| 4. Drought tolerance | Calculation of minimum stomatal leaf conductance. | Replaced complex methodologies for evaluation with a scalable mass-loss technique using detached leaves. | (Moriana et al., 2002) |
| 5. Frost tolerance | Susceptibility to freezing temperatures (−10 °C). | De novo protocol subjecting detached leaves to controlled freezing and visually evaluating physiological damage. | De novo within GEN4OLIVE project (Valverde et al., 2024) |
| III. Biotic Stress Tolerance
| 6. Olive Scab | Venturia oleaginea susceptibility. | Standardised 0–10 visual field incidence scale, complemented by a rapid 5% NaOH test to reveal latent infections. | (Moral et al., 2017, p. 20; Viruega et al., 2011) |
| 7. Cercospora leaf spot | Pseudocercospora cladosporioides susceptibility. | Standardised 0–10 visual field incidence scale, complemented by humid chamber incubation to visually differentiate natural leaf senescence from fungal conidia presence. | (Moral et al., 2015) |
| 8. Anthracnose | Colletotrichum spp. fruit rot and branch dieback. | Standardised 0–10 visual field incidence scale and synchronisation of evaluation with the fruit maturity index (MI > 3) to avoid phenological bias. | (Moral et al., 2009, 2017) |
| 9. Verticillium wilt | Verticillium dahliae resistance using a 0–16 rating scale. | Standardised root-dipping procedure (30 min at conidia/mL) applied strictly to acclimatised rooted cuttings. | (López-Escudero et al., 2004) |
| 10. Xylella fastidiosa
| Bacterial resistance/susceptibility and molecular diagnosis. | Dual evaluation combining natural open-field exposure with strictly controlled needle-puncture inoculations. | (‘EPPO’, 2019; Saponari et al., 2017) |
| 11. Olive fruit fly | Bactrocera oleae susceptibility. | Harmonised spatial trapping network, visual superficial fruit in field evaluation linked to an in lab destructive interior evaluation. | (Petacchi et al., 2003; Quesada-Moraga et al., 2018) |
| IV. Oil Yield & Quality
| 12. Oil extraction and chemical quality | Total oil content, phenolic profile, and fatty acids. | Synchronised harvesting at MI = 2 and highly controlled Abencor extractions (30 min at 28 °C) to preserve phenolic integrity. Implementation of NMR, HPLC and GC instruments for oil content and chemical analysis. | (Castillo-Luna & Priego-Capote, 2024; Miho et al., 2021; Peres et al., 2014) |
| V. Table Olive Quality
| 13. Physical traits and bruising | Morphological characteristics, firmness, and olive bruising index (OBI). | Standardised easy to implement protocols for each evaluation: Visual scale indexes and implementation of small instruments for evaluations. | (Jimémez et al., 2016; Morales-Sillero et al., 2014) |
| VI. Photographing and Morphological Characterisation
| 14. Artistic and technical photography | Artistic documentation of fruiting shoots and technical composition of leaves, fruits, and endocarps. | De novo methodology standardising DSLR camera setups to build multi-site image databases through a dual approach: artistic photography of fruiting shoots (in-field and laboratory), and technical photography utilising a rigid millimetre-marked template to create a highly standardised composition of leaves, fruits, and endocarps. | Developed de novo within GEN4OLIVE project by University of Córdoba. |
2.3. The crucial role of benchmark cultivars and data standardisation
Given the inherently broad geographic distribution of the GEN4OLIVE trails, environmental heterogeneity posed a major challenge for data integration. Olive traits exhibit significant genotype-by-environment (GxE) interaction, particularly concerning phenology, fruit development, oil quality, and phenotypic stress responses (Lamoumni et al., 2025; Yılmaz-Düzyaman et al., 2023). Consequently, comparing raw trait values across different geographical trials introduces a high risk of misinterpretation (Miho et al., 2021; Mousavi et al., 2019).
To effectively avoid these GxE errors in the cultivar ranking and classifications, we integrated a specific panel of shared benchmark (reference) cultivars into every protocol. These genotypes (e.g., ‘Arbequina’, ‘Frantoio’, and ‘Picual’) acted as universal biological standards across all five germplasm banks that participated in the GEN4OLIVE project. By systematically evaluating these shared references alongside the local accessions, evaluators can detect site or seasonal bias, calibrate sensors in high-throughput phenotyping, and establish a reliable baseline for comparison (Li et al., 2025; Mousavi et al., 2019).
To fulfil with our targeted objective, we conducted data standardization to ensure dataset interoperability. The data standardization is a method of normalizing data when comparisons or classifications are required among cultivars evaluated in different environments or growing seasons. In those cases, researchers may mathematically normalise the raw trait values relative to the benchmark cultivar performance at each specific location. Evaluators should calculate the relative standardised data applying the following mathematical formula:
(Where GB1 represents the specific Germplasm Bank or local environment where the trial is conducted, and reference cultivar represent the shared cultivars among all the Germplasm Banks or other Experimental Tails).
This relative standardisation process allows researchers to express cultivar performance as a trait ratio, effectively mitigating environmental differential performance across disparate geographic locations. Consequently, the resulting multi-environment datasets become robust, fully interoperable, and highly suitable for advanced genetic association mapping and breeding selection (Bakkali et al., 2013; Wu et al., 2024).
3. Results: Implementation and use case
To validate the efficiency and reproducibility of this 14-protocol methodological framework, the compendium was deployed in a massive multi-environment pre-breeding trial. Between 2021 and 2024, the five participating germplasm banks (UCO, ELGO DIMITRA, CREA, ORI, and INRA) systematically applied these harmonised protocols to comprehensively evaluate over 500 olive accessions across Spain, Greece, Italy, Turkiye, and Morocco.
By rigorously adhering to the defined methodologies—including the de novo photographic cataloguing and frost tolerance assessments, and all the methods adaptations for the rest of the traits—evaluators successfully generated robust, inter-comparable phenotypic data. Crucially, the systematic integration of shared benchmark cultivars (e.g., ‘Arbequina’, ‘Frantoio’, ‘Picual’) allowed for the mathematical normalisation of trait values, effectively reducing the environmental variance (GxE interactions) inherent to such disparate geographical locations.
Consequently, this coordinated effort yielded the largest standardised, publicly available dataset on olive genetic resources to date (GEN4OLIVE
Database, 2025). The successful execution of this international use case demonstrates that the framework overcomes the traditional barriers of technical complexity and data fragmentation, enabling highly reliable, large-scale phenotypic comparisons.
4. Conclusions
The historical lack of standardised phenotyping protocols has long hindered the interoperability of multi-site olive evaluations. By establishing a consensus-based framework of 14 highly replicable protocols, covering six fundamental agronomic and quality dimensions, we provide a robust solution to this critical bottleneck.
This methodological compendium deliberately prioritises cost-effective, high-throughput techniques over highly complex laboratory methods, thereby democratising the evaluation process for diverse research facilities worldwide. Furthermore, the incorporation of benchmark reference cultivars guarantees the successful mitigation of environmental differences and seasonal bias.
Ultimately, this harmonised framework constitutes a new universal standard for olive germplasm banks and researchers. It successfully overcomes data fragmentation, facilitates global data integration, and accelerates the future international pre-breeding initiatives necessary to discover new elite and resilient cultivars.
Ethics and consent
Ethical approval and consent were not required for this study.
Data and software availability
The phenotypic dataset generated from the evaluation of over 500 olive varieties across the five Mediterranean germplasm banks using this compendium of protocols and methodologies, is publicly available via the GEN4OLIVE project repository.
Extended Data
A comprehensive methodological handbook containing the detailed step-by-step procedures, visual assessment scales, experimental designs, specific templates and practical implementation guides for all 14 protocols is provided as Extended Data.
Acknowledgments
The authors gratefully acknowledge the technical staff and field operators of the University of Cordoba (UCO), the Institute of Olive Tree, Subtropical Plants and Viticulture (ELGO DIMITRA), the Council for Agricultural Research and Economics (CREA), the Olive Research Institute (ORI), and the Institut National de la Recherche Agronomique (INRA) for their indispensable support during the massive multi-environment field evaluations and laboratory assessments.
Additionally, during the preparation of this manuscript, the authors utilised the machine learning features of Google NotebookLM for the sole purpose of English language editing, grammatical refinement, and stylistic review. The structural compilation, protocol data harmonisation, and scientific interpretations were entirely generated and verified by the authors, who accept full responsibility for the integrity of the published content.
References
- Bakkali AE, Haouane H, Moukhli A, et al.:
Construction of Core Collections Suitable for Association Mapping to Optimize Use of Mediterranean Olive (Olea europaea L.) Genetic Resources.
PLOS ONE.
2013; 8(5): e61265. PubMed Abstract
| Publisher Full Text
| Free Full Text
- Baquedano FJ, Castillo FJ:
Drought tolerance in the Mediterranean species Quercus coccifera, Quercus ilex, Pinus halepensis, and Juniperus phoenicea.
Photosynthetica.
2007; 45(2): 229–238. Publisher Full Text
- Castillo Ruiz FJ, Tombesi S, Farinelli D:
Olive fruit detachment force against pulling and torsional stress.
Span. J. Agric. Res.
2018; 16(1): e0202. Publisher Full Text
- Castillo-Luna A, Priego-Capote F:
Phenolic enrichment of foods curated in olive oil: Kinetics and chemical evaluation.
Food Chem. X.
2024; 22: 101398. PubMed Abstract
| Publisher Full Text
| Free Full Text
- Ćwiek-Kupczyńska H, Altmann T, Arend D, et al.:
Measures for interoperability of phenotypic data: Minimum information requirements and formatting.
Plant Methods.
2016; 12(1): 44. PubMed Abstract
| Publisher Full Text
| Free Full Text
- De Melo-Abreu JP, Barranco D, Cordeiro AM, et al.:
Modelling olive flowering date using chilling for dormancy release and thermal time.
Agric. For. Meteorol.
2004; 125(1): 117–127. Publisher Full Text
- EPPO:
PM 7/24 (4).
Xylella Fastidiosa.
2019; 49(2): 175–227. Publisher Full Text
- Estudillo C, Pérez-Rial A, Guerrero-Páez FA, et al.:
Characterization of Olive-Resistant Genes Against Spilocaea oleagina, the Causal Agent of Scab.
Agronomy.
2025; 15. Publisher Full Text
- Fleckinger J:
Observations recentes sur l’ecologie du pommier a cidre.
VII Congres Internationalle Botanique.
1954; 10.
- GEN4OLIVE Database:
European Commission.
University of Cordoba;
2025.
Reference Source
- GEN4OLIVE Project—H2020:
CORDIS.
European Commission;
2024.
Reference Source
- Jimémez MR, Rallo P, Rapoport HF, et al.:
Distribution and timing of cell damage associated with olive fruit bruising and its use in analyzing susceptibility.2016. Publisher Full Text
- Krajewski P, Chen D, Ćwiek H, et al.:
Towards recommendations for metadata and data handling in plant phenotyping.
J. Exp. Bot.
2015; 66(18): 5417–5427. PubMed Abstract
| Publisher Full Text
- Lamoumni O, El Bakkali A, Abou-Saaid O, et al.:
QTL mapping and candidate gene identification of the full flowering date trait in olive trees (Olea Europaea L.).
BMC Plant Biol.
2025; 25(1): 1768. Publisher Full Text
- Li J, Jia D, Zhou Z, et al.:
Stability Analysis and Multi-Trait Selection of Flowering Phenology Parameters in Olive Cultivars Under Multi-Environment Trials.
Plants.
2025; 14(13). PubMed Abstract
| Publisher Full Text
| Free Full Text
- López-Escudero FJ, del Río C , Caballero JM, et al.:
Evaluation of Olive Cultivars for Resistance to Verticillium dahliae.
Eur. J. Plant Pathol.
2004; 110(1): 79–85. Publisher Full Text
- Malyshev AV, Beil I, Kreyling J:
Differential Thermal Analysis: A Fast Alternative to Frost ToleranceFrost tolerance Measurements.Hincha DK, Zuther E, editors.
Plant Cold Acclimation: Methods and Protocols.
Springer US;
2020; pp. 23–31. Publisher Full Text
, PMID: PubMed Abstract
|
- Miho H, Barranco Navero D, Trujillo I, et al.:
A methodological framework for the standardised evaluation of olive genetic resources: GEN4OLIVE harmonized protocols.
Open Research Europe (ORE).
2026. Publisher Full Text
- Miho H, Moral J, Barranco D, et al.:
Influence of genetic and interannual factors on the phenolic profiles of virgin olive oils.
Food Chem.
2021; 342(August 2020): 128357. PubMed Abstract
| Publisher Full Text
- Moral J, Alsalimiya M, Roca LF, et al.:
Relative Susceptibility of New Olive Cultivars to Spilocaea oleagina, Colletotrichum acutatum, and Pseudocercospora cladosporioides.
Plant Dis.
2015; 99(1): 58–64. PubMed Abstract
| Publisher Full Text
- Moral J, Oliveira RD, Trapero A:
Elucidation of the disease cycle of olive anthracnose caused by colletotrichum acutatum.
Phytopathology.
2009; 99(5): 548–556. PubMed Abstract
| Publisher Full Text
- Moral J, Xaviér CJ, Viruega JR, et al.:
Variability in susceptibility to anthracnose in the world collection of olive cultivars of Cordoba (Spain).
Front. Plant Sci.
2017; 8(November): 1–11. PubMed Abstract
| Publisher Full Text
| Free Full Text
- Morales-Sillero A, Rallo P, Jiménez MR, et al.:
Suitability of Two Table Olive Cultivars (‘Manzanilla de Sevilla’ and ‘Manzanilla Cacereña’) for Mechanical Harvesting in Superhigh-density Hedgerows.
HortScience.
2014; 49(8): 1028–1033. Publisher Full Text
- Moriana A, Villalobos FJ, Fereres E:
Stomatal and photosynthetic responses of olive (Olea europaea L.) leaves to water deficits.
Plant Cell Environ.
2002; 25(3): 395–405. Publisher Full Text
- Mousavi S, de la Rosa R , Moukhli A, et al.:
Plasticity of fruit and oil traits in olive among different environments.
Sci. Rep.
2019; 9(1): 16968. PubMed Abstract
| Publisher Full Text
| Free Full Text
- Navero DB, Escobar RF, Romero LR:
El cultivo del olivo.
7th edEdiciones Mundi-Prensa;
2017; vol. 2017. .
Reference Source
- Papoutsoglou EA, Faria D, Arend D, et al.:
Enabling reusability of plant phenomic datasets with MIAPPE 1.1.
New Phytol.
2020; 227(1): 260–273. Publisher Full Text
- Peres F, Martins LL, Ferreira-Dias S:
Laboratory-scale optimization of olive oil extraction: Simultaneous addition of enzymes and microtalc improves the yield.
Eur. J. Lipid Sci. Technol.
2014; 116(8): 1054–1062. Publisher Full Text
- Petacchi R, Rizzi I, Guidotti D:
The ‘lure and kill’ technique in Bactrocera oleae (Gmel.) control: Effectiveness indices and suitability of the technique in area-wide experimental trials.
Int. J. Pest Manag.
2003; 49(4): 305–311. Publisher Full Text
- Quesada-Moraga E, Santiago-Álvarez C, Cubero-González S, et al.:
Field evaluation of the susceptibility of mill and table olive varieties to egg-laying of olive fly.
J. Appl. Entomol.
2018; 142(8): 765–774. Publisher Full Text
- Rallo L, Barranco D, Díez CM, et al.:
Strategies for Olive (Olea europaea L.) Breeding: Cultivated Genetic Resources and Crossbreeding.Al-Khayri JM, Jain SM, Johnson DV, editors.
Advances in Plant Breeding Strategies: Fruits.
Springer International Publishing;
2018a; Vol. 3. : pp. 535–600. Publisher Full Text
- Rallo L, Díez CM, Morales-Sillero A, et al.:
Quality of olives: A focus on agricultural preharvest factors.
Sci. Hortic.
2018b; 233(December 2017): 491–509. Publisher Full Text
- Ramírez-Santa Pau M, Navarro C, Rallo L:
RELATIONSHIP AMONG FLOWERING, FRUITFULNESS AND CROP IN ‘MANZANILLA DE SEVILLA’ OLIVES.
Acta Hortic.
2002; 586: 317–320. Publisher Full Text
- Sanz-Cortés F, Martinez-Calvo J, Badenes ML, et al.:
Phenological growth stages of olive trees (Olea europaea).
Ann. Appl. Biol.
2002; 140(2): 151–157. Publisher Full Text
- Saponari M, Boscia D, Altamura G, et al.:
Isolation and pathogenicity of Xylella fastidiosa associated to the olive quick decline syndrome in southern Italy.
Sci. Rep.
2017; 7(1): 17723. PubMed Abstract
| Publisher Full Text
| Free Full Text
- Scholander PF, Bradstreet ED, Hemmingsen EA, et al.:
Sap Pressure in Vascular Plants.
Science.
1965; 148(3668): 339–346. Publisher Full Text
- Thalhammer A, Pagter M, Hincha DK, et al.:
Measuring Freezing ToleranceFreezingtolerance of Leaves and Rosettes: Electrolyte Leakage and Chlorophyll FluorescenceChlorophyll fluorescence Assays.Hincha DK, Zuther E, editors.
Plant Cold Acclimation: Methods and Protocols.
Springer US;
2020; pp. 9–21. PubMed Abstract
| Publisher Full Text
- UPOV:
Community Plant Variety Office. ROTOCOL FOR DISTINCTNESS, UNIFORMITY AND STABILITY TESTS. Olea europaea L. OLIVE. UPOV Code: OLEAA_EUR.2011.
Reference Source
- Valverde P, Lodolini EM, Giorgi V, et al.:
An easy methodology for frost tolerance assessment in olive cultivars.
Front. Plant Sci.
2024; 15. Publisher Full Text
- Viruega JR, Roca LF, Moral J, et al.:
Factors Affecting Infection and Disease Development on Olive Leaves Inoculated with Fusicladium oleagineum.
Plant Dis.
2011; 95(9): 1139–1146. Publisher Full Text
- Wu W, Shao M, Qi J, et al.:
Integrating genetic analysis of germplasm wealth for enhanced selection and improvement in olive (Olea europaea L.): Insights from leaves.
Plant Cell Rep.
2024; 43: 247. PubMed Abstract
| Publisher Full Text
- Yılmaz-Düzyaman H, León L, de la Rosa R , et al.:
Genotypic Effect on Olive (Olea europaea) Fruit Phenolic Profile.
Plants.
2025; 14(13). PubMed Abstract
| Publisher Full Text
| Free Full Text
- Yılmaz-Düzyaman H, Medina-Alonso MG, Sanz C, et al.:
Influence of Genotype and Environment on Fruit Phenolic Composition of Olive.
Horticulturae.
2023; 9(10). Publisher Full Text
