José Quero‐García

1.7k total citations
38 papers, 1.1k citations indexed

About

José Quero‐García is a scholar working on Plant Science, Molecular Biology and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, José Quero‐García has authored 38 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Plant Science, 20 papers in Molecular Biology and 5 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in José Quero‐García's work include Plant Physiology and Cultivation Studies (25 papers), Horticultural and Viticultural Research (21 papers) and Plant Reproductive Biology (17 papers). José Quero‐García is often cited by papers focused on Plant Physiology and Cultivation Studies (25 papers), Horticultural and Viticultural Research (21 papers) and Plant Reproductive Biology (17 papers). José Quero‐García collaborates with scholars based in France, United States and Germany. José Quero‐García's co-authors include Elisabeth Dirlewanger, José Antonio Campoy, Teresa Barreneche, Gregory A. Lang, Joanna Puławska, Loı̈ck Le Dantec, Bénédicte Wenden, Umesh R. Rosyara, Vincent Lebot and Luca Dondini and has published in prestigious journals such as PLoS ONE, Scientific Reports and Frontiers in Plant Science.

In The Last Decade

José Quero‐García

36 papers receiving 1.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
José Quero‐García France 19 954 504 159 85 66 38 1.1k
G. M. Halloran Australia 21 1.4k 1.4× 261 0.5× 238 1.5× 16 0.2× 98 1.5× 107 1.5k
Gennaro Fazio United States 32 2.5k 2.6× 747 1.5× 438 2.8× 12 0.1× 180 2.7× 111 2.7k
Craig Hardner Australia 21 637 0.7× 224 0.4× 244 1.5× 4 0.0× 215 3.3× 77 978
Augusto Tulmann Neto Brazil 13 993 1.0× 471 0.9× 236 1.5× 5 0.1× 150 2.3× 74 1.2k
L. S. Lee Australia 6 651 0.7× 222 0.4× 264 1.7× 8 0.1× 98 1.5× 8 875
Marie-Noëlle Ndjiondjop Ivory Coast 20 1.2k 1.3× 234 0.5× 619 3.9× 8 0.1× 64 1.0× 59 1.4k
Ricardo Goenaga United States 15 528 0.6× 164 0.3× 26 0.2× 27 0.3× 79 1.2× 118 780
Geoffrey P. Gill New Zealand 8 360 0.4× 342 0.7× 292 1.8× 4 0.0× 102 1.5× 12 656
M. Fregene Colombia 21 1.5k 1.5× 155 0.3× 179 1.1× 48 0.6× 38 0.6× 32 1.6k
Karen McLean United Kingdom 19 1.5k 1.6× 300 0.6× 263 1.7× 6 0.1× 50 0.8× 33 1.7k

Countries citing papers authored by José Quero‐García

Since Specialization
Citations

This map shows the geographic impact of José Quero‐García's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by José Quero‐García with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites José Quero‐García more than expected).

Fields of papers citing papers by José Quero‐García

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by José Quero‐García. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by José Quero‐García. The network helps show where José Quero‐García may publish in the future.

Co-authorship network of co-authors of José Quero‐García

This figure shows the co-authorship network connecting the top 25 collaborators of José Quero‐García. A scholar is included among the top collaborators of José Quero‐García based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with José Quero‐García. José Quero‐García is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Hardner, Craig, Elisabeth Dirlewanger, Bénédicte Wenden, et al.. (2023). Genotype-by-environment and QTL-by-environment interactions in sweet cherry (Prunus avium L.) for flowering date. Frontiers in Plant Science. 14. 1142974–1142974. 13 indexed citations
4.
Barreneche, Teresa, et al.. (2023). Genetic diversity and structure of Slovenian native germplasm of plum species (P. domestica L., P. cerasifera Ehrh. and P. spinosa L.). Frontiers in Plant Science. 14. 1150459–1150459. 3 indexed citations
5.
Donkpegan, Armel, Anthony Bernard, Teresa Barreneche, et al.. (2023). Genome-wide association mapping in a sweet cherry germplasm collection (Prunus avium L.) reveals candidate genes for fruit quality traits. Horticulture Research. 10(10). uhad191–uhad191. 17 indexed citations
6.
Quero‐García, José, Mathieu Fouché, Bénédicte Wenden, et al.. (2022). New insights into flowering date in Prunus: fine mapping of a major QTL in sweet cherry. Horticulture Research. 9. 10 indexed citations
7.
Quero‐García, José, et al.. (2021). Multi-year analyses on three populations reveal the first stable QTLs for tolerance to rain-induced fruit cracking in sweet cherry (Prunus avium L.). Horticulture Research. 8(1). 136–136. 24 indexed citations
8.
Barreneche, Teresa, et al.. (2018). S-locus diversity of sweet cherry cultivars in Tunisia.. Journal of New Sciences. 58. 3738–3742. 4 indexed citations
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Campoy, José Antonio, Rebecca Darbyshire, Elisabeth Dirlewanger, José Quero‐García, & Bénédicte Wenden. (2018). Yield potential definition of the chilling requirement reveals likely underestimation of the risk of climate change on winter chill accumulation. International Journal of Biometeorology. 63(2). 183–192. 61 indexed citations
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Hardner, Craig, Ben J. Hayes, Satish Kumar, et al.. (2018). Prediction of genetic value for sweet cherry fruit maturity among environments using a 6K SNP array. Horticulture Research. 6(1). 6–6. 20 indexed citations
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Campoy, José Antonio, Loı̈ck Le Dantec, José Quero‐García, et al.. (2015). Mapping of Candidate Genes Involved in Bud Dormancy and Flowering Time in Sweet Cherry (Prunus avium). PLoS ONE. 10(11). e0143250–e0143250. 58 indexed citations
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Franceschi, Paolo De, A. Cabrera, Esther van der Knaap, et al.. (2013). Cell number regulator genes in Prunus provide candidate genes for the control of fruit size in sweet and sour cherry. Molecular Breeding. 32(2). 311–326. 96 indexed citations
14.
Campoy, José Antonio, José Quero‐García, L. M. Mansur, et al.. (2013). Construction and Comparative Analyses of Highly Dense Linkage Maps of Two Sweet Cherry Intra-Specific Progenies of Commercial Cultivars. PLoS ONE. 8(1). e54743–e54743. 56 indexed citations
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Dirlewanger, Elisabeth, José Quero‐García, Loı̈ck Le Dantec, et al.. (2012). Comparison of the genetic determinism of two key phenological traits, flowering and maturity dates, in three Prunus species: peach, apricot and sweet cherry. Heredity. 109(5). 280–292. 120 indexed citations
16.
Cabrera, Antonio, Umesh R. Rosyara, Paolo De Franceschi, et al.. (2011). Rosaceae conserved orthologous sequences marker polymorphism in sweet cherry germplasm and construction of a SNP-based map. Tree Genetics & Genomes. 8(2). 237–247. 26 indexed citations
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Quero‐García, José, et al.. (2010). Cherry splitting: what solutions to provide?. 27–31. 1 indexed citations
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Quero‐García, José, Philippe Letourmy, Anton Ivančič, et al.. (2009). Hybrid performance in taro (Colocasia esculenta) in relation to genetic dissimilarity of parents. Theoretical and Applied Genetics. 119(2). 213–221. 14 indexed citations
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Ivančič, Anton, et al.. (2009). Topology of thermogenic tissues ofAlocasia macrorrhizos(Araceae) inflorescences. Botany. 87(12). 1232–1241. 4 indexed citations
20.
Quero‐García, José, Brigitte Courtois, Anton Ivančič, et al.. (2006). First genetic maps and QTL studies of yield traits of taro (Colocasia esculenta (L.) Schott). Euphytica. 151(2). 20 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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