Diego Jarquín

5.9k total citations · 2 hit papers
90 papers, 3.3k citations indexed

About

Diego Jarquín is a scholar working on Plant Science, Genetics and Ecology. According to data from OpenAlex, Diego Jarquín has authored 90 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 87 papers in Plant Science, 66 papers in Genetics and 4 papers in Ecology. Recurrent topics in Diego Jarquín's work include Genetics and Plant Breeding (72 papers), Genetic Mapping and Diversity in Plants and Animals (55 papers) and Genetic and phenotypic traits in livestock (47 papers). Diego Jarquín is often cited by papers focused on Genetics and Plant Breeding (72 papers), Genetic Mapping and Diversity in Plants and Animals (55 papers) and Genetic and phenotypic traits in livestock (47 papers). Diego Jarquín collaborates with scholars based in United States, Mexico and Brazil. Diego Jarquín's co-authors include José Crossa, Juan Burgueño, Paulino Pérez‐Rodríguez, Aaron J. Lorenz, Gustavo de los Campos, Sergio Pérez‐Elizalde, Jaime Cuevas, Osval A. Montesinos‐López, Yoseph Beyene and Rajeev K. Varshney and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and New Phytologist.

In The Last Decade

Diego Jarquín

85 papers receiving 3.2k citations

Hit Papers

Genomic Selection in Plant Breeding: Methods, Models, and... 2013 2026 2017 2021 2017 2013 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Genomic Selection in Plant Breeding: Methods, Models, and Perspectives
    2017 963 citations José Crossa, Paulino Pérez‐Rodríguez et al. profile →
  2. A reaction norm model for genomic selection using high-dimensional genomic and environmental data
    2013 426 citations Diego Jarquín, José Crossa et al. Theoretical and Applied Genetics profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Diego Jarquín United States 23 2.9k 2.2k 226 190 118 90 3.3k
Frank Technow Germany 20 2.0k 0.7× 1.6k 0.7× 310 1.4× 234 1.2× 130 1.1× 39 2.3k
Roberto Fritsche‐Neto Brazil 26 1.9k 0.7× 1.1k 0.5× 360 1.6× 167 0.9× 103 0.9× 142 2.2k
Marcos Malosetti Netherlands 31 3.0k 1.0× 1.7k 0.8× 449 2.0× 251 1.3× 115 1.0× 56 3.3k
Jaime Cuevas Mexico 15 1.5k 0.5× 1.3k 0.6× 119 0.5× 127 0.7× 71 0.6× 28 1.8k
Manje Gowda Kenya 36 4.1k 1.4× 2.7k 1.2× 604 2.7× 511 2.7× 74 0.6× 96 4.5k
Raman Babu Mexico 30 3.4k 1.2× 2.3k 1.1× 395 1.7× 547 2.9× 55 0.5× 52 3.9k
Haydn Kuchel Australia 29 3.1k 1.1× 1.4k 0.6× 815 3.6× 237 1.2× 206 1.7× 49 3.4k
Enrique Autrique Mexico 19 1.7k 0.6× 955 0.4× 285 1.3× 120 0.6× 113 1.0× 21 1.8k
Juan Manuel González‐Camacho Mexico 9 1.3k 0.5× 1.1k 0.5× 91 0.4× 142 0.7× 52 0.4× 33 1.6k
Manish Roorkiwal India 29 3.4k 1.2× 1.2k 0.5× 253 1.1× 492 2.6× 60 0.5× 69 3.7k

Countries citing papers authored by Diego Jarquín

Since Specialization
Citations

This map shows the geographic impact of Diego Jarquín'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 Diego Jarquín with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego Jarquín more than expected).

Fields of papers citing papers by Diego Jarquín

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Diego Jarquín. 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 Diego Jarquín. The network helps show where Diego Jarquín may publish in the future.

Co-authorship network of co-authors of Diego Jarquín

This figure shows the co-authorship network connecting the top 25 collaborators of Diego Jarquín. A scholar is included among the top collaborators of Diego Jarquín 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 Diego Jarquín. Diego Jarquín 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
1.
Babar, Md Ali, Diego Jarquín, Noah DeWitt, et al.. (2026). Effectiveness of low‐density high‐throughput marker platform and easy‐to‐measure traits for genomic prediction of biomass yield in oat ( Avena sativa L.). The Plant Genome. 19(1). e70179–e70179. 1 indexed citations
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Riascos, John J., et al.. (2024). Sparse testing designs for optimizing predictive ability in sugarcane populations. Frontiers in Plant Science. 15. 1400000–1400000. 1 indexed citations
4.
Nascimento, Moysés, et al.. (2024). Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee. Frontiers in Plant Science. 15. 1373318–1373318. 6 indexed citations
5.
Nascimento, Moysés, Ana Carolina Campana Nascimento, Camila Ferreira Azevedo, et al.. (2024). Integrating Environmental Covariates into Adaptability and Stability Analyses: A Structural Equation Modeling Approach for Cotton Breeding. Agriculture. 14(11). 1914–1914.
6.
Resende, Rafael Tassinari, et al.. (2024). GIS‐based G × E modeling of maize hybrids through enviromic markers engineering. New Phytologist. 245(1). 102–116. 8 indexed citations
7.
Kakani, Vijaya Gopal, et al.. (2024). Hyperspectral imaging combined with machine learning for high‐throughput phenotyping in winter wheat. SHILAP Revista de lepidopterología. 7(1). 9 indexed citations
8.
Díaz‐García, Luis, Diego Jarquín, Shinya Ikeda, et al.. (2024). Multiparametric Cranberry (Vaccinium macrocarpon Ait.) Fruit Textural Trait Development for Harvest and Postharvest Evaluation in Representative Cultivars. Journal of Texture Studies. 55(5). e12866–e12866. 2 indexed citations
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González, Francisco, et al.. (2024). Introducing CHiDO—A No Code Genomic Prediction software implementation for the characterization and integration of driven omics. The Plant Genome. 18(1). e20519–e20519. 4 indexed citations
12.
Arief, Vivi N., et al.. (2024). Simulations of multiple breeding strategy scenarios in common bean for assessing genomic selection accuracy and model updating. The Plant Genome. 17(1). e20388–e20388. 4 indexed citations
13.
Jarquín, Diego, et al.. (2023). Genome-wide association mapping highlights candidate genes and immune genotypes for net blotch and powdery mildew resistance in barley. Computational and Structural Biotechnology Journal. 21. 4923–4932. 6 indexed citations
14.
Semagn, Kassa, Muhammad Iqbal, Diego Jarquín, et al.. (2022). Genomic Predictions for Common Bunt, FHB, Stripe Rust, Leaf Rust, and Leaf Spotting Resistance in Spring Wheat. Genes. 13(4). 565–565. 16 indexed citations
15.
Semagn, Kassa, Muhammad Iqbal, José Crossa, et al.. (2021). Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems. Theoretical and Applied Genetics. 135(2). 537–552. 10 indexed citations
16.
Liu, Caiyun, Sivakumar Sukumaran, Diego Jarquín, et al.. (2020). Comparison of array‐ and sequencing‐based markers for genome‐wide association mapping and genomic prediction in spring wheat. Crop Science. 60(1). 211–225. 15 indexed citations
17.
Roorkiwal, Manish, Diego Jarquín, Muneendra Kumar Singh, et al.. (2018). Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea. Scientific Reports. 8(1). 11701–11701. 50 indexed citations
18.
Jarquín, Diego, James E. Specht, & Aaron J. Lorenz. (2016). Prospects of Genomic Prediction in the USDA Soybean Germplasm Collection: Historical Data Creates Robust Models for Enhancing Selection of Accessions. G3 Genes Genomes Genetics. 6(8). 2329–2341. 66 indexed citations
19.
Jarquín, Diego, et al.. (2014). Genotyping by sequencing for genomic prediction in a soybean breeding population. BMC Genomics. 15(1). 740–740. 166 indexed citations
20.
Jarquín, Diego, José Crossa, Xavier Lacaze, et al.. (2013). A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theoretical and Applied Genetics. 127(3). 595–607. 426 indexed citations breakdown →

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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