Réka Howard

1.6k total citations · 1 hit paper
38 papers, 896 citations indexed

About

Réka Howard is a scholar working on Plant Science, Genetics and Agronomy and Crop Science. According to data from OpenAlex, Réka Howard has authored 38 papers receiving a total of 896 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Plant Science, 20 papers in Genetics and 8 papers in Agronomy and Crop Science. Recurrent topics in Réka Howard's work include Genetics and Plant Breeding (22 papers), Genetic and phenotypic traits in livestock (15 papers) and Genetic Mapping and Diversity in Plants and Animals (14 papers). Réka Howard is often cited by papers focused on Genetics and Plant Breeding (22 papers), Genetic and phenotypic traits in livestock (15 papers) and Genetic Mapping and Diversity in Plants and Animals (14 papers). Réka Howard collaborates with scholars based in United States, Mexico and Canada. Réka Howard's co-authors include William D. Beavis, Diego Jarquín, Alicia L. Carriquiry, José Crossa, Kenneth G. Cassman, Patricio Grassini, Fatima A. Tenorio, Juan Pablo Monzón, Jesse Poland and R. Chris Gaynor and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Scientific Reports.

In The Last Decade

Réka Howard

35 papers receiving 886 citations

Hit Papers

Climate and agronomy, not genetics, underpin recent maize... 2022 2026 2023 2024 2022 40 80 120
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. Climate and agronomy, not genetics, underpin recent maize yield gains in favorable environments
    2022 129 citations Juan Pablo Monzón, Fatima A. Tenorio et al. Proceedings of the National Academy of Sciences profile →

Peers

Réka Howard
B. Hau Germany
Isacco Beritognolo Italy
Jérôme Enjalbert France
A. R. de Freitas Brazil
K. Emrich Germany
José Lindenberg Rocha Sarmento Brazil
P. W. Voigt United States
Fernando Brito Lopes Brazil
Marcin Pszczoła Poland
Baloua Nebié Mali
B. Hau Germany
Réka Howard
Citations per year, relative to Réka Howard Réka Howard (= 1×) peers B. Hau

Countries citing papers authored by Réka Howard

Since Specialization
Citations

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

Fields of papers citing papers by Réka Howard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Réka Howard

This figure shows the co-authorship network connecting the top 25 collaborators of Réka Howard. A scholar is included among the top collaborators of Réka Howard 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 Réka Howard. Réka Howard 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.
2.
Specht, James E., Réka Howard, Walter D. Carciochi, et al.. (2025). Soybean seed yield distribution within the canopy as affected by nitrogen supply. Crop Science. 65(2). 1 indexed citations
3.
Robinson, Emily A., et al.. (2025). Perception and Cognitive Implications of Logarithmic Scales for Exponentially Increasing Data: Perceptual Sensitivity Tested with Statistical Lineups. Journal of Computational and Graphical Statistics. 34(4). 1680–1691.
4.
Schram, A. W., et al.. (2024). A reproducibility crisis for clinical metabolomics studies. TrAC Trends in Analytical Chemistry. 180. 117918–117918. 2 indexed citations
5.
McMechan, Anthony J., et al.. (2023). Abnormal ear development in corn: Does hybrid, environment, and seeding rate matter?. Agronomy Journal. 115(4). 1796–1811. 1 indexed citations
6.
Agus, Fahmuddin, José F. Andrade, Juan I. Rattalino Edreira, et al.. (2023). A farmer data-driven approach for prioritization of agricultural research and development: A case study for intensive crop systems in the humid tropics. Field Crops Research. 297. 108942–108942. 9 indexed citations
7.
Robinson, Emily A., et al.. (2023). ‘You Draw It’: Implementation of Visually Fitted Trends with r2d3. Journal of Data Science. 281–294.
8.
Jarquín, Diego, et al.. (2023). Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification. Frontiers in Genetics. 13. 1 indexed citations
9.
Menza, Nicolás Cafaro La, Timothy J. Arkebauer, John L. Lindquist, et al.. (2022). Decoupling between leaf nitrogen and radiation-use efficiency in vegetative and early reproductive stages in high-yielding soybean. Journal of Experimental Botany. 74(1). 352–363. 7 indexed citations
10.
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
11.
Monzón, Juan Pablo, et al.. (2022). Climate and agronomy, not genetics, underpin recent maize yield gains in favorable environments. Proceedings of the National Academy of Sciences. 119(4). 129 indexed citations breakdown →
12.
Howard, Réka, Diego Jarquín, & José Crossa. (2022). Overview of Genomic Prediction Methods and the Associated Assumptions on the Variance of Marker Effect, and on the Architecture of the Target Trait. Methods in molecular biology. 2467. 139–156. 2 indexed citations
13.
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
14.
15.
Jarquín, Diego, Réka Howard, Zhikai Liang, et al.. (2020). Enhancing Hybrid Prediction in Pearl Millet Using Genomic and/or Multi-Environment Phenotypic Information of Inbreds. Frontiers in Genetics. 10. 1294–1294. 19 indexed citations
16.
Howard, Réka, et al.. (2020). The local stability of a modified multi-strain SIR model for emerging viral strains. PLoS ONE. 15(12). e0243408–e0243408. 55 indexed citations
17.
Li, Jiating, Madhav Bhatta, P. Stephen Baenziger, et al.. (2019). Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery. Plant Methods. 15(1). 123–123. 35 indexed citations
18.
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
19.
Howard, Réka, Alicia L. Carriquiry, & William D. Beavis. (2017). Application of Response Surface Methods To Determine Conditions for Optimal Genomic Prediction. G3 Genes Genomes Genetics. 7(9). 3103–3113. 5 indexed citations
20.
Xavier, Alencar, Diego Jarquín, Réka Howard, et al.. (2017). Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population. G3 Genes Genomes Genetics. 8(2). 519–529. 65 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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