Guillermo Gerard

569 total citations
30 papers, 303 citations indexed

About

Guillermo Gerard is a scholar working on Plant Science, Genetics and Agronomy and Crop Science. According to data from OpenAlex, Guillermo Gerard has authored 30 papers receiving a total of 303 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Plant Science, 17 papers in Genetics and 8 papers in Agronomy and Crop Science. Recurrent topics in Guillermo Gerard's work include Genetics and Plant Breeding (21 papers), Genetic Mapping and Diversity in Plants and Animals (17 papers) and Genetic and phenotypic traits in livestock (15 papers). Guillermo Gerard is often cited by papers focused on Genetics and Plant Breeding (21 papers), Genetic Mapping and Diversity in Plants and Animals (17 papers) and Genetic and phenotypic traits in livestock (15 papers). Guillermo Gerard collaborates with scholars based in Mexico, Argentina and United States. Guillermo Gerard's co-authors include Marı́a Rosa Simón, María Constanza Fleitas, Matías Schierenbeck, José Crossa, Andreas Börner, U. Lohwasser, Leonardo Crespo‐Herrera, Suchismita Mondal, Ravi P. Singh and Carolina Saint Pierre and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Guillermo Gerard

27 papers receiving 296 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guillermo Gerard Mexico 12 260 97 92 14 14 30 303
Amadou Tidiane Sall Senegal 6 297 1.1× 71 0.7× 110 1.2× 12 0.9× 18 1.3× 9 318
K. Ganesamurthy India 9 254 1.0× 85 0.9× 67 0.7× 14 1.0× 28 2.0× 65 289
Norman Philipp Germany 9 232 0.9× 78 0.8× 115 1.3× 8 0.6× 12 0.9× 13 253
P. Ramya India 9 291 1.1× 74 0.8× 150 1.6× 9 0.6× 10 0.7× 12 311
R S Mahala India 7 180 0.7× 41 0.4× 46 0.5× 21 1.5× 23 1.6× 11 208
Suma S. Biradar India 8 182 0.7× 59 0.6× 51 0.6× 17 1.2× 15 1.1× 43 205
Paulo Izquierdo United States 8 333 1.3× 37 0.4× 58 0.6× 11 0.8× 22 1.6× 15 348
P. G. S. Melo Brazil 13 468 1.8× 75 0.8× 53 0.6× 23 1.6× 20 1.4× 49 478
Sanja Mikić Serbia 11 281 1.1× 138 1.4× 64 0.7× 9 0.6× 18 1.3× 50 312
Sarah Battenfield United States 6 404 1.6× 81 0.8× 264 2.9× 11 0.8× 9 0.6× 6 425

Countries citing papers authored by Guillermo Gerard

Since Specialization
Citations

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

Fields of papers citing papers by Guillermo Gerard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guillermo Gerard

This figure shows the co-authorship network connecting the top 25 collaborators of Guillermo Gerard. A scholar is included among the top collaborators of Guillermo Gerard 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 Guillermo Gerard. Guillermo Gerard 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.
Montesinos‐López, Osval A., Josafhat Salinas‐Ruíz, Abelardo Montesinos‐López, et al.. (2025). Optimizing genomic prediction with transfer learning under a ridge regression framework. The Plant Genome. 18(3). e70049–e70049. 1 indexed citations
2.
Montesinos‐López, Osval A., Guillermo Gerard, Velu Govindan, et al.. (2025). Improving wheat grain yield genomic prediction accuracy using historical data. G3 Genes Genomes Genetics. 15(4). 2 indexed citations
3.
Fradgley, Nick, Guillermo Gerard, Velu Govindan, et al.. (2025). Prediction of Australian wheat genotype by environment interactions and mega-environments. Theoretical and Applied Genetics. 138(9). 241–241.
4.
Cuevas, Jaime, Johannes W. R. Martini, Guillermo Gerard, et al.. (2025). Enhancing wheat genomic prediction by a hybrid kernel approach. Frontiers in Plant Science. 16. 1605202–1605202. 1 indexed citations
5.
Crossa, José, Johannes W. R. Martini, Paulino Pérez‐Rodríguez, et al.. (2025). Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software. Trends in Plant Science. 30(7). 756–774. 13 indexed citations
6.
7.
Montesinos‐López, Osval A., Leonardo Crespo‐Herrera, Carolina Saint Pierre, et al.. (2024). Data Augmentation Enhances Plant-Genomic-Enabled Predictions. Genes. 15(3). 286–286. 3 indexed citations
8.
Crossa, José, Osval A. Montesinos‐López, Germano Costa‐Neto, et al.. (2024). Machine learning algorithms translate big data into predictive breeding accuracy. Trends in Plant Science. 30(2). 167–184. 19 indexed citations
9.
Crespo‐Herrera, Leonardo, Serafín Cruz-Izquierdo, José Sergio Sandoval-Islas, et al.. (2024). Genomic Prediction from Multi-Environment Trials of Wheat Breeding. Genes. 15(4). 417–417. 5 indexed citations
10.
Montesinos‐López, Abelardo, Leonardo Crespo‐Herrera, Guillermo Gerard, et al.. (2024). Deep learning methods improve genomic prediction of wheat breeding. Frontiers in Plant Science. 15. 1324090–1324090. 15 indexed citations
11.
Gerard, Guillermo, Suchismita Mondal, Francisco J. Piñera‐Chávez, et al.. (2024). Enhanced radiation use efficiency and grain filling rate as the main drivers of grain yield genetic gains in the CIMMYT elite spring wheat yield trial. Scientific Reports. 14(1). 10975–10975. 1 indexed citations
12.
Montesinos‐López, Osval A., Carolina Saint Pierre, Salvador A. Gezan, et al.. (2023). Optimizing Sparse Testing for Genomic Prediction of Plant Breeding Crops. Genes. 14(4). 927–927. 11 indexed citations
13.
Montesinos‐López, Osval A., José Crossa, Carolina Saint Pierre, et al.. (2023). Multivariate Genomic Hybrid Prediction with Kernels and Parental Information. International Journal of Molecular Sciences. 24(18). 13799–13799. 1 indexed citations
14.
Montesinos‐López, Osval A., Leonardo Crespo‐Herrera, Alencar Xavier, et al.. (2023). A marker weighting approach for enhancing within-family accuracy in genomic prediction. G3 Genes Genomes Genetics. 14(2). 5 indexed citations
15.
Montesinos‐López, Osval A., Leonardo Crespo‐Herrera, Carolina Saint Pierre, et al.. (2023). Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?. Frontiers in Genetics. 14. 1209275–1209275. 11 indexed citations
16.
Gerard, Guillermo, Pierre Hucl, F.A. Holm, et al.. (2022). Competitive ability of western Canadian spring wheat cultivars in a model weed system. Canadian Journal of Plant Science. 102(6). 1101–1114.
17.
Gerard, Guillermo, Leonardo Crespo‐Herrera, José Crossa, et al.. (2020). Grain yield genetic gains and changes in physiological related traits for CIMMYT’s High Rainfall Wheat Screening Nursery tested across international environments. Field Crops Research. 249. 107742–107742. 31 indexed citations
18.
19.
Fleitas, María Constanza, et al.. (2018). Evaluation of different fungicides and nitrogen rates on grain yield and bread-making quality in wheat affected by Septoria tritici blotch and yellow spot. Journal of Cereal Science. 83. 49–57. 30 indexed citations
20.
Gerard, Guillermo, et al.. (2016). EFICACIA DE CONTROL Y RESIDUALIDAD DE CURASEMILLAS SOBRE ENFERMEDADES FOLIARES DE TRIGO. SHILAP Revista de lepidopterología. 14(2). 85–102. 4 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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