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Analysis of institutional authors

Garcia-Abadillo, JulianAuthorIsidro Y Sánchez JAuthor

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August 18, 2024
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Sparse testing designs for optimizing predictive ability in sugarcane populations

Publicated to: Frontiers in Plant Science. 15 1400000- - 2024-07-23 15(), DOI: 10.3389/fpls.2024.1400000

Authors:

Garcia-Abadillo, J; Adunola, P; Aguilar, FS; Trujillo-Montenegro, JH; Riascos, JJ; Persa, R; Sanchez, J; Jarquín, D
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Affiliations

Cenicana, Colombian Sugarcane Res Ctr, Cali, Valle Del Cauca, Colombia - Author
Univ Florida, Agron Dept, Gainesville, FL 32611 USA - Author
Univ Florida, Hort Sci Dept, Gainesville, FL USA - Author
Univ Politecn Madrid, Ctr Biotecnol & Genom Plantas, Madrid, Spain - Author
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Abstract

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (GxE) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6x186=1,116 phenotypes). Our study employed three predictive models, including environment, genotype, and genomic markers as main effects, as well as the GxE to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for GxE pattern prediction. Results demonstrate that maximum accuracy for SA ( rho = 0.611 ) and for TCH ( rho=0.341 ) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72=12x6) being the most convenient cost-benefit combination.
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Keywords

Genomic prediction gpGenomic selectionGenomic selection gsOptimizationPedigreeSparse testing designsSugarcane breedinSugarcane breedingValuesYiel

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Frontiers in Plant Science due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 33/273, thus managing to position itself as a Q1 (Primer Cuartil), in the category Plant Sciences.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-27:

  • WoS: 3
  • Scopus: 2
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-27:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 16.

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 2 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/86538/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 113
  • Downloads: 55
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Colombia; United States of America.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (GARCIA-ABADILLO VELASCO, JULIAN) .

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Awards linked to the item

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. JG-A was funded a UPM predoctoral grant as part of the program "Programa Propio I +D+i" financed by the Universidad Politecnica de Madrid. JIS was supported by the Beatriz Galindo Program (BEAGAL18/00115) from the Ministerio de Educacion y Formacion Profesional of Spain and the Severo Ochoa Program for Centers of Excellence in R&D from the "Agencia Estatal de Investigacion" of Spain, grant SEV-2016SEV- -0672 (2017SEV- -2021)) to the CBGP. JIS was also supported by Grant PID2021-123718OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe, CEX2020-000999-S. DAS:The datasets and pipeline generated for this study can be found in the following link: https://uflorida-my.sharepoint.com/:f:/g/personal/jhernandezjarqui_ufl_edu/Ele_tW5RgC5PrfRHWnet5xsBzgOHCLzCZ_Yxz8SftLUv-Q?e=WhhHaq.
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