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Analysis of institutional authors

Carvalho, Humberto FanelliAuthorGarcia-Abadillo, JulianAuthorIsidro Y Sanchez, JulioCorresponding Author

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June 24, 2024
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Revisiting superiority and stability metrics of cultivar performances using genomic data: derivations of new estimators

Publicated to:Plant Methods. 20 (1): 85- - 2024-06-06 20(1), DOI: 10.1186/s13007-024-01207-1

Authors: Carvalho, HF; Rio, S; García-Abadillo, J; Sanchez, JIY

Affiliations

UMR AGAP Inst, CIRAD, F-34398 Montpellier, France - Author
Univ Montpellier, UMR AGAP Inst, Inst Agro, CIRAD,INRAE, Montpellier, France - Author
Univ Politecn Madrid UPM, Ctr Biotecnol & Genomica Plantas CBGP UPM INIA, Inst Nacl Invest & Tecnol Agr & Alimentaria INIA, Campus Montegancedo UPM, Pozuelo De Alarcon 28223, Madrid, Spain - Author

Abstract

The selection of highly productive genotypes with stable performance across environments is a major challenge of plant breeding programs due to genotype-by-environment (GE) interactions. Over the years, different metrics have been proposed that aim at characterizing the superiority and/or stability of genotype performance across environments. However, these metrics are traditionally estimated using phenotypic values only and are not well suited to an unbalanced design in which genotypes are not observed in all environments. The objective of this research was to propose and evaluate new estimators of the following GE metrics: Ecovalence, Environmental Variance, Finlay-Wilkinson regression coefficient, and Lin-Binns superiority measure. Drawing from a multi-environment genomic prediction model, we derived the best linear unbiased prediction for each GE metric. These derivations included both a squared expectation and a variance term. To assess the effectiveness of our new estimators, we conducted simulations that varied in traits and environment parameters. In our results, new estimators consistently outperformed traditional phenotype-based estimators in terms of accuracy. By incorporating a variance term into our new estimators, in addition to the squared expectation term, we were able to improve the precision of our estimates, particularly for Ecovalence in situations where heritability was low and/or sparseness was high. All methods are implemented in a new R-package: GEmetrics. These genomic-based estimators enable estimating GE metrics in unbalanced designs and predicting GE metrics for new genotypes, which should help improve the selection efficiency of high-performance and stable genotypes across environments.

Keywords

EcovalenceEfficiencyEnvironmental varianceFinlay-wilkinson regression coefficientFinlay–wilkinson regression coefficientGenomic predictionGenotype-by-environmentLin-binns superiority measurLin–binns superiority measureMixed modelsPedigreePredictionRegressionSelectionTrialsValueVariety

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Plant Methods 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 13/86, thus managing to position itself as a Q1 (Primer Cuartil), in the category Biochemical Research Methods.

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 2025-07-25:

  • 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: 11.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 2 (PlumX).

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: 0.75.
  • The number of mentions on the social network Facebook: 1 (Altmetric).
  • The number of mentions on the social network X (formerly Twitter): 1 (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/86481/

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: 37
  • Downloads: 3

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: France.

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 (FANELLI CARVALHO, HUMBERTO) and Last Author (ISIDRO SANCHEZ, JULIO).

the author responsible for correspondence tasks has been ISIDRO SANCHEZ, JULIO.