Integrating Environmental Covariates into Adaptability and Stability Analyses: A Structural Equation Modeling Approach for Cotton Breeding

Matheus Massariol Suela, Moysés Nascimento (), Ana Carolina Campana Nascimento, Camila Ferreira Azevedo, Paulo Eduardo Teodoro, Francisco José Correia Farias, Luiz Paulo de Carvalho and Diego Jarquin ()
Additional contact information
Matheus Massariol Suela: Laboratory of Intelligence Computational and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Viçosa 36570-900, MG, Brazil
Moysés Nascimento: Laboratory of Intelligence Computational and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Viçosa 36570-900, MG, Brazil
Ana Carolina Campana Nascimento: Laboratory of Intelligence Computational and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Viçosa 36570-900, MG, Brazil
Camila Ferreira Azevedo: Laboratory of Intelligence Computational and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Viçosa 36570-900, MG, Brazil
Paulo Eduardo Teodoro: Department of Agronomy, Federal University of Mato Grosso do Sul, Chapadão do Sul 79560-000, MS, Brazil
Francisco José Correia Farias: National Center for Cotton Research, Brazilian Agricultural Research Corporation, Campina Grande 58428-095, PB, Brazil
Luiz Paulo de Carvalho: National Center for Cotton Research, Brazilian Agricultural Research Corporation, Campina Grande 58428-095, PB, Brazil
Diego Jarquin: Agronomy Department, University of Florida, Gainesville, FL 32611-0180, USA

Agriculture, 2024, vol. 14, issue 11, 1-16

Abstract: Breeding programs rely on genotype-by-environment interaction (GEI) to recommend cultivars for specific locations. GEI describes how different genotypes perform under varying environmental conditions. Several methods were proposed to assess adaptability and stability across environments. These methods utilize various statistical approaches like parametric and non-parametric regression, multivariate analysis techniques, and even Bayesian frameworks and artificial intelligence. The accessibility of environmental data through platforms like NASA POWER allows breeders to integrate this information into a breeding process. It has been done by using multi-omics integration models that combine data across various biological levels to create accurate predictive models. In the context of phenotypic adaptability and stability analysis, structural equation modeling (SEM) offers an interesting approach to integrating environmental covariates. This work aimed to propose a novel approach that integrates weather information into adaptability and stability analysis, combining SEM with the established Eberhart and Russell model. Additionally, a user-friendly applet, denoted ECERSEM-AdaptStab, was made available to perform the analysis. This approach utilized data from 12 cotton cultivar trials conducted across two growing seasons at 19 sites. This approach successfully integrated environmental covariates into a phenotypic adaptability and stability analysis of cotton cultivars. Specifically, the genotypes TMG 41 WS, IMA CV 690, DP 555 BGRR, BRS 286 and BRS 369 RF were recommended for favorable environments, while the genotypes TMG 43 WS, IMA 5675 B2RF, IMA 08 WS, NUOPAL, DELTA OPAL, BRS 335, and BRS 368 RF are more suitable for unfavorable environments.

Keywords: ECERSEM-AdaptStab; adaptability and stability; structural equation models; shiny app; Gossypium hirsutum L.; fiber yield (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/11/1914/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/11/1914/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:11:p:1914-:d:1508504

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().