Adaptability and Stability of Cotton Genotypes Regarding Fiber Yield and Quality Traits

Teodoro, Paulo Eduardo; Farias, F. J. C.; Carvalho, Luiz Paulo de; Ribeiro, Larissa Pereira; Nascimento, Moysés; Azevedo, Camila Ferreira; Cruz, Cosme Damião; Bhering, Leonardo Lopes

doi:10.2135/cropsci2018.04.0250

Cited by 21 publications

(21 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These two scenarios occur specifically in cotton genotypes, where a short MIC index (between 0.149 and 0.165 μg mm −1 ) and a long FL prevent yarn twisting. Long FL and short MIC give the ideal condition for increasing the efficiency of spinning machines, which results in a softer, bulky, flexible, and malleable fabric (Freire, 2007;Teodoro et al, 2019). Some negative values for the areas under reaction norms for MIC were observed.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Reaction norms‐based approach applied to optimizing recommendations of cotton genotypes

et al. 2020

Self Cite

View full text Add to dashboard Cite

Reaction norms fitted through random regression models (RRM) have been widely used in animal and plant breeding for analyses of genotype × environment (G × E) interaction. However, in annual crops, they remain unexplored. Thus, this study aimed to evaluate the applicability and efficiency of RRM fitted through Legendre polynomials as a tool to recommend cotton (Gossypium hirsutum L.) genotypes. To this end, a data set with 12 genotypes of cotton evaluated in 10 environments for fiber length (FL) and fiber fineness was used. The restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) procedure was used to estimate the variance components and to predict the genetic values. Results showed that there was genetic variability among cotton genotypes and that the reaction norms over the environmental gradient illustrated the G × E interaction. Very high selective accuracies (̂> 0.90) were found for both traits in all environments, which indicates high reliability in the genotype's recommendation. The areas under the reaction norms were calculated for the recommendation of genotypes for unfavorable, favorable, and overall environments. Regarding genotypes recommendation, areas under reaction norms allow recommending genotypes for unfavorable and favorable environments, as well as for overall recommendation, for both traits. This study is the first considering reaction norms fitted through RRM for the recommendation of cotton genotypes and demonstrated the potential of this technique in cotton breeding, besides its great potential to deal with G × E interactions.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The significance of the genotype effects was tested using the likelihood ratio test (LRT) (Wilks, 1938), as follows:

L R T = - 2 (L o g L - L o g L_{R}),

where

L o g L_{R}

is the logarithm of the restricted likelihood function of the reduced model (without the genotype effects).…”

Section: Methodsmentioning

confidence: 99%

Reaction norms‐based approach applied to optimizing recommendations of cotton genotypes

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…When a significant G × E is identified, subsequent stability analysis is often carried out to identify stable genotypes which exhibit consistently higher yield over several environments 4 . The presence of significant G × E has been found in growth and yield traits of several field 5 – 8 and vegetable crops 3 , 9 – 11 . In tomato ( Solanum lycopersicum L.), an economically important vegetable widely grown and consumed in the world, yield and quality traits were found to demonstrate strong G × E 12 – 16 .…”

Section: Introductionmentioning

confidence: 99%

Stability of yield and its components in grafted tomato tested across multiple environments in Texas

Djidonou

Leskovar

Joshi

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Grafting with vigorous rootstocks could offer tomato growers in Texas sustainable and efficient option to achieve reliable yield across a range of production systems and locations. Genotypes (G) of grafted and non-grafted tomato were grown in different environments (E) in the 2017 and 2018 spring seasons. The objectives of the study were to (i) evaluate the effects of production system and grafting on tomato yield traits, (ii) determine the size of genotypic and genotype by environment interaction (G × e) variance components, and (iii) evaluate the relative stability of tested genotypes for yield and its components across production environments. In 2017, genotypes were non-grafted 'TAMU Hot Ty' (TAM) and 'Tycoon' (TY) and each grafted on commercial tomato rootstocks 'Estamino' (TAM/ ES, TY/ES) and 'Multifort' (TAM/MU, TY/MU) while in 2018, TAM and 'HM1823' (HM) were grafted on 'Estamino' (TAM/ES, HM/ES) and 'Multifort' (TAM/MU, HM/MU). Testing environments were high tunnel (HT) and open-field (OF) in Uvalde in 2017 while in 2018, these were HT and OF in Lubbock (LU-HT, LU-OF), Overton (OV-HT, OV-OF), Uvalde (UV-HT, UV-OF), and Weslaco (WE-HT, WE-OF). Total and marketable yields, fruit number per plant, and average fruit weight were significantly affected by E, G, and G × E interaction. Environmental component contributed 71-86% to the total variation for all these traits, while genotype explained 1.5-10.8%, and the contribution of G × E ranged between 4.3 to 6.7%. Estimation of the univariate statistic parameters and genotype plus genotype × environment (GGE) biplot analysis indicated that HM/MU and HM/ES were the most stable graft combination with the highest total and marketable yields, while TAM/ES was very unstable for yields across test environments. TAM/MU was stable but with yield lower than the grand mean. These results suggest that high tomato yields could be consistently achieved with grafted combination (HM/MU and HM/ES) especially under high tunnel production system across the regions of texas. Genetics, environmental factors, and cultural management practices typically determine the extent of quantitative traits of economic importance such as genotype or cultivar yield. As such, the proportion of the variation in this phenotypic trait due to the main effect of genotype (G), the environment (E) and their interactions (G × E) is routinely assessed during the process of selecting top-performing lines 1. In this regard, multilocation trials are routinely conducted to assess the pattern of genotypic response over a number of locations and years (test environments). Typically, a significant G × E exists whenever the genotype performance greatly differs with environments 2,3. When a significant G × E is identified, subsequent stability analysis is often carried out to identify stable genotypes which exhibit consistently higher yield over several environments 4. The presence of significant G × E has been found in growth and yield traits of several field 5-8 and vegetable crops 3,9-11. In tomato (Solanum lyc...

show abstract

“…For example, GGEBiplots requires as input data a two‐way table containing genotype by environment means with genotypes in rows and environments in columns, but doesn't provide any function to create quickly such table from data that often is in a ‘ long ’ format in R. In addition, several studies often compare different stability methods (e.g. Bornhofen et al, 2017; Freiria et al, 2018; Scapim et al, 2010; Shahbazi, 2019; Teodoro et al, 2019; Woyann et al, 2018). This requires a range of different packages to be used, making the coding tedious and difficult to follow.…”

Section: Introductionmentioning

confidence: 99%

metan: An R package for multi‐environment trial analysis

2020

View full text Add to dashboard Cite

1. Multi-environment trials (MET) are crucial steps in plant breeding programs that aim at increasing crop productivity to ensure global food security. The analysis of MET data requires the combination of several approaches including data manipulation, visualization and modelling. As new methods are proposed, analysing MET data correctly and completely remains a challenge, often intractable with existing tools. 2. Here we describe the metan R package, a collection of functions that implement a workflow-based approach to (a) check, manipulate and summarize typical MET data; (b) analyse individual environments using both fixed and mixed-effect models; (c) compute parametric and nonparametric stability statistics; (d) implement biometrical models widely used in MET analysis and (e) plot typical MET data quickly.3. In this paper, we present a summary of the functions implemented in metan and how they integrate into a workflow to explore and analyse MET data. We guide the user along a gentle learning curve and show how adding only a few commands or options at a time, powerful analyses can be implemented. 4. metan offers a flexible, intuitive and richly documented working environment with tools that will facilitate the implementation of a complete analysis of MET datasets. K E Y W O R D Sadditive main effect and multiplicative interaction, biometry, genotype-environment interaction, GGE biplot, multi-environment trials, R software, stability, statistics

show abstract

Adaptability and Stability of Cotton Genotypes Regarding Fiber Yield and Quality Traits

Cited by 21 publications

References 19 publications

Reaction norms‐based approach applied to optimizing recommendations of cotton genotypes

Reaction norms‐based approach applied to optimizing recommendations of cotton genotypes

Stability of yield and its components in grafted tomato tested across multiple environments in Texas

metan: An R package for multi‐environment trial analysis

Contact Info

Product

Resources

About