Adaptability and stability of maize hybrids in unreplicated multienvironment
            trials

Santos, Diego Coelho dos; Pereira, Cristiane Aparecida; Nunes, José Aírton Rodrigues; Lepre, André Luiz

doi:10.5935/1806-6690.20190010

Cited by 6 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some hybrids were shown to have regression coefficients b2 that were significantly different from zero, suggesting behavior that is better captured by the bit segmented model of [23] . The use of Eberhart and Russell's simple linear regression model would be sufficient for the other hybrids, for which this hypothesis was not disproved, to explain how the hybrids behaved in the various environments [24] . Concentrating to the character EL cross 2 x 8 with the mean value of 18.94, bi=1.45 and S 2 di=0.35,and the cross 2 x 9 with the mean value of 21.81, bi=1.25, and S2di=-0.16, and cross 3 x 6 with the mean 17.83,bi=1.74, and S 2 di=1.07, and parent 9 with the mean value 17.31, bi=1.61, and S 2 d=-0.61 were adaptable, but the cross 2 x 7 with the means value of 20.92,bi=0.30, and S 2 di= -1.34 was stable.…”

Section: Resultsmentioning

confidence: 99%

Multi-Environment Test for Stability and Adaptability Using Line X Tester Crossing Design in Maize

Mohammed¹,

Towfiq²

2022

PJBAS

View full text Add to dashboard Cite

Stability and adaptability are two main Characteristics that affect most field crops' growth and yield. maize is mainly affected and highly sensitive to environmental conditions. Therefore, the present study aimed to evaluate the stability and adaptability of maize crop genotypes for some agronomic traits related to kernel yield under four different environmental conditions. The field experiments consisted of 9 parents and 20 maize hybrids and were evaluated in randomized block design during the autumn and spring seasons at each location of Kalar and Khanaqeen. Genetic analysis of stability and adaptability according to the methodology of Eberhart and Russel regression coefficient (bi) and Elsahookie, and Al-Rawi genotypic resultant (GR) were performed based on the average of all environments. Results indicated that the cross HS x 844 was stable and showed maximum value of genotypic resultant to kernel yield according to ElSahookie and Al-Rawi, while the crosses 4218 x zp-595, zp-430 x zp-595 and HS x sym-5 were adaptable for this trait according to Eberhart and Russell.

show abstract

Section: Resultsmentioning

confidence: 99%

Multi-Environment Test for Stability and Adaptability Using Line X Tester Crossing Design in Maize

Mohammed¹,

Towfiq²

2022

PJBAS

View full text Add to dashboard Cite

show abstract

“…Several inferences have been made using methods based on deviations from linear (Carvalho et al, 2019) and bissegmented regression (Carvalho et al, 2016), decomposition of sums of squares, factor analysis, nonparametric models, and multivariate models such as those of additive main effects and multiplicative interaction (AMMI) and genotype main effects + genotype-by-environment interaction (GGE) (Szareski et al, 2018;Schneider et al, 2021). These models fragment the sums of squares of the GxE interaction and allow expressing them through principal component biplots, bringing together all the variation expressed by the nature of the experiment in the form of a graph, enabling a better understanding of tests with a high number of genotypes and environments (Jeberson et al, 2017;Santos et al, 2019). However, due to doubts regarding the strategic positioning of cultivars and the causes of the observed variations, other models, besides those of adaptability and phenotypic stability, are also used.…”

Section: Introductionmentioning

confidence: 99%

Strategic positioning of soybean based on the agronomic ideotype and on fixed and mixed multivariate models

Kehl¹,

Carvalho

Sacon

et al. 2022

Pesq. agropec. bras.

View full text Add to dashboard Cite

The objective of this work was to decompose the variations of the genotype × environment interaction through fixed multivariate models, as well as to understand the genetic variations through mixed models, for the estimation and prediction of the genetic value of soybean (Glycine max) genotypes in the state of Rio Grande do Sul, Brazil. Tests were carried out during the 2016/2017, 2017/2018, and 2018/2019 crop seasons in different municipalities in six regions of the state, using the additive main effects and multiplicative interaction (AMMI) and genotype main effects + genotype-by-environment interaction (GGE) models. The genotypes were also evaluated using an index that allows weighting between mean performance and stability (WAASBY) and by the restricted maximum likelihood (REML) and the best linear unbiased prediction (BLUP) models. The used experimental design was randomized complete blocks (18 environments x 12 genotypes), with three replicates. The best performing genotypes in favorable environments are: 'BMX Valente RR', 'BMX Alvo RR', 'NS 5959 IPRO', 'DM 5958RSF IPRO', and 'BMX Ativa RR'. The favorable environments are the 2017/2018 season in the municipality of Bagé and the 2016/2017 season in the municipalities of São Luiz Gonzaga and Cachoeira do Sul, where higher grain yields were obtained. The genotypes that show excellent performance in unfavorable environments are cultivars BMX Ativa RR, DM 5958RSF IPRO, NS 5959 IPRO, and TMG 7262 RR. The 2016/2017 season is considered unfavorable in the municipalities of São Luiz Gonzaga and Cachoeira do Sul. The AMMI, GGE, and WAASBY or BLUP models for genotype selection must be used simultaneously.

show abstract

Adaptability and stability of maize genotypes in growing regions of central Brazil

et al. 2021

View full text Add to dashboard Cite

This study aimed to estimate and compare parameters of adaptability and stability for maize grain yield in a variety of environments by different projection methods. Data from experiments on 36 maize genotypes, in simple lattice 6x6, in 2012/13 season performed at nine growing locations in central Brazil were used. Adaptability and stability analyses were performed using the methods of Lin & Binns (1988) with decomposition, MHPRVG through REML/BLUP, AMMI-Biplot, and GGE-Biplot analysis. These methods have similarities in terms of genotype ordering but differ in precision and amount of information provided on genotype-environment (GxE) interactions. When compared to GGE-Biplot, AMMI method retained a good percentage of the total square sum, based on pattern of GxE interaction. The method of Lin & Binns with decomposition is similar to MHPRVG, but these one is more accurate, practice and informative. MHPRVG and GGE-Biplot methods should be used together to select the most promising genotypes. The genotypes G5 and G8 can be recommended for cultivation in central Brazil due to their adaptability, stability, and yield.

show abstract

Adaptability and stability of maize hybrids in unreplicated multienvironment trials

Cited by 6 publications

References 2 publications

Multi-Environment Test for Stability and Adaptability Using Line X Tester Crossing Design in Maize

Multi-Environment Test for Stability and Adaptability Using Line X Tester Crossing Design in Maize

Strategic positioning of soybean based on the agronomic ideotype and on fixed and mixed multivariate models

Adaptability and stability of maize genotypes in growing regions of central Brazil

Contact Info

Product

Resources

About