Boosting predictive ability of tropical maize hybrids via genotype‐by‐environment interaction under multivariate GBLUP models

Crop Science ◽  
2020 ◽  
Vol 60 (6) ◽  
pp. 3049-3065
Author(s):  
Matheus Dalsente Krause ◽  
Kaio Olímpio das Graças Dias ◽  
Jhonathan Pedroso Rigal dos Santos ◽  
Amanda Avelar Oliveira ◽  
Lauro José Moreira Guimarães ◽  
...  
Keyword(s):  
Predictive Ability ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Tropical Maize ◽  
Maize Hybrids ◽  
Genotype By Environment

Stability Analysis of Agronomic Traits for Maize (Zea Mays L.) Genotypes Based on Ammi Model

2021 ◽  
Vol 50 (2) ◽  
pp. 343-350
Author(s):  
Meijin Ye ◽  
Zhaoyang Chen ◽  
Bingbing Liu ◽  
Haiwang Yue
Keyword(s):  
Grain Yield ◽  
Agronomic Traits ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Kernel Weight ◽  
Environment Interaction ◽  
Maize Hybrids ◽  
Genotype By Environment ◽  
Ammi Model ◽  
The Ideal

Stability and adaptability of promising maize hybrids in terms of three agronomic traits (grain yield, ear weight and 100-kernel weight) in multi-environments trials were evaluated. The analysis of AMMI model indicated that the all three agronomic traits showed highly significant differences (p < 0.01) on genotype, environment and genotype by environment interaction. Results showed that genotypes Hengyu321 (G9), Yufeng303 (G10) and Huanong138 (G3) were of higher stability on grain yield, ear weight and 100-kernel weight, respectively. Genotypes Hengyu1587 (G8) and Hengyu321 (G9) showed good performance in terms of grain yield, whereas Longping208 (G2) and Weike966 (G12) showed broad adaptability for ear weight. It was also found that the genotypes with better adaptability in terms of 100-kernel weight were Zhengdan958 (G5) and Weike966 (G12). The genotype and environment interaction model based on AMMI analysis indicated that Hengyu1587 and Hengyu321 were the ideal genotypes, due to extensive adaptability and high grain yield under both testing sites. Bangladesh J. Bot. 50(2): 343-350, 2021 (June)

scholarly journals Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize

2021 ◽  
Author(s):  
Raysa Gevartosky ◽  
Humberto Fanelli Carvalho ◽  
Germano Costa-Neto ◽  
Osval A. Montesinos-Lopez ◽  
Jose Crossa ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Genomic Prediction ◽  
Predictive Ability ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Apply Genetic ◽  
Ear Height ◽  
Predictor Model ◽  
Training Sets

Genomic prediction (GP) success is directly dependent on establishing a training population, where incorporating envirotyping data and correlated traits may increase the GP accuracy. Therefore, we aimed to design optimized training sets for multi-trait for multi-environment trials (MTMET). For that, we evaluated the predictive ability of five GP models using the genomic best linear unbiased predictor model (GBLUP) with additive + dominance effects (M1) as the baseline and then adding genotype by environment interaction (G × E) (M2), enviromic data (W) (M3), W+G × E (M4), and finally W+G × W (M5), where G × W denotes the genotype by enviromic interaction. Moreover, we considered single-trait multi-environment trials (STMET) and MTMET for three traits: grain yield (GY), plant height (PH), and ear height (EH), with two datasets and two cross-validation schemes. Afterward, we built two kernels for genotype by environment by trait interaction (GET) and genotype by enviromic by trait interaction (GWT) to apply genetic algorithms to select genotype:environment:trait combinations that represent 98% of the variation of the whole dataset and composed the optimized training set (OTS). Using OTS based on enviromic data, it was possible to increase the response to selection per amount invested by 142%. Consequently, our results suggested that genetic algorithms of optimization associated with genomic and enviromic data efficiently design optimized training sets for genomic prediction and improve the genetic gains per dollar invested.

scholarly journals Analysis of genotype by environment interaction for grain yield of intermediate maturing drought tolerant top-cross maize hybrids under rain-fed conditions

2017 ◽  
Vol 3 (1) ◽  
pp. 1333243 ◽  
Author(s):  
Alidu Haruna ◽  
Gloria Boakyewaa Adu ◽  
Samuel Saaka Buah ◽  
Roger A.L. Kanton ◽  
Amegbor Isaac Kudzo ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Maize Hybrids ◽  
Genotype By Environment ◽  
Drought Tolerant

scholarly journals EFFECTS OF GENOTYPE-BY-ENVIRONMENT INTERACTION ON THE MAIN AGRONOMIC TRAITS OF MAIZE HYBRIDS

2020 ◽  
Vol 18 (1) ◽  
pp. 1437-1458 ◽  
Author(s):  
H.W. YUE ◽  
Y.B. WANG ◽  
J.W. WEI ◽  
Q.M. MENG ◽  
B.L. YANG ◽  
...  
Keyword(s):  
Agronomic Traits ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Maize Hybrids ◽  
Genotype By Environment

scholarly journals Stability of Maize Hybrids across Environments Using Gge Biplot and Ammi Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 188-194
Author(s):  
Saleem Abid ◽  
Saleem Zahid
Keyword(s):  
Total Yield ◽  
Principal Component ◽  
Maize Yield ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Maize Hybrids ◽  
Genotype By Environment ◽  
Yield Variation ◽  
Ammi Analysis

Twenty six yellow maize hybrids on the basis of stability analysis were evaluated in National Uniform Maize Hybrid Yield Trials conducted across eight diversified environments of Pakistan. Combined analysis of variance based AMMI analysis shown highly significant differences for environments, genotypes and their interactions. The environments explained about 78 percent of the total yield variation followed by genotype by environment interaction. Environment was the main aspect that influences the performance of maize yield in study area. The first two interaction principal component axes (IPCA1 and IPCA2) explained about 63 percent of the grain yield variation due to genotype and genotype by environment interaction (GGE). The GGE biplot analysis shown that entry-2 (Mex-YLHY2) was the most stable hybrid and can be considered as adaptable to all the environments.

scholarly journals Genomic Prediction in a Multiploid Crop: Genotype by Environment Interaction and Allele Dosage Effects on Predictive Ability in Banana

2018 ◽  
Vol 11 (2) ◽  
pp. 170090 ◽  
Author(s):  
Moses Nyine ◽  
Brigitte Uwimana ◽  
Nicolas Blavet ◽  
Eva Hřibová ◽  
Helena Vanrespaille ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Predictive Ability ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Dosage Effects ◽  
Allele Dosage

scholarly journals Stability of combining ability effects in maize hybrids

2009 ◽  
Vol 66 (4) ◽  
pp. 494-498 ◽  
Author(s):  
Juarez Campolina Machado ◽  
João Cândido de Souza ◽  
Magno Antonio Patto Ramalho ◽  
José Luís Lima
Keyword(s):  
Combining Ability ◽  
Block Design ◽  
Diallel Analysis ◽  
Genotype By Environment Interaction ◽  
Specific Combining Ability ◽  
Environment Interaction ◽  
Additive Effects ◽  
Maize Hybrids ◽  
Genotype By Environment ◽  
The Stability

General and specific combining ability effects are important indicators in a maize (Zea mays L.) breeding program aiming hybrid development. The objectives of the present study were to estimate the general (GCA) and specific combining abilities (SCA) effects of commercial maize hybrids using a complete diallel scheme and to assess the stabilities of these estimates. Fifty-five entries were assessed; ten commercial single-crosses and all possible double-crosses. The experiments were carried out in 12 environments in the 2005/06 growing season. A randomized complete block design was used with three replications per environment. Ear yield was evaluated, corrected to 13% of moisture content. The combined diallel analysis involving all environments was performed and the stability of general and specific combining ability effects was investigated. The underlying nonparametric statistics evaluated the contribution of each effect to the genotype by environment interaction. Non-additive effects were more important for this set of hybrids than the additive effects. It was possible to select parents with high stability for combining ability and with high GCA.

Yield stability studies in indigenous and exotic maize hybrids under genotype by environment interaction

2020 ◽  
Vol 53 (3) ◽  
Author(s):  
Muhammad Irfan Yousaf ◽  
Naeem Akhtar ◽  
Aamer Mumtaz ◽  
Aamar Shehzad ◽  
Muhammad Arshad ◽  
...  
Keyword(s):  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
Environment Interaction ◽  
Stability Studies ◽  
Maize Hybrids ◽  
Genotype By Environment
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