Genotype-by-environment interaction of grain yield among candidate dryland wheat genotypes

2019 ◽  
Vol 36 (4) ◽  
pp. 299-306 ◽  
Author(s):  
Miriam Matlala ◽  
Hussein Shimelis ◽  
Jacob Mashilo
Keyword(s):  
Grain Yield ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Wheat Genotypes ◽  
Genotype By Environment

scholarly journals Genotype by environment interaction analysis of wheat (Triticum aestivum L.) grain yield under rain-fed conditions in Zambia

2021 ◽  
Vol 53 (4) ◽  
pp. 609-619
Author(s):  
B. Tembo
Keyword(s):  
Grain Yield ◽  
Agricultural Research ◽  
Interaction Analysis ◽  
Crop Improvement ◽  
Genotype By Environment Interaction ◽  
Research Station ◽  
Environment Interaction ◽  
Wheat Genotypes ◽  
Lattice Design ◽  
Genotype By Environment

Understanding genotype by environment interaction (GEI) is important for crop improvement because it aids in the recommendation of cultivars and the identification of appropriate production environments. The objective of this study was to determine the magnitude of GEI for the grain yield of wheat grown under rain-fed conditions in Zambia by using the additive main effects and multiplicative interaction (AMMI) model. The study was conducted in 2015/16 at Mutanda Research Station, Mt. Makulu Research Station and Golden Valley Agricultural Research Trust (GART) in Chibombo. During2016/17, the experiment was performed at Mpongwe, Mt. Makulu Research Station and GART Chibombo, Zambia. Fifty-five rain-fed wheat genotypes were evaluated for grain yield in a 5 × 11 alpha lattice design with two replications. Results revealed the presence of significant variation in yield across genotypes, environments, and GEI indicating the differential performance of genotypes across environments. The variance due to the effect of environments was higher than the variances due to genotypes and GEI. The variances ascribed to environments, genotypes, and GEI accounted for 45.79%, 12.96%, and 22.56% of the total variation, respectively. These results indicated that in rain-fed wheat genotypes under study, grain yield was more controlled by the environment than by genetics. AMMI biplot analysis demonstrated that E2 was the main contributor to the GEI given that it was located farthest from the origin. Furthermore, E2 was unstable yet recorded the highest yield. Genotype G47 contributed highly to the GEI sum of squares considering that it was also located far from the origin. Genotypes G12 and G18 were relatively stable because they were situated close to the origin. Their position indicated that they had minimal interaction with the environment. Genotype 47 was the highest-yielding genotype but was unstable, whereas G34 was the lowest-yielding genotype and was unstable.

Using AMMI approach to delineate genotype by environment interaction and stability of barley (Hordeum vulgare L.) genotypes under northern Indian shivalik hill conditions

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Om Prakash Yadav ◽  
A. K. Razdan ◽  
Bupesh Kumar ◽  
Praveen Singh ◽  
Anjani K. Singh
Keyword(s):  
Hordeum Vulgare ◽  
Grain Yield ◽  
Principal Component ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Hordeum Vulgare L ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Principal Component Axis ◽  
Ammi Analysis

Genotype by environment interaction (GEI) of 18 barley varieties was assessed during two successive rabi crop seasons so as to identify high yielding and stable barley varieties. AMMI analysis showed that genotypes (G), environment (E) and GEI accounted for 1672.35, 78.25 and 20.51 of total variance, respectively. Partitioning of sum of squares due to GEI revealed significance of interaction principal component axis IPCA1 only On the basis of AMMI biplot analysis DWRB 137 (41.03qha–1), RD 2715 (32.54qha–1), BH 902 (37.53qha–1) and RD 2907 (33.29qha–1) exhibited grain yield superiority of 64.45, 30.42, 50.42 and 33.42 per cent, respectively over farmers’ recycled variety (24.43qha–1).

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 Grain yield, stability and bacterial brown spot disease of dark red kidney dry bean (Phaseolus vulgaris L.) genotypes across six environments in South Africa

2020 ◽  
pp. 1433-1442
Author(s):  
Venâncio Salegua ◽  
Rob Melis ◽  
Deidré Fourie ◽  
Julia Sibiya ◽  
Cousin Musvosvi
Keyword(s):  
South Africa ◽  
Disease Resistance ◽  
Grain Yield ◽  
Genotype By Environment Interaction ◽  
Brown Spot ◽  
Environment Interaction ◽  
Phaseolus Vulgaris L ◽  
Dry Bean ◽  
Genotype By Environment ◽  
Bacterial Brown Spot

Dry bean (Phaseolus vulgaris L.) is grown under an extensive range of agro-climatic conditions and is an essential source of protein and income globally. This study aimed to evaluate yield performance, stability, and bacterial brown spot (BBS) disease resistance of fourteen dark red kidney genotypes across environments in South Africa namely Carolina, Clarens, Cedara, Middelburg, Potchefstroom, and Warden. Analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI) and the genotype plus genotype by environment interaction (GGE-biplot) analysis were used to evaluate grain yield performance, stability, and BBS disease resistance. The AMMI ANOVA revealed that mean squares for grain yield and BBS severity for the environment, genotype, and genotype by environment interaction were highly significant (P<0.001). Four interaction principal components (IPCA1 - 4) for grain yield and IPCA1 for BBS severity were highly significant (P<0.001, P<0.01). Genotype G12 showed broad adaptation for both high grain yield and low BBS severity across the six environments, while genotypes G08, G06, G03, G02, G05, and G04 had specific adaption for high grain yield and low BBS severity. These genotypes recorded grain yield above the grand mean and the best check cultivar, both with 1.43 t ha-1 , and BBS severity below the grand mean (31.90%) and the best check (48.89%). The genotypes identified with either broad or specific adaptation can be released in the environments they are adapted to, or used as parents in breeding programmes aiming to improve grain yield and BBS disease resistance of dry bean for farmers in South Africa.

scholarly journals Genotype by Environment Interaction for Grain Yield of Barley Mutant Lines

2019 ◽  
Vol 65 (2) ◽  
pp. 51-58
Author(s):  
Boryana Dyulgerova ◽  
Nikolay Dyulgerov
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Genotype By Environment Interaction ◽  
Winter Barley ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Growing Seasons ◽  
Stable Mutant ◽  
Check Variety ◽  
Mutant Lines

Abstract The aim of this study was to examine the genotype by environment interaction for grain yield and to identify high-yielding and stable mutant lines of 6-rowed winter barley under different growing seasons. The study was carried out during 7 growing seasons from 2010 – 2011 to 2016 – 2017 in the experimental field of the Institute of Agriculture – Karnobat, Southeastern Bulgaria. Fourteen advanced mutant lines and the check variety Vesletc were studied using a complete block design with 4 replications. The AMMI analysis of variance indicated that 20.54% of the variation for grain yield was explained by the effect of genotype and 37.34% and 42.12% were attributable to the environmental effects and genotype by environment interaction. The magnitude of the genotype by environment interaction was two times larger than that of genotypes, indicating that there was a substantial difference in genotype response across environments. The AMMI and GGE biplot analyses identified G9 as the highest yielding and stable genotype. This mutant line can be recommended for further evaluation for variety release. The mutant lines G6, G13 and G15 were suggested for inclusion in the breeding program of winter barley due to its high grain yield and intermediate stability.

scholarly journals Correlations between grain yield and related traits in winter wheat under multi-environmental traits

2020 ◽  
Vol 12 (4) ◽  
pp. 295-300
Author(s):  
N. Tsenov ◽  
T. Gubatov ◽  
I. Yanchev
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Multiple Correspondence Analysis ◽  
Triticum Aestivum L ◽  
Field Trials ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Wheat Grain ◽  
Genotype By Environment ◽  
Grain Number Per Spike

Abstract. In a series of field trials, a database of quantitative traits associated with winter wheat grain yield has been collected. The aim of the present study is to determine the relationships between the winter wheat (Triticum aestivum L.) traits of productivity in environments causing the maximum possible variation of each of the traits. In order to determine the correlations between the quantitative characters studied, all possible statistical methods have been applied (regression analysis, PCA, Multiple Correspondence analysis), which complement each other. It was found that the nature of the correlations between traits depends to a large extent on the methods for their evaluation. There are high and significant correlations between grain yields and the grain number per spike (weight of grain per spike and number of grains per m2) even under strong genotype by environment interaction of all the traits in trails. The established results are related to possible options for increasing winter wheat grain yield by breeding.

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