scholarly journals Analysis of Genotype by Environment Interaction of Improved Pearl Millet for Grain Yield and Rust Resistance

2017 ◽  
Vol 9 (2) ◽  
pp. 188
Author(s):  
G. Lubadde ◽  
P. Tongoona ◽  
J. Derera ◽  
J. Sibiya
Keyword(s):  
Grain Yield ◽  
Pearl Millet ◽  
Rust Resistance ◽  
Genotype By Environment Interaction ◽  
Climatic Conditions ◽  
Arid Zones ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Superior Genotypes ◽  
Main Effects

Pearl millet is grown by inhabitants of the semi-arid zones. Due to the unpredictable climatic conditions the genotype-by-environment interaction (GEI) makes it hard to select genotypes adapted to such conditions. The study objectives therefore were to analyse the patterns of GEI and to identify superior genotypes for grain yield and rust resistance. Seventy six genotypes were planted in four environments in 4×19 alpha design with two replications. The ANOVA results showed that main effects of environments were significant (p ≤ 0.05) for grain yield and highly significant (p ≤ 0.001) for rust resistance while the main effects of the genotypes and their interactions with environments were also important for grain yield and rust severity at 50% physiological maturity. The GGE biplot analysis revealed that environments associated with more rains received during vegetative phase performed better than those receiving more rains during post-anthesis phase. The winner in the best environment for grain yield was ICMV3771×SDMV96053 while Shibe×CIVT9206 and Shibe×GGB8735 were the best for rust resistance.

scholarly journals Yield Stability and Farmer Preference of Cowpea (Vigna Uncuilata) Lines in semi-arid Eastern Kenya

Afrika Focus ◽  
2019 ◽  
Vol 32 (2) ◽  
Author(s):  
Rose Wangari Kuruma ◽  
Patrick Sheunda ◽  
Charles Muriuki Kahwaga
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Early Maturing ◽  
Superior Genotypes ◽  
High Yielding Varieties ◽  
Main Effects ◽  
Pod Length

Stability in yields of agronomically acceptable cultivars is generally regarded as the ultimate goal in cowpea improvement. Nine advanced cowpea lines and 3 local checks were evaluated for grain yield in eastern Kenya with the aim of identifying stable genotypes and integrating farmer preferences. The study was conducted in 3 locations over 2 years under a randomized complete block design with 3 replications. Stability was estimated using additive main effects and multiplicative interaction (AMMI) and genotype by environment (GGE) models. There was variation among genotypes, locations and their interactions for grain yield. Genotype G5, G9 and G2 were found to be stable and high yielding. Environments Kit16 and Kit15 were considered as the most suitable for selecting superior genotypes for adaptability and stability. Farmers’ criteria for selecting genotypes included early maturing, pod length, disease tolerant and high yielding varieties. Cowpea performance for grain yield was greatly influenced by inherent genotypic factors, environment and their interaction effects. KEY WORDS: COWPEA, ENVIRONMENT, GENOTYPE BY ENVIRONMENT INTERACTION, STABILITY, GRAIN YIELD

scholarly journals Additive Main Effects and Multiplicative Interaction and Genotype Main Effect and Genotype by Environment Interaction Effects-Biplot Analysis of Sorghum Grain Yield in Uganda

2020 ◽  
Vol 12 (6) ◽  
pp. 98
Author(s):  
Charles Andiku ◽  
Geofrey Lubadde ◽  
Charles J. Aru ◽  
Michael A. Ugen ◽  
Johnie Ebiyau
Keyword(s):  
Grain Yield ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Test Environment ◽  
Yield Performance ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Main Effect ◽  
Sorghum Grain ◽  
Main Effects

Genotype-by-environment interaction analysis is vital for cultivar release, and to identify suitable crop production sites. The current study aimed to determine sorghum grain yield stability and adaptability and to identify the most informative and representative environments for sorghum grain yield performance in Uganda. Sorghum grain yield data of eight (08) genotypes; ICSR 160, IS8193, IESV92043DL, IESV92172DL, GE17/1/2013A, GE35/1/2013A, SESO1, and SESO3 tested across eight (08) major sorghum production area in Uganda for two consecutive seasons of 2017 using randomised complete block design with 2 replications were analysed via Additive Main effects and Multiplicative Interaction (AMMI) and Genotype Main Effect and Genotype by Environment interaction effects (GGE) using PB tools. Genotype IESV92043DL was the ideal genotype in the entire test environments with mean grain yield of 2783 kg ha-1 however genotype ICSR 160 had the highest grain yield of 2823 kg ha-1 across all the test environment. On the other hand, GE17/1/2013A was the most stable and adapted genotype across all the test environment. Of the eight (08) environments tested, biplot analysis precisely grouped the test environments into two presumed mega-environments with the best genotype being IS8193 and ICSR 160. Out of eight (08) trial sites, two (02) environments; Abi and Mayuge were the most representative and informative environment for sorghum grain yield performance in Uganda.

scholarly journals Genotype-by-environment interaction for seed yield and oil content of safflower (Carthamus tinctorius L.) genotypes

Genetika ◽  
2021 ◽  
Vol 53 (1) ◽  
pp. 11-22
Author(s):  
Hasan Koç
Keyword(s):  
Seed Yield ◽  
Oil Content ◽  
Genotype By Environment Interaction ◽  
Carthamus Tinctorius ◽  
Climatic Conditions ◽  
High Yield ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Main Effects

This research was carried out to determine genotype-by-environment interaction of safflower genotypes tested from 2014 to 2017. Konya, where the research was carried out, is the location with the most irregular and the lowest precipitation in Turkey. In this research, the variance analysis over years and genotypes showed that the main effects on genotypes made by year and genotype-by-year interaction were statistically significant (p<0.01) for all characteristics examined. The climatic conditions, especially the amount and distribution of precipitation, over the years allowed genotypes to perform substantially differently in seed yield and oil content of safflower genotypes. The biplot analysis provided significant advantages in identifying the promising genotypes. The genotypes showed similar patterns of performance across the years, while the amount and distribution of precipitation showed similar patterns. The experimental results revealed that the desired genotypes in terms of both stability and high yield, such as G?kt?rk, G7, and Din?er, G5 and G9 and oil content, such as G?kt?rk, Balc?, and Linas, existed. In comparison to oil content, seed yield was more sensitive to environmental factors.

scholarly journals Capturing GEI Patterns for Quality Traits in Biparental Wheat Populations

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1022
Author(s):  
Ivana Plavšin ◽  
Jerko Gunjača ◽  
Ruđer Šimek ◽  
Dario Novoselović
Keyword(s):  
Genotype By Environment Interaction ◽  
Grain Protein Content ◽  
Environment Interaction ◽  
Quality Traits ◽  
Dominant Source ◽  
Genotype By Environment ◽  
Ammi Model ◽  
Superior Genotypes ◽  
Main Effects ◽  
Selection Of

Genotype-by-environment interaction (GEI) is often a great challenge for breeders since it makes the selection of stable or superior genotypes more difficult. In order to reduce drawbacks caused by GEI and make the selection for wheat quality more effective, it is important to properly assess the effects of genotype, environment, and GEI on the trait of interest. In the present study, GEI patterns for the selected quality and mixograph traits were studied using the Additive Main Effects and Multiplicative Interaction (AMMI) model. Two biparental wheat populations consisting of 145 and 175 RILs were evaluated in six environments. The environment was the dominant source of variation for grain protein content (GPC), wet gluten content (WGC), and test weight (TW), accounting for approximately 40% to 85% of the total variation. The pattern was less consistent for mixograph traits for which the dominant source of variation has been shown to be trait and population-dependent. Overall, GEI has been shown to play a more important role for mixograph traits compared to other quality traits. Inspection of the AMMI2 biplot revealed some broadly adapted RILs, among which, MG124 is the most interesting, being the prevalent “winner” for GPC and WGC, but also the “winner” for non-correlated trait TW in environment SB10.

scholarly journals Genotype by Environment Interaction and Grain Yield Stability of Drought Tolerant Cowpea landraces in Ethiopia

2021 ◽  
Author(s):  
Tesfaye Walle Mekonnen ◽  
Firew Mekbib ◽  
Berhanu Amsalu ◽  
Melaku Gedil ◽  
Maryke Labuschagne
Keyword(s):  
Grain Yield ◽  
Primary Source ◽  
Genotype By Environment Interaction ◽  
Climatic Conditions ◽  
Yield Stability ◽  
Environment Interaction ◽  
Lattice Design ◽  
Genotype By Environment ◽  
Drought Tolerant ◽  
Wide Range

Abstract Cowpea is one of the most important indigenous food and forage legumes in Africa. It serves as a primary source of protein for poor farmers in drought-prone areas of Ethiopia. The crop is used as a source of food, and insurance crop during the dry season. Cowpea is adaptable to a wide range of climatic conditions. Despite this, the productivity of the crop is generally low due to lack of stable and drought tolerant varieties. In this study, 25 cowpea genotypes were evaluated in five environments using a triple lattice design during the 2017 and 2018 main cropping seasons. The objectives of this study were to estimate the magnitude of genotype by environment interaction (GEI) and grain yield stability of selected drought tolerant cowpea genotypes across different environments. The additive main effect and multiplicative interaction (AMMI) model indicated the contribution of environment, genotype and GEI as 63.98 6%, 2.66% and 16.30% of the total variation for grain yield, respectively. The magnitudes of the GEI sum of squares were 6.12 times that of the genotypes for grain yield. The IPCA1, IPCA2 and IPCA3 were all significant and explained 45.47%, 28.05% and 16.59% of the GEI variation, respectively. The results from AMMI, cultivar superior measure (Pi), genotype plus genotype-by-environment (GGE) biplot yield stability index (YSI), and AMMI stability value (ASV) analyses identified NLLP-CPC-07-145-21, NLLP-CPC-103-B and NLLP_CPC-07-54 as stable and high yielding genotypes across environments. Thus, these genotypes should be recommended for release for production for drought prone areas. NLLP-CPC-07-143, Kanketi and CP-EXTERETIS were the least stable. The AMMI1 biplot showed that Jinka was a high potential and favorable environment while Babile was an unfavorable environment for cowpea production.

scholarly journals Genotype-by-Environment Interaction and Stability Analysis in Grain Yield of Improved Tef (Eragrostis tef) Varieties Evaluated in Ethiopia

2019 ◽  
pp. 1-13
Author(s):  
Habte Jifar ◽  
Kebebew Assefa ◽  
Kassahun Tesfaye ◽  
Kifle Dagne ◽  
Zerihun Tadele
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Genotype By Environment Interaction ◽  
Eragrostis Tef ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Testing Environments ◽  
One Year ◽  
Main Effects

Aims: To assess the magnitude of genotype by environment interaction; possible existence of different mega-environments; and discriminating ability and representativeness of the testing environments. Study Design: Randomized complete Block Design with three replications. Place and Duration of Study: The study was conducted at Debre Zeit, Holetta and Alem Tena for two years (2015 and 2016) and at Adet, Axum and Bako for one year (2015). Methodology: Thirty-five improved tef varieties were evaluated at nine environments. The G × E interaction were quantified using additive main effects and multiplicative interaction (AMMI) and the genotype and genotype by environment (GGE) biplot models. Results: Combined analysis of variance revealed highly significant (P = 0.01) variations due to genotype, environment and genotype by environment interaction effects. AMMI analysis revealed 4.3%, 79.7% and 16% variation in grain yield due to genotypes, environments and G x E effects, respectively. G6 gave the highest mean grain yield (3.33 t/ha) over environments whereas G29 gave the lowest mean yield (2.49 t/ha). The GGE biplot grouped the nine testing environments and the 35 genotypes into four mega environments and seven genotypic groups. The four mega environments include: G-I (E1, E4 and E6); G-II (E2, E3, E7 and E8); G-III (E9), and G-IV (E5). E5, E6, E7 and E8 which had the longest vector were the most discriminating of all environments while, E1 and E4 which had the smallest angle with the average environmental axis were the most representative of all environments. Regarding genotypes, G6, G25, G34 and G16 were identified as the best yielding and relatively stable genotypes to increase tef productivity. Conclusion: AMMI and GGE were found to be efficient in grouping the tef growing environments and genotypes.

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)

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