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.

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).

Evaluation of sugarcane genotypes with respect to sucrose yield across three crop cycles using GGE biplot analysis

2021 ◽  
pp. 1-13
Author(s):  
Aliya Momotaz ◽  
Per H. McCord ◽  
R. Wayne Davidson ◽  
Duli Zhao ◽  
Miguel Baltazar ◽  
...  
Keyword(s):  
Block Design ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Area 5 ◽  
Randomized Complete Block Design ◽  
Main Effects

Summary The experiment was carried out in three crop cycles as plant cane, first ratoon, and second ratoon at five locations on Florida muck soils (histosols) to evaluate the genotypes, test locations, and identify the superior and stable sugarcane genotypes. There were 13 sugarcane genotypes along with three commercial cultivars as checks included in this study. Five locations were considered as environments to analyze genotype-by-environment interaction (GEI) in 13 genotypes in three crop cycles. The sugarcane genotypes were planted in a randomized complete block design with six replications at each location. Performance was measured by the traits of sucrose yield tons per hectare (SY) and commercial recoverable sugar (CRS) in kilograms of sugar per ton of cane. The data were subjected to genotype main effects and genotype × environment interaction (GGE) analyses. The results showed significant effects for genotype (G), locations (E), and G × E (genotype × environment interaction) with respect to both traits. The GGE biplot analysis showed that the sugarcane genotype CP 12-1417 was high yielding and stable in terms of sucrose yield. The most discriminating and non-representative locations were Knight Farm (KN) for both SY and CRS. For sucrose yield only, the most discriminating and non-representative locations were Knight Farm (KN), Duda and Sons, Inc. USSC, Area 5 (A5), and Okeelanta (OK).

scholarly journals Identification of Spring Barley Breeding Lines With Superior Yield Performance and Stability

2020 ◽  
Vol 68 (6) ◽  
pp. 947-958
Author(s):  
Volodymyr Hudzenko ◽  
Tetiana Polishchuk ◽  
Oleksandr Demydov ◽  
Mykola Sardak ◽  
Nataliia Buniak ◽  
...  
Keyword(s):  
Spring Barley ◽  
Genotype By Environment Interaction ◽  
Forest Steppe ◽  
Environment Interaction ◽  
Yield Performance ◽  
Practical Result ◽  
Breeding Lines ◽  
Genotype By Environment ◽  
Barley Breeding ◽  
Main Effects

The aim of the present study was to substantiate theoretically and to test in practice scheme of multi-environment trials at the final stage of spring barley breeding process and to distinguish the genotypes which combine superior yield performance and stability. In the first year of competitive testing (2015) nine promising spring barley breeding lines have been selected under condition of the Central part of Forest-Steppe of Ukraine (latitude 49°64′, longitude 31°08′, altitude 153 m). In 2016 and 2017, the genotypes were additionally tested in two other different agro-climatic zones of Ukraine: Polissia (latitude 50°93′, longitude 31°69′, altitude 126 m) and Northern Steppe (latitude 48°56′, longitude 32°32′, altitude 171 m). In addition to the standard variety Vzirets, the breeding lines were compared with ten widespread spring barley varieties in agricultural production. Significant total yield variability of the genotypes and cross-over genotype by environment interaction has been revealed. It confirmed the validity of proposed combination of spatial (zones) and temporal (years) gradients for more efficient evaluation of the genotype by environment interaction and differentiation of genotypes in terms of yield performance and stability. As a practical result, using additive main effects and multiplicative interaction (AMMI) and genotype main effects plus genotype by environment interaction (GGE) models, four spring barley breeding lines with combination of superior yield performance and high stability have been identified.

scholarly journals Identification of stable genotypes and genotype by environment interaction for grain yield in sorghum (Sorghum bicolor L. Moench)

2018 ◽  
Vol 17 (1) ◽  
pp. 81-86 ◽  
Author(s):  
Massaoudou Hamidou ◽  
Oumarou Souleymane ◽  
Malick N. Ba ◽  
Eric Yirenkyi Danquah ◽  
Issoufou Kapran ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genotype By Environment Interaction ◽  
Planting Date ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Food Crop ◽  
Planting Dates ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Sorghum Bicolor L

AbstractSorghum is a staple food crop in Niger and its production is constrained by sorghum midge and the use of low yielding, local sorghum varieties. To improve sorghum productivity, it is crucial to provide farmers with high yielding sorghum cultivars that are resistant to midge. We evaluated 282 genotypes in four environments of Niger Republic. Alpha (0.1) lattice with two replications was the experimental design. Genotype and genotype by environment (GGE) biplot analysis was used to study grain yield (GY) stability and G × E interactions. The results revealed that two distinct mega environments were present. Genotype L232 was the best genotype for GY in the first planting date at Konni and the first and second planting dates (PDs) at Maradi. Genotype L17 was the best for GY in the second PD at Konni. The second PD at Konni was the most discriminating environment while the first PD at Konni is suitable for selecting widely adapted genotypes for GY.

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 Adaptability and grain yield stability of rice hybrids and varieties in Venezuela

2021 ◽  
Vol 33 (3) ◽  
pp. 181-190
Author(s):  
Marco Acevedo Barona ◽  
Rubén Silva Díaz ◽  
Ramón Rea Suárez
Keyword(s):  
Grain Yield ◽  
High Performance ◽  
Mixed Model ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Rice Varieties ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Moderate Yield

The development of new high-performance and stable cultivars requires test multi-environmental validation to deal with the effect of genotype by environment interaction (GEI). With the objective to determine adaptability and stability for grain yield in hybrids and rice varieties through the models AMMI, SREG and REML/BLUP. Six experiments were evaluated during the 2015-2016 dry season in the main producing regions of Venezuela. The ANOVA detected differences for genotype (G), environment (E) and their interaction (GEI), representing 19, 65 and 16 % of the total variation, respectively, with prevalence of hybrid by localities interaction. The first major components of the AMMI and GGE biplot models explained 77 and 83 % of GEI, respectively. The three models coincided and identified the hybrid RHA-180 (H6) with improved average performance, adapted and stable. The hybrid HIAAL (H3) was the most prominent. Among the checks, 'Pionero FL' (V3) was the most stable with moderate yield; the opposite occurred with ‘Soberana FL’ (V4) and ‘SD-20A’ (V1), that the AMMI and GGE biplot models identified with high and unstable performances and specific adaptation to locality INIA Guárico (L1), not coinciding with the mixed model. Two mega-environments were identified with the winning genotypes H6 and V4. There was divergence between AMMI and GGE biplot to identify discriminatory and representative locations. The Plot 199 (L3) was the most representative, while the location L1 discriminated better the genotypes. The GGE biplot analysis was more informative and complete for the GEI analysis.

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

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