Additive Main Effect and Multiplicative Interaction analysis for grain yield of early maturing sorghum [Sorghum bicolor (L.) Moench] varieties in drought prone areas of Central Tigray, Northern Ethiopia

2020 ◽  
Author(s):  
Fantaye Belay ◽  
Hintsa Meresa ◽  
Shambel Syum
Keyword(s):  
Grain Yield ◽  
Analysis Of Variance ◽  
Interaction Analysis ◽  
Block Design ◽  
Northern Ethiopia ◽  
Environment Interaction ◽  
Multiplicative Interaction ◽  
Tigray Region ◽  
Early Maturing ◽  
Testing Environments

Abstract Shortage of widely adapted and high yielding variety is one of the major bottlenecks for production and productivity of sorghum in dry lowlands of Tigray region, northern Ethiopia. A field experiment was conducted during the main seasons of 2017and 2019 at four locations using randomized complete block design with three replications to evaluate the performance of ten early maturing sorghum genotypes for grain yield using AMMI (Additive Main Effects and Multiplicative Interaction) model. The combined analysis of variance revealed highly significant (P≤0.01) genotype (G), environment (E) and genotype × environment interaction (GEI). The significant genotype by environment interaction effects were further partitioned in to two significant interaction principal components by using AMMI model. The AMMI analysis of variance showed that the genotype, environment and interaction sum squares contributed 41.55 %, 28.67 % and 29.78 % to the treatment sum squares for grain yield respectively. In addition the first two IPCAs and interaction residual were significant. The first two IPCAs accounted for a total of 82.20 % of the interaction sum square. The results revealed that the observed yield variation among genotypes were due to genetic potential of genotypes and interaction rather than location differences. The highest yield was obtained from ESH-1 (3276 kg ha-1), while the lowest was from Grana-1 (2094 kg ha-1) and the average grain yield of genotypes was 2462 kg ha-1. Therefore, ESH-1 is selected as the best stable hybrid with consistent yielding performance across the testing environments in dry lowland areas of Abergelle and similar agro-ecologies in Tigray region, northern Ethiopia.

scholarly journals Genotype by environment interaction and stability analysis for rice genotypes under Boro condition

Genetika ◽  
2014 ◽  
Vol 46 (2) ◽  
pp. 521-528 ◽  
Author(s):  
Lotan Bose ◽  
Nitiprasad Jambhulkar ◽  
Kanailal Pande
Keyword(s):  
Interaction Analysis ◽  
Block Design ◽  
Principal Component ◽  
Stability Index ◽  
Yield Stability ◽  
Environment Interaction ◽  
Multiplicative Interaction ◽  
Genotype Effect ◽  
Rice Genotypes ◽  
Stability Statistics

Genotype (G)?Environment (E) interaction of nine rice genotypes possessing cold tolerance at seedling stage tested over four environments was analyzed to identify stable high yielding genotypes suitable for boro environments. The genotypes were grown in a randomized complete block design with three replications. The genotype ? environment (G?E) interaction was studied using different stability statistics viz. Additive Main effects and Multiplicative Interaction (AMMI), AMMI stability value (ASV), rank-sum (RS) and yield stability index (YSI). Combined analysis of variance shows that genotype, environment and G?E interaction are highly significant. This indicates possibility of selection of stable genotypes across the environments. The results of AMMI (additive main effect and multiplicative interaction) analysis indicated that the first two principal components (PC1-PC2) were highly significant (P<0.05). The partitioning of TSS (total sum of squares) exhibited that the genotype effect was a predominant source of variation followed by G?E interaction and environment. The genotype effect was nine times higher than that of the G?E interaction, suggesting the possible existence of different environment groups. The first two interaction principal component axes (IPCA) cumulatively explained 92 % of the total interaction effects. The study revealed that genotypes GEN6 and GEN4 were found to be stable based on all stability statistics. Grain yield (GY) is positively and significantly correlated with rank-sum (RS) and yield stability index (YSI). The above mentioned stability statistics could be useful for identification of stable high yielding genotypes and facilitates visual comparisons of high yielding genotype across the multi-environments.

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 Agronomic evaluation of corn (Zea mays L.) genotypes in the warm dry region of Chiapas, Mexico

2020 ◽  
Vol 13 (9) ◽  
Author(s):  
Jesús Martínez-Sánchez ◽  
Néstor Espinosa-Paz ◽  
Pedro Cadena-Iñiguez ◽  
Rafael Ariza-Flores ◽  
Robertony Camas-Gómez
Keyword(s):  
Zea Mays ◽  
Grain Yield ◽  
Plant Height ◽  
Analysis Of Variance ◽  
Central Region ◽  
Zea Mays L ◽  
Block Design ◽  
Experimental Unit ◽  
Environment Interaction ◽  
Female Flowering

Objective. To evaluate the agronomic behavior of corn (Zea mays L.) experimental genotypes in three contrasting environments in the Central region of Chiapas, Mexico. Design / methodology / approach. The experiments took place during the 2016 spring-summer agricultural cycle at Francisco Villa, Villaflores (730 m); San Luis, Suchiapa (600 m) and Ocozocoautla (800 m), at the Central region in the state of Chiapas, Mexico. At the three assessed sites, the climate is warm subhumid with rains in summer and intra-stival drought during the second half of July and the first half of August. The genotypes XT-5614, XT-3402, XT-5610, XT-5612, XT-5627, and BG7415W from the Biogene Company were evaluated, which are used in commercial crops at the Center of Chiapas. All genotypes showed viability greater than 90%. Three experimental sites were evaluated, in a randomized complete block design with four replications. The experimental unit consisted of four 5 m long rows 0.8 m apart. The useful plot was formed by two central furrows. The evaluated variables were: days to male flowering (DMF), days to female flowering (DFF), plant height (PH), cob height (CH), cob length (CL), cob diameter (CD), rows per cob (RC), grains per row and grain yield (YLD) at 14% moisture. These were analyzed with an analysis of variance (ANOVA) and for the genotype x environment interaction (GEI) the additive main effects and multiplicative interaction model (AMMI) were used, with the SAS statistical software and the GEA-R software. Results: The combined analysis of variance detected differences between genotypes (G) for most of the variables except in grains per row. and days to male and female flowering; there were significant differences between environments (A) for all variables, while for the GEI, there were significant differences for the number of rows per cob. The CV was 1.26 (DFF) at 10% (YLD), which indicates an acceptable control (<20%) of the experimental variability. The results indicated genetic variation between evaluated genotypes, which allows the selection of the most outstanding ones. The evaluation environments showed differential effects and this condition is necessary for the evaluation of germplasm for a genetic improvement process. Study limitations/implications: Cob height registered acceptable values given that short plants favor rotting in hot climates when weeds are present before harvest. The flowering of the genotypes was considered acceptable and was earlier (55 d) at the Suchiapa site. Findings/conclusions: Among the assessed genotypes there were significant differences for grain yield, plant height, cob height, cob length and the number of rows; the genotype-by-environment interaction was not significant. The XT 5627 and XT 5610 genotypes showed higher stability, and the former showed higher grain yield. The highest yields were recorded in the environment from Francisco Villa, Villaflores, at the Frailesca region, Chiapas.

scholarly journals Yield Stability of Sweet Sorghum Genotypes for Bioenergy Production Under Contrasting Temperate and Tropical Environments

2018 ◽  
Vol 10 (12) ◽  
pp. 42
Author(s):  
Diana-Abasi Udoh ◽  
Søren K. Rasmussen ◽  
Sven-Erik Jacobsen ◽  
Godfrey A. Iwo ◽  
Walter de Milliano
Keyword(s):  
Grain Yield ◽  
Analysis Of Variance ◽  
Sweet Sorghum ◽  
Block Design ◽  
Yield Stability ◽  
Biofuel Production ◽  
Environment Interaction ◽  
Yield Attributes ◽  
Fresh Biomass ◽  
Sorghum Genotypes

Forty-three sweet sorghum accessions were grown in two contrasting environments; Nigeria (tropical environment) and Denmark (temperate environment). The objectives were to determine the interaction between genotype and environment on grain yield, fresh biomass and stem sugar, and to assess yield stability of sweet sorghum and identify the best genotypes for biofuel production. The sweet sorghum originating from a Dutch and ICRISAT collection was grown in randomized complete block design in three replicates for two years (2014 and 2015). The combined analysis of variance of the sweet sorghum genotypes in two years over the two contrasting environments revealed that year (Y), genotype (G), environment (E) and genotype by environment interaction (GEI) were significant in the entire biofuel yield attributes obtained from both Dutch and ICRISAT collections except the degree of Brix and fresh biomass respectively across the year. The year and genotype interaction (Y&times;G) was not significant in all the biofuel attributes of Dutch accessions. Additive main effect and multiplicative interaction (AMMI) analysis of variance showed significant effect of G, E and the GEI. The AMMI was used to identify the best performing, adaptable and more stable genotypes. Twenty-two genotypes of both ICRISAT and Dutch accessions were identified to be stable across the two locations with respect to different biofuel attributes. Nine, seven, and six genotypes were found to be stable for grain yield, biomass yield and brix value, respectively. The best performing genotypes for stem sugar across locations were identified. From the available data collected, the performance of the sweet sorghum was attributed to both genetic and environmental effects. High GE was observed to influence stability, hence will influence the selection criteria of the sweet sorghum genotypes.

scholarly journals Genotype X Environment Interaction and Yield Stability in Early-Maturing Cowpea (Vigna unguiculata (L.) Walp.) Landraces in Ethiopia

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yirga Kindie ◽  
Bulti Tesso ◽  
Berhanu Amsalu
Keyword(s):  
Grain Yield ◽  
Analysis Of Variance ◽  
Vigna Unguiculata ◽  
Regression Coefficient ◽  
Yield Stability ◽  
Environment Interaction ◽  
Genotype X Environment ◽  
Genotype Environment Interaction ◽  
Cropping Season ◽  
Early Maturing

The study was conducted to estimate the effects of genotype, environment, and genotype × environment interaction on grain yield and yield-related traits and to identify stability genotype. At six environments, twenty-four cowpea landraces and one check were evaluated in a 5 × 5 triple lattice during the 2019 cropping season. Data were collected on yield and yield-related traits. The analysis of variance for each environment and across environments showed significant differences among genotypes, environments, and GEI for most traits including yield. Environment, genotype, and GEI showed 27.45%, 20.9%, and 49.55% contribution to the total sum of squares, respectively, for grain yield. This indicated that the environments were diverse and most of the variation in grain yield was caused due to interaction and environmental means. G24 (2632 kg ha−1) and G16 (2290 kg ha−1) were the highest yielder and stable genotypes with mean grain yields above the grand mean (2049.28 kg ha−1) and standard check (2273 kg ha−1). G24 and G16 were the most stable genotypes according to cultivar superiority, Wricke’s ecovalence, regression coefficient, and devotion from regression stability models.

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.

scholarly journals Genotype x environment interaction and stability of indigenous coriander (Coriandrum sativum L.) genotypes for seed yield in different agro-climatic zones of Chhattisgarh

1970 ◽  
pp. 122-130
Author(s):  
S L Sawargaonkar ◽  
A K Singh ◽  
J Tiwari ◽  
K P Singh ◽  
A Sao ◽  
...  
Keyword(s):  
Seed Yield ◽  
Interaction Analysis ◽  
Block Design ◽  
Principal Component ◽  
Genotype X Environment Interaction ◽  
Coriandrum Sativum ◽  
Environment Interaction ◽  
Climatic Zones ◽  
Genotype X Environment ◽  
Testing Environments

The present study was conducted to find out the stability and yield performances of 13 genotypes of indigenous coriander (Coriandrum sativum L.) evaluated in different agro climatic zones of Chhattisgarh. The trials were laid out in a Randomized Block Design (RBD) with three replications at three locations for three years resulting in nine environments (Genotype × year interactions). The genotypes and G × E interactions revealed significant differences at p <0.01 for seed yield indicating varieties and testing environments were distinct from each other. Additive main effects and multiplicative interaction analysis (AMMI-biplot) indicated that the yield performances of indigenous coriander genotypes were highly affected by the environments. The first two principal component axes (PCA 1 and PCA 2) were significant and they explained 67% of the total genotype x environment interaction of which 42.4% and 24.6% were represented by PCA 1 and PCA 2, respectively. A biplot generated using genotypic and environmental scores of the first two AMMI components demonstrated that genotype with larger PCA 1 and lower PCA 2 scores were high yielding and stable genotypes and genotypes with lower PCA 1 and larger PCA 2 scores were low yielding and unstable cultivars in tested locations. The genotype GC 5 C-101 (ICS 4) showed higher grain yields (16.35 q ha-1) over grand mean (13.03 q ha-1) and also had the minimum PCA 1 score, minimum AMMI stability value (ASV) and yield stability index (YSI). Therefore genotype ICS 4 (Chhattisgarh Shri Chandrahasini Dhaniya -2) showed wider stability across different agro climatic environments of Chhattisgarh.

scholarly journals Genotype X Environment Interaction of Food Barley

2015 ◽  
Vol 21 ◽  
pp. 41-48
Author(s):  
Gebremedhin Welu
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Genotype X Environment ◽  
Genotype Effect ◽  
Environment Effects ◽  
Randomized Complete Block Design ◽  
Barley Genotypes

The objective of this experiment was to estimate the magnitude of genotype X environment interaction on grain yield and yield related traits. Twelve varieties of food barley were included in the study planted in randomized complete block design with three replications. The ANOVA of combined and individual location revealed significant differences among the food barley genotypes for grain yield and other traits. The results of ANOVA for grain yield showed highly significant (p≤0.01) differences among genotypes evaluated for grain yield at Maychew and significant (p≤0.05) differences in Korem, Alage and Mugulat. The ANOVA over locations showed a highly significant (p≤0.01) variation for the genotype effect, environment effects, genotype X environment interaction (GEI) effect and significant (p≤0.05) variation for GEI effect of yield and for most of the yield related traits of food barley genotypes. Haftysene, Yidogit, Estayish and Basso were the genotypes with relatively high mean grain yield across all locations and they are highly performing genotypes to the area. Among locations, the highest mean grain yield was recorded at Korem and it was a suited environment to all the genotypes whereas Mugulat is unfavoured one. ECOPRINT 21: 41-48, 2014DOI: http://dx.doi.org/10.3126/eco.v21i0.11903

scholarly journals Genotype by environment interaction for seeds yield in pea (Pisum sativum L.) using additive main effects and multiplicative interaction model

Euphytica ◽  
2019 ◽  
Vol 215 (11) ◽  
Author(s):  
Jan Bocianowski ◽  
Jerzy Księżak ◽  
Kamila Nowosad
Keyword(s):  
Pisum Sativum ◽  
Block Design ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Multiplicative Interaction ◽  
Genotype By Environment ◽  
Randomized Complete Block Design ◽  
Main Effects

Abstract The objective of this study was to evaluate the genotype by environment interaction using the additive main effects and multiplicative interaction model for seeds yield of pea cultivars grown in Poland. Twelve pea (Pisum sativum L.) cultivars: Bohun, Boruta, Cysterski, Ezop, Kavalir, Lasso, Medal, Santana, Tarchalska, Terno, Wenus and Zekon were evaluated in 20 environments (ten locations in 2 years). The experiment was laid out as randomized complete block design with three replicates. Seeds yield ranged from 26.10 dt ha−1 (for Wenus in Radostowo 2011) to 79.73 dt ha−1 (for Lasso in Słupia 2010), with an average of 50.70 dt ha−1. AMMI analyses revealed significant genotype and environmental effects as well as genotype-by-environment interaction with respect to seeds yield. In the analysis of variance, 89.19% of the total seeds yield variation was explained by environment, 1.65% by differences between genotypes, and 8.33% by GE interaction. The cultivar Terno is the highest stability. The cultivar Tarchalska is recommended for further inclusion in the breeding program because its stability and the highest averages of seeds yield.

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