Multi-environment trial of newly developed white yam genotypes I: Analysis using genotype main effect and genotype – by environment interaction (GGE Biplot) model

2008 ◽  
Vol 20 (1) ◽  
Author(s):  
EC Nwachukwu ◽  
JEG Ikeorgu ◽  
R Asiedu
Keyword(s):  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Main Effect ◽  
White Yam

scholarly journals Genotype and environment interaction of sweetpotato varieties

2019 ◽  
Vol 44 (3) ◽  
pp. 501-512
Author(s):  
S Sultana ◽  
HC Mohanta ◽  
Z Alam ◽  
S Naznin ◽  
S Begum
Keyword(s):  
Tuber Yield ◽  
Agricultural Research ◽  
Genotype By Environment Interaction ◽  
Yield Potential ◽  
Environment Interaction ◽  
Dry Matter Content ◽  
Genotype By Environment ◽  
Discriminating Power ◽  
Study Results ◽  
Main Effect

The article presents results of additive main effect and multiplicative interaction (AMMI) and genotype (G) main effect and genotype by environment (GE) interaction (G × GE) biplot analysis of a multi environmental trial (MET) data of 15 sweetpotato varieties released from Bangladesh Agricultural Research Institute conducted during 2015–2018. The objective of this study was to determine the effects of genotype, environment and their interaction on tuber yield and to identify stable sweetpotato genotypes over the years. The experimental layout was a randomized complete block design with three replications at Gazipur location. Combined analysis of variance (ANOVA) indicated that the main effects due to genotypes, environments and genotype by environment interaction were highly significant. The contribution of genotypes, environments and genotype by environment interaction to the total variation in tuber yield was about 60.16, 10.72 and 12.82%, respectively. The first two principal components obtained by singular value decomposition of the centred data of yield accounted for 100% of the total variability caused by G × GE. Out of these variations, PC1 and PC2 accounted for 71.5% and 28.5% of variability, respectively. The study results identified BARI Mistialu- 5, BARI Mistialu- 14 and BARI Mistialu- 15 as the closest to the “ideal” genotype in terms of yield potential and stability. Varieties ‘BARI Mistialu- 8, BARI Mistialu- 11 and BARI Mistialu- 12’ were also selected as superior genotypes. BARI Mistialu- 3 and BARI Mistialu- 13 was comparatively low yielder but was stable over the environment. Among them BARI Mistialu-12, BARI Mistialu-14 and BARI Mistialu-15 are rich in nutrient content while BARI Mistialu-8 and BARI Mistialu-11 are the best with dry matter content and organoleptic taste. Environments representing in 1st and 3rd year with comparatively short vectors had a low discriminating power and environment in 2nd year was characterized by a high discriminating power. Bangladesh J. Agril. Res. 44(3): 501-512, September 2019

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 GENOTYPE BY ENVIRONMENT INTERACTION IN COWPEA LINES USING GGE BIPLOT METHOD

2018 ◽  
Vol 31 (1) ◽  
pp. 64-71 ◽  
Author(s):  
MASSAINE BANDEIRA E SOUSA ◽  
KAESEL JACKSON DAMASCENO-SILVA ◽  
MAURISRAEL DE MOURA ROCHA ◽  
JOSÉ ÂNGELO NOGUEIRA DE MENEZES JÚNIOR ◽  
LAÍZE RAPHAELLE LEMOS LIMA
Keyword(s):  
Maximum Yield ◽  
Genotype By Environment Interaction ◽  
Superior Performance ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Brazilian Cerrado ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Adjusted Means ◽  
Selection Of

ABSTRACT The GGE Biplot method is efficien to identify favorable genotypes and ideal environments for evaluation. Therefore, the objective of this work was to evaluate the genotype by environment interaction (G×E) and select elite lines of cowpea from genotypes, which are part of the cultivation and use value tests of the Embrapa Meio-Norte Breeding Program, for regions of the Brazilian Cerrado, by the GGE-Biplot method. The grain yield of 40 cowpea genotypes, 30 lines and 10 cultivars, was evaluated during three years (2010, 2011 and 2012) in three locations: Balsas (BAL), São Raimundo das Mangabeiras (SRM) and Primavera do Leste (PRL). The data were subjected to analysis of variance, and adjusted means were obtained to perform the GGE-Biplot analysis. The graphic results showed variation in the performance of the genotypes in the locations evaluated over the years. The performance of the lines MNC02-675F-4-9 and MNC02-675F-4-10 were considered ideal, with maximum yield and good stability in the locations evaluated. There mega-environments were formed, encompassing environments correlated positively. The lines MNC02-675F-4-9, MNC02-675F-9-3 and MNC02-701F-2 had the best performance within each mega-environment. The environment PRL10 and lines near this environment, such as MNC02-677F-2, MNC02-677F-5 and the control cultivar (BRS-Marataoã) could be classified as those of greater reliability, determined basically by the genotypic effects, with reduced G×E. Most of the environments evaluated were ideal for evaluation of G×E, since the genotypes were well discriminated on them. Therefore, the selection of genotypes with adaptability and superior performance for specific environments through the GGE-Biplot analysis was possible.

scholarly journals Analysis of Genotype by Environment Interaction on Cocoa Hybrids (Theobroma cacao L.) Resistance to Phytophthora Pod Rot

2016 ◽  
Vol 32 (3) ◽  
pp. 162-170
Author(s):  
Agung Wahyu Soesilo ◽  
Indah Anita Sari ◽  
Bayu Setyawan
Keyword(s):  
Block Design ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Experimental Station ◽  
Stability Performance ◽  
Stable Performance ◽  
Wet Climate ◽  
The Stability

Phenomenon of genotype by environment interaction was able to influence the stability performance of cocoa resistance to Phytophthora pod rot (PPR). This research had an objective to evaluate the effect of genotype by environment interaction on resistance of cocoa hybrids to PPR. The tested hybrids were F1 crosses between selected clones of TSH 858, Sulawesi 1, Sulawesi 2, NIC 7, ICS 13, KEE 2 and KW 165. There were 14 tested hybrids and an open pollinated hybrid of ICS 60 x Sca 12 was used as control in multilocation trials at four different agroclimatic locations, namely Jatirono Estate ((highland-wet climate), Kalitelepak Estate (lowland-wet climate), Kaliwining Experimental Station (low land-dry climate) and Sumber Asin Experimental Station (highland-dry climate). Trials were established in the randomized complete block design with four replications. Resistance to PPR were evaluated based on the percentage of infected pod for the years during wet climate of 2010 in Jatirono, Kalitelepak and Kaliwining followed in dry climate of 2011–2015 in Kaliwining and Sumber Asin. Variance of data were analyzed for detecting the effect of genotype by environment interaction (GxE) then visualized with a graph of genotype main effect and genotype by environment interaction (a graph of GGE) biplot. There was consistently no interaction effect between hybrid and location to PPR incidence which was affected by single factor of hybrid, year, location and interaction between year and location. The effect of year indicated yearly change of weather was more important to PPR incidence than location difference. A graph of GGE biplot indicated a stable performance of the tested hybrids among locations.

scholarly journals Assessment of ecological stability in yield for breeding of spring barley cultivars with increased adaptive potential

2020 ◽  
Vol 11 (3) ◽  
pp. 425-430
Author(s):  
V. M. Hudzenko ◽  
O. A. Demydov ◽  
V. P. Kavunets ◽  
L. M. Kachan ◽  
V. A. Ishchenko ◽  
...  
Keyword(s):  
Spring Barley ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Ecological Stability ◽  
Breeding Lines ◽  
Barley Cultivars ◽  
Genotype By Environment ◽  
Barley Breeding ◽  
Agroclimatic Zones

Increasing crop adaptability in terms of ensuring a stable level of productivity in the genotype – environment interaction is still the central problem of plant breeding theory and practice. The aim of the present study is to theoretically substantiate and practically test a scheme of multi-environment trials, as well as interpret experimental data using modern statistical tools for evaluation of the genotype by environment interaction, and highlight the best genotypes with combining yield performance and ecological stability at the final stage of the spring barley breeding process. For this purpose in the first year of competitive testing (2016) at the V. M. Remeslo Myronivka Institute of Wheat of the National Academy of Agrarian Sciences of Ukraine we selected nine promising spring barley breeding lines. In 2017 and 2018 these breeding lines were additionally tested in two other scientific institutions located in different agroclimatic zones of Ukraine. For a more reliable assessment, the breeding lines were compared not only with standard cultivar, but also with ten spring barley cultivars widespread in agricultural production of Ukraine. Thus, for three years of competitive testing, we received experimental genotype-environmental data from seven environments, which represent a combination of contrasting agroclimatic zones (Central part of the Forest-Steppe, Polissia and Northern Steppe of Ukraine) and different years (2016–2018). Our results revealed significant variability of mean yield of genotypes, as well as cross-over genotype by environment interaction. The first two principal components of both AMMI and GGE biplot explained more than 80% of the genotype by environment interaction. In general, the peculiarities we revealed indicate the effectiveness of the proposed combination of spatial (agroclimatic zones) and temporal (years) gradients to identify the best spring barley genotypes with the optimal combination of yield performance and ecological stability. Using AMMI and GGE biplot models was effective for the comprehensive differentiation of genotypes in terms of wide and specific adaptability, as well as for qualitative characterization of test environments and providing mega-environment analysis. As a practical result of the multi-environment trial, four spring barley breeding lines have been submitted to the State Variety Testing of Ukraine as new cultivars MIP Sharm, MIP Tytul, MIP Deviz and MIP Zakhysnyk, respectively.

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.

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