Yield Stability of Different Elite Wheat Lines under Drought and Irrigated Environments using AMMI and GGE Biplots

2021 ◽  
Vol 9 (2) ◽  
pp. 98-106
Author(s):  
Dipendra Regmi ◽  
Mukti Ram Poudel ◽  
Bishwas K.C. ◽  
Padam Bahadur Poudel
Keyword(s):  
Principal Component ◽  
Environment Interaction ◽  
Lattice Design ◽  
Genotype Environment Interaction ◽  
Stability Model ◽  
Winter Crop ◽  
Ideal Line ◽  
Genotype Evaluation ◽  
The Mean ◽  
Wheat Lines

Wheat is the principal winter crop in Nepal. Drought affects 44% of the lands of the total wheat area in the country with a yield loss of 15–20%. This research focuses to minimize this loss through the identification of high-yielding lines stable across the drought stress and irrigated environments. The experiment was conducted in Alpha Lattice Design with 20 genotypes replicated twice with five blocks per replication from November 2019 to April 2020. The findings showed that genotypes, environments, and genotype-environment interaction have a highly significant effect on grain yield and explained 28.95%, 52.57%, and 18.47% of variation on yield, respectively. The which-won-where model revealed elite line NL 1420 is the most responsive line in the drought environment, followed by BL 4407, while elite line NL 1179 is the most stable line in irrigated environment. The mean vs stability model with principal component 1 and 2 explaining 65.76% and 34.24% respectively, showed that elite line NL 1420, BL 4407, BL 4919, Bhrikuti are both high yielding and stable lines while line NL 1179, Gautam, and NL 1384 are less stable in both test environments. Similarly, the ranking genotypes model indicated lines close to the ideal line are NL 1420, BL 4407, BL 4919, Bhrikuti as the most representative line for genotype evaluation. Thus, elite wheat line NL 1420 and NL 1179 are recommended as specifically adapted to drought and irrigated environments, respectively, and elite line NL 1420, BL 4407, BL 4919, Bhrikuti are recommended for further evaluation for stability. Int. J. Appl. Sci. Biotechnol. Vol 9(2): 98-106

Simultaneous selection of yield and yield stability in chickpea genotypes using the GGE biplot technique

2013 ◽  
Vol 61 (3) ◽  
pp. 185-194 ◽  
Author(s):  
E. Farshadfar
Keyword(s):  
Grain Yield ◽  
Principal Component ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Yield Data ◽  
Genotype By Environment ◽  
Growing Seasons ◽  
Genotype Environment Interaction ◽  
The Mean ◽  
Selection Of

GGE biplot analysis is an effective method, based on principal component analysis (PCA), to fully explore multi-environment trials (METs). It allows visual examination of the relationships among the test environments, genotypes and the genotype-by-environment interactions (G×E interaction). The objective of this study was to explore the effect of genotype (G) and the genotype × environment interaction (GEI) on the grain yield of 20 chickpea genotypes under two different rainfed and irrigated environments for 4 consecutive growing seasons (2008–2011). The yield data were analysed using the GGE biplot method. The first mega-environment contained environments E1, E3, E4 and E6, with genotype G17 (X96TH41K4) being the winner; the second mega-environment contained environments E5, E7 and E8, with genotype G12 (X96TH46) being the winner. The E2 environment made up another mega-environment, with G19 (FLIP-82-115) the winner. The mean performance and stability of the genotypes indicated that genotypes G4, G16 and G20 were highly stable with high grain yield.

GROUPING LOCATIONS FOR EFFICIENT CASSAVA EVALUATION IN MALAWI

2003 ◽  
Vol 39 (2) ◽  
pp. 167-179 ◽  
Author(s):  
J. MKUMBIRA ◽  
N. M. MAHUNGU ◽  
U. GULLBERG
Keyword(s):  
Environmental Heterogeneity ◽  
Principal Component ◽  
Environment Interaction ◽  
Scatter Plot ◽  
Genotype Environment Interaction ◽  
Breeding Zone ◽  
Adverse Environmental Conditions ◽  
The Tropics ◽  
Pair Wise Comparison ◽  
Better Than

Cassava, a crop widely adapted in the tropics, has the important attribute of withstanding adverse environmental conditions better than do many other staple crops. The performance of an individual genotype, however, is influenced by the environment in which it grows. In Malawi, the heterogeneity of agro-ecologies requires the cumbersome and costly assessment of new cassava genotypes at many sites. This study was conducted, therefore, to test the feasibility of selecting only a few locations for cassava evaluation that would be representative of all the agro-ecologies in which cassava is grown in Malawi. Enormous environmental effects, largely contributed by the interaction between season and location, were manifested. Genotype×environment interaction, due largely to a third level interaction (genotype×season×location), was highly significant for all the traits studied. A principal component analysis scatter plot showed no particular grouping of environments, but a pair-wise comparison showed that some of the locations had limited genotype×environment interaction, indicating that it would be sufficient to use one of these sites for evaluating these traits. The value of the residual was often large, probably as an effect of environmental heterogeneity in the test sites. The authors conclude that cassava genetic improvement will continue to be slow if Malawi is used as a single breeding zone. They recommend a much finer grouping of the locations and the use of smaller plot sizes to allow more clones to be tested at more sites for the same cost. Locations may be selected for intensive cassava breeding work from those that give the best discrimination between genotypes while having insignificant genotype×environment interactions in a relatively large number of environments.

DESEMPENHO AGRONÔMICO E ESTABILIDADE DE TOPCROSSES DE MILHO AVALIADOS EM MINAS GERAIS E PARANÁ

2018 ◽  
Vol 17 (2) ◽  
pp. 303
Author(s):  
ANDRÉ GABRIEL ◽  
MARCOS VENTURA FARIA ◽  
GUILHERME MENDES BATTISTELLI ◽  
EVANDREI SANTOS ROSSI ◽  
CARLOS AUGUSTO DA SILVA ◽  
...  
Keyword(s):  
Plant Height ◽  
Minas Gerais ◽  
Environment Interaction ◽  
Agronomic Performance ◽  
Maize Hybrids ◽  
Genotype X Environment ◽  
Lattice Design ◽  
Genotype Environment Interaction ◽  
Genetic Potential ◽  
Ear Height

 RESUMO – O objetivo deste trabalho foi avaliar o desempenho agronômico e estabilidade fenotípica de híbridos topcrosses de milho oriundos do cruzamento com uma linhagem elite (testador), em quatro ambientes (Guarapuava- PR, Candói-PR, Guarda-Mor-MG e Paracatu-MG), utilizando os métodos Annicchiarico e AMMI para selecionar linhagens com elevado potencial genético. Foram avaliados 88 híbridos topcrosses e 12 híbridos comerciais (2B688, AG9010, AS1575, DKB390, GNZ2500, GNZ8132, GNZ9501, GNZ9505, P30F53, P30R50, P30F35 e Penta). O delineamento utilizado foi o látice triplo 10x10 e foram avaliadas as características agronômicas altura de planta (AP), altura de espiga (AE) e estabilidade produtiva de grãos (PG). Observou-se efeito significativo para genótipo, ambiente e interação genótipo x ambiente para a PG. A média de produtividade dos genótipos foi maior em Paracatu-MG (11.100 kg ha-1) que em Candói (9.654 kg ha-1), Guarapuava (9.456 kg ha-1) e Guarda-Mor (9.446 kg ha-1). Com base nos resultados de PG e estabilidade utilizando os métodos Annicchiarico e AMMI, o híbrido topcross HTC 136 mostrou ser o melhor para seguir no programa de melhoramento no processo de endogamia.Palavras-chave: Annicchiarico, AMMI, interação genótipo x ambiente, testador, Zea mayz L. AGRONOMIC PERFORMANCE AND STABILITY OF MAIZE TOPCROSSES IN MINAS GERAIS AND PARANÁ ABSTRACT – The objective of this study was to evaluate the agronomic performance and phenotype stability of top cross maize hybrids originated by crossing with an elite line (tester), in four environments (Guarapuava-PR, Candói- PR, Guarda-Mor-MG and Paracatu-MG), using the methods Annicchiarico and AMMI to select lines with high genetic potential. Thus, 88 top cross hybrids and 12 commercial hybrids (2B688, AG9010, AS1575, DKB390, GNZ2500, GNZ8132, GNZ9501, GNZ9505, P30F53, P30R50, P30F35 and Penta) were evaluated. The 10x10 triple lattice design was used evaluating the agronomic characteristics plant height (PH), ear height (EH) and yield stability of grain (YG). Significant differences were observed for genotype, environment and genotype x environment interaction for YG. The average productivity of the genotypes was superior in Paracatu-MG (11,100 kg ha-1) compared to Candói (9,654 kg ha-1), Guarapuava (9,456 kg ha-1) and Guarda-Mor (9,446 kg ha-1). The data on YG and stability using Annicchiarico and AMMI methods showed that the top cross hybrid HTC 136 is suitable to continue in the inbreeding process of the breeding program.Keywords: Annicchiarico, AMMI, genotype-environment interaction, tester, Zea mayz L.

Genotype × environment interaction and genetic association of grain iron and zinc content with other agronomic traits in RIL population of pearl millet

2018 ◽  
Vol 69 (11) ◽  
pp. 1092
Author(s):  
Tripti Singhal ◽  
C. Tara Satyavathi ◽  
Aruna Kumar ◽  
S. Mukesh Sankar ◽  
S. P. Singh ◽  
...  
Keyword(s):  
Pearl Millet ◽  
Agronomic Traits ◽  
Pennisetum Glaucum ◽  
Recombinant Inbred Lines ◽  
Selection Index ◽  
Principal Component ◽  
Zinc Content ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Zn Content

Biofortification of lines of pearl millet (Pennisetum glaucum (L.) R.Br.) with increased iron (Fe) and zinc (Zn) will have great impact because pearl millet is an indispensable component of food and nutritional security of inhabitants of arid and semi-arid regions. The aim of the present study was to assess the stability of Fe and Zn content in recombinant inbred lines (RILs) developed for grain Fe and Zn content, and to use these lines in developing micronutrient-rich pearl millet hybrids. A mapping population consisting of 210 RILs along, with parents and checks, was assessed in three consecutive years (2014–16) under rainfed conditions at the same experimental location in an alpha design with two repetitions. Significant differences were observed in genotype, environment and genotype × environment interaction mean squares for all variables, particularly grain micronutrients. The first two principal components of an interaction principal component analysis cumulatively explained 100% of the total variation; respective contributions of the first and second components were 64.0% and 36.0% for Fe, and 58.1% and 41.9% for Zn. A positive and moderately high correlation (0.696**) between Fe and Zn contents suggests good prospects of simultaneous improvement for both micronutrients. Among the 210 RILs, RIL 69, RIL 186, RIL 191, RIL 149 and RIL 45 were found to be more stable with higher mean micronutrient content, additive main effects and multiplicative interaction stability value (ASV) and genotype selection index (GSI) under rainfed condition. These RILs are promising and can be tested further for their combining ability for yield as well as grain micronutrient content for developing superior biofortified, heterotic pearl millet hybrids.

scholarly journals Multiattribute Response of Maize Genotypes Tested in Different Coastal Regions of Brazil

2011 ◽  
Vol 2011 ◽  
pp. 1-6
Author(s):  
Lúcio Borges de Araújo ◽  
Mario Varela Nualles ◽  
Mirian Fernandes Carvalho Araújo ◽  
Carlos Tadeu dos Santos Dias
Keyword(s):  
Principal Components ◽  
Environment Interaction ◽  
Coastal Regions ◽  
Multivariate Response ◽  
Genotype Environment Interaction ◽  
Maize Genotypes ◽  
The Mean ◽  
Components Analysis ◽  
And Performance ◽  
Main Effects

This work applies the three mode principal components analysis to analyze simultaneously the multiple attributes; to fit of models with additive main effects and multiplicative interaction effects (AMMI models) and the regressions models on sites (SREG models); to evaluate, respectively, the multivariate response of the genotype × environment interaction and the mean response of 36 genotypes of corn tested in 4 locations in Brazil. The results were presented by joint plots to identify the best genotypes for their adaptability and performance in the set of attributes.

Agro-morphological diversity and stability of durum wheat lines (Triticum durum Desf.) in Algeria

2013 ◽  
Vol 61 (2) ◽  
pp. 149-159 ◽  
Author(s):  
A. Mekliche ◽  
F. Dahlia ◽  
L. Hanifi-Mekliche
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Morphological Diversity ◽  
Environment Interaction ◽  
The North ◽  
Genotype Environment Interaction ◽  
Genotypic Stability ◽  
Triticum Durum Desf ◽  
Heterogeneity Variance ◽  
Wheat Lines

This study focuses on the genetic potential and genotypic stability of 17 durum wheat genotypes during three crop years under wet conditions in the north of Algeria (Algiers). The results showed highly significant (P<0.001) agro-morphological diversity between the genotypes and a genotype × environment interaction for all the traits except for fertile spikelet number. Wricke’s ecovalance (wi), Shukla's stability variance (σi2), heterogeneity variance (%HV) and the incomplete correlation (%IC) method were used to analyse the genotype × environment interaction on grain yield. The genotypes Ardente/Waha L2, Ardente and Saadi/Simeto L3 exhibited great instability with the highest values of wi, σi2, %HV and %IC. Ardente/Waha L1, Simeto/Vitron L5, Simeto and Ardente/Vitron L1 had the highest grain yield and average stability (wi, %HV and %IC were weak). Significant correlations were found between %HV, Rij2, bi, wi, σi2 and %IC, implying that they were similarly efficient in detecting stable genotypes and in measuring stability.

GENOTYPE-ENVIRONMENT INTERACTION VARIANCES IN YIELD TRIALS OF FALL RYE

1970 ◽  
Vol 50 (1) ◽  
pp. 77-80 ◽  
Author(s):  
P. J. KALTSIKES
Keyword(s):  
Genotype By Environment Interaction ◽  
Western Canada ◽  
Environment Interaction ◽  
First Order ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Yield Difference ◽  
The Mean ◽  
Yield Trials ◽  
Interaction Variance

Estimates of genotype by environment interaction variances were obtained from the western Canada Co-operative fall rye tests grown in 1963–1967. All first-order interactions and the second-order interactions were significantly greater than zero at the 0.05 level of probability. Although the estimate of cultivar by year interaction variance was relatively small, it accounted for 40% of the variance of a cultivar mean when only three years of testing were considered. However, testing in 20 locations for three years with four replicates could detect yield differences of approximately 10% of the mean of the highest yielding cultivar. If further reduction of the yield difference detectable is desired, more locations should be included in the test.

scholarly journals Single‐Step Genomic and Pedigree Genotype × Environment Interaction Models for Predicting Wheat Lines in International Environments

2017 ◽  
Vol 10 (2) ◽  
Author(s):  
Paulino Pérez‐Rodríguez ◽  
José Crossa ◽  
Jessica Rutkoski ◽  
Jesse Poland ◽  
Ravi Singh ◽  
...  
Keyword(s):  
Single Step ◽  
Environment Interaction ◽  
Interaction Models ◽  
Genotype Environment Interaction ◽  
Wheat Lines

scholarly journals Climate change has increased genotype-environment interactions in wheat breeding

2020 ◽  
Author(s):  
Wei Xiong ◽  
Matthew Reynolds ◽  
Jose Crossa ◽  
Thomas Payne ◽  
Urs Schulthess ◽  
...  
Keyword(s):  
Climate Change ◽  
Genetic Model ◽  
Climatic Factors ◽  
Wheat Breeding ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Quantitative Genetic ◽  
Quantitative Genetic Model ◽  
Yield Responses ◽  
Wheat Lines

Abstract The International Maize and Wheat Improvement Center (CIMMYT) develops and distributes annually elite wheat lines as international trials worldwide to assess their performance in different environments and utilization by partners for use in breeding or release as varieties. However, as elsewhere, the collaborator test sites where trials are evaluated have experienced climate change, with implications for how adapted wheat genotypes are bred. Using a standard quantitative genetic model and archived datasets for four global spring wheat trials, we show that the genotype-environment-interaction (GEI) has increased by up to 500% over recent decades. Notably crossover has increased over time, a critical indicator of changes in the ranking of cultivar performance in different environments. Climatic factors explain over 70% of the year-to-year variability in GEI and crossover interactions for yield. Examining yield responses of all genotypes in all trial environments from 1985 to 2017 reveals that climate change has increased GEI by ~ 49% and ranking change by ~38%. Genetic improvement of wheat targeted to high-yielding environments has exacerbated this increase, but the performance of new wheat germplasm developed to withstand heat and drought stress is more adapted and stable, offsetting the increase in ranking changes due to the warmer climate.

scholarly journals Genotype × Environment (G × E) Interaction in Southernpea [Vigna unguiculata (L.) Walp] Genotypes in the Southeastern USA

HortScience ◽  
1998 ◽  
Vol 33 (4) ◽  
pp. 596c-596
Author(s):  
John C. Alleyne ◽  
Teddy E. Morelock ◽  
Clay H. Sneller
Keyword(s):  
Vigna Unguiculata ◽  
Principal Component ◽  
Visual Assessment ◽  
Yield Potential ◽  
Environment Interaction ◽  
Interaction Patterns ◽  
Genotype Environment Interaction ◽  
Principal Component Axis ◽  
Biological Interpretation ◽  
Testing Environments

Genotype by environment (G × E) effects in Regional Cooperative Southernpea trials for the southeastern United States were investigated to characterize the extent, pattern, and potential impact of G × E on seed yield of southernpea [Vigna unguiculata (L.) Walp] genotypes. The structure of G × E effects was investigated using the Additive Main Effect and Multiplicative Interaction (AMMI) method. AMMI analyses revealed a highly significant genotype × environment interaction, most of which was partitioned into a genotype × location component of variance. AMMI first principal component axis scores stratified environments into two groups that minimized variation within groups. Biological interpretation of groupings and visual assessment of the AMMI biplot, revealed high-yielding genotypes interacting positively with one group of environments and conversely, low-yielding genotypes interacting positively with the other group. There were some significant rank changes of genotypes as yield potential varied across environments. Some environments showed similar main effects and interaction patterns indicating that most of the G × E effects could be captured with fewer testing sites, and consequently redundancy of some testing environments over years.

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