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.

scholarly journals QTL mapping of genotype–environment interaction for fitness in Drosophila melanogaster

1998 ◽  
Vol 71 (2) ◽  
pp. 133-141 ◽  
Author(s):  
JAMES D. FRY ◽  
SERGEY V. NUZHDIN ◽  
ELENA G. PASYUKOVA ◽  
TRUDY F. C. MACKAY
Keyword(s):  
Environmental Heterogeneity ◽  
Natural Populations ◽  
Antagonistic Pleiotropy ◽  
Environment Interaction ◽  
Standard Medium ◽  
Fitness Effects ◽  
Genotype Environment Interaction ◽  
Different Temperatures ◽  
Temperature Interaction ◽  
Ri Lines

A fundamental assumption of models for the maintenance of genetic variation by environmental heterogeneity is that selection favours alternative alleles in different environments. It is not clear, however, whether such antagonistic pleiotropy is common. We mapped quantitative trait loci (QTLs) causing variation for reproductive performance in each of three environmental treatments among a set of 98 recombinant inbred (RI) lines derived from a cross between two D. melanogaster laboratory strains. The three treatments were standard medium at 25°C, ethanol-supplemented medium at 25°C, and standard medium at 18°C. The RI lines showed highly significant genotype–environment interaction for the fitness measure. Of six QTLs with significant effects on fitness in at least one of the environments, five had significantly different effects at the different temperatures. In each case, the QTL by temperature interaction arose because the QTL had stronger effects at one temperature than at the other. No evidence for QTLs with opposite fitness effects in different environments was found. These results, together with those of recent studies of crop plants, suggest that antagonistic pleiotropy is a relatively uncommon form of genotype–environment interaction for fitness, but additional studies of natural populations are needed to confirm this conclusion.

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 GGE Biplot Analysis of Multi-Environment Yield Trials in Soybean Promising Lines

2019 ◽  
Vol 3 (2) ◽  
pp. 72
Author(s):  
Ayda Krisnawati ◽  
M. Muchlish Adie
Keyword(s):  
Block Design ◽  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Tropical Regions ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Promising Line ◽  
The Tropics

Soybean in Indonesia is grown in diverse agro-ecological environments. The performance of soybean yield often varies due to significant genotype × environment interaction (GEI), therefore the yield stability of performance is an important consideration in the breeding program. The aim of the research was to exploring the GEI pattern and yield stability of soybean promising lines in the tropics using GGE (Genotype and Genotype by Environment Interaction) biplot method. A total of 16 soybean promising lines were evaluated in ten environments during 2016 growing season. The experiment was arranged in a randomized completely block design with four replicates. The analysis of variance revealed that environments (E) explained the highest percentage of variation (51.45%), meanwhile the genotypes (G) and genotype × environment interactions (GEI) contributed for 3.24%, and 14.59% of the total variation, respectively. Seed yield of 16 soybean promising lines ranged from 2.41 to 2.83 t.ha-1 with an average of 2.74 t.ha-1. Joint effects of genotype and interaction (G+GE) which was partitioned using GGE biplot analysis showed that the first two components were significant, explaining 60.88% (37.89% PC1 and 22.98% PC2) of the GGE sum of squares. Indonesia can be divided into at least four putative mega environments for soybean production. The GGE biplot identified G10 as high yielding and stable promising line, thus recommended to be developed in multi-environment in tropical regions of Indonesia.

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.

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

scholarly journals Determination of the adaptability and stability of soybean cultivars in different locations and at different sowing times in Paraná state using the AMMI and Eberhart and Russel methods

2016 ◽  
Vol 37 (6) ◽  
pp. 3973 ◽  
Author(s):  
Daniel Augusto Silveira ◽  
Luiz Fernando Pricinotto ◽  
Maicon Nardino ◽  
Carlos André Bahry ◽  
Cássio Egídio Cavenaghi Prete ◽  
...  
Keyword(s):  
Interaction Analysis ◽  
Block Design ◽  
Principal Component ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Sowing Dates ◽  
Ammi Analysis ◽  
Soybean Genotypes ◽  
The Stability ◽  
Performance Adaptation

This study aimed to evaluate the adaptability and phenotypic stability of 10 soybean genotypes in 12 environments in Paraná state by using the additive main effects and multiplicative interaction analysis (AMMI) and Eberhart and Russell models. The assays were conducted in a randomized complete block design with three replicates, in the 2010/2011 season in four locations in Paraná state (Assaí, São Pedro do Ivaí, Cornélio Procópio, and Marilândia do Sul), and with three sowing dates (15/-20/10/10; 29/10-03/11/10; 15/-20/11/10). The cultivars tested with Roundup Ready® technology included SYN 1049, SYN 1152, SYN 1059, SYN 3358, SYN 1163, SYN 1157, V-MAX, FT Campo Mourão, BMX Potência, and SYN 9070. The yield character was analyzed. Data were submitted to analysis of variance and the adaptability and stability were then analyzed. The results of the AMMI and Eberhart and Russell models were somewhat consistent for the stability parameter only. The AMMI analysis was able to capture 66% of the variance associated with residue no additives, of which 43.18% was retained in the first principal component of interaction and 23.58%, in the second component. This is sufficient to explain the genotype × environment interaction. The SYN 1059, SYN 1163, and VMAX genotypes are distinguished by their considerably higher yield and productive adaptation. In the AMMI analysis, the cultivar SYN 1163 showed commercial promise among the other cultivars for high grain yield performance, adaptation, and response predictability.

Genotype × environment interaction, environmental heterogeneity and the lek paradox

2012 ◽  
Vol 25 (4) ◽  
pp. 601-613 ◽  
Author(s):  
M. D. GREENFIELD ◽  
R. G. DANKA ◽  
J. M. GLEASON ◽  
B. R. HARRIS ◽  
Y. ZHOU
Keyword(s):  
Environmental Heterogeneity ◽  
Environment Interaction ◽  
Lek Paradox ◽  
Genotype Environment Interaction

Interpreting genotype×environment interaction in sesame (Sesamum indicum L.)

2006 ◽  
Vol 145 (3) ◽  
pp. 263-271 ◽  
Author(s):  
H. LAURENTIN ◽  
D. MONTILLA ◽  
V. GARCIA
Keyword(s):  
Yield Components ◽  
Principal Component ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Silt Content ◽  
Genotype Environment Interaction ◽  
Principal Component Axis ◽  
Superior Genotypes ◽  
Edaphic Properties ◽  
Selection Of

An understanding of genotype by environment (G×E) interaction would be useful for establishing breeding objectives, identifying the best test conditions, and finding areas of optimal cultivar adaptation. Data from field assays including eight environments and eight elite lines were analysed to identify environmental and genotypic variables related with G×E interaction for yield in sesame multi-environment trials in Venezuela. Both predictable and unpredictable environmental variables were recorded. Yield components were recorded as genotypic variables. Yield and yield components were used to perform additive main effect and multiplicative interaction (AMMI) analysis. Significant differences (P<0·01) for G×E interaction were observed for all variables examined, except for the number of branches per plant. For yield, 0·28 of the total sum of squares corresponded to G×E interaction. Using environmental and genotypic data, correlation analysis was carried out between genotypic and environmental scores of the first interaction principal component axis (IPCA 1) for all variables examined. Significant correlations (P<0·05) were observed between IPCA 1 for yield and content of sand and silt in soil. No significant correlation was found between IPCA 1 score for yield and genotypic variables. These results indicate that edaphic properties at the trial locations play an important role in yield G×E interaction in Venezuelan sesame. These results should help select test sites for sesame in Venezuela to minimize G×E interaction and make selection of superior genotypes easier. Two strategies can be recommended: multi-environment trials at sites with average, not extreme, sand and silt content, or stratification of sites according to sand and silt content.

scholarly journals ADAPTIBILITY PERFORMANCES OF CHICKPEA (CICER ARIETINUM L.) GENOTYPES UNDER DIFFERENT ENVIRONMENTS FOR STABILITY OF QUANTITATIVE TRAITS

2020 ◽  
Vol 53 (1) ◽  
pp. 62-72
Author(s):  
M. G. AZAM ◽  
M. A. HOSSAIN ◽  
J. HOSSAIN ◽  
M. A. HOSSAIN ◽  
M. O. ALI
Keyword(s):  
Regression Analysis ◽  
Cicer Arietinum ◽  
Block Design ◽  
Principal Component ◽  
High Yield ◽  
Environment Interaction ◽  
Phenotypic Characters ◽  
Cicer Arietinum L ◽  
Genotype Environment Interaction ◽  
Wide Range

The evaluation and computation of yield stability of a genotype over environments is a critical component of a certain breeding program. The present study was intended to screen 11 advance chickpea (Cicer arietinum L.) genotypes and one check for genotype × environment interaction (G × E) at six locations with varying micro and macro climatic conditions for yield correlated phenotypic characters. A number of 11 advanced genotypes of chickpea and one check variety were assessed for their adaptability at six different locations of Bangladesh. The randomized complete block design (RCBD) with three replications was chosen to experiment. The means were used to compute Additive Main Effects and Multiplicative Interaction (AMMI) analysis of variance, followed by regression analysis to measure × E. The regression analysis showed significant genotype × environment interaction for all the phenotypic characters. The mean values of days to flowering, days to maturity, plant height, number of pods per plant and seed yield were highly significant for linear, as well as non-linear components of G × E. Chickpea yield was significantly (p< 0.01) affected by genotypes, the environments and G × E interaction, indicating that the varieties and the test environments were diverse. G × E was further partitioned by principal component axes. The first two principal components cumulatively explained 86.59% of the total variation, of which 53.34% and 33.25% were contributed by IPCA1 and IPCA2, respectively. The AMMI stability value discriminated genotypes G2 (BCX 09010-9), G3 (BCX 09010-2) and G8 (BCX 01008-4) the stable genotypes. The investigated genotypes exhibited varying adaptability in different environments. Genotypes G3 (BCX 09010-9) and G9 (BCX 01008-3) were stable genotypes with high yield over a wide range of environments are promising candidate chickpea varieties.

Sign in / Sign up

Export Citation Format

Share Document