scholarly journals Interpretation of genotype × environment interaction in perennial ryegrass (Lolium perenne L.)

Genetika ◽  
2015 ◽  
Vol 47 (2) ◽  
pp. 509-522
Author(s):  
Zeljko Lakic ◽  
Igor Balalic ◽  
Svetko Vojin
Keyword(s):  
Perennial Ryegrass ◽  
Seed Weight ◽  
Interaction Model ◽  
Environment Interaction ◽  
Seed Number ◽  
Local Varieties ◽  
Lolium Perenne L ◽  
Growing Seasons ◽  
Genotype Environment Interaction ◽  
Main Effects

The aim of this field study was to evaluate genotype ? year interaction for spike length, 1000 seed weight, seed number per g and hectoliter mass in perennial ryegrass, using eight divergent genotypes across two growing seasons. Interaction was evaluated using AMMI (Additive Main Effects and Multiplicative Interaction) model. All traits schowed additive (genotype, year) and non additive (genotype ? year interaction) variation. On the basis od AMMI1 biplot genotype G4 showed most stability for all examined traits, so it can be recommended for sowing in different environmental conditions. Also, the genotype G4 could be used in the creation of new local varieties of perennial ryegrass.

scholarly journals Identification of pigeonpea genotypes with wider adaptability to rainfed environments through AMMI and GGE biplot analyses

2021 ◽  
Vol 81 (01) ◽  
pp. 63-73
Author(s):  
M. V. Nagesh Kumar ◽  
V. Ramya ◽  
C. V. Sameer Kumar ◽  
T. Raju ◽  
N. M. Sunil Kumar ◽  
...  
Keyword(s):  
Cajanus Cajan ◽  
Large Scale ◽  
Interaction Model ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Rainfed Agriculture ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Wide Range ◽  
Main Effects

Pigeonpea [Cajanus cajan (L.) Millspaugh] is an important pulse crop grown under Indian rainfed agriculture. Twenty eight pigeonpea genotypes were tested for stability and adaptability across ten rainfed locations in the States of Telangana and Karnataka, India using AMMI (additive main effects and multiplicative interaction) model and GGE (genotype and genotype by environment) biplot method. The grain yields were significantly affected by environment (56.8%) followed by genotype × environment interaction (27.6%) and genotype (18.6%) variances. Two mega environments were identified with several winning genotypes viz., ICPH 2740 (G15), TS 3R (G10), PRG 176 (G8) and ICPL 96058 (G22). E2 (Gulbarga, Karnataka), E3 (Bidar, Karnataka) and E6 (Vikarabad, Telangana) were the most discriminating environments. Genotypes, ICPH 2740, PRG 176 and TS 3R were the best cultivars in all the environments whereas PRG 158 (G9), ICPL 87119 (G12), ICPL 20098 (G19) and ICPL 96058 (G22) were suitable across a wide range of environments. Genotypes, ICPH 2740 and PRG 176 can be recommended on a large scale to the farmers with small holdings to enhance pigeonpea productivity and improve the food security

scholarly journals Genome-based trait prediction in multi- environment breeding trials in groundnut

2020 ◽  
Vol 133 (11) ◽  
pp. 3101-3117 ◽  
Author(s):  
Manish K. Pandey ◽  
Sunil Chaudhari ◽  
Diego Jarquin ◽  
Pasupuleti Janila ◽  
Jose Crossa ◽  
...  
Keyword(s):  
Complex Traits ◽  
Prediction Accuracy ◽  
Agronomic Traits ◽  
Total Yield ◽  
Snp Array ◽  
Interaction Model ◽  
Cost Effective ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Main Effects

Abstract Key message Comparative assessment identified naïve interaction model, and naïve and informed interaction GS models suitable for achieving higher prediction accuracy in groundnut keeping in mind the high genotype × environment interaction for complex traits. Abstract Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling  %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut.

scholarly journals Genotype × environment interactions and phenotypic stability for wheat grown in stressful conditions

Genetika ◽  
2014 ◽  
Vol 46 (3) ◽  
pp. 799-806 ◽  
Author(s):  
Borislav Banjac ◽  
Velimir Mladenov ◽  
Miodrag Dimitrijevic ◽  
Sofija Petrovic ◽  
Jan Bocanski
Keyword(s):  
Interaction Model ◽  
Triticum Aestivum L ◽  
Genotype By Environment Interaction ◽  
Control Treatment ◽  
Environment Interaction ◽  
Phenotypic Stability ◽  
Genotype By Environment ◽  
Growing Seasons ◽  
Sources Of Variation ◽  
Main Effects

The objective of this study was to present the results of experiment conducted on 11 cultivars of wheat (Triticum aestivum L.) and 1 cultivar of triticale (Triticosecale W) on stressful conditions of halomorphic solonetz in Kumane, Banat, Serbia. Across three growing seasons genotypic variability, monitoring of phenotypic variation and genotype by environment interaction (GEI) for number of grains per spike and yield was studied. The cultivar were grown in field trails of control treatment and treatments with measures repairs solonetz using phosphogypsum in the amount of 25 t?ha-1 and 50 t?ha-1. GEI was tested using AMMI (Additive Main Effects and Multiplicative Interaction) model. The expression of tested traits were statistically significant and showed additive and non-additive sources of variation. The first source of variation, quantified IPCA1 axis explained most of the structure of GEI.

scholarly journals Evaluation of promising maize hybrids in different Regions of Bangladesh

2018 ◽  
Vol 43 (2) ◽  
pp. 301-307
Author(s):  
ANMS Karim ◽  
S Ahmed ◽  
AH Akhi ◽  
MZA Talukder ◽  
TA Mujahidi
Keyword(s):  
Significant Variation ◽  
Interaction Model ◽  
Environment Interaction ◽  
Maize Hybrids ◽  
Genotype Environment Interaction ◽  
Single Cross ◽  
The Mean ◽  
Different Response ◽  
Main Effects ◽  
Selection Of

Nine single cross promising maize hybrids and three check varieties (NK40, BHM9 and 900 MGold) were assessed for genotype environment interaction (GEI) and stability for the selection of promising one(s) in seven different locations of Bangladesh. The AMMI (additive main effects and multiplicative interaction) model was used to analyze the genotype-environment interaction over seven locations to select desired hybrid having higher yield and other potential attributes. Regarding genotypes (G), significant variation was found in all the characters except yield while environment (E) was found significant for all the characters. The environment of Gazipur was poor; Jamalpur, Hathazari, Ishurdi, Rahmatpur, Burirhat, and Jessore were positive environments for tested maize hybrids. Considering the mean, bi and S2di for all the parameters, it was evident that all the genotypes showed different response of adaptability under different environmental conditions. Among the hybrids E12 (900MG), E7(CML502 × CML491), E1(BIL95 × BIL28), E10 (NK40) and E2 (BIL95 × BIL31) were exhibited high yielder.Considering bi ̴1(regression co-efficient), S2di ̴ 0 (deviation from regression)and mean versace IPCA1 (fig1)indicated that hybrids E7and E1showed the higher yield as well as stable across locations.Bangladesh J. Agril. Res. 43(2): 301-307, June 2018

scholarly journals Genotype by environment interaction for physiological traits in sugar beet (Beta vulgaris L.) parents and hybrids using additive main effects and multiplicative interaction model

2021 ◽  
Author(s):  
Zahra Abbasi ◽  
Jan Bocianowski
Keyword(s):  
Sugar Beet ◽  
Beta Vulgaris ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Physiological Traits ◽  
Environment Interaction ◽  
Extraction Coefficient ◽  
Multiplicative Interaction ◽  
Genotype By Environment ◽  
Main Effects

AbstractThe objective of this study was to assess genotype by environment interaction for 21 physiological traits in sugar beet (Beta vulgaris L.) parents and hybrids grown in Rodasht Agricultural Research Station in Iran by the additive main effects and multiplicative interaction model. The study comprised of 51 sugar beet genotypes [10 multigerm pollen parents, four monogerm seed parents and 36 F1 hybrids], evaluated at four environments in a randomized complete block design, with three replicates. The additive main effects and multiplicative interaction analyses revealed significant environment main effects with respect to all observed traits, except extraction coefficient of sugar. The additive main effects and multiplicative interaction stability values ranged from 0.009 (G17 for leaf Ca2+) to 9.698 (G09 for extraction coefficient of sugar). The parental forms 2 7233-P.29 (G38) and C CMS (G49) as well as hybrids 2(6)*C (G27) and 5*C (G33) are recommended for further inclusion in the breeding programs because of their stability and good average values of observed traits.

scholarly journals Adaptive Abilities of Broad Bean (Vicia faba L.) Accession in Terms of Main Quantitative Traits

2019 ◽  
Vol 65 (4) ◽  
pp. 136-146
Author(s):  
Natalia Georgieva ◽  
Valentin Kosev
Keyword(s):  
Plant Height ◽  
Seed Weight ◽  
Quantitative Traits ◽  
Broad Bean ◽  
Environment Interaction ◽  
High Plasticity ◽  
Forage Crops ◽  
Genotype Environment Interaction ◽  
Adaptive Ability ◽  
Adaptive Abilities

Abstract The experimental activity was conducted at the Institute of Forage Crops (Pleven) during the period 2016 – 2018. The adaptive ability of 10 broad bean accessions was determined with respect to main quantitative traits based on parametric and nonparametric analysis. The environment influences to the highest degree the traits of 1st pod height, pods number and seed weight per plant. The plant height and seeds number were strongly influenced by the genotype, and the mass of 100 seeds was determined by the genotype × environment interaction. The broad bean accessions can be distributed as follows: Fb 1929 has a high value of the 1st pod height (34 cm) and is characterized by high plasticity and stability; BGE 029055 and Fb 1896 are stable and form a large number of pods per plant (11 – 15); Fb 1896 and Fb 2486 are distinguished with good adaptability and stability, increased seed weight (28.01 and 30.28 g, respectively) and 100 seeds mass (105.48 g and 91.31 g). Accessions BGE 032012 and Fb 2481 represent a selection value in terms of plant height (61.36 and 65.83 cm); Fb 1929 – in 1st pod height (32.46 cm); and BGE 029055, Fb 1896 and Fb 2486 – in pods number (10.59, 9.67 and 11.89). Fb 1896, Fb 2486 and BGE 041470 can be used to develop a new genetic diversity in breeding aimed at increasing the mass of 100 seeds and seed productivity.

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).

GENOTYPE-ENVIRONMENT INTERACTION OF YIELD IN CEREAL CROPS IN NORTHWESTERN CANADA

1981 ◽  
Vol 61 (2) ◽  
pp. 255-263 ◽  
Author(s):  
R. M. De PAUW ◽  
D. G. FARIS ◽  
C. J. WILLIAMS
Keyword(s):  
Triticum Aestivum L ◽  
Regression Coefficients ◽  
Cereal Crops ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Hordeum Vulgare L ◽  
Genotype Environment Interaction ◽  
Early Maturing ◽  
Frost Free Period ◽  
Main Effects

Three cultivars of each crop, wheat (Triticum aestivum L.), oats (Avena sativa L.), and barley (Hordeum vulgare L.), were grown for 4 yr at five locations north of the 55th parallel in northwestern Canada. There were highly significant differences among all main effects and interactions. Galt barley produced the highest seed yield followed by Centennial barley, Random oats and Harmon oats. Victory oats, Olli barley, Neepawa wheat and Pitic 62 wheat yielded similarly to each other while Thatcher wheat was significantly lower yielding. Mean environment yields ranged from 2080 to 5610 kg/ha. The genotype-environment (GE) interaction of species and cultivars was sufficiently complicated that it could not be characterized by one or two statistics (e.g., stability variances or regression coefficients). However, variability in frost-free period among years and locations contributed to the GE interaction because, for example, some cultivars yielded well (e.g., Pitic 62) only in those year-location environments with a relatively long frost-free period while other early maturing cultivars (e.g., Olli) performed well even in a short frost-free period environment.

Sign in / Sign up

Export Citation Format

Share Document