scholarly journals Statistical and graphical (GGE biplot) evaluation of the adaptive ability and stability of winter barley breeding lines

2019 ◽  
Vol 23 (1) ◽  
pp. 110-118 ◽  
Author(s):  
V. N. Gudzenko
Keyword(s):  
Optimal Combination ◽  
Winter Barley ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Statistical Parameters ◽  
Breeding Lines ◽  
Barley Breeding ◽  
Yield Variation ◽  
Genotype Environment Interaction ◽  
Sowing Dates

Due to current global climate changes, the issue of improving adaptive capacity of crops is of high importance. It is important to create winter crop varieties with both ecological adaptability and yield stability in years with different hydrothermal conditions. In order to develop winter barley varieties with a combination of yield and stability, 14 promising breeding lines have been evaluated in the conditions of the V.M. Remeslo Myronovka Institute of Wheat of NAAS of Ukraine in 2012/2013–2014/2015 using four different sowing dates. The ANOVA revealed a reliable part in yield variation: 64.59 % for environment, 16.84 % for genotype–environment interaction, and 15.57 % for genotype. The sowing dates significantly increased the yield variation of the breeding lines. The differences between the average yields of the lines depending on sowing date within the year were 1.05 t/ha in 2012/2013, 0.90 t/ha in 2013/2014, and 1.25 t/ha in 2014/2015. For genotype–environment interaction interpretation and ranking lines by yield a number of the most known statistical parameters of adaptability, stability, and plasticity and GGE biplot were applied. The use of different sowing dates at the final stage of the winter barley breeding process is a simple but effective approach that allows a more detailed assessment of the adaptive potential of breeding lines in various growing conditions. As compared to statistical parameters, GGE biplot has some advantages for interpretation of genotype–environment interaction. This graphic model allows ranking environments to be visualized for their discriminating ability and representativeness, as well as both specifically adapted genotypes and the ones with the optimal combination of yield potential and stability to be identified in a set of environments (mega-environment). The breeding line Pallidum 4816 with the optimal combination of yield and stability, as well as the high-yielding breeding lines Pallidum 4857 and Pallidum 4659 were identified and submitted to the State Variety Testing of Ukraine as the new winter barley varieties MIP Yason, MIP Oskar and MIP Hladiator.

scholarly journals Genotype environment interaction and stability for yield and its components in advanced breeding lines of red rice (Oryza sativa L.)

2020 ◽  
Vol 49 (3) ◽  
pp. 425-435
Author(s):  
BM Dushyantha Kumar ◽  
AP Purushottam ◽  
P Raghavendra ◽  
T Vittal ◽  
KN Shubha ◽  
...  
Keyword(s):  
Grain Yield ◽  
Oryza Sativa L ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Red Rice ◽  
Yield Performance ◽  
Breeding Lines ◽  
Genotype Environment Interaction ◽  
Ammi Analysis ◽  
Total Variability

Effects of genotype, environment and their interaction for grain yield and yield attributing characters in 20 advanced breeding lines of rice across six environments was investigated. Yield stability and adaptability of yield performance were analyzed by Eberhart and Russel model and (GGE) bi-plot. The AMMI analysis of variance indicated that mean squares due to genotypes, location and genotype location contributed per cent 59.08, 5.79 and 21.63, respectively for total variability in grain yield per hectare. Estimates of GGE bi-plot revealed that the lines G1, G3, G11, G13, G15, G12, G16, G7 and G10 were positioned near GGL bi-plot origin indicating wider adaptation for the trait grain yield per hectare. Eberhart and Russel Model and GGE biplot model showed the advanced breeding lines viz., JB 1-11-7 (G1) and JA 6-2 (G15) exhibited wider adaptability across the tested environments for number of productive tillers per plant and yield per hectare.

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 Comprehensive evaluation of spring barley breeding lines in yield, stability and tolerance to biotic and abiotic factors under condition of the central part of the Ukrainian Forest-Steppe

2021 ◽  
Vol 17 (1) ◽  
pp. 30-42
Author(s):  
V. M. Hudzenko ◽  
T. P. Polischuk ◽  
O. O. Babii ◽  
A. A. Lysenko ◽  
T. V. Yurchenko
Keyword(s):  
Drought Tolerance ◽  
Spring Barley ◽  
Yield Stability ◽  
Forest Steppe ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Adaptive Traits ◽  
Yield Performance ◽  
Breeding Lines ◽  
Barley Breeding

Purpose. Identification of spring barley promising breeding lines with combination of adaptive traits under conditions of the central part of the Ukrainian Forest-Steppe. Methods. Field trial, laboratory-field analysis of drought tolerance, statistical and graphical analysis of experimental data. Results. The analysis of variance of the AMMI model showed that the largest contribution to the general variation (85.78%) had environmental conditions (years of research). The value of the genotype was 8.21%, and the genotype by environment interaction was 6.01%. The first and second principal components of both AMMI and GGE biplot explained more than 85% of the genotype-environment interaction. Spring barley breeding lines ‘Deficiens 5162’, ‘Nutans 5073’ and ‘Deficiens 5161’ had the superior combination of yield performance and relative stability through the years according to GGE biplot. With GYT biplot analysis it has been determined that the breeding lines ‘Deficiens 5162’ and ‘Nutans 5073’ also significantly predominated over the other genotypes in terms of combination of yield performance and a number of other traits – 1000 kernels weight, drought tolerance, resistance to pathogens. Breeding lines ‘Deficiens 5161’, ‘Nutans 4966’, ‘Nutans 4705’, ‘Nutans 4816’, ‘Nutans 5184’, ‘Nutans 5193’, which exceeded the mean value in the trial in terms of combination of yield performance and a number of adaptive traits may have practical significance in the breeding process for creation of new initial material. Conclusions. As a result of the complex evaluation when using AMMI, GGE biplot and GYT biplot graphical models the breeding lines ‘Deficiens 5162’ and ‘Nutans 5073’ with the optimal combination of yield, stability, thousand kernel weight and tolerance to abiotic and biotic environmental factors have been identified

Breeding for improved blanchability in peanut: phenotyping, genotype × environment interaction and selection

2018 ◽  
Vol 69 (12) ◽  
pp. 1237 ◽  
Author(s):  
G. C. Wright ◽  
M. G. Borgognone ◽  
D. J. O Connor ◽  
R. C. N. Rachaputi ◽  
R. J. Henry ◽  
...  
Keyword(s):  
Low Cost ◽  
Peanut Butter ◽  
Environment Interaction ◽  
Breeding Lines ◽  
Seed Supply ◽  
Genotype Environment Interaction ◽  
Effective Selection ◽  
The Usa ◽  
Speed Up ◽  
Very High

Breeding for improved blanchability—the propensity of the testa (skin) to be removed from the kernel following rapid heat treatment—is a priority for improvement in the Australian Peanut Breeding Program (APBP). Easy removal of the testa by blanching is required for processing of peanuts into peanut butter and various other confectionary products. Thus, blanchability is an economically important trait in any newly released cultivar in Australia. A better understanding of the range of genetic variation, nature of inheritance and genotype×environment (G×E) interactions, and the development of a low-cost method to phenotype in early generations, could speed up breeding for this trait. Studies were conducted to develop a low-cost, rapid method utilising minimal amounts of seed to phenotype in early generations, along with an assessment of G×E interactions over a range of years and environments to derive optimal selection protocols. Use of a smaller kernel sample size than standard (50 vs 200g) was effective for accurately assessing blanchability in breeding lines and could allow selection in early generations (e.g. in seed produced from a single F2 plant where seed supply is adequate). G×E interaction for blanchability was shown to be very low. Genotypic variance explained 62–100% of the total variance for blanchability, assessed in two diverse germplasm pools including 107 accessions in the USA mini-core over three environments and multiple APBP breeding lines grown over nine different years–environments. Genotypic correlations between all environments were very high (~0.60–0.96), with heritability for the blanchability trait estimated to be very high (0.74–0.97) across the 13 trials. The results clearly demonstrate that effective selection for improved blanchability can be conducted in early generations and in a limited number of contrasting environments to ensure consistency of results.

scholarly journals Delineation of Genotype × Environment Interaction for Identification of Stable Genotypes to Grain Yield in Mungbean

2020 ◽  
Vol 2 ◽  
Author(s):  
Santhi Madhavan Samyuktha ◽  
Devarajan Malarvizhi ◽  
Adhimoolam Karthikeyan ◽  
Manickam Dhasarathan ◽  
Arumugam Thanga Hemavathy ◽  
...  
Keyword(s):  
Grain Yield ◽  
Tamil Nadu ◽  
Stability Index ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype Environment Interaction ◽  
South Indian ◽  
Indian State ◽  
Wide Adaptation ◽  
High Yields

In the present study, fifty-two mungbean (Vigna radiata) genotypes were evaluated for seven morphological traits at three different environments in South Indian state Tamil Nadu, namely Virinjipuram (E1), Eachangkottai (E2), and Bhavanisagar (E3) during Kharif 2017, 2018, and 2019, respectively. The data collected were subjected to variability and correlation analyses, followed by stability analysis using additive main effects and multiplicative interaction (AMMI) model, genotype and genotype × environment interaction effects (GGE) biplot. Variablility was observed among the genotypes for the following traits viz., plant height, days to fifty per cent flowering, number of pods per plant, pod length, number of seeds per pod, hundred seed weight and grain yield. Correlation analysis showed that the trait number of pods per plant was significantly associated with grain yield. The G × E was smaller than the genetic variation of grain yield as it portrayed the maximum contribution of genotypic effects (61.07%). GGE biplot showed E3 as a highly discriminating and representative environment. It also identified environment-specific genotypes viz., EC 396111 for E1, EC 396125 for E2 and EC 396101 for E3 environments. The genotypes with minimum genotype stability index (GSI) viz., V2802BG (7), HG 22 (13), and EC 396098 (13) were observed with wide adaptation and high yields across all the three environments. In summary, we identified stable genotypes adapted across environments for grain yield. These genotypes can be used as parent/pre-breeding materials in future mungbean breeding programs.

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).

Genetic variation in winter barley and selection of high yielding lines

2015 ◽  
Vol 49 (6) ◽  
Author(s):  
Adnan Al-Yassin ◽  
Murari Singh ◽  
Michael Baum
Keyword(s):  
Genetic Material ◽  
Winter Barley ◽  
High Yield ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Heritable Trait ◽  
Genetic Mechanisms ◽  
Yield Gain ◽  
Food Grain ◽  
Yield Trials

Barley (<italic>Hordeum vulgare</italic> L.) is an important crop with excellent nutritious feed and food grain. Winter barley, in particular, is predominantly grown in highland under rainfed systems due to its ability to tolerate cold. However, it has low productivity due to complex genetic mechanisms and limitations in determining an optimal environment for its selection and evaluation. This study evaluated the genetic variability, heritability and genetic gain for yield in barley, using preliminary un-replicated yield trials in 2011 at two locations and followed by replicated sets of yield trials in 2012, all in Syria. Significant genotypic variability was found at both stages of the evaluation/selection. During 2011, the best linear unbiased predictor means of test genotypes adjusted for spatial variability were found in the range of 1.75–3.75 t/ha at Tel Hadya and 0.03–1.58 t/ha at Breda. A set of 22 advanced yield trials comprising a total of 601 lines at Tel Hadya in 2012 yielded in the range of 1.85–3.13 t/ha. Based on the mean over these set of trials, the highest heritable trait was days to heading (broad-sense heritability on mean-basis= 0.64) followed by yield (the heritability = 0.30). The yield gain due to selection, at 20% intensity of selection, was 5.66% at Tel Hadya and 27.1% at Breda in 2011 using un-replicated genetic material, while it was 7.01 % for the replicated trials at Tel Hadya in 2012. We recommend use of the best lines selected in 2012 at Tel Hadya for further exploitation in genotype × environment interaction studies for high yield and specific and broad adaptation.

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