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 Spring Barley Breeding Lines With Superior Yield Performance and Stability

2020 ◽  
Vol 68 (6) ◽  
pp. 947-958
Author(s):  
Volodymyr Hudzenko ◽  
Tetiana Polishchuk ◽  
Oleksandr Demydov ◽  
Mykola Sardak ◽  
Nataliia Buniak ◽  
...  
Keyword(s):  
Spring Barley ◽  
Genotype By Environment Interaction ◽  
Forest Steppe ◽  
Environment Interaction ◽  
Yield Performance ◽  
Practical Result ◽  
Breeding Lines ◽  
Genotype By Environment ◽  
Barley Breeding ◽  
Main Effects

The aim of the present study was to substantiate theoretically and to test in practice scheme of multi-environment trials at the final stage of spring barley breeding process and to distinguish the genotypes which combine superior yield performance and stability. In the first year of competitive testing (2015) nine promising spring barley breeding lines have been selected under condition of the Central part of Forest-Steppe of Ukraine (latitude 49°64′, longitude 31°08′, altitude 153 m). In 2016 and 2017, the genotypes were additionally tested in two other different agro-climatic zones of Ukraine: Polissia (latitude 50°93′, longitude 31°69′, altitude 126 m) and Northern Steppe (latitude 48°56′, longitude 32°32′, altitude 171 m). In addition to the standard variety Vzirets, the breeding lines were compared with ten widespread spring barley varieties in agricultural production. Significant total yield variability of the genotypes and cross-over genotype by environment interaction has been revealed. It confirmed the validity of proposed combination of spatial (zones) and temporal (years) gradients for more efficient evaluation of the genotype by environment interaction and differentiation of genotypes in terms of yield performance and stability. As a practical result, using additive main effects and multiplicative interaction (AMMI) and genotype main effects plus genotype by environment interaction (GGE) models, four spring barley breeding lines with combination of superior yield performance and high stability have been identified.

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

scholarly journals GGE biplot analysis of genotype by environment interaction of spring barley varieties

2015 ◽  
Vol 102 (4) ◽  
pp. 431-436 ◽  
Author(s):  
Pavel Solonechnyi ◽  
Nataliya Vasko ◽  
Aleksey Naumov ◽  
Olga Solonechnaya ◽  
Olga Vazhenina ◽  
...  
Keyword(s):  
Spring Barley ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Biplot Analysis

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.

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