scholarly journals Analysis of Genotype-Environment Interaction and Yield Stability of Introduced Upland Rice in the Groundnut Basin Agroclimatic Zone of Senegal

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ghislain Kanfany ◽  
Mathieu Anatole Tele Ayenan ◽  
Yedomon Ange Bovys Zoclanclounon ◽  
Talla Kane ◽  
Malick Ndiaye ◽  
...  
Keyword(s):  
Grain Yield ◽  
Upland Rice ◽  
Block Design ◽  
Principal Component ◽  
Stability Index ◽  
Rice Production ◽  
Yield Stability ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype Environment Interaction

Identification of highly performing varieties under Senegalese environment is crucial to sustain rice production. Genotype-environment interaction and stability performance on the grain yield of ten upland rice genotypes were investigated across 11 environments in Senegal during the rainy seasons of 2016 and 2017 to identify adapted varieties. The experiment was conducted using a randomized complete block design with three replications at each environment. Data on grain yield were recorded and analyzed using the additive main effects and multiplicative interaction (AMMI) model. The combined analysis of variance revealed that the grain yield was significantly affected by environment (67.9%), followed by genotype × environment (G × E) interaction (23.6%) and genotype (8.5%). The first two principal component axes were highly significant with 37.5 and 26% of the total observed G × E interaction variation, respectively. GGE biplot grouped the environments into four potential megaenvironments. Based on the yield stability index parameter and ranking GGE biplot, NERICA 8 and ART3-7-L9P8-1-B-B-1 were stable and high-yielding varieties compared to the local check NERICA 6. These varieties should be proposed for cultivation in order to sustain the rice production in the southern part of the groundnut basin of Senegal and used as parental lines in rice breeding program for grain yield improvement.

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.

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 by environment interaction and stability analysis for rice genotypes under Boro condition

Genetika ◽  
2014 ◽  
Vol 46 (2) ◽  
pp. 521-528 ◽  
Author(s):  
Lotan Bose ◽  
Nitiprasad Jambhulkar ◽  
Kanailal Pande
Keyword(s):  
Interaction Analysis ◽  
Block Design ◽  
Principal Component ◽  
Stability Index ◽  
Yield Stability ◽  
Environment Interaction ◽  
Multiplicative Interaction ◽  
Genotype Effect ◽  
Rice Genotypes ◽  
Stability Statistics

Genotype (G)?Environment (E) interaction of nine rice genotypes possessing cold tolerance at seedling stage tested over four environments was analyzed to identify stable high yielding genotypes suitable for boro environments. The genotypes were grown in a randomized complete block design with three replications. The genotype ? environment (G?E) interaction was studied using different stability statistics viz. Additive Main effects and Multiplicative Interaction (AMMI), AMMI stability value (ASV), rank-sum (RS) and yield stability index (YSI). Combined analysis of variance shows that genotype, environment and G?E interaction are highly significant. This indicates possibility of selection of stable genotypes across the environments. The results of AMMI (additive main effect and multiplicative interaction) analysis indicated that the first two principal components (PC1-PC2) were highly significant (P<0.05). The partitioning of TSS (total sum of squares) exhibited that the genotype effect was a predominant source of variation followed by G?E interaction and environment. The genotype effect was nine times higher than that of the G?E interaction, suggesting the possible existence of different environment groups. The first two interaction principal component axes (IPCA) cumulatively explained 92 % of the total interaction effects. The study revealed that genotypes GEN6 and GEN4 were found to be stable based on all stability statistics. Grain yield (GY) is positively and significantly correlated with rank-sum (RS) and yield stability index (YSI). The above mentioned stability statistics could be useful for identification of stable high yielding genotypes and facilitates visual comparisons of high yielding genotype across the multi-environments.

scholarly journals Analysis of genotype-environment interaction and yield stability of Thai upland rice (Oryza sativa L.) genotypes using AMMI model

2020 ◽  
pp. 362-370
Author(s):  
Shams Shaila Islam ◽  
Jakarat Anothai ◽  
Charassri Nualsri ◽  
Watcharin Soonsuwon
Keyword(s):  
Principal Component Analysis ◽  
Grain Yield ◽  
Upland Rice ◽  
Oryza Sativa L ◽  
Principal Component ◽  
Vital Role ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Ammi Model ◽  
Rice Genotypes

Genotype-environment interaction and stability analysis has been important for plant breeders and plays a vital role in identifying genotypes that are stable or unstable in a given environment. The experiments in this research were conducted to determine the effects of genotype, environment and genotype-environment interaction on grain yield using the AMMI statistical model, and to recognize the most stable rice genotypes among ten genotypes in southern Thailand’s provinces of environments in Songkhla, Satun and Phatthalung. Highly significant differences were shown from the combined analysis for environments with grain yields, revealing that environments were different and indicated change ability between the genotypes and their interactions. The average grain yield assessment of the tested genotypes was around the environments where genotype G8 (Nahng Kian) had the highest grain yield 6234.11 kg/ha. AMMI biplot of the Interaction Principal Component Analysis (IPCA) scores visualized 90.7% for IPCA1 and 9.3% for IPCA2 with the genotypes and environments for grain yield. In the AMMI stability value method, G8 (Nahng Kian) was the most stable genotype followed by the genotypes G2 (Mai Tahk) and G10 (Hawm Jet Ban) Songkhla, Satun and Phatthalung environments.

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.

scholarly journals Evaluasi Stabilitas Hasil Jagung Hibrida Menggunakan Metode Genotype and Genotype by Environment Interaction Biplot (GGE BIPLOT)

2017 ◽  
Vol 1 (2) ◽  
pp. 97
Author(s):  
Slamet Bambang Priyanto ◽  
Roy Efendi ◽  
Bunyamin Z. ◽  
M. Azrai ◽  
M. Syakir
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Maize Hybrids ◽  
Genotype By Environment ◽  
North Sulawesi ◽  
South Sulawesi

<p class="Abstrak">Visualization of GGE biplot analyses was able to explain the genotype by environment interaction. This research was aimed to determine the yield stability of promising experimental maize hybrids in eight locations based GGE biplot method. Ten promising experimental maize hybrids and two commercial hybrid varieties as check, namely: HBSTK01, HBSTK03, HBSTK05, HBSTK06, HBSTK07, HBSTK08, HBSTK09, HBSTK10, HBSTK11, HBSTK13 and Bima 16 and Pertiwi 3 were evaluated in eight locations, ie. Bangka (Bangka Belitung), Probolinggo (East Java), Minahasa Utara (North Sulawesi), Donggala (Central Sulawesi), Soppeng, South Sulawesi, Gowa (South Sulawesi, Konawe (Southeast Sulawesi)and Lombok Barat (West Nusa Tenggara) from May to October 2013. The treatments were arranged in a randomized complete block design (RCBD) with 3 replications. Variable measured was grain yield. Analysis of variance was performed for data from each study site, to determine the performance of each genotype at each location. Yield stability analysis was performed by GGE biplot method using PB tools software. Results showed that genotype H9 (HBSTK11) had the highest biological stability with grain yield of 10.37 t/ha, higer than the overall mean yield. The best hybrid with the highest yield and good stability was hybrid H6 (HBSTK08) of 11.08 t/ha. This experimental hybrid is considered potential to be released as new hybrid variety. North Minahasa is considered the most suitable location for testing, whereas Konawe and West Lombok are least suitable, compared with the other locations.</p>

scholarly journals Grain Yield Stability of Rice Genotypes

2020 ◽  
Vol 3 (2) ◽  
pp. 116-126
Author(s):  
Jiban Shrestha ◽  
Ujjawal Kumar Singh Kushwaha ◽  
Bidhya Maharjan ◽  
Manoj Kandel ◽  
Suk Bahadur Gurung ◽  
...  
Keyword(s):  
Grain Yield ◽  
Climatic Factors ◽  
Block Design ◽  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
Soil Conditions ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Rice Genotypes ◽  
B 31

Stability analysis identifies the adaptation of a crop genotype in different environments. The objective of this study was to evaluate promising rice genotypes for yield stability at different mid-hill environments of Nepal. The multilocation trials were conducted in 2017 and 2018 at three locations viz Lumle, Kaski; Pakhribas, Dhankuta; and Kabre, Dolakha. Seven rice genotypes namely NR11115-B-B-31-3, NR11139-B-B-B-13-3, NR10676-B-5-3, NR11011-B-B-B-B-29, NR11105-B-B-27, 08FAN10, and Khumal-4 were evaluated in each location. The experiment was laid out in a randomized complete block design with three replications. The rice genotype NR10676-B-5-3 produced the highest grain yield (6.72 t/ha) among all genotypes. The growing environmental factors (climate and soil conditions) affect the grain yield performance of rice genotypes. The variation in climatic factors greatly contributed to the variation in grain yield. Polygon view of genotypic main effect plus genotype-by-environment interaction (GGE) biplot showed that the genotypes NR10676-B-53 and NR11105-B-B-27 were suitable for Lumle; NR11115-B-B-31-3 and NR11139-B-B-B-13-3 for Pakhribas; and 08FAN10 and NR11011-B-B-B-B-29 for Kabre. The GGE biplot showed that genotype NR10676-B-5-3 was stable hence it was near to the point of ideal genotype. This study suggests that NR10676-B-5-3 can be grown for higher grain yield production in mid-hills of Nepal.

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 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 Genotype × environment interaction and grain yield stability in Chinese hybrid rice

2020 ◽  
Vol 11 (1) ◽  
pp. 47
Author(s):  
Jiban Shrestha ◽  
Ujjawal Kumar Singh Kushwaha ◽  
Bidhya Maharjan ◽  
Sushil Raj Subedi ◽  
Manoj Kandel ◽  
...  
Keyword(s):  
Grain Yield ◽  
Hybrid Rice ◽  
Yield Stability ◽  
Environment Interaction ◽  
Genotype Environment Interaction
Sign in / Sign up

Export Citation Format

Share Document