scholarly journals Integrating Multivariate and Univariate Statistical Models to Investigate Genotype–Environment Interaction of Advanced Fragrant Rice Genotypes under Rainfed Condition

2021 ◽  
Vol 13 (8) ◽  
pp. 4555
Author(s):  
Norainy Hashim ◽  
Mohd Y. Rafii ◽  
Yusuff Oladosu ◽  
Mohd Razi Ismail ◽  
Asfaliza Ramli ◽  
...  
Keyword(s):  
High Stability ◽  
Block Design ◽  
Univariate Analysis ◽  
Primary Objective ◽  
Environment Interaction ◽  
Fragrant Rice ◽  
Commercial Cultivation ◽  
Genotype Environment Interaction ◽  
Premium Price ◽  
Second Category

Specialty fragrant rice is sold at a premium price in both local and international trade because of its superior grain qualities. In this research, 40 advanced fragrant rice accessions were evaluated in different environments. The primary objective was to identify genotypes with high grain yield and high stability using multivariate (GGE biplot) and univariate analysis (regression slope, deviation from regression, Shukla’s stability variance, Wricke’s ecovalence, and Kang’s stability statistic). The field experiment trials were laid in a randomized complete block design in three replications. The analysis of variance showed highly significant differences among genotypes, locations, seasons, and the interactions between genotype, locations, and seasons. The environment significantly explained about 43.32% (37.01 and 6.31% for locations and seasons) of the total sum of squares. Based on average ranking generated from multivariate and univariate stability measured, rice accessions were classified into three major categories, viz., genotypes having high trait performance, and high stability as category 1. The second category consists of genotypes that exhibit high mean performance but low stability, while the third category includes genotypes with high stability but low trait performance. Our results showed that breeding for yield performance was possible, and the identified genotypes could be recommended for commercial cultivation.

scholarly journals Elucidation of Genotype × Environment Interaction for Identification of Stable Genotypes to Grain Yield of Rice (Oryza sativa L.) Varieties in Bangladesh Rainfed Condition

2021 ◽  
Vol 24 (1) ◽  
pp. 59-71
Author(s):  
SK Debsharma ◽  
PR Roy ◽  
RA Begum ◽  
KM Iftekharuddaula
Keyword(s):  
Grain Yield ◽  
Oryza Sativa L ◽  
Block Design ◽  
Environment Interaction ◽  
Rainfed Condition ◽  
Ecological Zones ◽  
Commercial Cultivation ◽  
Genotype Environment Interaction ◽  
Randomized Complete Block Design ◽  
Sources Of Variation

The research was evaluated in seven ecological zones throughout the country following randomized complete block design with three replications in each location. The combined analysis of variance for grain yield indicated that the differences among all sources of variation were highly significant (P<0.01). Environment (E), Genotype (G) and G × E interaction effects explained by 25.54%, 17.02% and 48.97% for inbred and 35.26%, 17.03%, 31.23% for hybrid of the total sum of square, respectively. Locations E2 (Chattogram); E3 (Dhaka) for inbred and E2 (Chattogram); E4 (Khulna); E5 (Rajshahi); E7 (Sylhet) for hybrid varieties were found most discriminating and more informative among all. Ideal environment was found in E6 (Rangpur) for inbred and E4 (Khulna) for hybrid varieties. According to GGE mean and stability biplot, the most stable genotypes were G18 (BRRI dhan34); G26 (BRRI dhan52); G28 (BRRI dhan57); G19 (BRRI dhan37); G13 (BR25); G27 (BRRI dhan56); G21 (BRRI dhan39) for inbred and G2 (BADC hybrid dhan-2); G4 (BADC hybrid dhan-6); G5 (Bayer hybrid dhan-4) for hybrid compared to the other genotypes. The ideal genotypes were found G18 (BRRI dhan34) for inbred and G26 (BRRI hybrid dhan6) for hybrid varieties. In summary, we identified stable genotypes adapted across the environments for grain yield. These genotypes can be used as commercial cultivation with sustainable production as well as a parent/pre-breeding material in future rice breeding program. Bangladesh Rice J. 24 (1): 59-71, 2020

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 Models for multi-environment yield trials with fixed and random block effects and homogeneous and heterogeneous residual variances.

1969 ◽  
Vol 91 (3-4) ◽  
pp. 117-131
Author(s):  
Fernando Casanoves ◽  
Raúl Macchiavelli ◽  
Mónica Balzarini
Keyword(s):  
Analysis Of Variance ◽  
Block Design ◽  
Breeding Program ◽  
Residual Variance ◽  
Environment Interaction ◽  
Breeding Programs ◽  
Heterogeneous Variances ◽  
Genotype Environment Interaction ◽  
Random Block ◽  
Residual Variances

Multi-Environment Trials (METs) are used to make recommendations about genotypes at many stages of plant breeding programs. Because of the genotype-environment interaction, METs are usually conducted in various environments (locations and/or years), using designs which involve several repetitions (plots) for each genotype at each environment. The stratification or blocking of plots within each environment enables one to consider part of the variability due to differences between plots. The objective of this study was to see how frequently the problem of heterogeneous variances across environments appears in Peanut Breeding Program METs, and to evaluate the effects of diverse spatial modeling strategies on the comparison of genotype means in each environment. A series of 18 METs in a peanut breeding program with randomized complete block design in each environment were simultaneously adjusted by using 1) classic analysis of variance models (fixed and random block effects); 2) mixed models adjusted with homogenous and heterogeneous residual variances to take into account that experiments conducted in different environments may vary in precision (residual variances). The results suggest that the analysis of variance models with a block design and heteroscedastic errors between locations are more appropriate than their homogeneous residual variance versions.

scholarly journals AMMI and GGE Biplot Analysis for Stability of Yield in Mid-early Pigeonpea [Cajanus cajan (L.) Millspaugh] Genotypes

2022 ◽  
Author(s):  
P. Jagan Mohan Rao ◽  
N. Sandhyakishore ◽  
S. Sandeep ◽  
G. Neelima ◽  
A. Saritha ◽  
...  
Keyword(s):  
Block Design ◽  
High Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Single Location ◽  
Randomized Complete Block Design ◽  
Advanced Stages ◽  
Superior Genotypes

Background: The genotype × environment interaction greatly influences the success of breeding and in multi-location trials complicates the identification of superior genotypes for a single location, due to magnitude of genotype by location interaction are often greater than genotype by year interaction. This necessitates genotype evaluation in multi environments trials in the advanced stages of selection. Methods: Nine elite pigeonpea genotypes of mid-early duration were evaluated in six diverse locations in randomized complete block design with three replications during kharif, 2019 to ascertain the stable genotypes, environments discrimination and genotype by environment crossovers using AMMI and GGE biplot stability models. Result: The results in the present investigation revealed that first two principal components explained 73.4% of variation interaction, while, 80.50% in GGE biplot. Both the models identified WRGE-126 (G6) as stable performer with high yield (1733 kg ha-1) and among the locations Tandur (E1) measured as the ideal environment. Whereas, the environments, Adilabad (E3) and Warangal (E4) were observed representative with better discriminating ability.

scholarly journals Phenotypic stability of hybrids of Gália melon in Rio Grande do Norte state, Brazil

2011 ◽  
Vol 83 (4) ◽  
pp. 1421-1434 ◽  
Author(s):  
Glauber H.S. Nunes ◽  
Haroldo Santos Júnior ◽  
Leilson C. Grangeiro ◽  
Francisco Bezerra Neto ◽  
Carlos T.S. Dias ◽  
...  
Keyword(s):  
Block Design ◽  
Rio Grande ◽  
Soluble Solids ◽  
Environment Interaction ◽  
Industrial Complex ◽  
Phenotypic Stability ◽  
High Productivity ◽  
Genotype Environment Interaction ◽  
Randomized Complete Block Design ◽  
Main Effect

The objectives of this study were to determine the importance of simple and complex components of the interaction genotype × environment and to evaluate the adaptability and stability of Gália melon hybrids. Nine hybrids were tested in twelve environments of Rio Grande Norte State from 2000 to 2001. The experiments were carried out in a randomized complete block design with three replications. The statistical methods of Toler and Burrows, Wricke and AMMI (Additive Main effect and Multiplicative Interaction) were used to study the adaptability and stability. The complex component is responsible for most of the genotype × environment interaction for the yield and content of solids soluble of fruits. The environments associated with Mossoró and Assu municipalities are the most suitable to evaluate melon hybrids in the state. The hybrid DRG 1537 was the most likely to be grown in the Agro-industrial Complex Mossoró-Assu due to its stability, high productivity and high content of soluble solids.

scholarly journals Genotype × Environment Interaction of Mosaic Disease, Root Yields and Total Carotene Concentration of Yellow-Fleshed Cassava in Nigeria

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Norbert G. Maroya ◽  
Peter Kulakow ◽  
Alfred G. O. Dixon ◽  
Busie B. Maziya-Dixon
Keyword(s):  
Block Design ◽  
Matter Content ◽  
Mosaic Disease ◽  
Sum Of Squares ◽  
Cassava Mosaic Disease ◽  
Environment Interaction ◽  
Dry Matter Content ◽  
Root Yield ◽  
Genotype Environment Interaction ◽  
Agroecological Zones

Twenty-one yellow-fleshed cassava genotypes were evaluated over two years in five major cassava growing agroecological zones in Nigeria. The trials were established in a randomized complete block design with four replications to assess genotype performance and Genotype × Environment interaction for cassava mosaic disease (CMD), fresh and dry root yield (FYLD; DYLD), root dry matter content (DMC), and total carotene concentration (TCC). Combined analysis of variance showed significant differences (P<0.001) among genotypes (G), environment (E), and Genotype × Environment interaction (GE) for all the traits tested. For reaction to CMD, the best genotypes showing stable resistance were TMS 07/0539 and TMS 07/0628. For root yield, the best genotypes were TMS 01/1368 and TMS 07/0553. Genotype TMS 07/0593 was the best for DMC and TCC across the 10 environments. Variation among genotypes accounted for most of the Total Sum of Squares for CMD (72.1%) and TCC (34.4%). Environmental variation accounted for most of the Total Sum of Squares for FYLD (42.8%), DYLD (39.6%), and DMC (29.2%). This study revealed that TMS 07/0593 has the highest and most stable TCC, DMC with the lowest CMD severity score and appeared to be the best genotype.

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 Stability and adaptability of elite upland rice lines using Bayesian-AMMI model

2021 ◽  
pp. 244-250
Author(s):  
Joel Jorge Nuvunga ◽  
Alessandra Querino da Silva ◽  
Cristian Tiago Erazo Mendes ◽  
Gabriel Cossa ◽  
Luciano Antonio de Oliveira ◽  
...  
Keyword(s):  
Upland Rice ◽  
Block Design ◽  
Environment Interaction ◽  
Improvement Program ◽  
Genotype Environment Interaction ◽  
Randomized Block Design ◽  
Main Variable ◽  
Wide Adaptation ◽  
Ammi Model ◽  
Extra Assumption

Rice is one of the world’s most important crops. The search for genotypes that are more productive and have wide adaptation to different environments is paramount. One of the major breeder’s obstacles faced is identification of superior strains is the presence of Genotype × Environment Interaction (GEI), which motivated the development of countless statistical procedures aiming to offer more efficient studies. In this work we analysed adaptability and stability of 13 upland rice lineages as part of a genetic improvement program in nine different environments, resulting from local combination and years of agriculture. The experiment was conducted in a completely randomized block design, with three replicates. The main variable is the grain storage in kg/ha. The model applied is the Bayesian Main Additive Effects and Multiplicative Interaction (Bayesian-AMMI). Our implementation implies an extra assumption of random effects from genotypes coming from a single population as opposed to previous works in the literature. Credibility regions with maximum posteriori density allowed identification of cultivars with higher average yield. Stable genotypes showed an initial evidence of adaptation to an environment in this rice breeding program. Bayesian-AMMI is flexible, and starts to be more widely used, but our suggestion is promising in making it a more powerful tool

scholarly journals AGRONOMIC PERFORMANCE OF IMPROVED MAIZE (Zea mays) CULTIVARS IN SOUTHERN CHAD

2020 ◽  
Vol 8 (6) ◽  
pp. 789-798
Author(s):  
Gapili Naoura ◽  
◽  
Nadjiam Djirabaye ◽  
Hortense Noëlle Apala Mafouasson ◽  
Mamahat Alhabib Hassane ◽  
...  
Keyword(s):  
High Performance ◽  
Block Design ◽  
Environment Interaction ◽  
Days To Flowering ◽  
High Performing ◽  
Maize Cultivars ◽  
Genotype Environment Interaction ◽  
Randomized Complete Block Design ◽  
Ear Height ◽  
New Cultivars

This study was carried out to identify high-performing maize cultivars adapted to the environmental conditions of Southern Chad. Twelve new cultivars associated with two local cultivars often used by farmers in Southern Chad were evaluated in three different environments in a randomized complete block design (RCBD) with three replications. The analysis of variance showed significant differences (p<0.001) between cultivars for the measured traits except for plant height (p=0.157) and ear height (p=0.624). Genotype × environment interaction was also reported significant among cultivars only for days to flowering and number of kernels per row, but yield did not show any statistical difference, although assessed cultivars differed in their relative main performance across the three sites. Potential grain yield showed a negative correlation with days to flowering, ear height, and days to silking. The study revealed different yield performances for the assessed cultivars across different environments. The cultivar 2013 TZEE WPOP DT STR was the highest yield producing cultivars at the combined sites. The cultivars IWD C2 SYN, TZEE WPOP DT C5 STR C5, 2011 TZE YDT STR SYN, and DTE STR YSYN POP C4 showed high-performance cultivars across the different environments and can be therefore recommended for release as new cultivars to farmers of Southern Chad.

scholarly journals DESEMPENHO DE HÍBRIDOS DE MILHO TIPO AMILÁCEO EM RELAÇÃO A CARACTERÍSTICAS DO RENDIMENTO DE GRÃOS

2019 ◽  
Vol 18 (1) ◽  
pp. 74-87
Author(s):  
AMALIO RAMÓN MENDOZA GONZÁLEZ ◽  
COSME DAMIÃO CRUZ ◽  
VERONICA MACHADO
Keyword(s):  
Grain Yield ◽  
Significant Interaction ◽  
Block Design ◽  
Eastern Region ◽  
Environment Interaction ◽  
Maize Starch ◽  
Complex Type ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Randomized Block Design

RESUMO - O objetivo deste trabalho foi avaliar a existência de interação entre genótipos e ambientes e estimar osparâmetros de adaptabilidade e estabilidade de genótipos de milho amiláceo. Para isto, 44 genótipos de milho amiláceode três ambientes das localidades da Região Oriental do Paraguai foram utilizados para estimar os parâmetros deadaptabilidade e estabilidade para quatro características. As características usadas foram floração masculina, altura deplanta até o ápice da panícula, aspecto da planta e rendimento. O delineamento experimental utilizado foi o de blocosao acaso, com três repetições e parcelas contendo 25 plantas. O teste F demonstrou diferenças significativas para todasas características avaliadas na localidade de Choré com nível de 1%. Para a localidade de Capitán Miranda, no entanto,produtividade de grãos (PG) e aspecto da planta (AP) não foram significativos. Na localidade Yjhovy, o (PG) foisignificativo aos 5%. A análise de variância conjunta mostrou uma interação significativa para genótipos por ambientesao nível de 5% de significância pelo teste F, para todas as características, com exceção de floração masculina. A maioriadas características apresentou interação do tipo complexa. Os materiais 41 e 24 foram os genótipos que contiveram osmenores valores de índice de superioridade do cultivar (Pi) geral.Palavras chave: Adaptabilidade e estabilidade, interação genótipo ambiente, Avati Morotî, raças.PERFORMANCE OF AMYLACE TYPE CORN HYBRIDSIN RELATION TO GRAIN YIELD CHARACTERISTICSABSTRACT - In this study, the aim was to evaluate the interactions between genotypes and environments, and estimatethe parameters of adaptability and stability of genotypes of maize starch. Forty four genotypes of maize starch from threeenvironments of the locations of the Eastern Region of Paraguay were used to estimate the parameters of adaptabilityand stability for four features. The features used were the male flowering, height to the panicle apex, plant aspect andyield. The experimental design was a randomized block design with three replications, each containing 25 plants. The Ftest showed significant differences for all traits in the locality of Choré with level of 1%, while the location of CaptainMiranda grain yield (GY) and plant aspect (AP) were not significant. In the locality Yjhovy, the (PG) was significantat 5 %. The analysis of variance showed a significant interaction for genotype by environment at the 5 % significancelevel for the F test, for all traits except male flowering. Most features of the showed interaction of complex type. Thematerials were 41e 24 genotypes that contained the lowest values of index of superiority do cultivar (Pi) general.Keywords: Adaptability and stability, Avati Morotî, breeds, genotype environment interaction.

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