Multienvironment Evaluation of Tannin-Free Photoperiod-Insensitive Sorghum (Sorghum bicolor (L) Moench) for Yield and Resistance to Grain Mold in Senegal

Combining resistance to grain mold with high grain yield in tannin-free white-grained photoperiod-insensitive sorghum is of major interest for farmers in Senegal. In this study, GGE biplot analysis was used to assess the performance, adaptability, and stability of eleven sorghum parental lines and their hybrid combinations for yield and grain mold resistance under Senegalese environments. Eleven inbred lines along with their 22 hybrid combinations and one check were evaluated across three sites during the 2015 and 2016 rainy seasons under natural grain mold infestation. The results of this study showed strong genetic variability among studied genotypes for all measured traits. The highly significant G × E interaction effects for grain yield and panicle grain mold rating score (PGMR) indicated that both traits are influenced by genetics and to some extent by environment. Broad-sense heritability computed was high for all these traits except PGMR, showing a high environmental pressure on this later. The study showed that grain mold pressure in the studied sites decreased following a South-North gradient similar to the rainfall pattern, with the south region wetter, explaining the high disease pressure in Darou and Sinthiou Maleme contrary to Bambey. The GGE biplot analysis performed showed that the first two principal components explained 85.84% of the total variation of GGE sum of squares for grain yield. The which-won-where view of the GGE biplot for grain yield showed that the hybrid HB16 was the most adapted for Bambey area. The ranking of genotypes based on both yield performance and stability showed that HB16, HB5, HB21, HB18, and HB7 were the best hybrids combining high grain yield, high stability performance, and tolerance to grain mold disease across the test environments. These hybrids outperformed the best yielding inbred line P29 (2196.7 kg ha−1) with grain yield advantages ranging 17–60%. Therefore, these hybrids could be recommended to farmers in order to improve sorghum yield in Senegal.

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Identification of stable genotypes and genotype by environment interaction for grain yield in sorghum (Sorghum bicolor L. Moench)

Plant Genetic Resources ◽

10.1017/s1479262118000382 ◽

2018 ◽

Vol 17 (1) ◽

pp. 81-86 ◽

Cited By ~ 1

Author(s):

Massaoudou Hamidou ◽

Oumarou Souleymane ◽

Malick N. Ba ◽

Eric Yirenkyi Danquah ◽

Issoufou Kapran ◽

...

Keyword(s):

Grain Yield ◽

Genotype By Environment Interaction ◽

Planting Date ◽

Environment Interaction ◽

Gge Biplot ◽

Food Crop ◽

Planting Dates ◽

Genotype By Environment ◽

Biplot Analysis ◽

Sorghum Bicolor L

AbstractSorghum is a staple food crop in Niger and its production is constrained by sorghum midge and the use of low yielding, local sorghum varieties. To improve sorghum productivity, it is crucial to provide farmers with high yielding sorghum cultivars that are resistant to midge. We evaluated 282 genotypes in four environments of Niger Republic. Alpha (0.1) lattice with two replications was the experimental design. Genotype and genotype by environment (GGE) biplot analysis was used to study grain yield (GY) stability and G × E interactions. The results revealed that two distinct mega environments were present. Genotype L232 was the best genotype for GY in the first planting date at Konni and the first and second planting dates (PDs) at Maradi. Genotype L17 was the best for GY in the second PD at Konni. The second PD at Konni was the most discriminating environment while the first PD at Konni is suitable for selecting widely adapted genotypes for GY.

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GGE Biplot Analysis for Yield Stability in Multi-environment Trials of Promising Hybrid Rice (Oryza sativa L.)

Bangladesh Rice Journal ◽

10.3329/brj.v19i1.25213 ◽

2015 ◽

Vol 19 (1) ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

Anowara Akter ◽

M Jamil Hasan ◽

Umma Kulsum ◽

MH Rahman ◽

M Khatun ◽

...

Keyword(s):

Grain Yield ◽

Oryza Sativa L ◽

High Yield ◽

Gge Biplot ◽

Yield Data ◽

Biplot Analysis ◽

Stability Performance ◽

Sources Of Variation ◽

The Difference ◽

Ideal Genotype

The genotype and genotype by environment biplot model is an excellent tool for visual multi-environment trials data analysis. In this study we investigated grain yield of six rice genotypes (three tested, one released hybrids and two inbred check varieties) in five environments. The combined analysis of variance for grain yield data indicated that the differences among all sources of variation were highly significant (P<0.001). Environment (E), Genotype (G) and G × E interaction effects accounted for 12.49, 76.51 and 10.21% of the total sum of squares respectively. The first two principal components (PC1 and PC2) were used to display a two-dimensional GGE biplot. Thus, genotypic PC1 scores>0 classified the high yielding genotypes while PC1 scores<0 identified low yielding genotypes. Unlike genotypic PC1, genotypic PC2 scores discriminated the unstable ones. The GGE biplot analysis was useful in identifying stable genotypes with high yield performance. In this study, the polygon view of GGE biplot showed that the vertex genotypes were BRRI1A/BR168R (G1), BRRI10A/BRRI10R (G2) and BRRI dhan28 (G5) having the largest distance from the origin, which was most discriminated genotypes with the unstable ones. These vertex genotypes BRRI1A/BR168R (G1) and BRRI10A/BRRI10R (G2) gave higher yield (PC1 scores>0) while another vertex genotype BRRI dhan28 (G5) produced low yield (PC1 score<0). Hence, the vertex genotype BRRI10A/BRRI10R (G2) was high yielding for all environments and it fell into section 1 following IR58025A/BRRI10R (G3) and BRRI hybrid dhan1 (G4). Mean yield and stability performance over environments of each genotype is explored by using the average environment (tester) coordinate (AEC) methods. These methods show that the genotypes BRRI10A/BRRI10R (G2), IR58025A/BRRI10R (G3) and BRRI hybrid dhan1 (G4) had higher stability as well as higher mean yield while the genotype IR58025A/BRRI10R (G3) had the highest stability out of these three genotypes. The ideal genotype biplot suggests that the closer to ideal genotype was IR58025A/BRRI10R (G3) followed by G2 and G4 being more desirable than the other genotypes. Similarly, the environment Barisal (E3) was ideal environment followed by E1 (Gazipur), E2 (Comilla) and E5 (Satkhira). Hence, the environment Barisal (E3) is more stable and suitable for all genotypes following Satkhira (E5) because it has large PC1 and small PC2 score but Rangpur (E4) is a discriminating environment because it has large PC2 score. The interrelationship among the environments according to the small angles of test environments was highly positively correlated. Gazipur (E1), Comilla (E2), Barisal (E3) and Satkhira (E5) were closely correlated with small angles but Rangpur (E4) had medium long angles. Comparison between two genotypes showed that BRRI10A/BRRI10R (G2) and IR58025A/BRRI10R (G3) were high yielder in test environments. Thus, the difference between G2 and G3 was relatively small in test environments.Bangladesh Rice j. 2015, 19(1): 1-8

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AMMI and GGE biplot analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes at moisture deficit environment of Wollo, Ethiopia

Journal of Agricultural Science and Practice ◽

10.31248/jasp2019.123 ◽

2019 ◽

Vol 4 (1) ◽

pp. 9-19 ◽

Cited By ~ 1

Author(s):

Agegnehu Mekonnen ◽

◽

Firew Mekbib ◽

Arega Gashaw ◽

◽

...

Keyword(s):

Triticum Aestivum ◽

Grain Yield ◽

Bread Wheat ◽

Triticum Aestivum L ◽

Gge Biplot ◽

Biplot Analysis ◽

Moisture Deficit

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GGE biplot analysis of genotype × environment interaction inrabigrain sorghum [Sorghum bicolor(L.) Moench]

Indian Journal of Genetics and Plant Breeding (The) ◽

10.5958/0975-6906.2014.00889.x ◽

2014 ◽

Vol 74 (4s) ◽

pp. 558 ◽

Cited By ~ 3

Author(s):

Sujay Rakshit ◽

K. N. Ganapathy ◽

S. S. Gomashe ◽

M. Swapna ◽

A. More ◽

...

Keyword(s):

Sorghum Bicolor ◽

Environment Interaction ◽

Gge Biplot ◽

Biplot Analysis ◽

Genotype Environment Interaction ◽

Sorghum Bicolor L

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Grain yield stability of barley genotypes in uniform regional yield trails in warm and semi warm dry land area

Indian Journal of Agricultural Research ◽

10.18805/ijare.a-290 ◽

2017 ◽

Author(s):

Hassan Khanzadeh ◽

Behroz Vaezi ◽

Rahmatolah Mohammadi ◽

Asghar Mehraban1 ◽

Tahmaseb Hosseinpor ◽

...

Keyword(s):

Grain Yield ◽

Agricultural Research ◽

Gge Biplot ◽

Ge Interaction ◽

Dry Land ◽

Biplot Analysis ◽

Ammi Model ◽

Cropping Seasons ◽

Ideal Genotype ◽

Better Than

The aim of this study was to assess the effect of GEI on grain yield of barley advanced lines and exploit the positive GEI effect using AMMI and SREG GGE biplot analysis. Therefore, 18 lines were evaluated at five research stations (Ghachsaran, Mogan, Lorestan, Gonbad and Ilam) of Dryland Agricultural Research Institute (DARI), in the semi-warm regions in Iran, in 2012, 2013 and 2014 cropping seasons under rain-fed conditions. Analysis of variance showed that grain yield variation due to the environments, genotypes and GE interaction were highly significant (p>0.01), which accounted for 68.9%, 9.3% and 22.7% of the treatment combination sum of squares, respectively. To determine the effects of GEI on yields, the data were subjected to AMMI and GGE biplot analysis. The first five AMMI model terms were highly significant (p>0.01) and the first two terms explained 59.56% of the GEI. There were two mega-environments according to the SREG GGE model. The best genotype in one location was not always the best in other test locations. According to AMMI1 biplot, G2, G4, G5 and G6 were better than all other genotypes across environments. G2 was the ideal genotype to plant in Gachsaran. It seems that Ghachsaran is the stable environment between the environments studied and next in rank was Gonbad. In finally, the ATC method indicated that G1, G3, G4 and G6 were more stable as well as high yielding.

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Use of gross income as a measure of productivity in rice breeding

Canadian Journal of Plant Science ◽

10.4141/cjps07190 ◽

2008 ◽

Vol 88 (5) ◽

pp. 1015-1022 ◽

Cited By ~ 2

Author(s):

S. O. PB. Samonte ◽

L. T. Wilson ◽

R. E. Tabien ◽

A. M. McClung

Keyword(s):

Grain Yield ◽

Selection Criterion ◽

Gge Biplot ◽

Milled Rice ◽

Market Prices ◽

Biplot Analysis ◽

Main Crop ◽

Rice Genotypes ◽

Gross Income ◽

Key Words Rice

Rice breeders consider grain yield and milled rice percentages in developing cultivars, but usually do not consider gross income. This study’s objectives were to identify rice genotypes that produced high and stable expected gross incomes using genotype plus genotype × environment (GGE) biplot analysis. Uniform Regional Rice Nursery data on 47 long-grain genotypes grown at five locations (AR, LA, MO, MS, and TX) during 2001 to 2003 were analyzed. Gross income of each genotype was estimated based on grain yield, milled rice percentages, market prices, and direct and counter-cyclical payments. Based on GGE biplot analysis, the genotypes with the highest yield and highest gross income for the main crop were different in 12 out of 13 environments. RU0103184, Francis, and RU0003178 were the genotypes with the highest gross income in six, four, and three environments, respectively. Rice breeders should consider gross income as a selection criterion in the release of new cultivars. Key words: Rice, GGE, GE, breeding, gross income

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EFFECT OF NITROGEN FERTILIZATION LEVELS ON GRAIN YIELD AND YIELD COMPONENTS IN TRITICALE BASED ON AMMI AND GGE BIPLOT ANALYSIS

Applied Ecology and Environmental Research ◽

10.15666/aeer/1604_48654878 ◽

2018 ◽

Vol 16 (4) ◽

pp. 4865-4878

Author(s):

E ORAL

Keyword(s):

Grain Yield ◽

Nitrogen Fertilization ◽

Yield Components ◽

Gge Biplot ◽

Biplot Analysis ◽

Yield And Yield Components

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Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh

10.21203/rs.3.rs-1043738/v1 ◽

2021 ◽

Author(s):

Marium Khatun ◽

A. K. M. Aminul Islam ◽

M. Rafiqul Islam ◽

M. A. Rahman Khan ◽

M. Kamal Hossain

Keyword(s):

Stability Analysis ◽

Grain Yield ◽

Primary Component ◽

Yield Stability ◽

Second Primary ◽

Gge Biplot ◽

Environment Analysis ◽

Biplot Analysis ◽

Ammi Analysis ◽

Breeding Zone

Abstract During the 2018-2019 Boro season (dry season), 70 rice genotypes were examined with alpha lattice experimental design with the goal of measuring grain yield stability analysis. Results indicated that AMMI analysis explained 100% of the G×E variance, while captured 81.74% variance. Based on the GGE and AMMI analysis, the most stable and high yielding genotype was identified G41 followed by G22, G26, G58, G24 and G61. The AMMI 1 biplot analysis revealed that the first primary component of interaction (IPC1) factor was responsible for 64.2 % variation due to G × E interaction. On other hand, the second primary component (PC2) factor accounted for 35.8% variation of the G × E interaction. These two-primary component (PC1 and PC2), all together accounted for 100% variation of the G × E interaction. The contribution of G68 was highest to the interaction followed by G70, G58, G42, G61, G45, G38, G14, G33, G60, G53, and G9. Best environment analysis indicated that the ranking was Rajshahi < Gazipur < Cumilla. GGE biplot analysis accounted for 81.74% variation comprising two principal components PC1 and PC2 with 45.62% and 36.12% variations respectively. Rajshahi was more stable than Gazipur. Based on environment analysis genotypes, G22, G26, G58, and G44 can be recommended as best stable genotypes that breeding zone. However, the genotype G61 was identified adapted to Cumilla breeding zone.

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