scholarly journals Photosynthetic heat tolerance in wheat: Evidence for genotype-by-environment interactions

2021 ◽  
Author(s):  
Onoriode Coast ◽  
Bradley Posch ◽  
Bethany Rognoni ◽  
Helen Bramley ◽  
Oorbessy Gaju ◽  
...  
Keyword(s):  
Photosystem Ii ◽  
Heat Tolerance ◽  
Flag Leaf ◽  
Wheat Breeding ◽  
Environment Interaction ◽  
Genotypic Effect ◽  
Daily Growth ◽  
Wheat Genotypes ◽  
Genotype By Environment ◽  
Factor Analytic Model

High temperature stress inhibits wheat photosynthetic processes and threatens wheat production. Photosynthetic heat tolerance (commonly measured as T – the critical temperature at which incipient damage to photosystem II occurs) in wheat genotypes could be improved by exploiting genetic variation and genotype-by-environment interaction (GEI) for this trait. Flag leaf T of a total of 54 wheat genotypes were evaluated in 12 thermal environments over three years in Australia using linear mixed models for assessing GEI effects. Nine of the 12 environments had significant genotypic effect and highly variable broad-sense heritability (H ranged from 0.15 to 0.75). T GEI was variable, with 55.6% of the genetic variance across environments accounted for by the factor analytic model. Mean daily growth temperature preceding anthesis was the most influential environmental driver of T GEI, suggesting varied scales of biochemical, physiological, and structural adaptations to temperature requiring different durations to manifest at the thylakoid membrane and leaf levels. These changes help protect or repair photosystem II upon exposure to heat stress. To support current wheat breeding, we identified genotypes superior to commercial cultivars commonly grown by farmers, and showed that there is potential for developing genotypes with greater photosynthetic heat tolerance.

scholarly journals Genotype by environment interaction analysis of wheat (Triticum aestivum L.) grain yield under rain-fed conditions in Zambia

2021 ◽  
Vol 53 (4) ◽  
pp. 609-619
Author(s):  
B. Tembo
Keyword(s):  
Grain Yield ◽  
Agricultural Research ◽  
Interaction Analysis ◽  
Crop Improvement ◽  
Genotype By Environment Interaction ◽  
Research Station ◽  
Environment Interaction ◽  
Wheat Genotypes ◽  
Lattice Design ◽  
Genotype By Environment

Understanding genotype by environment interaction (GEI) is important for crop improvement because it aids in the recommendation of cultivars and the identification of appropriate production environments. The objective of this study was to determine the magnitude of GEI for the grain yield of wheat grown under rain-fed conditions in Zambia by using the additive main effects and multiplicative interaction (AMMI) model. The study was conducted in 2015/16 at Mutanda Research Station, Mt. Makulu Research Station and Golden Valley Agricultural Research Trust (GART) in Chibombo. During2016/17, the experiment was performed at Mpongwe, Mt. Makulu Research Station and GART Chibombo, Zambia. Fifty-five rain-fed wheat genotypes were evaluated for grain yield in a 5 × 11 alpha lattice design with two replications. Results revealed the presence of significant variation in yield across genotypes, environments, and GEI indicating the differential performance of genotypes across environments. The variance due to the effect of environments was higher than the variances due to genotypes and GEI. The variances ascribed to environments, genotypes, and GEI accounted for 45.79%, 12.96%, and 22.56% of the total variation, respectively. These results indicated that in rain-fed wheat genotypes under study, grain yield was more controlled by the environment than by genetics. AMMI biplot analysis demonstrated that E2 was the main contributor to the GEI given that it was located farthest from the origin. Furthermore, E2 was unstable yet recorded the highest yield. Genotype G47 contributed highly to the GEI sum of squares considering that it was also located far from the origin. Genotypes G12 and G18 were relatively stable because they were situated close to the origin. Their position indicated that they had minimal interaction with the environment. Genotype 47 was the highest-yielding genotype but was unstable, whereas G34 was the lowest-yielding genotype and was unstable.

scholarly journals Genotype X Environment Interaction for Quality Traits in Bread Wheat Genotype Tested At Six Environments

2019 ◽  
Vol 4 (2) ◽  
Keyword(s):  
Bread Wheat ◽  
Grain Quality ◽  
Wheat Breeding ◽  
Environment Interaction ◽  
Quality Traits ◽  
Wheat Genotypes ◽  
Significant Difference ◽  
Wide Adaptation ◽  
New Varieties

The study was conducted to evaluate the effect of GEI and its magnitude on the grain quality of bread wheat genotypes in Ethiopia. 15 bread wheat genotypes were evaluated using RCBD with four replications at six different locations in Ethiopia during 2017/18 cropping season. Combine Analysis of variance showed highly significant (P<0.001) differences among genotype, environment and GEI for investigated quality traits except GEI shows non-significant difference in dry gluten and gluten index. The environment contributed more than 50% only in PC (83.6%) and HLW (56.1%). The three components, G, E and GxE made almost similar contribution to most of the quality traits (WG, DG and GI), although the contribution of the environment was a little higher. Hardness index was determined mainly by the genotype (69.3%). The contribution of GxE was higher than that of genotype in all quality traits except in HDI and GI, again indicating the important role of GxE in the determination of wheat quality traits. Genotype ETBW9045 and ETBW8065 gave the best value of protein in the favorable means (15.05% and 14.75%) respectively. The Hidase had the highest value of wet gluten (58.2%) and dry gluten (24.38%) in average for all investigated locations (58.2%). GGE biplot declared ETBW9045 (#10) and ETBW8065 (#6) genotypes as stable in all quality. These two genotypes ETBW9045 (#10) and ETBW8065 (#6) are recommended for wide adaptation and for crossing. This study demonstrates success in wheat breeding for improved quality in bread wheat. The study also provides information on the combined stability of improved quality of the nationally important bread wheat genotypes. Therefore, the results of this study could be valuable for national bread wheat breeding programs to develop new varieties with high stable grain quality.

scholarly journals Assessment of Morpho-Physiological, Yield and Yield Attributing Traits Related to Post Anthesis Drought in Wheat Genotypes Under Rainfed Condition in Rampur, Chitwan

2020 ◽  
Vol 8 (3) ◽  
pp. 323-335
Author(s):  
A. Ojha ◽  
B.R. Ojha
Keyword(s):  
Grain Yield ◽  
Flag Leaf ◽  
Block Design ◽  
Winter Season ◽  
Growth Period ◽  
Grain Filling ◽  
Genotypic Effect ◽  
Rainfed Condition ◽  
Wheat Genotypes ◽  
Genotypic Effects

A set of twenty wheat (Triticum aestivum L.) genotypes was evaluated to assess morpho-physiological, yield and yield attributing traits related to post-anthesis drought in wheat genotypes under rainfed condition in a Randomized completely block design with three replications at research farm of Faculty of Agriculture, Rampur, Chitwan, during winter season of 2016/2017. The result revealed highly significant genotypic effects for number of tillers per m2 area, plant height, spike length, number of grains per spike, weight of grains per spike, 1000 kernels weight, days to booting, days to heading, days to anthesis, days to flag leaf senescence, days to maturity, SPAD meter reading, peduncle length, grain filling duration and reproductive growth period. Significant genotypic effects were found for grain yield, biological yield and harvest index and an array of variation was found among the genotypes for each trait. But non-significant genotypic effect was found for canopy temperature depression. WK2373 gave highest grain yield kg/ha (3035 kg/ha) and biomass yield kg/ha (8080 kg/ha). This study presented WK2373, WK2379, WK2380, WK2386, WK2388, WK2383, WK 2378 and WK1481 the best genotypes governing different valuable traits. These potential genotypes for valuable traits found in different clusters. Crossing genotypes belonging to different clusters could maximize the opportunities for transgressive segregation as there is a higher probability that unrelated genotypes would contribute unique desirable alleles at different loci.  Therefore, this study can help breeders to increase genetic diversity by selecting materials of divergent parentage for crosses, thereby reducing vulnerability to diseases and climate changes.  Int. J. Appl. Sci. Biotechnol. Vol 8(3): 323-335

scholarly journals Multi-Environment Trial Analysis of Food Barley in Ethiopia Using AMMI and GGE Biplot Methods

2018 ◽  
Vol 6 (3) ◽  
pp. 75-85 ◽  
Author(s):  
Girma Fana ◽  
Diriba Tadese ◽  
Hiwot Sebsibe ◽  
Ramesh P.S. Verma
Keyword(s):  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Lower Yield ◽  
Genotypic Effect ◽  
Genotype By Environment ◽  
Trial Analysis ◽  
Ammi Model ◽  
The Mean ◽  
The Ideal

Food barley released varieties were tested in 2012 for performance across major environments in Ethiopia consisting of 12 varieties Diribe, Tilla, Abbay, Biftu, Defo, Dinsho, Mulu, Setegn, Misiratch, Basso, Mezezo and local checks over six locations Gergera, Estayish, Shambu, Arjo, Robe and Sinana. The objective was to determine genotype by environment interaction using AMMI and GGE biplot, compare the two models for identifying the adaptable and stable genotypes. Sinana was identified as the high yielding environment and MULU the high yielding variety with mean yields of 3466.31 and 3137.67 kg/ha, respectively. The mean yield at Estayish was lower (1535 kg/ha) than other environments whereas lower yield (2212.16 kg/ha) was also obtained from the variety DINSHO. The AMMI analysis of Variance indicated that 47% of the total sum of squares is attributed to the Environmental effect, 8% to the genotypic effect and 25% to the interaction. The first three principal components of the GEI explained 81% of the variation. Genotypes Basso, Biftu and Setegn were the most stable whereas Diribe was unstable. Variety Mulu was identified as the winner genotype by AMMI model whereas Diribe was identified as the winner by the GGE model. GGE model better explains the which-won-where scenario and hence preferred to AMMI model. The discriminating and representative view of the GGE biplot depicted that Sinana and Shambu are discriminating environments whereas Sinana, Estayish and Gergera are representative environments. Therefore, Sinana is the ideal environment for discriminating genotypes and representing other environments for selecting ideal genotypes.

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