The investigation was aimed to identify stable genotypes of durum wheat which can perform consistently under multiple environments. The experiment was conducted in Research Area of CCS Haryana Agricultural University, Hisar during rabi 2015-16. In the present investigation seven released varieties of durum wheat were evaluated on four environments (timely
Significant variability for genotype, environment and their interaction was recorded.
Four environments were grouped into two groups.
Genotype WHD 948 was found most stable under different environments.
Durum wheat (
The present investigation was carried out in Research Area of Wheat and Barley Section, Department of Genetics and Plant Breeding, CCS Haryana Agricultural University. The experiment was conducted during
The genotypes were evaluated under four environmental conditions i.e. timely sown irrigated condition (E1), late sown irrigated condition (E2), timely sown rainfed condition (E3), and late sown rainfed condition (E4). After restricting these conditions other recommended packages and practices were followed throughout the cropping season of wheat. The experiment was conducted in Randomized Block Design (RBD) with three replications.
In the present experiment data was collected from randomly selected five plants from each genotype. Plants were tagged and harvested separately at the time of maturity. Threshing of these plants was done and grain yield of each plant was weighted. The data was then analyzed for the stability of genotypes and grouping of environments based on GGE biplot analysis given by Yan
Grain yield in wheat is a complex trait as it has both genotypic and environmental origin (Farshadfar
Pooled analysis of yield data for seven durum wheat genotypes from four environments was carried out to partition the total variability into its component parts. Analysis of variance (ANOVA) showed that the studied genotypes were significantly different from each other for grain yield (
Source of Variation |
DF |
Mean Sum of Squares |
% SS |
---|---|---|---|
Genotype |
18 |
6.301** |
28.105 |
Environment |
3 |
67.918** |
50.489 |
Rep within environment |
8 |
1.072 |
2.124 |
Genotype × Environment |
6 |
4.970** |
7.388 |
Pooled Error |
48 |
1.000 |
11.894 |
Total |
83 |
**
Environmental effect can make the evaluation more confusing. In the present case about 50% of variability was caused due to environment. Under such situation the data was further analyzed for genotype and genotype plus environment interaction (GGE) using the standard procedure of GGE biplot analysis suggested by Yan
Basic features of the GGE biplot. Note that about 88% of variability was explained by first two principal components
In our present GGE biplot, green color was used to indicate positions of genotypes on biplot whereas environments were represented by blue color. To get the maximum information from the biplot, black dotted lines were used to represnt origin of biplot and blue dotted lines as environment vectors i.e. line joining the environments to the origin of biplot. The environment vectors were formed by calculating standard deviations of genotypes in a particular environment. Thus, the length of environmental vectors can be utilized in identification of a suitable environment. In the present study, environment E3 had maximum length of environment vector (
Ranking of environments based on discriminating ability and representativeness
Thus, environment E3 had more discriminating power as genotypes perform differentially in this environment. All other environments had similar environment vectors and thus had same discriminating powers. The other information we get from environment vectors is the correlation between the environments. This correlation is a measure of cosine angular distance between two environment vectors. Larger the angle, lesser will be correlation between the environments and vice versa. In the present case, environment E1 and E2 were highly correlated while others with high angles were less correlated. Thus instead of E1 and E2 we can use either of these environment for future studies as both environments are alike and can give similar results. Another feature of GGE biplot is that environments can be grouped based on average or ideal environment. In
The genotypes WHD 948 was best for three environments viz. E1, E2 and E3 whereas WHD 896 was most suitable for E4 (
(a) Polygon view of GGE biplot representing “which- won-where” pattern of genotypes and environments. (b) Average environment coordination (AEC) views of the GGE biplot based on environment-focused scaling for the means of performance and stability of genotypes
From this study it can be concluded that