Document Type : Original Article

Authors

1 Department of Plant Breeding, Science and Research branch, Islamic Azad University, Tehran, Iran.

2 2. Full Professor, Dryland Agricultural Research Institute (DARI), Agricultural Research Education and Extension Organization (AREEO), Iran.

3 Full Professor, Seed and Plant Improvement Institute, Karaj, Iran.

Abstract

Introduction
Sunflower is an important oilseed crops that about 11.8 percent of global vegetable oil production to be allocated. GGE Biplot is a multi-dimensional method in quantitative genetic decomposition and plant breeding that in addition to decomposition GEI performs decomposition of the interaction of genotype× quality, genotype× marker and data analysis related to Diallel confluence. The purposes of research are included:Studying the interaction of genotype and environment in area laboratory of sunflower crop plant performance and introducing the most stable and desirable number or sunflower numbers in under investigation areas and environments.

Materials and Methods
For investigating the interaction of genotype and environment of 17 artificial sunflower numbers by the names of Sil-96, Sil-54, Sil-42, Sil-94, Sil-140, Sil-162, Sil-200, Sil-205, Sil-231, Sil-210, Sil-211, Sil-224, Sil-80, Sil-82, Sil-75, Sil-53, Sil-20 and three numbers of pollinator Lacomka, Zaria and Armaverski as witnesses of the experiment were studied. In this experiment four places as the names of Sararood (Kermanshah), Gachsaran, Ghamlou (Kurdistan) and Gobad were participated that in Sararood of Kermanshah in crop year 2011-2012, 2012-2013, 2014-2015 was planted in two situations of without irrigation and supplementary irrigation. In Ghamlou in two crop years 2012-2013, 2014-2015, in Gachsaran and Gobad also in crop year 2014-2015 was planted only in the situation of without irrigation. But in general 10 environments were tested. Experiments in all stations were done in the form of complete random blocks design with three repeats.

Results and Discussion
The results of variance analysis showed that the effects of environment and the interaction of genotype and environment in one percent probability level was meaningful for seed performance. Surveying Polygon of genotype interaction in environment showed that genotypes G14، G9، G8، G4، G18، G19، G20 which had the most distance from Bipolt center and placed in Polygon vertices that they were premier genotypes. The lines which designed from Biplot center divided the shape of Polygon to seven Mega environments. The first Mega environment was included into the environments E4, E5 and E6 in which genotype G17 had the most performance. The second Mega environment was included into the environments E3 and E7 that G2, G14 and G9 were premier genotypes of this environment. The third Mega environment was included into environment E1 that genotype G8 was high performance genotype in this environment. The fourth Mega environment was included into the environments E2, E9, E8 and E10 in which genotype G20 was premier genotype. The relationship among environments showed that the angle between environments E3 and E7 and also between the environments E4, E5 and E6 and also between environments E2, E10 and E8 was very close each other and it indicates high correlation of these environments each other. The environments E7, E2 and E1 had relatively short length of vector that indicates low value for the capability of their difference. In contrast, the environments E3, E5, E6, E4, E10, E8 and E9 had tall length of vector and at last they had more capability for difference. Graphic average surveying of performance and the stability of genotypes showed that genotypes G17 and then genotypes G15, G3, G2, G14 and G6 had the highest performance. Genotypes G11, G10, G12, G13, G19 and G7 had low performance and high stability. Artificial number G9 had the most performance of seed and suitable stability.
Surveying genotypes towards ideal genotypes showed that genotype G17 was the closest genotype to ideal genotype that had the most performance and also it was among stable genotypes. Genotypes G3 and G15 which had high stability was also close to ideal genotype and they can select as suitable genotypes.
Surveying environments towards ideal environment showed that the most desirable environment is environment E4, then environments E5, E6, E10, E2, E8, E7, E9, E3 and E1 were in next priorities respectively that among them, environment E1 was the most undesirable environment.

Conclusion
Genotypes G3, G14, G15 and G17 were the most stable and desirable genotypes. On the other hand genotypes G9, G4 and G20 were known as the most undesirable genotypes. Furthermore, the results of decomposition GGE Biplot could introduce environment E4 which have the least distance from hypothetical ideal environment as the most desirable environment.

Keywords

Allard, R.W., Bradshaw, A.D., 1964. Implication of Genotype-Environment Interactions in Applied Plant Breeding. Crop Science. 4, 503-508.
Gabriel, K.R., 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika. 58, 453-467.
Gauch, H.G., 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Science. 46, 1488- 1500.
Kang, M.S., 1993. Simultaneous selection for yield and stability in crop performance trials: Consequences for growers. Agronomy Journal. 85, 754-757.
Kempton, R.A., 1984. The use of biplots in interpreting variety by environment interactions. Journal of Agricultural Science. 103, 123-135.
Pourdad, S.S., Jamshidmoghaddam, M., 2012. Evaluation of yield stability of spring sanflower (Carthamus tinctorius L.) genotypes using GGE biplot. 12th Iranian Crop Sciences Congress, September 4-6. Karaj, Iran. [In Persian].
Pourdad, S.S., Ghaffari, A., 2009. Comparison of parametric and non-parametric yield stability measures and their relationship in spring rapeseed (Brassica napus L.) in warm dry-lands of Iran. Middle Eastern and Russian Journal of Plant Science and Biotechnology. 3, 35-40.
Purchase, J., 1997. Parametric analysis to describe genotype×environment interaction and yield stability in winter wheat. PhD. University of the Free State, South Africa.
Seiler, G.J., 2007. Wild annual Helianthus anomalus and H. deserticola for improving oil content. Agriculture and Forest Meteorology. 74, 22-29.
Yan,W., 2001. GGE Biplot-A windows application for graphical analysis of multi- environment trial data and other types of two-way data. Agronomy Journal. 93, 1111-1118.
Yan, W., 2002. Singular-value partitioning in biplot analysis of multi environment trial data. Agronomy Journal. 94, 990-996.
Yan, W. Kang, M.S., Woods, B.M., Cornelius, P.L., 2007. GGE biplot vs. AMMI analysis of genotype by environment data. Crop Science. 47, 643-655.
Yan, W., Rajcan, I., 2002. Biplot analysis of sites and trait relations of soybean in Ontario. Crop Science. 42, 11-20.
Yan, W., Tinker, N.A., 2005. An integrated biplot analysis system for displaying, interpreting and exploring genotype × environment interaction. Crop Science. 45, 1004-1016.
Yan, W., Kang, M.S., 2003. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists. CRC Press. Boca Raton, FL. 30.
Yan, W., Hunt, Sheng, A.Q., Szlavnics, Z., 2000. Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Science. 40, 597-605.
Zobel, R.W., Wright, M.J., Gauch, H.G., 1988. Statistical analysis of a yield trial. Agronomy Journal. 80, 388-393.