The objective of this study was to investigate the stability of rapeseed genotypes using GTBiplot and GGEBiplot in two normal and stress conditions. The experiment was conducted in a randomized complete block design with three replications and ten treatments in the research field of Birjand University in 2012-2013. The GTBiplot explained %59.31, %62.33 and %62.01 of total variation of the standard data in normal, stress, and both, respectively. The most of variation was explained by GTBiplot caused by seed and biological yield, number of seeds per pod and number of pods per main branch in normal environment. The most of variation was explained by GTBiplot caused by seed yield, harvest index, pod length, number of auxiliary branches and number of pods per plant in stress condition. The most of variation was explained by GTBiplot caused by seed yield, pod length, number of pods per auxiliary branch and the total of pods per plant in normal and stress conditions. According to GTBiplot polygon it was revealed that the Hay-308 genotype in view of day to 50% flowering, biological yield, number of auxiliary branches, number of pods per auxiliary branch and number of pods per plant was the best genotypes in normal and stress conditions. Licord and Zarfam genotypes had the most of 1000-seed weight. Hay-401 genotype had the most of pod length. Genotypes comparison using Biplot cleared that the Hay-308 had the highest yield. The Hay-401, SLM046, Sarigol and opera genotypes had the lowest yield. The other genotypes had high yield.
Cooper, M. & Hammer, G. L. (1996). Plant adaptation and crop improvement. CAB International/ IRRI/ICRISAT, Wallingford, Oxon, UK.
Dehghani, H., Omidi, H. & Sabaghnia, N. (2008). Graphic analysis of trait relations of Rapeseed using the Biplot method. Agronomy Journal, 100(100), 1443-1449.
Escobar, M., Berti, M., Matus, I., Tapia, M. & Johnson, B. (2011). Genotype × environment interaction in Canola (Brassica napus L.), seed yield in Chile. Chilean Journal of Agricultural Research, 71(2), 175-186.
Faramarzi, A., Saleh Pile Rod, F., Mostefaei, H. & Mardan, R. (2010). Evaluation and comparison of 17 promising genotypes of spring canola under dry land conditions in the Meyaneh. 3th International Seminar oilseeds and edible oils. Tehran, pp.139-144.
Gabriel, K. R. (1971). The Biplot graphic display of matrices with application to principal component analysis. Biometrika, 58, 453-467.
Gunasekera, C. P., Martin, L. D., Siddique, K. H. M. & Walton, G. H. (2006). Genotype by Environment interactions of Indian mustard (B. juncea L.) and canola (B. napus L.) in Mediterranean-type environments: 1. Crop growth and seed yield. European Journal of Agronomy, 25(1), 1-12.
Kang, M. S. (1990). Genotype-by-environment interaction and plant breeding. Louisiana State University Agriculture Center, Baton Rouge, Louisiana. pp. 261-272.
Kang, M. S. & Gauch, H. G. (1996). Genotype-by-environment interaction. CRC. Press, Boca Raton, Florida, pp. 199-234.
Mostafavi, KH., Mohammadi, A., Khodarahmi, M. & Zabet, M. (2011). Response study of canola cultivars to multi-environments using genotype plus genotype environment interaction (GGE) Biplot method in Iran. African Journal of Biotechnology, 10(53), 10877-10881.
Ozoni, D. A. & Esfahani, M. (2009). Relationships between yield and related traits in Rapeseed (Brassica napus L.). 1th National Conference on Oilseeds, Esfahan, Iran, pp.130-135. (in Farsi)
Sabaghnia, N., Dehghani, H., Alizadeh, B. & Moghaddam, M. (2011).Yield analysis of Rapeseed under water-stress conditions using GGE Biplot methodology. Journal of Crop Improvement, 5, 26-45.
Yan, W. (1999). Methodology of cultivar evaluation based on yield trial data with special reference to winter wheat in Ontario. Ph. D Thesis, University of Guelph, Guelph, ON, Canada.
Yan, W. & Wallace, D. H. (1995). Breeding for negatively associated traits.Plant Science, Plant Breeding Review, 13, 141-177
Yan, W. & Rajcan, I. (2002). Biplot Analysis of test sites and trait relations of Soybean in Ontario. Crop Science, 42, 11-20.
Yan, W., Hunt, L. A., Sheng, Q. & Szlavnics, Z. (2000). Cultivar evaluation and mega environment investigation based on the GGEBiplot. Crop Science, 40, 597-605.
Yan, W., Kang, M., Ma, B., Woods, S. & Cornelius, P. (2007). GGE Biplot vs. AMMI analysis of genotype by environment data. Crop Science, 47, 643-653.
Zabet, M. (2017). Identification of superior genotypes of Rapeseed by GTBiplot and GGEBiplot methodology in normal and stressed conditions. Iranian Journal of Field Crop Science, 48(1), 207-220. doi: 10.22059/ijfcs.2017.138252.653999
MLA
Mohammad Zabet. "Identification of superior genotypes of Rapeseed by GTBiplot and GGEBiplot methodology in normal and stressed conditions", Iranian Journal of Field Crop Science, 48, 1, 2017, 207-220. doi: 10.22059/ijfcs.2017.138252.653999
HARVARD
Zabet, M. (2017). 'Identification of superior genotypes of Rapeseed by GTBiplot and GGEBiplot methodology in normal and stressed conditions', Iranian Journal of Field Crop Science, 48(1), pp. 207-220. doi: 10.22059/ijfcs.2017.138252.653999
VANCOUVER
Zabet, M. Identification of superior genotypes of Rapeseed by GTBiplot and GGEBiplot methodology in normal and stressed conditions. Iranian Journal of Field Crop Science, 2017; 48(1): 207-220. doi: 10.22059/ijfcs.2017.138252.653999