Document Type : Research Paper

Authors

1 Assistant Professor, Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

2 Professor, University of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

3 Associate Professor, Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

Abstract

Abstract
Introduction
Oilseed rape is the third most important oil crop in the world after oil palm and soybean. The world's oilseed rape production was 4.6 million tons in 2018-2019 and has the ability to compensate for the lack of edible oil in Iran (containing 40-45% oil). The production of this crop is mainly done by using two zero cultivars with a low level of glucosinolate in the feed and the absence of Erucic acid in the oil. Two types of oilseed rape are cultivated in Iran, spring and autumn. Spring type is cultivated in warm regions of the Caspian Sea coast and southern regions of the country and its autumn type is mostly cultivated in cold and mild cold regions.
 
Materials and Methods
In this study 16 rapeseed mutant lines obtained through Gama radiation of three rapeseed cultivars Talaye, Zarfam and Express with 800, 900 and 1200 gray dose rates, followed by 7 selfing generation, are compared during two years for earliness, seed and oil yield and other important agronomic traits in four regions Karaj, Kermanshah, Esfahan and Zarghan with three check varieties. The oil content of rapeseed varieties and lines were determined by NMR (nuclear magnetic resonance) at chemic laboratory of Oilseed Crops Research Department of Seed and Plant Improvement Institute. Finally, the highest yield early maturity lines were defined. Each treatment was sown in plot with four rows, four meters lenght and 30 cm between row distances. The experimental field was prepared in late summer and chemical fertilizer were given to it. The sowing experiment were planned for late week of September until the first week of October.At the end of each cropping year, the yield of each plot was harvested separately and the statistical calculations were carried out after the gathering of two years results by GGE biplot method.
 
Results and Discussion
Results of combined ANOVA showed that main effects of year and genotype, interaction of location ×genotype; year × location, and interaction of year, location and genotype had significant effects on grain yield. The genotypes showed the highest and the lowest grain yield in Kermanshah 4016 kg/ha and Zarghan 2886 kg/ha stations, respectively. Line T-900-4 produced the highest grain yield 3840 kg/ha in all locations. To study the interaction of genotypes and environments, GGE biplot method was used. Based on the polygonal graphs related to genotypes, line Z-800-6, Z-900-7, Okapi and Z-800-3 produced the highest yield in Karaj, Zarghan, Kermanshah and Esfahan, respectively. Regarding the imaginary ideal genotype graph and biplot of genotypes and environments and seed yield ranking, line Z-900-7 were identified as the best genotypes due to its higher yield and stability.
 
Conclusion
In Iran, 70% of rapeseed production is done in warm regions and only 30% in cold regions. Therefore, breeding cold-tolerant cultivars by using mutations will increase rapeseed cultivation in the cold region of Iran.
 
 

Keywords

Main Subjects

References

Ahmadi, M., Omidi, M., Alizadeh, B., & Shah Nejat Bushehri, A. A. (2019). Study of advanced mutant rapeseed lines in cold regions of Iran. Agricultural Science and Sustainable Production, 29(4), 175-184. [In Farsi]

Crossa, J., Fox, P. N., Pfeiffer, W. H., Rajaram, S., & Gauch Jr. H.G. (1991). AMMI adjustment for statistical analysis of an international wheat yield trial. Theoretical and Applied Genetics, 81, 27-37.

Escobar, M., Berti, M., Matus, I., Tapia, M., & Johnson, B. (2010). Genotype × environment interaction in canola (Brassica napus l.) seed yield in Chile. Chilean Journal of Agricultural Research, 71(2), 175-186.

FAO (2019). FAOSTAT. Rome, Italy: FAO, Statistics Division.

Gabriel, K. R. (1971). The biplot-graphical display of matrices with applications to principal components analysis. Biometrika, 58(3), 453-467.

Gauch, H. G. (2006). Statistical analysis of yield trials by AMMI and GGE. Crop Science, 46(4), 1488-1500.

Malekshahi, F., Dehghani, H., & Alizadeh, B. (2012). Biplot trait analysis of some of canola (brassica napus l.) genotypes in irrigation and drought stress conditions. Plant Productions, 35(2), 1-16. [In Farsi]

Mostafavi, Kh., Mohammadi, A., Khodarahmi, M., Zabet, M., & Zare, M. (2012). The response of rapeseed commercial cultivars in different environments using the GGE biplot graphical method. Agronomy and Plant Breeding, 8(4), 133-143. [In Farsi]

Mozaffari, K., & Ahmadi, M. R. (2010). Final report breeding rapeseed varieties for early maturity by inducting gamma rays. Nuclear Science and Technology Research Center, 1-66. [In Farsi]

Naserian Khiabani, B., & Alizadeh, B. (2018). Evaluation of grain yield and yield stability in rapeseed (brassica napus) mutant lines using GGE biplot. Journal of Nuclear Science and Technology, 83(1), 96-102. [In Farsi]

Nowosad, K., Liersch, A., Popławska, W., & Bocianowski, J. (2016). Genotype by environment interaction for seed yield in rapeseed (Brassica napus L.) using additive main effects and multiplicative interaction model. Euphytica, 208(16), 187–194.

Oghan, H. A., Zeinalzadeh-Tabrizi, H., Fanaei, H. R., Kazerani, N. K., Ghodrati, G. R., Danaie, A. Kh., & Valipuor, M. B. (2019). Stability study of seed yield in promising lines of spring oilseed rape in outhern-worm regions of Iran. Journal of Crop Breeding, 11(31), 42-54. [In Farsi]

Pourdad, S. S., & Jamshid Moghaddam, M. (2013). Study on genotype×environment interaction through gge biplot for seed yield in spring rapeseed (Brassica Napus L.) in rain-fed condition. Journal of Crop Breeding, 5(12), 1-14. [In Farsi]

Rodriguez, J., Sahagun, J., Villaseñor, H., Molina, J., & Martinez, Y. A. (2002). Estabilidad de siete variedades comerciales de trigo (Triticum aestivum L.) de temporal. Revista Fitotecnia Mexicana, 25(2), 143-151.

Rondanini, D. P., Gomez, N. V., Agosti, M. B., & Miralles, D. J. (2012). Global trend of rapeseed grain yield stability and rapeseed-to-wheat yield ratio in the last four decades. European Journal of Agronomy, 37(1), 56-65.

Roozeboom, K. L., Schapaugh, T. W., Tuinstra, M. R., Vanderlip., R. L., & Milliken, G. (2008). Testing wheat in variable environments: Genotype, environment, interaction effects, and grouping test locations. Crop Science, 48(1), 317-330.

Sohrabi, S., dehghani, H., & Alizadeh, B. (2014). Grouping of Promising Winter Rapeseed (Brassica napus L.) Lines Based on Genotype × Environment Interaction. Seed and Plant Improvement Journal, 30(4), 807-819. [In Farsi]

Tahira, A. R., Khan, M. A., & Amjad, M. (2013). Stability analysis of canola (Brassica napus) genotypes in Pakistan. Global Advanced Research Journal of Agricultural Science, 10(2), 270-275.

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(5), 1111-1118.

Yan, W., Kang, M. S., Baoluo, M., Woods, Sh., & Cornelius, P. L. (2007). GGE Biplot vs. AMMI analysis of genotype-by-environment data. Crop Science, 47(2), 641-653.

Yan, W., Hunt, L. A., Sheng, Q., & Szlavnics, Z. (2000). Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science, 40(3), 597-605.

Yang, R., Crossa, J., Cornelius, P., & Bugueno, J. (2009). Biplot analysis of genotype x environment interaction: Proceed with caution. Crop Science, 49(5), 1564-1576.

Zhang, H., Berger, J. D., & Herrmann, C. (2017). Yield stability and adaptability of canola (Brassica napus L.) in multiple environment trials. Euphytica, 213(7), 1-21.

Zhang, H., Berger, J. D., & Milroy, S. P. (2013). Genotype × environment interaction studies highlight the role of phenology in specific adaptation of canola (Brassica napus) to contrasting Mediterranean climates. Field Crops Research, 144, 77-88.

Zobel, R. W., Wright, M. J., & Gauch Jr, H. G. (1988). Statistical analysis of a yield trial. Agronomy Journal, 80(3), 388-393.

References
Ahmadi, M., Omidi, M., Alizadeh, B., & Shah Nejat Bushehri, A. A. (2019). Study of advanced mutant rapeseed lines in cold regions of Iran. Agricultural Science and Sustainable Production, 29(4), 175-184. [In Farsi]
Crossa, J., Fox, P. N., Pfeiffer, W. H., Rajaram, S., & Gauch Jr. H.G. (1991). AMMI adjustment for statistical analysis of an international wheat yield trial. Theoretical and Applied Genetics, 81, 27-37.
Escobar, M., Berti, M., Matus, I., Tapia, M., & Johnson, B. (2010). Genotype × environment interaction in canola (Brassica napus l.) seed yield in Chile. Chilean Journal of Agricultural Research, 71(2), 175-186.
FAO (2019). FAOSTAT. Rome, Italy: FAO, Statistics Division.
Gabriel, K. R. (1971). The biplot-graphical display of matrices with applications to principal components analysis. Biometrika, 58(3), 453-467.
Gauch, H. G. (2006). Statistical analysis of yield trials by AMMI and GGE. Crop Science, 46(4), 1488-1500.
Malekshahi, F., Dehghani, H., & Alizadeh, B. (2012). Biplot trait analysis of some of canola (brassica napus l.) genotypes in irrigation and drought stress conditions. Plant Productions, 35(2), 1-16. [In Farsi]
Mostafavi, Kh., Mohammadi, A., Khodarahmi, M., Zabet, M., & Zare, M. (2012). The response of rapeseed commercial cultivars in different environments using the GGE biplot graphical method. Agronomy and Plant Breeding, 8(4), 133-143. [In Farsi]
Mozaffari, K., & Ahmadi, M. R. (2010). Final report breeding rapeseed varieties for early maturity by inducting gamma rays. Nuclear Science and Technology Research Center, 1-66. [In Farsi]
Naserian Khiabani, B., & Alizadeh, B. (2018). Evaluation of grain yield and yield stability in rapeseed (brassica napus) mutant lines using GGE biplot. Journal of Nuclear Science and Technology, 83(1), 96-102. [In Farsi]
Nowosad, K., Liersch, A., Popławska, W., & Bocianowski, J. (2016). Genotype by environment interaction for seed yield in rapeseed (Brassica napus L.) using additive main effects and multiplicative interaction model. Euphytica, 208(16), 187–194.
Oghan, H. A., Zeinalzadeh-Tabrizi, H., Fanaei, H. R., Kazerani, N. K., Ghodrati, G. R., Danaie, A. Kh., & Valipuor, M. B. (2019). Stability study of seed yield in promising lines of spring oilseed rape in outhern-worm regions of Iran. Journal of Crop Breeding, 11(31), 42-54. [In Farsi]
Pourdad, S. S., & Jamshid Moghaddam, M. (2013). Study on genotype×environment interaction through gge biplot for seed yield in spring rapeseed (Brassica Napus L.) in rain-fed condition. Journal of Crop Breeding, 5(12), 1-14. [In Farsi]
Rodriguez, J., Sahagun, J., Villaseñor, H., Molina, J., & Martinez, Y. A. (2002). Estabilidad de siete variedades comerciales de trigo (Triticum aestivum L.) de temporal. Revista Fitotecnia Mexicana, 25(2), 143-151.
Rondanini, D. P., Gomez, N. V., Agosti, M. B., & Miralles, D. J. (2012). Global trend of rapeseed grain yield stability and rapeseed-to-wheat yield ratio in the last four decades. European Journal of Agronomy, 37(1), 56-65.
Roozeboom, K. L., Schapaugh, T. W., Tuinstra, M. R., Vanderlip., R. L., & Milliken, G. (2008). Testing wheat in variable environments: Genotype, environment, interaction effects, and grouping test locations. Crop Science, 48(1), 317-330.
Sohrabi, S., dehghani, H., & Alizadeh, B. (2014). Grouping of Promising Winter Rapeseed (Brassica napus L.) Lines Based on Genotype × Environment Interaction. Seed and Plant Improvement Journal, 30(4), 807-819. [In Farsi]
Tahira, A. R., Khan, M. A., & Amjad, M. (2013). Stability analysis of canola (Brassica napus) genotypes in Pakistan. Global Advanced Research Journal of Agricultural Science, 10(2), 270-275.
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(5), 1111-1118.
Yan, W., Kang, M. S., Baoluo, M., Woods, Sh., & Cornelius, P. L. (2007). GGE Biplot vs. AMMI analysis of genotype-by-environment data. Crop Science, 47(2), 641-653.
Yan, W., Hunt, L. A., Sheng, Q., & Szlavnics, Z. (2000). Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science, 40(3), 597-605.
Yang, R., Crossa, J., Cornelius, P., & Bugueno, J. (2009). Biplot analysis of genotype x environment interaction: Proceed with caution. Crop Science, 49(5), 1564-1576.
Zhang, H., Berger, J. D., & Herrmann, C. (2017). Yield stability and adaptability of canola (Brassica napus L.) in multiple environment trials. Euphytica, 213(7), 1-21.
Zhang, H., Berger, J. D., & Milroy, S. P. (2013). Genotype × environment interaction studies highlight the role of phenology in specific adaptation of canola (Brassica napus) to contrasting Mediterranean climates. Field Crops Research, 144, 77-88.
Zobel, R. W., Wright, M. J., & Gauch Jr, H. G. (1988). Statistical analysis of a yield trial. Agronomy Journal, 80(3), 388-393.