ارزیابی پایداری عملکرد دانه ژنوتیپ های کلزا بهاره با استفاده از تجزیه بای پلات GGE

نوع مقاله : مقاله پژوهشی

نویسندگان

1 موسسه تحقیقات اصلاح وتهیه و نهال بذر، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران.

2 بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان مازندران، سازمان تحقیقات، آموزش و ترویج کشاورزی، ساری، ایران.

3 بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان گلستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، گرگان، ایران.

4 بخش تحقیقات علوم زراعی و باغی مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی سیستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، زابل، ایران.

5 بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان بوشهر، سازمان تحقیقات، آموزش و ترویج کشاورزی، برازجان، ایران.

چکیده

بررسی اثر متقابل ژنوتیپ × محیط در انتخاب ژنوتیپ‌های برتر نقش مهمی دارد و یکی از مهمترین پدیده ها در برنامه‌های به‌نژادی محصولات زراعی است. به منظور بررسی سازگاری و پایداری عملکرد دانه کلزا در چهار منطقه از اقلیم گرم و خشک جنوب و گرم مرطوب شمال ایران، 16 لاین‌ آزاد گرده ‌افشان کلزای بهاره در قالب طرح بلوک‌های کامل تصادفی با سه تکرار در دو سال زراعی (95-1394 و 96-1395) کشت شدند. تجزیه واریانس مرکب داده‌ها نشان داد که اثر محیط بر و اثر متقابل ژنوتیپ × محیط بر عملکرد دانه در سطح احتمال یک درصد معنی‌دار بود. تجزیه داده‌ها با استفاده از روش GGE منجر به شناسایی چهار محیط کلان: گرگان (شامل لاین G8)، ساری (شامل لاین‌ G4)، ساری-زابل (شامل لاین G13) و زابل (شامل لاین G10) شد. لاین‌های G4 (آسا)، G8 (روشنا) و G13 (آرام) به‌ترتیب با 2581، 2509 و 2774 کیلوگرم در هکتار دارای بالاترین میانگین عملکرد دانه و پایداری عملکرد بودند که در بین آنها لاین G4 بیشترین پایداری عملکرد را داشت. رقم G11 (شاهد) با 2155 کیلوگرم در هکتار در گروه لاین‌های با عملکرد دانه پایین و ناپایدار طبقه‌بندی شد. به‌طورکلی، گروه‌بندی لاین‌های بهاره کلزا بر اساس اثر متقابل ژنوتیپ × محیط با استفاده از روش GGE بای‌پلات رهیافتی مفید برای آزادسازی ارقام کلزای بهاره با پتانسیل عملکرد بالا و پایدار برای محیط‌های هدف است.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluation of Seed Yield Stability of Spring Rapeseed Genotypes Using GGE Biplot Analysis

نویسندگان [English]

  • H. Amiri Oghan 1
  • V. Rameeh 2
  • A. Faraji 3
  • , H. R. Fanaei 4
  • N. Kh. Kazerani 5
  • S. Rahmanpour 1
1 Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran.
2 Field and Horticultural Crops Sciences Research Department, Agricultural and Natural Resources Research and Education Center of Mazandaran, Agricultural Research, Education and Extension Organization, Sari, Iran.
3 Field and Horticultural Crops Sciences Research Department, Agricultural and Natural Resources Research and Education Center of Golestan, Agricultural Research, Education and Extension Organization, Gorgan, Iran.
4 Field and Horticultural Crops Sciences Research Department Agricultural and Natural Resources Research and Education Center of Sistan, Agricultural Research, Education and Extension Organization, Zabol, Iran.
5 Field and Horticultural Crops Sciences Research Department, Agricultural and Natural Resources Research and Education Center of Bushehr, Agricultural Research, Education and Extension Organization, Borazjan, Iran.
چکیده [English]

The phenomenon of genotype × environment interaction has important implications for selection of superior genotypes which remains as one of the main goals of crop breeding programs. To investigate the seed yield stability of promising spring rapeseed lines in four field stations in southern warm and dry and northern warm and humid agro-climatic zones of Iran. Sixteen open pollinated spring promising rapeseed lines were sown in randomized complete block design with three replications in two cropping cycles (2015-16 and 2016-17). Combined analysis of variance revealed that the effect of environment on seed yield was significant (P < 0.01). Genotype × environment interaction effect was also significant (P < 0.01) on seed yield. GGE biplot analysis identified four mega-environments; Gorgan (Line G8), Sari (Line G4), Sari-Zabol (Line G13) and Zabol (Line G7). Lines G4 (Asa), G8 (Roshana) and G13 (Aram) with average seed yield of 2714, 3349 and 3817 kg ha-1, respectively, had higher seed yield and yield stability. Line G4 had the highest seed yield stability. Line G11 (RGS003) with 2155 kg ha-1 was identified as low yielding with low seed yield stability. Generally, grouping of spring rapeseed lines based on genotype × environment interaction using GGE biplot analysis is a useful approach for selection and releasing of spring rapeseed cultivars with high seed yield potential and yield stability for target environments.

کلیدواژه‌ها [English]

  • Spring rapeseed
  • mega-environment
  • wide adaptation
  • specific adaptation

مراجع

Abdel-Moneim, A. M. E., Sabic, E. M., Abu-Taleb, A. M., and Ibrahim, N. S. 2020. Growth performance, hemato-biochemical indices, thyroid activity, antioxidant status, and immune response of growing Japanese quail fed diet with full-fat canola seeds. Tropical Animal Health and Production 1–10.
 
Ali, N., Javidfar, F., and Mirza, Y. 2003. Selection of stable rapeseed (Brassica napus L.) genotypes through regression analysis. Pakistan Journal of Botany 35 (2): 175–180.
 
Anonymous, 2018. FAO Statistical Data. Available on: www.faostat.org. Arun, N., and Dalai, A. K. 2020. Environmental and socioeconomic impact assessment of biofuels from lignocellulosic biomass. Lignocellulosic Biomass to Liquid Biofuels 283–299.
 
Azizinia, S., and Mortazavian, M. M. 2015. A yield stability survey in winter type canola using univariate methods and genotypic distribution pattern. Journal of Crop Production and Processing 5 (15): 57–68.
 
Bocianowski, J., Liersch, A., and Nowosad, K. 2020. Genotype by environment interaction for alkenyl glucosinolates content in winter oilseed rape (Brassica napus L.) using additive main effects and multiplicative interaction model. Current Plant Biology 100137.
 
Brandiej, E., and Meverty, B. E. 1994. Genotype × environmental interaction and stability of seed yield of oil rapeseed. Crop Science 18: 344–353.
 
Comstock, R. E., and Moll, R. H. 1963. Genotype-environment interactions. pp. 164-196. In: Hanson, D., and Robinson, H. F. (eds.) statistical genetics and plant breeding.
 
Washington, National Academy of Science. Crossa, J. 1990. Statistical analyses of multilocation trials. Advances in Agronomy 44: 55–85.
 
Dehghani, H., Sabaghnia, N., and Moghaddam, M. 2009. Interpretation of genotype-by-environment interaction for late maize hybrids' grain yield using a biplot method. Turkish Journal of Agriculture and Forestry 33 (2): 139–148.
 
Eberhart, S. A. T, and Russell, W. A. 1966. Stability parameters for comparing varieties. Crop Science 6 (1): 36–40. Excel. 2016. Microsoft office excel spreadsheet software. Microsoft Corporation One Microsoft Way Redmond, WA 98052-6399, USA.
 
Falconer, D. S. 1952. The problem of environment and selection. The American Naturalist 86 (830): 293–298.
 
Finlay, K. W., and Wilkinson, G. N. 1963. The analysis of adaptation in a plant-breeding programme. Australian Journal of Agricultural Research 14 (6): 742–754.
 
Freeman, G. H., and Perkins, J. M. 1971. Environmental and genotype-environmental components of variability VIII. Relations between genotypes grown in different environments and measures of these environments. Heredity 27 (1): 15–23.
 
Gauch, H. G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Science 46 (4): 1488–1500.
 
Gauch, H. G. 2013. A simple protocol for AMMI analysis of yield trials. Crop Science 53 (5): 1860–1869.
 
Getahun, A. 2017. Adaptability and stability analysis of groundnut genotypes using AMMI model and GGE-biplot. Journal of Crop Science and Biotechnology 20 (5): 343–349.
 
Ghazvini, H., Marandi, M., and Amini Sefidab, A. 2019. Evaluation of grain yield stability and genetic variation in salt-tolerant bread wheat promising lines and cultivars. Seed and Plant Journal 35 (1): 1–25 (in Persian).
 
Hongyu, K., García-Peña, M., de Araújo, L. B., dos Santos Dias, C. T., Araújo, L. B. de, Santos Dias, C. T. dos, de Araújo, L. B., and dos Santos Dias, C. T. 2014. Statistical analysis of yield trials by AMMI analysis of genotype×environment interaction. Biometrical Letters 51 (2): 89–102.
 
Jankowski, K. J., Załuski, D., and Sokólski, M. 2020. Canola-quality white mustard: Agronomic management and seed yield. Industrial Crops and Products 145: 112138.
 
Javidfar, F., Alizadeh, B., Amiri Oghan, H., and Sabaghnia, N. 2010. A study of genotype by environment interaction in oilseed rape genotypes using GGE Biplot method. Iranian Journal of Field Crop Science 41 (4): 771–779.
 
Kaya, Y., Akçura, M., and Taner, S. 2006. GGE-biplot analysis of multi-environment yield trials in bread wheat. Turkish Journal of Agriculture and Forestry 30 (5): 325–337.
 
Lewis, D. 1954. Gene-environment interaction: A relationship between dominance, heterosis, phenotypic stability and variability. Heredity 8 (3): 333–356.
 
Lin, C. S., Binns, M. R., and Lefkovitch, L. P. 1986. Stability analysis: Where do we stand? Crop Science 26 (5): 894–900.
 
Marjanović-Jeromela, A., Nagl, N., Gvozdanović-Varga, J., Hristov, N., Kondić-Špika, A., and Marinković, M. V. R. 2011. Genotype by environment interaction for seed yield per plant in rapeseed using AMMI model. Pesquisa Agropecuária Brasileira 46 (2): 174–181.
 
Miah, M. A., Rasul, M. G., Mian, M. A. K., and Rohman, M. M. 2015. Evaluation of rapeseed lines for seed yield stability. International Journal of Agronomy and Agricultural Research 7: 12–19.
 
Moreno-Gonzalez, J., Crossa, J., and Cornelius, P. L. 2004. Genotype × environment interacion in multi-environment trials using shrinkage factors for AMMI models. Euphytica 137 (1): 119–127.
 
Muir, W., Nyquist, W. E., and Xu, S. 1992. Alternative partitioning of the genotype-by-environment interaction. Theoretical and Applied Genetics 84 (1–2): 193–200.
 
Nowosad, K., Liersch, A., Popławska, W., and 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 (1): 187–194.
 
Pacheco, Á., Vargas, M., Alvarado, G., Rodríguez, F., Crossa, J., and Burgueño, J. 2018. GEA-R (genotype × environment analysis with R for windows) version 4.1.
 
Perkins, J. M., and Jinks, J. L. 1968. Environmental and genotype-environmental components of variability. Heredity 23 (3): 339–356.
 
Rempel, C., Li, X., Geng, X., Liu, Q., and Zhang, Y. 2020. Manufacture of defatted canola meal with enhanced nutritive composition by air classification on an industrial scale. Journal of the Science of Food and Agriculture 100 (2): 764–774.
 
Roy, D. 2001. Plant Breeding: Analysis and exploitation of variation. Alpha Science International. 701 pp.
 
Samonte, S. O. P. B. B., Wilson, L. T., McClung, A. M., and Medley, J. C. 2005. Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analyses. Crop Science 45 (6): 2414–2424. SAS. 2017. Step-by-step programming with Base SAS® 9.4. Second Edition.
 
Cary, NC: SAS Institute Inc. Shukla, G. K. 1972. Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29 (2): 237–245.
 
Tadele, T., Gashaw, S., and Amanuel, T. 2018. Genotypes × environment interaction analysis for Ethiopian mustard (Brassica carinata L.) genotypes using AMMI model. Journal of Plant Breeding and Crop Science 10 (4): 86-92.
 
Tai, G. C. C. 1971. Genotypic stability analysis and its application to potato regional trials. Crop Science 11 (2): 184–190.
 
Wricke, G. 1962. Uber eine methode zur erfassung der okologischen streubreite in feldverzuchen. Z. Pflanzenzucht 47: 92–96.
 
Yan, W., and Hunt, L. A. 2001. Interpretation of genotype × environment interaction for winter wheat yield in Ontario. Crop Science 41 (1): 19–25.
 
Yan, W., Hunt, L. A., Sheng, Q., and Szlavnics, Z. 2000. Cultivar evaluation and mega environment investigation based on the GGE biplot. Crop Science 40 (3): 597–605.
 
Yan, W., and Kang, M. S. 2002. GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press. Boca Raton, Florida, USA. 288 pp.
 
Yan, W., Kang, M. S., Ma, B., Woods, S., and Cornelius, P. L. 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science 47 (2): 643–653.
 
Yan, W., and Rajcan, I. 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Science 42 (1): 11–20.
 
Yan, W., and Tinker, N. A. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86 (3): 623–645.
 
Yang, R. C., Crossa, J., Cornelius, P. L., and Burgueño, J. 2009. Biplot analysis of genotype × environment interaction: Proceed with caution. Crop Science 49 (5): 1564–1576.
 
Zeng, X., Zou, D., Wang, A., Zhou, Y., Liu, Y., Li, Z., Liu, F., Wang, H., Zeng, Q., and Xiao, Z. 2020. Remediation of cadmium-contaminated soils using Brassica napus: Effect of nitrogen fertilizers. Journal of Environmental Management 255: 109885.
 
 
نهال و بذر
دوره 36، شماره 2
تیر 1399
صفحه 207-222

فایل ها

هم رسانی

ارجاع به این مقاله

آمار