اثر برهمکنش ژنوتیپ × محیط بر عملکرد شکر سفید ارقام چغندرقند (Beta vulgaris L.) با دوره رشد کوتاه درکشت زمستانه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 مؤسسه تحقیقات اصلاح و تهیه بذر چغندرقند، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران.

2 موسسه تحقیقات اصلاح و تهیه بذر چغندر قند، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران.

3 مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی خراسان رضوی، سازمان تحقیقات، آموزش و ترویج کشاورزی، مشهد، ایران.

4 مؤسسه تحقیقات اصلاح و تهیه بذر چغندرقند، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران

چکیده

توسعه و ترویج کشت زمستانه چغندرقند یکی از راهکار‌های مهم برای استفاده از بارندگی‌های فصلی و صرفه‌جویی در مصرف آب آبیاری برای تولید این محصول است. مطالعه حاضر با هدف بررسی اثر برهمکنش ژنوتیپ × محیط بر عملکرد شکر سفید و گزینش ارقام برتر با دوره رشد کوتاه از میان 11 رقم چغندرقند درکشت زمستانه در قالب طرح بلوک‌های کامل تصادفی با چهار تکرار در ایستگاه تحقیقات کشاورزی مغان در سه سال زراعی (1398-1397، 1399-1398 و 1400-1399)، در ایستگاه تحقیقات کشاورزی تربت‌جام در دو سال زراعی (1399-1398 و 1400-1399) و در مزرعه تحقیقاتی کشت و صنعت جوین در یک سال زراعی (1399-1398) اجرا شد. تجزیه واریانس مرکب داده ها نشان داد که اثر محیط، ژنوتیپ و برهمکنش ژنوتیپ × محیط بر عملکرد شکر سفید در سطح احتمال یک درصد معنی‌داری بود. نتایج بدستآمده از روش گرافیکی GGE بایپلات نشان داد که مؤلفه اصلی اول و دوم در مجموع 64/83 درصد از تغییرات کل عملکرد شکر سفید را توجیه کرد. بر اساس روش GGE بای‌پلات در محیط‌ مغان در سال 1400 ارقام SVZB2019 و دراووس و در محیط‌های مغان در سال‌های 1398 و 1399، تربت‌جام در سال های 1399 و 1400 و جوین در سال 1399 ارقام FDIR19B3021، FDIR19B4028 و SVZA2019 به ترتیب، از نظر پایداری عملکرد شکر سفید، برتر بودند. از نتایج این پژوهش چنین نتیجه گیری شد که محیط نقش بسزایی در بیان فنوتیپی عملکرد شکر سفید در کشت زمستانه ارقام چغندر قند مورد بررسی داشت. بنابراین لازم است ارقام بر اساس شرایط اقلیمی و زراعی مناطق هدف انتخاب و برای کشت زمسستانه معرفی شوند.

کلیدواژه‌ها


عنوان مقاله [English]

Genotype × Environment Interaction Effect on White Sugar Yield of Winter-Sown Short-Season Sugar Beet (Beta vulgaris L.) Cultivars

نویسندگان [English]

  • D. Taleghani 1
  • A. Saremirad 2
  • M. Hosseinpour 2
  • M. Ahmadi 3
  • H Hamidi 3
  • R. Nemati 4
1 Sugar Beet Seed Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran.
2 Sugar Beet Seed Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran.
3 Khorasan Razavi Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Mashhad, Iran.
4 Sugar Beet Seed Institute (SBSI)- Agricultural Research Education and Extension, Karaj, Iran.
چکیده [English]

The promotion and development of winter-sown sugar beet is one of the significant approaches for using seasonal rainfalls and saving irrigation water for its production. For this purpose, the present study was conducted to study the of genotype × environment interaction effect on white sugar yield of 11 winter season short-season sugar beet cultivars, and selection of superior cultivars in three regions of Moghan,(2019, 2020 and 2021) Torbat-e-Jam (2020 and-2021), and Joveyn (2020) using randomized complete block design with four replications. Combined analysis of variance showed that the environment, genotype and genotype × environment interaction had significant (P ≤ 0. 01) effect on white sugar yield. The GGE biplot method revealed that the first and second main components explained 83.64% of the total variation in white sugar yield. Based on the GGE biplot method, in Moghan 2021, cv. SVZB2019 and Dravos, and in Moghan 2019 and 2020, in Torbat-e-Jam 2020 and 2021 and in Joveyn 2020, cv. FDIR19B3021, cv. FDIR19B4028 and cv. SVZA2019 identified as the best cultivars with high white sugar yield and yield stability, respectively. In general, it was concluded that the environment played a significant role in phenotypic expression of the white sugar yield in the winter-sown short season sugar beet cultivars. Therefore, it would be necessary to select and release sugar beet cultivars adapted to climatic and agronomic conditions for winter sowing in target environments.

کلیدواژه‌ها [English]

  • Sugar beet
  • yield stability
  • phenotypic expression
  • GGE biplot
  • ideal environment
Alimoradi, A. 2002. Autumn–sown sugar beet characteristics. pp. 192-198. In: Proceedings of the 24th Seminar of Iranian Sugar Factories. Mashhad, Iran. (in Persian).
 
Anonymous. 2022. Determining longitude, latitude and altitude. Iran. https://www.bahesab.ir/map/geographic/ Basafa, M., and Taherian, M. 2016. Analysis of stability and adaptability of forage yield among silage corn hybrids. Journal of Crop Breeding 8 (19): 185-191.
 
Basati, J., Kolivand, M., Neamati, A., and Zareii, A. 2003. Study of autumn sowing of sugar beet in the tropical areas of Kermanshah province. Journal of Sugar Beet 18 (2): 119-130 (in Persian).
 
Choluj, D., Karwowska, R., Jasinska, M., and Haber, G. 2004. Growth and dry matter partitioning in sugar beet plants (Beta vulgaris L.) under moderate drought. Plant, Soil and Environment 50 (6): 265-272.
 
Chołuj, D., Wiśniewska, A., Szafrański, K. M., Cebula, J., Gozdowski, D., and Podlaski, S. 2014. Assessment of the physiological responses to drought in different sugar beet genotypes in connection with their genetic distance. Journal of Plant Physiology 171 (14): 1221-1230.
 
Cook, D., and Scott, R. 1993. The sugar beet crop: science into practice. Champan and Hall Press. New York, USA. 704 pp.
 
Durr, C., and Boiffin, J. 1995. Sugar beet seedling growth from germination to first leaf stage. The Journal of Agricultural Science 124 (3): 427-435.
 
Hassani, M., Hamze, H., and Mansouri, H. 2021. Evaluation of adaptability and stability of root yield and white sugar yield in sugar beet (Beta vulgaris L.) genotypes using multivariate AMMI and GGE biplot method. Journal of Crop Breeding 13 (37): 222-235 (in Persian).
 
Hassani, M., Heidari, B., Dadkhodaie, A., and Stevanato, P. 2018. Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.). Euphytica 214 (4): 1-21.
 
Hoffmann, C. M., and Kluge-Severin, S. 2011. Growth analysis of autumn and spring sown sugar beet. European Journal of Agronomy 34 (1): 1-9.
 
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.
 
Mavi, H. S., and Tupper, G. J. 2004. Agrometeorology: principles and applications of climate studies in agriculture. CRC Press. 447 pp.
 
Metzger, M. J., Bunce, R. G. H., Jongman, R. H., Mücher, C. A., and Watkins, J. W. 2005. A climatic stratification of the environment of Europe. Global Ecology and Biogeography 14 (6): 549-563.
 
Milford, G., Jarvis, P., and Walters, C. 2010. A vernalization-intensity model to predict bolting in sugar beet. The Journal of Agricultural Science 148 (2): 127-137.
 
Milford, G., and Limb, R. 2008. Bolting in sugar beet–time to re-evaluate our advice. British Sugar Beet Review 76: 3-5.
 
Mohammadian, R., Moghaddam, M., Rahimian, H., and Sadeghian, S. 2005. Effect of early season drought stress on growth characteristics of sugar beet genotypes. Turkish Journal of Agriculture and Forestry 29 (5): 357-368.
 
Mostafavi, K., Orazizadeh, M., Rajabi, A., and Ilkaei, M. N. 2018. Stability and adaptability analysis in sugar beet varieties for sugar content using GGE-biplot and AMMI methods. Bulgarian Journal of Agricultural Science 24 (1): 40-45.
 
Mostafavi, K., and Saremirad, A. 2021. Genotype-environment interaction study in corn genotypes using additive main effects and multiplicative interaction method and GGE- biplot method. Journal of Crop Production 14 (3):1-12 (in Persian).
 
Olivoto, T., and Lúcio, A. D. C. 2020. METAN: An R package for multi‐environment trial analysis. Methods in Ecology and Evolution 11 (6): 783-789.
 
Ranji, Z., Mesbah, M., Amiri, R., and Vahedi, S. 2005. Study on the efficiency of AMMI method and pattern analysis for determination of stability in sugar beet varieties. Iranian Journal of Crop Science 7 (1): 1-20 (in Persian).
 
Rinaldi, M., and Vonella, A. V. 2006. The response of autumn and spring sown sugar beet (Beta vulgaris L.) to irrigation in southern Italy: water and radiation use efficiency. Field Crops Research 95 (2-3): 103-114.
 
Saremirad, A., and Mostafavi, K. 2021. Using AMMI and biplot graphical analysis multivariate methods to evaluate the effect of genotype-environment interaction in cotton genotypes. Iranian Journal of Cotton Researches 8 (2): 127-144 (in Persian).
 
Saremirad, A., Mostafavi, K., and Mohammadi, A. 2020. Genotype-environment interaction study based on the GGE biplot method for kernel yield in sunflower (Helianthus annuus L.) cultivars. Journal of Crop Breeding 12 (34): 43-53 (in Persian).
 
Saremirad, A., and Taleghani, D. 2022. Utilization of univariate parametric and non-parametric methods in the stability analysis of sugar yield in sugar beet (Beta vulgaris L.) hybrids. Journal of Crop Breeding 14 (43): 49-63 (in Persian).
 
Schnepel, K., and Hoffmann, C. 2016. Effect of extending the growing period on yield formation of sugar beet. Journal of Agronomy and Crop Science 202 (6): 530-541.
 
Streibie, J. C., Ritz, C., Pipper, C. B., Yndgaard, F., Fredlund, K., and Thomsen, J. N. 2009. Sugar beet, bioethanol, and climate change. pp. 820-821. In: IOP Conference Series. Earth and Environmental Science Volume 6. IOP Publishing.
 
Taleghani, D., Moharamzadeh, M., Hemayati, S. S., Mohammadian, R., and Farahmand, R. 2011. Effect of sowing and harvest time on yield of autumn-sown sugar beet in Moghan region in Iran. Seed and Plant Production Journal 27 (2): 355-371.
 
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. 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 Tinker, N. A. 2005. An integrated biplot analysis system for displaying, interpreting, and exploring genotype × environment interaction. Crop Science 45 (3): 1004-1016.
 
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.