| تعداد نشریات | 284 |
| تعداد نشریات سازمان | 82 |
| تعداد شماره‌ها | 3,034 |
| تعداد مقالات | 33,949 |
| تعداد مشاهده مقاله | 45,405,654 |
| تعداد دریافت فایل اصل مقاله | 68,382,574 |
اثرات متقابل ژنوتیپ در محیط بر روی عملکرد دانه ژنوتیپ‌های گندم زمستانه کشت شده در شرایط دیم | ||
| پژوهش های کاربردی زراعی | ||
| دوره 34، شماره 1 - شماره پیاپی 130، خرداد 1400، صفحه 55-70 اصل مقاله (684.6 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/aj.2021.343298.1478 | ||
| نویسنده | ||
| صابر گلکاری* | ||
| عضو هیات علمی بخش تحقیقات غلات موسسه تحقیقات کشاورزی دیم کشور | ||
| چکیده | ||
| به منظور مطالعه اثر متقابل ژنوتیپ × محیط بر روی عملکرد دانه، این تحقیق با 24 ژنوتیپ گندم نان در 21 محیط مختلف شامل ایستگاه‌های تحقیقات کشاورزی مراغه، سرارود، قاملو، شیروان، اردبیل، اراک و زنجان در شرایط دیم طی سه سال زراعی (۱۳۹۲-۹۵) انجام گرفت. نتایج تجزیه مرکب عملکرد دانه نشان داد که اثر اصلی ژنوتیپ، محیط و اثر متقابل ژنوتیپ × محیط در سطح احتمال 1% معنی‌دار بوده و بزرگی اثر متقابل ژنوتیپ × محیط نسبت به اثر ژنوتیپ تقریبا" سه برابر بود که بیانگر وجود گروه‌های محیطی مختلف در برنامه به نژادی گندم دیم کشور است. بر اساس نتایج GGE بای پلات، محیط‌ها در دو گروه و ژنوتیپ‌ها در پنج گروه مشخص گروهبندی شدند. ژنوتیپ‌های شماره 1 (رقم شاهد آذر2) و 21 (لاین به نژادی) هرکدام سازگاری خصوصی بالایی را به یکی از دو گروه محیطی متفاوت نشان دادند. در این تحقیق محیط‌های سرد به خوبی توسط تجزیه گرافیکی از محیط‌های معتدل سرد متمایز شده و لاین برتر در هر گروه شناسایی شد. در بین لاین‌های مورد ارزیابی ژنوتیپ شماره ۲۰ به عنوان ژنوتیپ ایده‌ال بهترین ترکیب را از نظر میانگین عملکرد دانه بیشتر و پایداری عملکرد بالا به نمایش گذاشت. بکارگیری تجزیه‌های گرافیکی در این تحقیق توانمندی بالای این روش را در ارزیابی همزمان ژنوتیپ‌ها و محیط‌های اجرای آزمایش به نمایش گذاشت و اطلاعات بسیار مفیدی از ژنوتیپ‌ها و محیط‌های برتر را برای استفاده در برنامه‌های به نژادی فراهم کرد. | ||
| کلیدواژه‌ها | ||
| پایداری عملکرد؛ GGE Bi-plot؛ گندم نان؛ مناطق سرد و معتدل سرد | ||
| عنوان مقاله [English] | ||
| Genotype × Environment Interactions effects on Grain Yield in Winter Wheat grown under rainfed conditions | ||
| نویسندگان [English] | ||
| SABER GOLKARI | ||
| Cereal department Dryland Agriculture Research Institute (DARI) | ||
| چکیده [English] | ||
| IIntroduction Wheat (Triticum aestivum L.) is widely grown across Central and West Asia. Evaluation of wheat genotypes in different environments is essentials to estimate Genotypes x Environment interactions (Fan et al., 2007). Breeding new cultivars require evaluation of yield stability and the adaptability of high yielding genotypes across different environments. Statistical approaches such as GGE Biplot analysis reveals genotypic main effects, as well as G × E interactions. GGE biplots analysis provides a graphical depiction of relationships among environments, genotypes and their interactions in an effective manner (Yan et al., 2000). This method has been used to examine the uniformity of different environments and to identify superior genotypes in multi-environment trials. This study aimed to determine the effects of genotype by environment interactions on the performance of 22 promising wheat lines along with two check cultivars and to identify the ideal genotypes for the examined environments. Material and Methods Wheat promising lines including 22 elite lines along with two commercial cultivars Azar2 and Ohadi were evaluated in a randomized complete block design (RCBD) experiment with 4 replications under rainfed dryland conditions. Uniform experiments were carried out for 3 years (2013-16) at the agricultural research stations of Maragheh, Sararod, Ghamlo, Zanjan, Ardabil, Shirvan and Arak representing cold and moderate cold dryland wheat growing regions of Iran. Entries were planted in plots with 6 x 1.2 dimension (6-m long rows, 17.5 cm apart and seeding rate of 380 kernel/m2). Morphological characteristics, phenological stages and yield components were scored during growth period. Grain yield and thousands kernel weight were determined for each plot after harvest. The collected yield data were subjected to combined ANOVA considering environment as random and genotypes as fixed effects using SAS (9.1) statistical software. Yield stability parameters were estimated and GGE biplot analysis was carried out using open-source software R packages. Results and Discussion The ANOVA revealed significant differences among genotypes for yield performance. Significant genotype by environment interactions were also identified. The main effect for environments and genotypes by environment interactions accounted for 82.3 and 6.5 percent of total variances in the experiment. The grand total mean grain yield for the evaluated wheat genotypes was 2165 kg/ha. Genotypes G21 and G23 produced the highest (2378 kg/ha) and the lowest (1747 kg/ha) yields, respectively. Genotype G20 was found as the most stable according to the estimated stability parameters (Table.3). Based on polygonal GGE biplot, two environmental groups and five different groups for the examined genotypes were detected (Fig. 1). The first environmental group included Maragheh (M92, M93), Ghamlo (G92, G93, G94), Ardabil (A93), Zanjan (Z92, Z93), Arak (Ak92, Ak93, Ak94) and Shirvan (Sh93, Sh94), with the G21 being as the best performer in the group. The second environmental group included 8 remaining environments with the G1 (Azar2) as the highest yielding genotype in the group (Fig. 1). Simultaneous evaluation of genotypes for yield and yield stability identified genotypes G20, G13 G2 and G6 as high yielding with high yield stability (Fig. 2). An environment is considered ideal tester for evaluating a set of genotypes if it is able to effectively differentiate genotypes based on traits of interest and if it properly represents other environments (Yan & Tinker, 2006). Our findings revealed environments Z92, Sh92 and Ak92 as the most ideal environments and environments A92, S93, Ak92, M92, M94 as the highly discriminative of the examined genotypes (Fig.3). The genotype G20 with higher average yield and the least contribution into GxE interactions showed the characteristics of an ideal genotype (Fig. 4). Conclusion Analysis of yield data revealed that, the variations due to G × E interactions were largely explained by the environment effects. In this study, cold environments were grouped closely, showing a similar behavior in discriminating the examined genotypes and were easily distinguished from the moderate cold environments. Acute angles among environments M92, G92, Sh93, Ak93, Sh94, G94, Ak94, Z93, A93, Ak92 and also among environments S94, Sh92, M94, Z94, S92 indicated high correlations in discriminating the studied genotypes. We found the genotype G20 as the ideal genotypes possessing high yield performance and high yield stability. | ||
| کلیدواژه‌ها [English] | ||
| Bread wheat, Cold and moderate cold regions, GGE Bi-plot, Yield stability | ||
| مراجع | ||
|
Barah, B. C., Binswanger, H. P., Rana, B.S., and Rao, N. G. P. 1981. The use of risk aversion in plant breeding; concept and application. Euphytica, 30: 451–458. doi: 10.1007/BF00034010 Bhan, M. K., Pal, S., Rao, B. L., Dhar, A. K., and Kang, M. S. 2005. GGE bi-plot analysis of oil yield in lemongrass. Journal of New Seeds, 7: 127–139. doi: 10.1300/J153v07n02_07 Blanche, S. B., and Myers, G. O. 2006. Identifying discriminating locations for cultivar selection in Louisiana. Crop Science, 46: 946–949. doi: 10.2135/cropsci2005.0279 Brancourt-Hulmel, M., and Lecomte, C. 2003. Effect of environmental varieties on genotype 3 environment interaction of winter wheat: A comparison of bi-additive factorial regression to AMMI. Crop Science, 43: 608–617. Casanoves, F., Baldessari, J., and Balzarini, M. 2005. Evaluation of multi-environment trials of peanut cultivars. Crop Science, 45: 18–26. Cooper, M., and Hammer, G. L. 1996. Plant adaptation and crop improvement. CAB International: Wallingford, UK; ICRISAT: Patancheru, India; and IRRI: Manila, The Philippines. Cooper, M., and DeLacy, I. H. 1994. Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiments. Theoretical and Applied Genetics, 88: 561–572. Dardanelli, J. L., Balzarini, M., Martínez, M. J., Cuniberti, M., Resnik, S., Ramunda, S. F., Herrerob, R., and Baigorrib, H. 2006. Soybean maturity groups, environments, and their interaction define mega-environments for seed composition in Argentina. Crop Science, 46: 1939–1947. doi: 10.2135/cropsci2005.12-0480 Baxevanos, D., Goulas, C., Rossi, J., and Braojos, E. 2008. Separation of cotton cultivar testing sites based on representativeness and discriminating ability using GGE Biplots. Agronomy Journal, 100: 1230–1236. Eskridge, K. M. 1990. Selection of stable cultivars using a safety-first rule. Crop Science, 30: 369–374. Fan, X. M., Kang, M. S., Chen, H., Zhang, Y., Tan, J., and Xu, C. 2007. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agronomy Journal, 99: 220–228. Gabriel, K. R. 1971. The bi-plot graphic display of matrices with application to principal component analysis. Biometrika, 58: 453–467. Gauch, H. G. 1992. Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. Elsevier Science Publishers, Amsterdam, Netherlands. Gauch, H. G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Science, 46: 1488–1500. Gauch, H. G., and Zobel, R. W. 1997. Identifying mega-environments and targeting genotypes. Crop Science, 37: 311–326. Gauch H. G., and Zobel R.W. 1996. AMMI analysis of yield trials. In ‘Genotype-by- environment interaction’. (Eds MS Kang, HG Gauch, Jr) pp. 85–122. CRC Press: Boca Raton, FL. Gravois K. A., and Bernhardt, J. L. 2000. Heritability and genotype by environment interactions for discolored rice kernels. Crop Science, 40: 314–318. Huhen, M. 1996. Nonparametric analysis of genotype_environment interactions by ranks. In ‘Genotype-by-environment interaction’. (Eds. MS Kang, HG Gauch, Jr) pp. 235–271. CRC Press: Boca Raton, FL. Kang, M. S. 1988. A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Research Communications, 16: 113–115. Kang, M. S. 1993. Simultaneous selection for yield and stability in crop performance trials: Consequences for growers. Agronomy Journal, 85: 754–757. Kang, M.S. 1998. Using genotype-by-environment interaction for crop cultivar development. Advances in Agronomy, 62:199–252. doi: 10.1016/S0065-2113(08)60569-6. Kang, M. S., Aggarwal, V. D., Chirwa, R. M. 2006. Adaptability and stability of bean cultivars as determined via yield-stability statistic and GGE bi-plot analysis. Journal of Crop Improvement, 15: 97–120. doi: 10.1300/J411v15n01_08 Kang, M. S., and Pham, H. N. 1991. Simultaneous selection for high yielding and stable crop genotypes. Agronomy Journal, 83: 161–165. Kaya, Y., Akcura, M., and Taner, S. 2006. GGE-bi-plot analysis of multi environment yield trials in bread wheat. Turkish Journal of Agriculture and Forestry, 30: 325–337. Kempton, R. A. 1984. The use of bi-plots in interpreting variety-by-environment interactions. Journal of Agricultural Science, 103: 123–135. Koemel, J. E., Guenzi, A. C., Carver, B. F. and Payton, M. E. 2004. Hybrid and pure line hard red winter wheat yield and stability. Crop Science, 40:107–113. Lage, J., Trethowan, R. M. and Hernandez, E. 2008. Identification of site similarities in western and central Asia using CIMMYT international wheat yield data. Plant Breeding, 127:350–354. Lin, C. S., Binns, M. R., and Lefcovitch, L. P. 1986. Stability analysis: Where do we stand? Crop Science, 26: 894– 900. Magari, R., and Kang, M.S. 1993. Genotype selection via a new yield stability statistic in maize yield trials. Euphytica, 70: 105–111. doi: 10.1007/BF00029647. Malvar, R. A., Revillaa, P., Butrona, A., Gouesnardc, B., Boyatc, A., Soengasa, P., Alvarezb, A., and Ordas, A. 2005. Performance of crosses among French and Spanish maize populations across environments. Crop Science, 45: 1052–1057. doi: 10.2135/cropsci2004.0301 Mohammadi, R., Armion, M., Zadhassan, E., Ahmadi, M.M., and Sadeghzadeh Ahari, D. 2012. Genotype × Environment interaction for grain yield of rainfed durum wheat using the GGE bipot Model. Seed and Plant I. Journal, 3: 503-518. Mohammadi, R., Haghparast, R., Amri, A., and Ceccarelli, S. 2010. Yield stability of rainfed durum wheat and GGE bi-plot analysis of multi-environment trials. Crop and Pasture Science, 61: 92–101. Özgen, M. 1991. Yield stability of winter wheat (Triticum sp.) cultivars and lines. Journal Agronomy Crop Science, 166: 318–327. Pazdernik, D. L., Hardman, L. L., and Orf, J. H. 1997 Agronomic performance and stability of soybean varieties grown in three maturity zones in Minnesota. Journal of Production Agriculture, 10: 425–430. Sharma, R. C., Morgounov, A. I., Braun, H. J., Akin, B., Keser, M., Bedoshvili, D. and van Ginkel, M. 2009. Identifying high yielding stable winter wheat genotypes for irrigated environments in Central and West Asia. Euphytica, 171: doi 10.1007/s10681-009-9992-6. Rose, I. V. L. W., Das, M. K., and Taliaferro, C. M. 2008. A comparison of dry matter yield stability assessment methods for small numbers of genotypes of bermudagrass. Euphytica, 164: 19–25. Samonte, S. O. P., Wilson, L. T., McClung, A. M. and Medley, J. C. 2005. Targeting cultivars onto rice growing environments using AMMI and SREG GGE bi-plot analyses. Crop Science, 45: 2414–2424. doi: 10.2135/cropsci2004.0627 Sharma, R. C., Smith, E. L., and McNew, R. W. 1987. Stability of harvest index and grain yield in winter wheat. Crop Science, 27:104–108. Shukla, G. K. 1972. Some statistical aspects of partitioning genotype environmental components of variability. Heredity, 29: 237–245. doi: 10.1038/hdy.1972.87. Thomason, W. E., and Phillips, S. B. 2006. Methods to evaluate wheat cultivar testing environments and improve cultivar selection protocols. Field Crops Research, 99: 87–95. doi: 10.1016/j.fcr.2006.03.007 Trethowan, R. M., Van Ginkel, M., Ammar, K. and Crossa, J. 2003. Associations among twenty years of bread wheat yield evaluation environments. Crop Science, 43:1698–1711. Van Ginkel, M., and Rajaram, S. 1993. Breeding for durable resistance to diseases in wheat: an international perspective. In: Jacobs T, Parleviet J (eds) Durability of disease resistance. Kluwer, Dordrecht, pp 259–272. Voltas, J., Lopez-Corcoles, H., and Borras, G. 2005. Use of bi-plot analysis and factorial regression for the investigation of superior genotypes in multi-environment trials. European Journal of Agronomy, 22: 309–324. doi: 10.1016/j.eja.2004.04.005 Waldron, B. L., Asay, K. H., and Jensen, K. B. 2002. Stability and yield of cool-season pasture grass species grown at five irrigation levels. Crop Science, 42: 890–896. 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., Hunt, L. A., Sheng, Q., and Szlavnics, Z. 2000. Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Science, 40: 596–605. Yan, W., and Kang, M. S. 2003. ‘GGE Biplot analysis: A graphical tool for breeders, geneticists, and agronomists.’ CRC Press: Boca Raton, FL. 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: 643–655. Yan, W., and Rajcan, I. 2002 Biplot evaluation of test sites and trait relations of soybean in Ontario. Crop Science, 42: 11–20. Yan, W. 2002. Singular-value partitioning in biplot analysis of multi-environment trial data. Agronomy Journal, 94: 990–996. Yan, W., and Tinker, N. A. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science, 86: 623–645. | ||
|
آمار تعداد مشاهده مقاله: 674 تعداد دریافت فایل اصل مقاله: 439 |
||