تجزیه پایداری عملکرد دانه در ژنوتیپ‌های گندم دوروم (Triticum turgidum L. var. durum) در شرایط مختلف اقلیمی با استفاده از مدل AMMI

نوع مقاله : علمی - پژوهشی

نویسندگان

1 بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی خراسان رضوی، سازمان تحقیقات، آموزش و ترویج کشاورزی، مشهد، ایران

2 بخش تحقیقات غلات، موسسه تحقیقات اصلاح و تهیه نهال و بذر، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران

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

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

چکیده

هدف از این پژوهش تجزیه اثر متقابل ژنوتیپ×محیط بر عملکرد دانه 18 لاین گندم دوروم (Triticum turgidum L. var. durum) با استفاده از تجزیه مدل اثر اصلی افزایشی و ضرب‌پذیر امی (AMMI) و نیز ارزیابی ژنوتیپ‌ها، محیط و اثر متقابل آنها با استفاده از آماره‌های پایداری و اکووالانس ریک بود. آزمایشات در سه ایستگاه تحقیقات کشاورزی کرج، نیشابور و کرمانشاه طی سال‌های 96-1394 به مدت دو سال زراعی اجرا شدند. نتایج حاصل از تجزیه امی بر عملکرد دانه نشان داد که اثر اصلی سال و مکان و اثر متقابل آنها با ژنوتیپ و همچنین دو مؤلفه اول اثر متقابل معنی‌دار بودند. نمودار بای‌پلات امی قادر به تفکیک ژنوتیپ‌های پایدار و محیط‌های با قدرت تفکیک بالا از محیط‌های ضعیف بود. نتایج نشان داد که محیط کرج طی هر دو سال زراعی بیشترین نقش را در ایجاد اثر متقابل ژنوتیپ×محیط دارا بود؛ در حالی­که محیط نیشابور طی هر دو سال زراعی پایدارترین محیط بود و کمترین نقش را در ایجاد اثر متقابل ژنوتیپ×محیط داشت. همچنین محیط کرمانشاه طی سال زراعی 96-1395 مشابه کرج و در سال زراعی 95-1394 مشابه محیط نیشابور عمل کرد. بر اساس نتایج، ژنوتیپ‌های 17 و 18 ناپایدارترین ژنوتیپ‌ها تشخیص داده شدند. همچنین در مجموع، سال‌ها و سه منطقه، ژنوتیپ 16 دارای قدرت پایداری بالا و عملکرد مطلوب در محیط‌های مورد مطالعه بود.

کلیدواژه‌ها


عنوان مقاله [English]

Stability Analysis of Seed Yield in Durum Wheat Genotypes (Triticum turgidum L. var durum) using AMMI Analysis

نویسندگان [English]

  • Ali Akbar Moayyedi 1
  • Tohid Najafi Mirak 2
  • Gholam Hissein Ahmadi 3
  • Akbar Ghandi 4
1 Department of Horticulture Crop Research , Khorasan Razavi Agricultural and Natural Resources Resaerch and Education Center, AREEO, Mashhad, Iran.
2 Department of Cereal Research , Seed and Plant Research Improvement Institute, AREEO, Karaj, Iran
3 Department, of Horticulture Crop Research Kermanshah Agricultural and Natural Resources Resaerch and Education Center, AREEO, Kermanshah, Iran.
4 Department of Horticulture Crop Research , Isfahan Agricultural and Natural Resources Resaerch and Education Center, AREEO, Isfahan, Iran.
چکیده [English]

Introduction[1]
Durum wheat (Triticum turgidum L. var durum) consists of only 5% of the world’s total cultivated wheat area and contributes about 10% to the total global wheat production. In recent years, the production level of durum wheat has risen to more than 30 million tons and EU, USA and Canada together representing 60% of the production. Durum wheat in Iran is grown on 300-400 thousand hectares with an average annual production of 500-600 thousand tons. Increase in yield is one of the primary aims pursued in plant breeding programs. Similar to other crops, insufficient yield stability in durum wheat is recognized as a one of the factors responsible for the gap between actual yield and potential yield. In breeding programs, the identification of superior genotypes is difficult due to environmental variability of target locations and the interaction of these variability with the investigated genotypes. Therefore, it is important to evaluate the advanced agronomic lines across various environments and over multiple years to ensure their yield stability and production. Many statistical models have been suggested to analyze G×E interaction. The additive main effects and multiplicative Interaction (AMMI) model is a multivariate statistical method that entirely justifies genotype and environment main effects as well as multiplicative G×E interaction effects. This method provides a clear interpretation of G×E interaction effect. The objectives of this study were to analyze genotype by environment (GE) interactions on the seed yield of some durum wheat lines by AMMI model and to evaluate genotype (G), environment (E) and genotype× environment (GE) interactions using statistics parameter i.e. AMMI stability value (ASV) and ecovalence (W2i).
 
Materials and Methods
Sixteen promising durum wheat lines (G1-G16) along with two check cultivars (durum wheat cv. Hana and bread wheat cv. Parsi), were investigated for two cropping seasons (2015-2016 and 2016-2017) at three Agricultural Research Stations (such as Karaj, Kermanshah and Neishabour cities) The experimental design at all locations was a randomized complete block design with three replications. Some agronomic attributes such as the number of days until anthesis stages, plant height, number of days till physiological maturity, 1000-kernel weight and grain yield were determined for each genotype. However, only the grain yield data was used to analyze G×E interactions. Combined analysis of variance for grain yield was performed using ADEL-R software. The GGE Biplot methodology was employed to analyze G×E interaction. The AMMI M model was used for the following purposes; (i): Evaluation of yield stability, (ii): The simultaneous selection for yield and stability, (iii): Identification of ideal durum wheat genotypes, and (iv): Assessment of the characteristics of and relationships among the testing environments.
 
 
Results and Discussion
The combined analysis of variance showed that the main effects of year and location were significant at 1% probability level, while the main effect of genotype had not significant. Genotype× year interaction and triple genotype × year × location interaction were significant at 1% probability level and also genotype × location interaction was significant at 5% probability level, indicating genotype × environment interaction. The results of AMMI ANOVA showed that about 86.5% of total variation was related to environment effect, 1.4% to genotype effect and 12.1% to genotype× environment interaction. Overall, the average grain yield of the evaluated lines ranged from 7.6 to 8.4 t.ha-1 and the G18 and G2 lines had the lowest and highest grain yield, respectively. Main effect due to environment and genotype × environment interaction as well as two first interaction principal components (IPCA1-2) were found to be significant, indicating that the agroclimatic environmental conditions were different, and that there was a differential response of the genotypes to the environments. The first two IPCA components of the GE interaction explained about 70.2% of the GE interaction. According to IPCA1, G9, G15 and G16 had the lowest scores and were the most stable genotypes whereas G17 and G18 with the highest scores were found to be unstable. The lowest ASV was observed for G16 that was the most stable genotype whose mean yield was higher than the grand mean. However, the highest ASV scores were achieved by G17 and G18. AMMI Biplot was used to visualize mean seed yield performance and stability of durum wheat genotypes. AMMI Biplot was able to distinguish stable genotypes with broad sense and narrow sense adaptation and environments with high and low genotype discrimination ability. The genotype G16 with higher seed yield than the total mean were the most stable genotypes, while the genotypes G17 and G18 with the highest contribution to GE interaction were the most unstable genotypes. Wricke’s ecovalence stability parameter (W2i) showed that the genotypes G16, G12, G5 and G4 were the most stable genotypes.
 
Conclusion
The results indicated that AMMI model and their biplots was an appropriate method for simultaneous selection of performance and stability of cultivars and lines. Also, according to all of methods, genotype G16 was selected as a stable and high genotype across all environments. Finally, it can be considered as a favorite promising line compared to the check cultivar Hana and as a candidate in the temperate climate.
 

کلیدواژه‌ها [English]

  • Genotype × Environment Interaction
  • Stability
  • Superior Genotype
Albert, M.J.A., 2004. A comparison of statistical methods to describe genotype× environment interaction and yield stability in multi- location maize trials. MSc Thesis. Department of Plant Science. The University of the Free State, Bloemfontein.
Annicchiarico, P., 1997. Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy. Euphytica 94: 53-62.
Basford, K.E., and Cooper, M., 1998. Genotype by environment interaction and some considerations of their implication for wheat breeding in Australia. Australian Journal of Agricultural Research 49: 154-175.
Cornelius, P.I., 1993. Statistical tests and retention of terms in the additive main effects and multiplicative interaction model for cultivar trials. Crop Science 33: 1186-1193.
Cornelius, P.L., Crossa, J., and Seyedsadr, M.S., 1996. Statistical tests and estimates of multiplicative models for GE interaction. In: kang, M.S. and H.G. Jr. Gauch (Eds.). Genotype-by- Environment Interaction. (pp.199-234). CRC Press, Boca Raton, Florida.
Croosa, J., Gauch, G.H., and Zobell, R.W., 1990. Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Science 30: 493-500.
Ebdon, J.S., and Gauch, H.G., 2002. Additive main effect and multiplicative interaction analysis of national turf grass performance trials: I. Interpretation of genotype× environment interaction. Crop Science 42: 489-496.
Eberhart, S.A., and Russell, W.A., 1966. Stability parameters for comparing varieties. Crop Science 6: 36-40.
Finlay, K.W., and Wilkinson, G.N., 1963. The analysis of adaptation in a plant breeding program. Australian Journal of Agricultural Research 14: 742-754.
Gauch, H.G., and Zobel, R.W., 1996. AMMI analysis of yield trials. In: Kang, M.S., and H.G. Jr. Gauch (Eds.), Genotype- by- Environment Interaction. (pp. 85-122). CRC Press, Boca Raton, Florida.
Grausgruber, H., Oberforster, M., Werteker, M., Ruckenbauer, P., and Vollmann, J., 2000. Stability of quality traits in Australian grown winter wheat. Field crop Research 66: 257-267.
Hayward, M., Bosemard, D., and Romagosa, L., 1993. Plant breeding. Chapman and Hall, UK.
Huhn, M., 1996. Nonparametric analysis of genotype× environment interaction by ranks. In: Kang, M.S., and Gauch, H.G.Jr. (Eds.), Genotype- by- Environment Interaction. (pp. 235-271). CRC Press, Boca Raton. Florida.
Isik, K., and Kleinschmit, J., 2005 .Similarities and effectiveness of test environments in selecting and deploying desirable genotypes. Theoretical and Applied Genetics 110: 311-322.
Kang, M.S., 1993. Simultaneous selection for yield and stability in crop performance genotype × environment interaction 239 trials: consequences for growers. Agronomy Journal 85: 754-757.
Kang, M.S., 1998. Using genotype× environment interaction for crop cultivar development. Advances in Agronomy 62: 199-252.
Kang, M.S., and Magari, R., 1996. New Developments in Selecting for Phenotypic Stability in Crop Breeding. In: M.S. Kang, and H.G. Zobel (Eds.), Genotype- by- Environment interaction, 1-14. CRC Press, Boca Raton.
Moayedi, A.A., Najafi Mirak, T., Taherian, M., Sasani, S., and Azarm, A., 2020. Evaluation of grain yield stability of durum wheat promising lines in moderate regions of Iran. Journal of Agroecology 12(2): 365-378. (In Persian with English Summary)
Mohammadi, R., Armion, M., Sadeghzadeh, B., Golkari, S., Khalilzadeh, H., Ahmadi, G., Abedi-Asl, M., and Eskandari, M., 2016. Assessment of grain yield stability and adaptability of rainfed durum wheat breeding lines. Applied Field Crops Research 29(4): 25-42. (In Persian with English Summary)
Mohammadi, R., Pourdad S.S., and Amri, A., 2008. Grain yield stability of spring safflower (Carthamus tinctorius L.). Australian Journal of Agricultural Research 59: 546-553.
Najafi Mirak, T., Dastfal, M., Andarzian, B., Farzadi, H., Bahari, M., and Zali, H., 2018. Evaluation of durum wheat cultivars and promising lines for yield and yield stability in warm and dry areas using AMMI model and GGE Biplot. Journal of Crop Breeding 10(28): 1-12. (In Persian with English Summary)
Najafi Mirak, T., Moayedi, A.A., Sasani, S., and Ghandi, A., 2019. Evaluation of adaptation and grain yield stability of durum wheat (Triticum turgidum L.) genotypes in temperate agro-climate zone of Iran. Iranian Journal of Crop Sciences 21(2): 127-138. (In Persian with English Summary)
Purchase, J., 1997. Parametric analysis to describe genotype× environment interaction and yield stability in winter wheat. Ph.D.University of the Free State, South Africa.
Purchase, J.L., Hatting, H., and Van Deventer, C.S., 2000. Genotype× environment interaction of winter wheat in South Africa: II. Stability analysis of yield performance. South Africa Journal of Plant and Soil 17(3): 101-107.
Schoeman, L.J., 2003. Genotype× environment interaction in sunflower (Helianthus annuus) in South Africa. MSc Thesis, Department of Agronomy, University of the Free State, Bloemfontein.
Shafi, B., Mahler, K.A., Price, W.J., and Auld, D.L., 1992. Genotype× environment interaction effects on winter rapeseed yield and oil content. Crop Science 32: 922-927.
Suadric, A., Simic, D., and Vratric, M., 2006. Characterization of genotype by environment interactions in soybean breeding program of Southeast Europe. Plant Breeding 125: 125-191.
Taherian, M., Bihamta, M.R., Peyghambari, S.A., Alizadeh, H., and Rasoulnia, A., 2019. Stability analysis and selection of salinity tolerant barley genotypes. Journal of Crop Breeding 11(29): 93-103.
Van Eeuwijk, F.F., 1992. Multiplicative models for genotype. Environment interaction in plant breeding. Statistical Applied Genetics 4: 393-406.
Wricke, G., 1962. Uber eine method zur refassung der okologischen streubretite in feldversuchen, Flazenzuecht 47: 92-96.
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., and 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., and Szlavnics, Z., 2000. Cultivar evaluation and mega- environment investigation based on the GGE Biplot. Crop Science 40: 597-605.
CAPTCHA Image