Evaluation of sesame yield stability using statistical parameters and GGE biplot graphical methods

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MASOUDI BAHRAM; Abbasali Mohammad; Aien Ahmad; Saif Amiri Saber

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Journal: Crop Production Year:2020 | Volume:13 | Issue:3 Page(s): 71-84

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Title

Evaluation of sesame yield stability using statistical parameters and GGE biplot graphical methods

Author(s)

MASOUDI BAHRAM | Abbasali Mohammad | Aien Ahmad | Saif Amiri Saber | Issue Writer Certificate

Keywords

Genotype-environment interaction

stability parameters

land race

sesame (Sesamum indicum)

Abstract

Evaluation of sesame yield stability using statistical parameters and GGE biplot graphical methods Background and objectives: Sesame is a short-day plant and sensitive to heat and moisture stresses and the yield show a wide variation as the environmental factor change in growing seasons. Therefore a commercially successful cultivar must perform well in a wide range of agricultural and climatic conditions. Plant breeders focus on estimating the interaction of genotypes and environment through repeated experiments to introduce stable cultivars able to perform better in changing environments. This study follows the same strategies to introduce sesame cultivars Materials and methods: In order to identify high yielding and stable sesame genotypes suitable different regions of Iran, a total of 36 native sesame populations (obtained from National Gene Bank of Seed and Plant Improvement Institute, Iran) were cultivated in three regions (Karaj, Moghan and Jiroft) in a randomized complete block design for two years (2016-2017). Each genotype was planted in a plot with three rows of 1. 5 m long. The distance between rows was 60 cm and seeds were planted with 8-7 cm intra-spacing. At the end of the growing period following the physiological, seeds were harvested and the yield of each genotype was calculated. Simple and combined analysis of variance were performed on data then the univariate statistics methods including regression coefficient, deviation from regression parameter, Shukla’ s stability variance, and Wricke’ s ecovalence were used to evaluate the grain yield stability of the genotypes. Finally, GGE biplot analysis was used to understand the interaction between genotype and the environment. Results: The results of combined analysis indicated that the effect of location, year, genotype and genotype-location interaction were significant for grain yield. The results also showed that the grain yield was significantly affected by environmental factors. Based on all stability parameters and biplot analysis, genotype no. 10 had higher yield, lower Wricke’ s ecovalence and Shukla’ s stability variance value. Furthermore regression coefficient was equivalent to one and deviation from regression was the least. Accordingly this genotype was selected as the most stable genotypes. This result was also reconfirmed by GGE biplot analysis. Other stable and high yielding genotypes include 11, 9, 8 and 7. Conclusion: Genotype 10 is a land race genotype from Haji Abad region of Hormozgan province and showed less fluctuations in tested environments, and in terms of the average yield, it is the most stable genotype compared to the others. So based on the findings from this study this genotype could be suggested to cultivate in future breeding programs.

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APA: Copy

MASOUDI, BAHRAM, Abbasali, Mohammad, Aien, Ahmad, & Saif Amiri, Saber. (2020). Evaluation of sesame yield stability using statistical parameters and GGE biplot graphical methods. ELECTRONIC JOURNAL OF CROP PRODUCTION, 13(3 ), 71-84. SID. https://sid.ir/paper/957747/en

Vancouver: Copy

MASOUDI BAHRAM, Abbasali Mohammad, Aien Ahmad, Saif Amiri Saber. Evaluation of sesame yield stability using statistical parameters and GGE biplot graphical methods. ELECTRONIC JOURNAL OF CROP PRODUCTION[Internet]. 2020;13(3 ):71-84. Available from: https://sid.ir/paper/957747/en

IEEE: Copy

BAHRAM MASOUDI, Mohammad Abbasali, Ahmad Aien, and Saber Saif Amiri, “Evaluation of sesame yield stability using statistical parameters and GGE biplot graphical methods,” ELECTRONIC JOURNAL OF CROP PRODUCTION, vol. 13, no. 3 , pp. 71–84, 2020, [Online]. Available: https://sid.ir/paper/957747/en

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