Document Type : Original Article

Authors

1 Department of Plant Sciences and Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran

2 Department of Vegetable Research, Seed and Plant Improvement Institute (SPII), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

Abstract

Introduction
The interaction of genotype with the environment provides the possibility of selecting stable genotypes for a wide range of environments. Evaluation of the interaction of genotype with the environment is necessary to increase the efficiency of selecting varieties with high and stable performance in a wide range of different environments. The objectives of this paper are: (1) evaluating the stability of 60 potato genotypes for tuber yield in two years using parametric and non-parametric stability methods, (2) identifying genotypes with good and stable performance when evaluated in variable environments and (3) investigating the relationship and correlation between stability statistics of tuber performance under water deficit conditions in Iran.
Materials and methods
To evaluate the performance stability and adaptability of the 60 potato genotypes, two cultivars and 58 advanced clones, 17 parametric and non-parametric statistics were evaluated for tuber yield across eight environments during the 2018-2019 growing seasons. The genotypes were evaluated under normal and water deficit conditions in Karaj and Ardabil. In this study, the parametric analysis for yield was determined by such parameters as regression coefficient (bi), environmental variance (Si2), coefficient of variation (CVi), deviation from regression (sdi2), and Wricke’s ecovalence (Wi2), The non-parametric analysis included Nassar and Huhn's statistics (S(1) and S(2)), Huhn's equation (S(3) and S(6)), Shukla’s stability variance (σ2i), Plaisted and Peterson’s (θi), Thennarasu’s non-parametric (NP(1), NP(2), NP(3), and NP(4)), and Kang’s rank-sum (KR) parameter. Parametric and non-parametric statistics are used by agronomists and plant breeders. Currently, researchers are interested in applying several stability statistics to obtain the desired results, crucial for the selection of stable varieties
Results and discussion
Composite variance analysis showed that the effect of place and year as well as the effect of genotype are significant. The interaction effect of year × location × genotype was significant at the probability level of 1%. The genotype effect was also significant at the 1% probability level. The interaction effect of genotype x location and genotype x year was not significant, which indicates that the average performance of genotypes is not different in different locations and years. Grouping of genotypes based on average performance and parametric and non-parametric stability statistics showed that genotypes are divided into four main groups. In general, based on the average rank of parametric and non-parametric stability parameters, genotypes G31, G21 and G36 had the least changes and were recognized as the most stable genotypes, and therefore they can be introduced as stable genotypes. The results of stability statistics and cluster analysis showed that G31, G21 and G36 genotypes can be introduced as stable and compatible genotypes.
Conclusion
Our results showed that G21, G31 and G36 genotypes contributed the least to the genetic × environment interaction (G*E) and were considered as stable genotypes under water deficit conditions. The different parametric and non-parametric stability procedures can be proposed to select drought tolerant genotypes under different environments conditions; these procedures could be useable for recognition of the best genotypes under drought stress conditions. Therefore, the analysis of yield stability could be utilized in combination with parametric and non-parametric methods to evaluate and identify drought tolerance genotypes. Dendrogram results confirmed each other with the results of parametric and non-parametric statistics. While G49, G51, G53 and G56 genotypes with the highest values were the most unstable genotypes.

Keywords

Main Subjects

 
 Abdipur, M., Vaezi, B., 2014. Analysis of the genotype-by-environment interaction of winter barley tested in the Rain-fed Regions of Iran by AMMI Adjustment. Bulgarian Journal of Agriculture Science, 20, 421-427.
Abdulahi, A., Mohammadi, R., Pourdad, S.S., 2007. Evaluation of safflower (Carthamus spp.) genotypes in multi-environment trials by nonparametric methods. Asian Journal of Plant Sciences, 6, 827-832. https://doi.org/10.3923/ajps.2007.827.832
Adugna, W., Labuschagne, M.T., 2003. Parametric and nonparametric measures of phenotopic stability in linseed (linum usitatissimum L.). Euphytica. 129, 211-218. https://doi.org/10.1023/A:1021979303319
Alwala, S., Kwolek, T., Mcpherson, M., Pellow, J., Meyer, D., 2010. A comprehensive comparison between Eberhart and Russell joint regression and GGE biplot analyses to identify stable and high yielding maize hybrids. Field Crop Reseach. 119, 225-230. https://doi.org/10.1016/j.fcr.2010.07.010
Becker, H.C., Leon, J., 1988. Stability analysis in plant breeding. Plant Breeding, 101, p.1-23. https://doi.org/10.1111/j.1439-0523.1988.tb00261.x
Ebdon, J.S., Gauch, H.G., 2002. Additive main effect and multiplicative interaction analysis of national turf grass performance trial: I. interpretation of genotype× environment interaction. Crop Science, 42, 489-496. https://doi.org/10.2135/cropsci2002.4890
Eberhart, S.A.T., Russell, W.A., 1966. Stability  parameters for comparing varieties. Crop Science, 6, 36–40. https://doi.org/10.2135/cropsci1966.0011183X000600010011x
EL-hashash, E.F., Agwa, A.M., 2018. Comparison of parametric stability statistics for grain yield in barley under different stress severities. Merit Research Journal of Agricultural Science and Soil Science. 6, 098-111.
Finlay, K.W., Wilkinson. G.N., 1963. The analysis of adaptation in a plant-breeding programme. Australian Journal of Agricultural Research, 14, 742–754. https://doi.org/10.1071/AR9630742
Flores, F., Moreno, M.T., Cubero, J.I., 1998.A comparison of univariate and multivariate methods to analyze environments. Field Crops Research, 56, 271-286. https://doi.org/10.1016/S0378-4290(97)00095-6
Francis, T.R., Kannenberg, L.W., 1978.Yield stability studies in short-season maize: I. A descriptive method for grouping genotypes. Canadian Journal of Plant Science. 58, 1029–1034. https://doi.org/10.4141/cjps78-157
Gupta, A.K., Mishra, R., Lal, R.K., 2015. Genetic resources, diversity, characterization and utilization of agronomical traits in turmeric (Curcuma longa L.). Industrial Crops and Products. 77, 708-712. https://doi.org/10.1016/j.indcrop.2015.09.030
Hassanpanah, D., Hassanabadi, H., 2011. Evaluation of quantitative and qualitative characteristics of promising potato clones in Ardabil region, Iran. Modern Science of Sustainable Agriculture Journal. 7, 37-48 [In Persian]. https://doi.org/10.22092/SPIJ.2018.117867
Huhn, M., 1990. Nonparametric measures of phenotypic stability. Part 1: Theory. Euphytica. 47, 189–1990. https://doi.org/10.1007/BF00024241
Kang, M.S., 1988. A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Research Communication. 16, 113–115.
Khalili, M., Pour, AA., 2016. Parametric and non-parametric measures for evaluating yield stability and adaptability in barley doubled haploid lines. Journal of Agricultural Science and Technology. 18, 789-803.
Kilic. H., Akçura, M., Aktaş, H., 2010. Assessment of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in multi-environments. Notulae Botanicae Horti Agrobotanici Cluj-Napoca. 38, 271-279. https://doi.org/10.15835/nbha3834742
Lipkovich, I.A., Smith, E.P., 2002. Biplot and Singular Value Decomposition Macros for Excel©. J. Stat. Softw. 7, 1-15. https://doi.org/10.18637/jss.v007.i05
Mishra, R., Gupta, AK., Lal, RK., 2020. Genotype x environment interaction, stability analysis for yield and quality traits in turmeric (Curcuma longa L.). Trends in Phytochemical Research. 4, 219-34.
Moghaddaszadeh, M., Asghari, Z.R., Hassanpanah, D., Zare, N., 2019. Non-parametric stability analysis of tuber yield In potato (Solanum Tuberosum L.) genotypes. Journal of Crop Breeding. 28, 58-63. https://doi.org/10.29252/jcb.10.28.50
Mohammadi, R., Abdulahi, A., Haghparast, R., Armion, M., 2007. Interpreting genotype × environment interactions for durum wheat grain yields using nonparametric methods. Euphytica. 157, 239-251. https://doi.org/10.1007/s10681-007-9417-3
Mut, Z., Gülümser, A., Sirat, A., 2010. Comparison of stability statistics for yield in barley (Hordeum vulgare L.). African Journal of Biotechnology. 9, 1610-1618.v https://doi.org/10.5897/AJB10.1404
Nassar, R., Huhn, M., 1987. Studies on estimation of phenotypic stability: tests of significance for nonparametric measures of phenotypic stability. Biometrics. 43, 45–53. https://doi.org/10.2307/2531947
Plaisted, R.I., Peterson, L.C., 1959. A technique for evaluating the ability of selection to yield consistently in different locations or seasons. American Potato Journal. 36, 381–385. https://doi.org/10.1007/BF02852735
Plaisted, R.L., 1960. A shorter method for evaluating the ability of selections to yield consistently over locations. American Potato Journal. 37, 166–172. https://doi.org/10.1007/BF02855271
Pour‐aboughadareh, A., Yousefian, M., Moradkhani, H., Poczai, P., Siddique, K.H., 2019. STABILITYSOFT: A new online program to calculate parametric and non‐parametric stability statistics for crop traits. Applications in Plant Sciences. 7, e01211. https://doi.org/10.1002/aps3.1211
Ravari, S.Z., Dehghani, H., Naghavi, H., 2017. Study of genetic control of salinity tolerance in bread wheat cv. Kavir-using generation mean analysis. Crop Breeding Journal. 7, pp.57-66. https://doi.org/10.22092/CBJ.2018.115180.1010
Roodi, D., Ghodrati, G., Kazerani, N., Masoudi, B., 2022. Investigation the yield stability of brassica genotypes (Brassica spp.) under drought stress by using statistical parameters and GGE biplot graphical methods. Journal of Crop Breeding. 42, 138-147. https://doi.org/10.52547/jcb.14.42.138
Sabaghnia, N. Dehghani, H. Sabaghpour, S.H., 2006. Non-parametric methods for interpreting genotype × environment interaction of lentil genotypes. Crop Science. 46, 1100-1106. https://doi.org/10.2135/cropsci2005.06-0122
Saidi, A., Akbari, A., Mozzaffari, J., Heidari, A., Seraj-azari, M., Pirayeshfar, B., Yazdansepas, A.,Torabi, M., Alizadeh, A., Vahabzadeh, M., Asadi, H., 2000. Iranian Wheat Pool. In The World Wheat Book, A History of Wheat Breeding, A.P. Bonjean and W.J. Angus, eds (Paris, France: Lavoisier Publishing), 1131.
Shukla G., 1972. Some statistical aspects of partitioning genotype environmental components of variability. Heredity. 29, 237-245. https://doi.org/10.1038/hdy.1972.87
Thennarasu, K., 1995. On certain nonparametric procedures for studying genotype Environment interactions and yield stability. PhD. thesis, PJ School IARI, New Delhi, India.
Solomon, T., 2011. Study of Useful Plants in and around GATE UDUMA (Traditional Gedeo Homegardens) in Kochere Wereda of Gedeo Zone, Ethiopia: An Ethnobotonical Approach. M.Sc. thesis Addis Ababa, Ethiopia.
Vaezi, B., Pour-Aboughadareh, A., Mohammadi, R., Armion, M., Mehraban, A., Hossein-Pour, T., Dorii, M., 2017. GGE biplot and AMMI analysis of barley yield performance in Iran. Cereal Research Communications, 45, 500–511. https://doi.org/10.1556/0806.45.2017.019
Verma, A., Kumar, V., Kharab, A.S., Singh, G.P., 2018. Parametric vis-à-vis non parametric measures to describe G × E interactions for fodder yield of dual purpose barley genotypes evaluated under MET. International Journal of Current Microbiology and Applied Sciences. 7, 226-234. https://doi.org/10.20546/ijcmas.2018.702.029
Wricke, G., 1962. Uber eine methode zur erfassung der oekologischen streubreite in feldversuchen. Zeitschr. f. Pflanzenz. 47, 92-96.
Yan, W., Hunt, L.A., 2001. Interpretation of genotype × environment interaction for winter wheat yield in Ontario. Crop Science. 41, 19–25. https://doi.org/10.2135/cropsci2001.41119x
Zali, H., Sofalian, O., Hasanloo, T., Asgharii, A., Hoseini, S.M., 2015. Appraising of drought tolerance relying on stability analysis indices in canola genotypes simultaneously, using selection index of ideal genotype (SIIG) technique: Introduction of new method. Biological Forum. 7, 703.   https://doi.org/10.29252/jcb.11.29.117