Grain yield stability analysis of oat (Avena sativa L.) genotypes using univariate parametric and non-parametric methods

Document Type : Original Article

Authors

1 Department of Plant Production and Genetics, Campus of Agriculture and Natural Resources, Razi University, Kermanshah, Iran.

2 Cereal Research Center, Razi University, Kermanshah, Iran.

10.22126/cbb.2024.11410.1093

Abstract

Introduction: Oats (Avena sativa L.), as one of the multi-purpose crops with high nutritional value, are used for both grain production and as fodder. Oat grain yield is strongly affected by biotic and abiotic factors. These factors can lead to a decrease in yield stability and complexity in predicting genetic results. Therefore, identifying superior and stable genotypes across different environments is crucial. This research aims to evaluate the yield and stability of oat genotypes under various climatic conditions to identify and introduce genotypes with high yield and optimal stability.
Materials and methods: In this research, 21 oat genotypes were evaluated using a randomised complete block design with three replications across 16 environments (combination of year and place) under different conditions, including full irrigation, post-anthesis drought stress, and rainfed conditions during the years 2009-2015. Stability analysis was conducted using univariate parametric methods, Lin and Binn’s superiority index (Pi), Romer’s environmental variance (S2i), Francis and Kannenberg’s coefficient of variation (CVi), Wricke’s ecovalence (W2i), Shukla’s stability variance (σ2i), Plaisted and Peterson's statistic (), geometric adaptability index (GAI), Pinthus's coefficient of determination (R2i), Finlay and Wilkinson’s linear regression coefficient (bi), Eberhart and Russell’s variance deviation from the linear regression (S2di), and Perkins and Jinks's regression coefficient (ßi). Non-parametric methods such as Kang’s rank-sum method (RSM), Nassar and Huehn’s and Huehn’s stability statistics, Thennarasu’s stability statistics, and Fox’s superiority index were also applied. In parametric analyses, 13 environments were selected due to non-homogeneity of error variances, while all 16 environments were included in the non-parametric methods.
Results: Based on parametric indices, genotypes 19, 7, 21, 15, 4, and 6 with the lowest Pi values, and genotypes 1, 6, 20, 14, and 3 with the lowest W2i, σ2i, and values were identified as the most stable genotypes. Furthermore, genotypes 2, 11, 5, and 3, with the lowest S2i values, and genotypes 5, 2, 6, 3, 12, 13, 9, and 21, with the lowest CVi values, were more stable than the other genotypes. The highest GAI values were observed in genotypes 19, 7, 21, 5, 17, and 4, which were identified as stable genotypes. Genotypes 2 and 15, with the highest R2i values and above-average yield, exhibited greater stability. Additionally, genotypes 1, 6, 8, 17, 20, and 21 were recognized as the most stable genotypes due to their bi values being close to one. Genotypes 1, 6, and 20 were noted for having bi value close to one and lower S2di values. Furthermore, genotypes 6 and 20 exhibited the lowest ßi and S2di values. Based on the non-parametric RSM statistics, Genotypes 7, 19, and 21 were introduced as the most stable genotypes. Utilising Nassar and Huehn’s indices including ,  Genotype 6, Genotypes 6 and 15, Genotypes 1, 6, and 18, and Genotypes 1, 6, 18, and 20 were also respectively identified as stable genotypes. In the evaluation of Thennarasu’s statistics including , Genotypes 1, 6, 18, and 20, Genotypes 1, 2, 11, 14, 18, and 20, Genotypes 1, 2, 8, 9, 11, 14, 18, and 20, and, Genotypes 1, 2, 11, 14, 18, and 20 were respectively introduced as the most stable. Additionally, based on the TOP index, genotypes 4, 7, 15, 19, and 21 were identified as the most stable with favourable yields. According to the MID index, Genotypes 1 and 6 were identified as the most stable with average yields.
Conclusion: Based on the results of the most parametric and non-parametric indices, Genotype 6 (GA Mitchell) was identified as the most suitable and stable genotype, exhibiting a grain yield higher than the mean. Therefore, this genotype can be utilized as a valuable genetic resource in future breeding programs.

Keywords

Main Subjects

References

Abyar, S., Navabpour, S., Karimizadeh, R., Nasrollahnejad Ghomi, A. A., Kiani, G., & Gholizadeh, A. 2021. Evaluation of genotype × environment interaction and grain yield stability of different bread wheat genotypes using non-parametric methods. Cereal Research, 11(2), 89-104. https://doi.org/10.22124/cr.2021.20461.1687. (In Persian)
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 Crops Research, 119(2-3), 225-230. https://doi.org/10.1016/J.
FCR.2010.07.010
Amini, A., Tabatabaee, M. T., Akbari Moghadam, H., Ravari, S. Z., Amin Azarm, D., & Tajalli, H. 2020. Evaluation of grain yield and its stability in bread wheat genotypes in saline regions of Iran. Iranian Journal of Field Crop Science, 51(4), 191-202. https://doi.org/10.22059/ijfcs.2020.
290344.654649. (In Persian)
Arya, R. K., Verma, A., & Vandana. 2022. Biplot analysis of non-parametric measures of stability for long term evaluation of fababean genotypes. Electronic Journal of Plant Breeding, 13(2), 350-360. https://doi.org/10.37992/2022.1302.055
Awoke, S., & Sharma, M. K. 2016. Parametric and non-parametric methods to describe genotype by environment interaction and grain yield stability of bread wheat. Statistics and Applications, 14(1-2), 9-29.
Bajpai, P. K., & Prabhakaran, V. T. 2003. Simultaneous testing of genotype × environment interaction and stability for more than one trait. Indian Journal of Genetics and Plant Breeding, 63(1), 11-14.
Bredenkamp, J. 1974. Nonparametric prüfung von wechselwirkungen. Psychologische Beiträge, 16, 398-416.
Cubukcu, P., Kocatürk, M., Ilker, E., Kadiroğlu, A., Vurarak, Y., Şahin, Y., Karakuş, M., Akgün Yildirim, Ü., Göksoy, A., & Sincik, M. 2021. Stability analysis of some soybean genotypes using parametric and non-parametric methods in multienvironments. Turkish Journal of Field Crops, 26(2), 262–271. https://doi.org/10.17557/tjfc.1033363
De Kroon, J., & Van der Laan, P. 1981. Distribution-free test procedures in two-way layouts: a concept of rank-interaction. Statistica Neerlandica, 35(4), 189-213. https://doi.org/10.1111/J.14
67-9574.1981.TB00730.X
Desheva, G., & Valchinova, E. 2024. Evaluation of yield stability and adaptability of oat genotypes (Avena sativa L.). Poljoprivreda, 30(1), 3-12. https://doi.org/10.18047/poljo.30.1.1
Devi, R., Sood, V. K., & Arora, A. 2023. Stability analysis for seed yield and related traits of oat (Avena sativa L.) under varied conditions of North-Western Himalayas. International Journal of Environment and Climate Change, 13(11), 2409-2418. https://doi.org/10.9734/ijecc/2023/v13i1
13407
Dhakal, A., Poland, J., Adhikari, L., Faryna, E., Fiedler, J., Rutkoski, J. E., & Arbelaez, J. D. 2024. Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.). The Plant Genome, 17(2), e20457. https://doi.org/10.1002/tpg2.20457
Eberhart, S. A., & Russell, W. A. 1966. Stability parameters for comparing varieties. Crop Science, 6(1), 36-40. https://doi.org/10.2135/cropsci1966.0011183X000600010011x
Erdogdu, Y., & Esendal, E. 2021. Multi-environment trial analysis by parametric and non-parametric stability parameters for seed yield in winter rapeseed (Brassica napus L.) genotypes. Turkish Journal of Field Crops, 26(1), 71–78. https://doi.org/10.17557/tjfc.943928
FAOSTAT, 2022. Food and Agriculture Organization of the United Nations-Statistic Division. https://www. fao. org/faostat/en/# data: QC.
Finlay, K. W., & Wilkinson, G. N. 1963. The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research, 14(6), 742-754. https://doi.org/10.1071/AR963074
2
Fox, P. N., Skovmand, B., Thompson, B. K., Braun, H. J., & Cormier, R. 1990. Yield and adaptation of hexaploid spring triticale. Euphytica, 47, 57-64. https://doi.org/10.1007/BF00040364
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(4), 1029-1034. https://doi.org/10.4141/cjps78-157
Grundy, M. M. L., Quint, J., Rieder, A., Ballance, S., Dreiss, C. A., Cross, K. L., Gray, R., Bajka, B. H., Butterworth, P. J., Ellis, P. R., & Wilde, P. J. 2017. The impact of oat structure and β-glucan on in vitro lipid digestion. Journal of Functional Foods, 38, 378–388. https://doi.org/10.1016/j.jff
.2017.09.011
Hameed, M., Shah, S. H., & Khan, H. H. 2020. A nonparametric analysis for stability of wheat genotypes tested in Southern Punjab, Pakistan. European Online Journal of Natural and Social Sciences, 9(1), 153-163. https://european-science.com/eojnss/article/view/5922
Huehn, M. 1990. Non-parametric measures of phenotypic stability: Part 1. Theory. Euphytica, 47, 189-194. https://doi.org/10.1007/BF00024241
Huhn, M., & Leon, J. 1995. Nonparametric analysis of cultivar performance trials: Experimental results and comparison of different procedures based on ranks. Agronomy Journal, 87(4), 627-632. https://doi.org/10.2134/AGRONJ1995.00021962008700040004X
Kang, M. S. 1991. Modified rank-sum method for selecting high-yielding, stable crop genotypes. Cereal Research Communications, 19(3), 361-364.
Karimizadeh, R., Mohammadi, M., Shefazadeh, M. K., Mahmoodi, A. A., Rostami, B., & Karimpour, F. 2012. Relationship among and repeatability of ten stability indices for grain yield of food lentil genotypes in Iran. Turkish Journal of Field Crops, 17(1), 51-61.
Kaya, Y., & Taner, S. 2003. Estimating genotypic ranks by nonparametric stability analysis in bread wheat (Triticum aestivum L.). Central Europe Agriculture Journal, 4(1), 47-53.
Kebede, G., Worku, W., Jifar, H., & Feyissa, F. 2023a. GGE biplot analysis of genotype by environment interaction and grain yield stability of oat (Avena sativa L.) in Ethiopia. Agrosystems, Geosciences & Environment, 6(3), e20410. https://doi.org/10.1002/agg2.20410
Kebede, G., Worku, W., Jifar, H., & Feyissa, F. 2023b. Grain yield stability analysis using parametric and nonparametric statistics in oat (Avena sativa L.) genotypes in Ethiopia. Grassland Research, 2(3), 182-196. https://doi.org/10.1002/glr2.12056
Kebede, G., Worku, W., Jifar, H., & Feyissa, F. 2023c. Stability analysis for fodder yield of oat (Avena sativa L.) genotypes using univariate statistical models under diverse environmental conditions in Ethiopia. Ecological Genetics and Genomics, 29, 100202. https://doi.org/10.1016/j
.egg.2023.100202
Khazaei, A., Golzardi, F., Torabi, M., Fyzbakhsh, M. T., Azari Nasrabad, A., Nazari, L., Ghasemi, A., & Motaghi, M. 2021. Evaluation of the yield stability of grain sorghum genotypes using AMMI analysis in different regions of Iran. Creal Research, 11(1), 77-88. https://doi.org/10.221
24/CR.2021.19921.1675. (In Persian)
Kiliç, H. 2012. Assessment of parametric and non-parametric methods for selecting stable and adapted spring bread wheat genotypes in multi-environments. Journal of Animal & Plant Sciences, 22(2), 390–398.
Lin, C. S., & Binns, M. R. 1988. A method of analyzing cultivar × location × year experiments: A new stability parameter. Theoretical and Applied Genetics, 76, 425-430. https://doi.org/10.1007/
bf00265344
Mahdavi, A. M., Babaeian Jelodar, N., Farshadfar, E., & Bagheri, N. 2020. Evaluation of stability and adaptation of bread wheat genotypes using univariate statistics parameters and AMMI. Plant Genetic Researches, 7(1), 19-32. http://dx.doi.org/10.52547/pgr.7.1.2
Mao, H., Xu, M., Ji, J., Zhou, M., Li, H., Wen, Y., Wang, J., & Sun, B. 2022. The utilization of oat for the production of wholegrain foods: Processing technology and products. Food Frontiers, 3(1), 28–45. https://doi.org/10.1002/fft2.120
Masoudi, B., Abbasali, M., Aien, A., & Saif Amiri, S. 2020. Evaluation of sesame yield stability using statistical parameters and GGE biplot graphical methods. Journal of Crop Production, 13(3), 71-84. https://doi.org/10.22069/ejcp.2021.17753.2306
Mehraj, U., Abidi, I., Ahmad, M., Zaffar, G., Dar, Z. A., Rather, M. A., & Lone A. A. 2017. Stability analysis for physiological traits, grain yield and its attributing parameters in oats (Avena sativa L.) in the Kashmir Valley. Electronic Journal of Plant Breeding, 8(1), 59-62. https://www.ejplant
breeding.org/index.php/EJPB/article/view/1210
Moghaddaszadeh, M., Asghari Zakaria, R., Hassanpanah, D., & Zare, N. 2019. Nonparametric stability analysis of tuber yield in potato (Solanum tuberosum L.) genotype. Journal of Crop Breeding, 10(28), 50-63. http://dx.doi.org/10.29252/jcb.10.28.50. (In Persian)
Mohammadi, R., & Amri, A. 2008. Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica, 159, 419-432. https://doi.org/10.1007/s10681-007-9600-6
Mohammadi, R., Abdulahi, A., Haghparast, R., Aghaee, M., & Rostee, M. 2007. Interpreting genotype × environment interactions for durum wheat grain yield using nonparametric methods. Euphytica, 157, 239-251. doi.org/10.1007/s10681-007-9417-3
Mohammadi, R., Armion, M., Zadhasan, E., Ahmadi, M. M., & Amri, A. 2017. The use of Ammi model for interpreting genotype × environment interaction in durum wheat. Experimental Agriculture, 54(5), 670–683. https://doi.org/10.1017/S0014479717000308
Nassar, R., & Huehn, M. 1987. Studies on estimation of phenotypic stability: Tests of significance for nonparametric measures of phenotypic stability. Biometrics, 43, 45-53. https://doi.org/10.23
07/2531947
Perkins, J. M., & Jinks, J. L. 1968. Environmental and genotype-environmental components of variability III. Multiple lines and crosses. Heredity, 23, 339–356. https://doi.org/10.1038/hdy.19
68.48
Pinthus, M. J. 1973. Estimate of genotypic value: A proposed method. Euphytica, 22, 121-123. https://doi.org/10.1007/BF00021563
Plaisted, R. L., & Peterson, L. C. 1959. A technique for evaluating the ability of selections to yield consistently in different locations or seasons. American Potato Journal, 36, 381-385. https://doi.o
rg/10.1007/BF02852735
Pour-Aboughadareh, A., Yousefian, M., Moradkhani, H., Poczai, P., & Siddique, K. H. M. 2019. STABILITYSOFT: A new online program to calculate parametric and non-parametric stability statistics for crop traits. Applications in Plant Sciences, 7(1), e01211. https://doi.org/10.1002/aps
3.1211
Pourdad, S. S. 2011. Repeatability and relationships among parametric and non-parametric yield stability measures in safflower (Carthamus tinctorius L.) genotypes. Crop Breeding Journal, 1(2), 109-118. https://doi.org/10.22092/CBJ.2011.100360
Rea, R., Sousa-Vieira, O. D., Briceño, R., Díaz, A., & George, J. 2020. Simultaneous selection indices for yield and stability in sugarcane. Revista de Ciencias Agrícolas, 37(2), 67-77.
Romer, T. H. 1917. Sind die ertragsreichsten Sorten ertragssicherer. DGL-Mitt, 32(1), 87-89.
Sanadya, S. K., Sood, V. K., Sharma, G., Enyew, M., & Katna, G. 2024. Adaptation of oat genotypes in organic and conventionally managed fields in the northwestern Himalayas. Agronomy Journal, 116(2), 433-445. http://dx.doi.org/10.1002/agj2.21542
Saremirad, A., & 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. http://dx.doi.org/10.52547/jcb.14.43.49. (In Persian)
Shobeiri, S. S., Pezeshkpour, P., & Sadeghzadeh Ahari, D. 2023. Non-Parametric Stability Analysis of Yield in Lentil Genotypes. Crop Production Journal, 16(3), 49-68. http://dx.doi.org/10.22069/
ejcp.2024.20804.2551 (In Persian)
Shukla, G. K. 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 non-parametric procedures for studying genotype-environment interactions and yield stability. Ph.D. thesis, PJ School, IARI, New Delhi, India.
Verma, A., Kumar, V., Kharab, A. S., & Singh, G. P. 2018. Comparative performance of parametric and non-parametric measures for analyzing G × E interactions of grain yield for dual purpose barley genotypes. Electronic Journal of Plant Breeding, 9(3), 846-855. https://doi.org/10.5958/0
975-928X.2018.00105.9
Wricke, G. 1962. Evaluation method for recording ecological differences in field trials. Z Pflanzenzucht, 47, 92-96.

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