Assessment of Seed Yield Stability of Advanced Rainfed Lentil Lines in Multi-Environmental Trials using WAASB and WAASBY Indices

Document Type : Research Paper

Authors

1 , Kohgiluyeh and Boyer-Ahmad Agricultural and Natural Resources Research and Education Center, Dryland Agricultural Research Institute, Agricultural Research, Education and Extension Organization, Gachsaran, Iran.

2 Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Khorramabad, Iran.

3 Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Moghan, Iran.

4 Ilam Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Ilam, Iran.

5 Golestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Gonbad, Iran.

Abstract

The aim of the present study was to evaluate and interpret the genotype × environment interaction for advanced rainfed lentil lines. Fifteen advanced rainfed lentil lines (genotypes) along with two commercial cultivars (cv. Gachsaran and cv. Sepehr) as check were evaluated using randomized complete block design with three replications in Gachsaran, Moghan, Khorramabad and Ilam field stations in Iran in 2019-20 and 2020-21 cropping seasons. The mean seed yield of trials was 891 kg ha-1. Genotype 10 with 1031 kg ha-1 had the highest mean seed yield, and genotype 16 (cv. Gachsaran) with 728 kg ha-1 had the lowest. Since genotype × environment interaction was significant, yield stability analysis was performed. Evaluation of the seed yield stability of genotypes by rank method showed that genotypes 12 and 10 had high seed yield and yield stability. In addition, yield stability analysis using WAASB index, as a multivariate method, was also performed. The seed yield × WAASB index biplot showed that most of the lentil genotypes had higher seed yield and yield stability than the check cultivars (cv. Gachsaran and cv. Sepehr). Genotypes 12, 13, 11, 10 and 1 had higher seed yield and yield stability than the superior check cultivar (cv. Sepehr). Considering the equal contribution for mean seed yield and yield stability in the calculation of WAASBY index, genotypes 10, 13, 11, 12, 1 were ranked as the most desirable genotypes for target environments.

Keywords


Abdulahi, A., Mohammadi, R., and Pourdad, S. S. 2007. Evaluation of safflower (Carthamus spp.) genotypes in multi-environment trials by nonparametric methods. Asian Journal of Plant Sciences 6 (5): 827-832.
 
Ajay, V., and Singh G. P. 2021. AMMI with BLUP analysis for stability assessment of wheat genotypes under multi locations timely sown trials in Central Zone of India. International Journal of Agricultural Science and Food Technology 7: 118-124.
 
Balestre, M., Von Pinho, R. G., Souza, J. C., and Oliveira, R. L. 2009. Genotypic stability and adaptability in tropical maize based on AMMI and GGE biplot analysis. Genetics and Molecular Research 8: 1311–1322.
 
Barbosa, M. H., Ferreira, A., Peixoto, L. A., Resende, M. D., Nascimento , M., and Silva. F. F. 2014. Selection of sugar cane families by using BLUP and multi-diverse analyses for planting in the Brazilian savannah. Genetics and Molecular Research 13: 1619-1626.
 
Baretta, D., Nardino, M., Carvalho, I. R., Oliveira, A. C., de Souza, V. Q., and Maia, L. C. 2016. Performance of maize genotypes of Rio Grande do Sul using mixed models. Científca 44: 403-411.
 
Biçer, T., and Şarkar, D. 2006. Stability parameters in lentil. Journal of Central European Agriculture 7 (3): 439-444.
 
Ebadi Segherloo, A., Sabaghpour, S. H., Dehghani, H., and Kamrani, M. 2008. Non-parametric measures of phenotypic stability in chickpea genotypes (Cicer arietinum L.). Euphytica 162: 221-229.
 
Farshadfar, E. 2013. Simultaneous selection of yield and yield stability in chickpea genotypes using the GGE biplot technique. Acta Biologica Hungarica 61: 185-194.
 
Fox, P., Skovmand, B., Thompson, B., Braun, H. J., and Cormier, R. 1990. Yield and adaptation of hexaploid spring triticale. Euphytica 47: 57-64.
 
Gurmu, F., Lire, E. A., Asfaw, A., Alemayehu, F., Rezene, Y., and Ambachew, D. 2012. GGE- biplot analysis of grain yield of faba bean genotypes in Southern Ethiopia. Electronic Journal of Plant Breeding 3: 898-907.
 
Hasani, M., Hamze, H., and Mansori, H. 2021. Evaluation of adaptability and stability of root yield and white sugar yield (Beta vulgaris L.) in sugar beet genotypes using multivariate AMMI and GGE biplot method. Journal of Crop Breeding 13 (37): 222-235.
 
Karimizadeh, R., Ghojogh, H., Hosseinpour, T., Armion, M., Shahbazi Homonlo, K., and Sharifi, P. 2021. Evaluating of the efficiency of AMMI and BLUP models and their integration for identifying high-yielding durum wheat (Triticum turgidum L. var. durum) genotypes adapted to warm rainfed regions of Iran. Iranian Journal of Crop Sciences 23: 30-48 (in Persian).
 
Kumar, R., Sharma, S. K., Luthra, O. P., and Sharma, S. 2005. Phenotypic stability of lentil genotypes under different environments. Annals of Biology 21: 155-158.
 
Mofidian, S. M. A., and Moghaddam, A. 2013. Analysis of ecotype × location interaction in cold-region alfalfa ecotypes. Iranian Journal of Crop Sciences 15 (2): 181-195 (in Persian).
 
Namdari, A., Pezeshkpoor, P., Mehraban, A., Mirzaei, A., and Vaezi, B. 2022a. Evaluation of genotype × environment interaction using WAASB and WAASBY indices in multi-environment yield trials of rainfed lentil (Lens culinaris L.) genotypes. Iranian Journal of Crop Sciences 24: 165-18 (in Persian).
 
Namdari, A., Pezeshkpoor, P., Mehraban, A., Mirzaei, A., and Vaezi, B. 2022b. Evaluation of grain yield stability of advanced rainfed lentil genotypes using multivariate AMMI method. Journal of Crop Breeding 14 (42): 169-176 (in Persian).
 
Nardino, M., Baretta, D., Carvalho, I. R., Olivoto, T., Follmann, D. N., Szareski, V. J., De Pelegrin, A. J., Konflanz, V. A., and de Souza, V. Q. 2016. Restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) for analyzing the agronomic performance of corn. African Journal of Agricultural Research 11: 4864–4872.
 
Olivoto, T., Lúcio, A. D. C., da Silva, J. A. G., Sari, B. G., and Diel, M. I. 2019a. Mean performance and stability in multi-environment trials II: selection based on multiple traits. Agronomy Journal 111 (6): 2961-2969.
 
Olivoto, T., Lúcio, A. D. C., da Silva, J. A. G., Marchioro, V.S., de Souza, V. Q., and Jost, E. 2019b. Mean performance and stability in multi-environment trials I: combining features of AMMI and BLUP techniques. Agronomy Journal 111 (6): 2949-2960.
 
Olivoto, T., Nardino, M., Carvalho, I. R., Follmann, D. N., Ferrari, M., Szareski, V. J., De Pelegrin, A. J., and de Souza, V. Q. 2017. REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits. Genetics and Molecular Research 16 (1): 1-19.
 
Pezeshkpour, P., and Afkar, S. 2019. Assessment of variability of lentil genotypes for agronomic traits using multivariate Analyses. Journal of Crop Breeding 11 (30): 142-151 (in Persian).
 
Pezeshkpour, P., Karimizadeh, R., Mirzaei, A., and Barzali. M. 2021. Analysis of yield stability of lentil genotypes using AMMI method. Journal of Crop Breeding 13 (37):132-145 (in Persian).
 
Piepho, H. P., Mohring, J., Melchinger, A. E., and Buchse, A. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161: 209–228.
 
Rahayu, S. 2020. Yield stability analysis of rice mutant lines using AMMI method. Journal of Physics: Conference Series 1436 (1): 1-9.
 
Ramburan, S., Zhou, M., and., Labuschagne, M. 2011. Interpretation of genotype × environment interactions of sugarcane: Identifying significant environmental factors. Field Crops Research 124: 392–399.
 
Sabaghnia, N., Dehghani, H., and Sabaghpour, S. H. 2008. Graphic analysis of genotype by environment interaction for lentil yield in Iran. Agronomy Journal 100: 760-764.
 
Sharifi, P. 2020. Application of multivariate analysis methods in agriculural sciences. Rasht branch, Islamic Azad University Press (in Persian). 308 pp.
 
Sharifi, P., Abbasian, A., and Mohaddesi, A. 2021. Evaluation the mean performance and stability of rice genotypes by combining features of AMMI and BLUP techniques and selection based on multiple traits. Plant Genetics Researches 7 (2): 163-180.
 
Smith, A. B., Cullis, B. R., and Thompson, R. 2005. The analysis of crop cultivar breeding and evaluation trials: An overview of current mixed model approaches. Journal of Agriculture Science 143 (1): 449-462.
 
Tigabu, D. A., Tadesse, Z., Zegeye, H., and Assefa. A. 2017. Seasonal variability and genetic response of elite bread wheat lines in drought prone environments of Ethiopia. Journal of Plant Breeding and Genetics 5: 15–21.
 
Van Eeuwijk, F. A., Bustos-Korts, D. V., and Malosetti, M. 2016. What should students in plant breeding know about the statistical aspects of genotype×environment interactions? Crop Science 56 (5): 2119-2140.
 
Veenstra, L. D., Santantonio, N., Jannink, J., and Sorrells. M. E. 2019. Influence of genotype and environment on wheat grain fructan content. Crop Science 59: 190–198.
 
Yan, W., Hunt, L. A., Sheng, Q., and Szlavnics, Z. 2000. Cultivar evaluation and mega environment investigations based on the GGE- biplot. Crop Science 40: 597-605.