Graphical analysis of forage yield stability under high and low potential circumstances in 16 grass pea (Lathyrus sativus L.) genotype

Behrouz VAEZI, Hamid HATAMI MALEKI, Saeed YOUSEFZADEH, Reza PIROOZ, Askar JOZEYAN, Raham MOHTASHAMI, Naser SABAGHNIA

Abstract


Introducing grass pea genotypes with wide adaptability across diverse environments is important. Dry forage yield of 16 grass pea genotypes, tested in a RCBD design with three replicates across 4 locations over 3 seasons in Iran. The GGE biplot method based on SREG model facilitated a visual evaluation of the best genotypes. The first two principal components accounted for 77 % of the GE interaction and revealed six winning genotypes and four mega-environments. The average location coordinate (ALC) was used to examine both yield performance and stability and indicated IFLA-1913, IFLA-1961, IFLA-1812, and IFLA-2025 were the best genotypes. Based on the ideal-genotype approach, genotype G5 was better than all other genotypes and showed both high forage yield and stability across locations. According to G + GE sources of variations, the genotypes (IFLA-1913, IFLA-1961, IFLA-1812, and IFLA-2025) were the most suitable varieties for the grass pea-producing regions in semi-arid and rain-fed conditions. An ideal location should be both discriminating of the genotypes and representative of the average location, but we could not find such location in this research. Results confirmed that G5 (IFLA-1961) has high stability and high yield performance (4.92 t ha-1), and could introduce as favorable genotype for commercial variety release.


Keywords


average location coordinate; biplot; GGE (Genotype+ Genotype Environment interaction)

Full Text:

PDF

References


Berteroa, H., Dela Vegab, A.J., Correaa, G., Jacobsen C, S.E., & Mujica, A. (2004). Genotype and genotype-by-environment interaction effects for grain yield and grain size of quinoa (Chenopodium quinoa Willd.) as revealed by pattern analysis of international multi-environment trials. Field Crops Research, 89, 299–318. https://doi.org/10.1016/j.fcr.2004.02.006

Burgueno, J., Crossa, J., & Vargas, M. (2003). Graphing GE and GGE biplots. Handbook of Formulas and Software for Plant Geneticists and Breeders. MS Kang (ed.). Food Products Press. New York pp. 193–203.

Croft, A., Pang E. C. K. M., & Taylor, P.W. J. (1999). Molecular analysis of Lathyrus sativus L. (grass pea) and related Lathyrus species. Euphytica, 107, 167–176. https://doi.org/10.1023/A:1003520721375

Eberhart, S.T., & Russell, W.A., (1966). Stability parameters for comparing varieties. Crop Science, 6, 36–40. https://doi.org/10.2135/cropsci1966.0011183X000600010011x

Gauch, H.G., Piepho, H. P., & Annicchiarico, P. (2008). Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Science, 48, 866–889. https://doi.org/10.2135/cropsci2007.09.0513

Gcdt (2007). Strategy for the ex situ conservation of Lathyrus (grass pea), with special reference to Lathyrus sativus, L. cicera L. ochrus. Global Crop Diversity Trust. Rome, Italy. http://www.croptrust.org/documents/web/Lathyrus-Strategy-FINAL-31Oct07.pdf.

Gollob, H. F. (1968). A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika, 33, 73–115. https://doi.org/10.1007/BF02289676

Kang, M.S. (2002). Genotype–environment interaction: progress and prospects. In ‘Quantitative genetics, genomics and plant breeding. (Ed. MS Kang) pp. 221–243. https://doi.org/10.1079/9780851996011.0221

Kang, M. S., Aggarwal, V. D., & Chirwa, R. M. (2006). Adaptability and stability of bean cultivars as determined via yield-stability statistic and GGE biplot analysis. Journal of Crop Improvement, 15, 97–120. https://doi.org/10.1300/J411v15n01_08

Karimizadeh, R., Mohammadi, M., & Sabaghmia, N. (2013). Site regression biplot analysis for matching new improved lentil genotypes into target environment. Journal of Plant Physiology and Breeding, 3, 49–63.

Lambein, F., & Kuo-Genth, Y.H. (1997). Lathyrus sativus, a neolithic crop with modern future? An overview of the present situation. Presented at the int. conference “Lathyrus sativus -cultivation and nutritional value in animals and human” - Poland, Lublin - Radom, 9-10.06, 1997, Materials of the Conf. 6–12.

Sabaghnia, N. (2010). Multivariate statistical analysis of genotype × environment interaction in multi-environment trials of breeding programs. Agriculture and Forestry, 56, 19–31.

Sabaghnia, N., Karimizadeh, R., & Mohammadi, M. (2012). Genotype by environment interaction and stability analysis for grain yield of lentil genotypes. Žemdirbyste – Agriculture, 99, 305–312.

Sabaghnia, N., Karimizadeh, R., & Mohammadi, M. (2013). GGL biplot analysis of durum wheat (Triticum turgidum spp. durum) yield in multi-environment trials. Bulgarian Journal of Agriculture Science, 19, 756–765.

Setimela, P.S., Vivek, B., Bänziger, M., Crossa, J., & Maideni F. (2007). Evaluation of early to medium maturing open pollinated maize varieties in SADC region using GGE biplot based on the SREG model. Field Crops Research, 103, 161–169. https://doi.org/10.1016/j.fcr.2007.05.010

Smartt, J., Kaul, A., Araya, W.A., Rahman, M.M., & Kearney, J. (1994). Grasspea (Lathyrus sativus L.) as a potentially safe food legume crop. p. 144–155. In: F.J. Muehlbauer and W.J. Kaiser (eds.), Expanding the Production and Use of Cool Season Food Legumes. Kluwer Academic Publishers. Dordrecht, Netherlands. https://doi.org/10.1007/978-94-011-0798-3_7

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, 1111–1118. https://doi.org/10.2134/agronj2001.9351111x

Yan, W. (2002). Singular value partitioning in biplot analysis of multi environment trial data. Agronomy Journal, 94, 990–996. https://doi.org/10.2134/agronj2002.9900

Yan, W., Hunt, L. A., Sheng, Q., & Szlavnics, Z. (2000). Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science, 40, 597–605. https://doi.org/10.2135/cropsci2000.403597x

Yan, W., Kang, M. S., Woods, B., MA, S., & Cornelius, P. L. (2007). GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science, 47, 643–655. https://doi.org/10.2135/cropsci2006.06.0374

Yan, W., & Tinker, N.A. (2006). Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science, 86, 623-645. https://doi.org/10.4141/P05-169

Yau, S. K. (1995). Regression and AMMI analyses of genotype × environment interactions: An empirical comparison. Agronomy Journal, 87, 121–126. https://doi.org/10.2134/agronj1995.00021962008700010021x

Yihunie, T.A., & Gesesse, C.A. (2018). GGE biplot analysis of genotype by environment interaction in field pea (Pisum sativum L.) genotypes in Northwestern Ethiopia. Journal of Crop Science and Biotechnology, 21, 67-74. https://doi.org/10.1007/s12892-017-0099-0




DOI: http://dx.doi.org/10.14720/aas.2023.119.1.2227

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Behrouz VAEZI, Hamid HATAMI MALEKI, Saeed YOUSEFZADEH, Reza PIROOZ, Askar JOZEYAN, Raham MOHTASHAMI, Naser SABAGHNIA

 

Acta agriculturae Slovenica is an Open Access journal published under the terms of the Creative Commons CC BY License.

                           


eISSN 1854-1941