Hostname: page-component-848d4c4894-wzw2p Total loading time: 0 Render date: 2024-06-03T04:15:46.143Z Has data issue: false hasContentIssue false
  • English
  • Français

Genotype × environment interaction and stability analyses of grain yield in rainfed winter bread wheat

Published online by Cambridge University Press:  06 October 2022

Mozaffar Roostaei Open the ORCID record for Mozaffar Roostaei [Opens in a new window] ,
Jaffar Jafarzadeh ,
Ebrahim Roohi ,
Hossein Nazary ,
Rahman Rajabi ,
Reza Mohammadi ,
Gholam Reza Khalilzadeh ,
Fereshteh Seif ,
Seyyed Mohammad Mehdi Mirfatah  and
Saber Seif Amiri
...Show all authors
Mozaffar Roostaei*
Affiliation:
Dryland Agricultural Research Institute, AREEO, Maragheh, Iran
Jaffar Jafarzadeh
Affiliation:
Dryland Agricultural Research Institute, AREEO, Maragheh, Iran
Ebrahim Roohi
Affiliation:
Field and Horticultural Crops Science Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
Hossein Nazary
Affiliation:
Field and Horticultural Crops Science Research Department, Zanjan Agricultural and Natural Resources Research and Education Center, AREEO, Zanjan, Iran
Rahman Rajabi
Affiliation:
Dryland Agricultural Research Institute (DARI), AREEO, Sararood Campus, Kermanshah, Iran
Reza Mohammadi
Affiliation:
Dryland Agricultural Research Institute (DARI), AREEO, Sararood Campus, Kermanshah, Iran
Gholam Reza Khalilzadeh
Affiliation:
Field and Horticultural Crops Science Research Department, West Azerbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran
Fereshteh Seif
Affiliation:
Field and Horticultural Crops Science Research Department, Hamedan Agricultural and Natural Resources Research and Education Center, AREEO, Hamedan, Iran
Seyyed Mohammad Mehdi Mirfatah
Affiliation:
Field and Horticultural Crops Science Research Department, Markazi Agricultural and Natural Resources Research and Education Center, AREEO, Arak, Iran
Saber Seif Amiri
Affiliation:
Field and Horticultural Crops Science Research Department, Ardabil Agricultural and Natural Resources Research and Education Center, AREEO, Ardabil, Iran
Hoosein Hatamzadeh
Affiliation:
Field and Horticultural Crops Science Research Department, North Khorasan Agricultural and Natural Resources Research and Education Center, AREEO, Bojnourd, Iran
Malek Masoud Ahmadi
Affiliation:
Field and Horticultural Crops Science Research Department, North Khorasan Agricultural and Natural Resources Research and Education Center, AREEO, Bojnourd, Iran
*
*Corresponding author. Email: m.roostaei@areeo.ac.ir
Get access Rights & Permissions [Opens in a new window]

Abstract

The genotype × environment (GE) interaction analysis is fundamental in crop breeding programs to guide selection and for recommendation of high performing and stable genotypes for breeding objectives. This study aimed at quantifying the GE interaction effects and determines grain yield stability among winter bread wheat genotypes under rainfed conditions of Iran. Twenty-four winter wheat genotypes were evaluated under nine test locations using a randomized complete blocks design with four replications during three cropping seasons (2019–21). The additive main effects and multiplicative interaction (AMMI) model and several parametric and nonparametric stability statistics were applied for analysis of grain yield data collected from the experiments. AMMI analysis of variance for grain yield revealed significant effects (p < 0.01) for genotype, environment, and GE interaction. The environment was the main source of variation and accounted for 83.5% of the total yield variation, followed by GE (6.5%) and genotype (1.0%) effects. The AMMI biplot analysis indicated the genotypes G3, G23, G22, G10, and G19 as high yielding with stability performance across environments. Genotypes G14, G13, G20, and G9 showed large positive interaction with the environments featuring the highest rainfall during growing season, while genotypes G7, G6, and G21 had a large positive interaction with environments with low rainfall. Spearman’s rank correlation analysis revealed that the AMMI stability value, Shukla’s stability variance (σ2i), Wricke’s ecovalence (W2i), coefficient of determination (R2i), variance in regression deviations (S2di), and nonparametric statistic of S2(i) were not correlated with mean yield in tested genotypes, showing they are related to static/biological concept of stability. In contrast, the genotypic superiority index (Pi) and regression coefficient (bi) were significantly correlated (p < 0.01) with mean yield and corresponded to dynamic/agronomic concept of stability. These findings suggest that selection of genotypes should be considered based on selection objectives of using the various stability parameters described here. In conclusion, the selected genotypes in this study should be recommended as new cultivars or parental lines for grain yield and stability improvement under rainfed conditions of Iran or similar agro-ecologies.

Keywords

Winter wheatGrain yieldGE interactionStability performance
Type
Research Article
Information
Experimental Agriculture , Volume 58 , 2022 , e37
Check for updates
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Annicchiarico, P. (1997). Joint regression vs. AMMI analysis of genotype–environment interactions for cereals in Italy. Euphytica 94, 53–62.CrossRefGoogle Scholar
Becker, H.C. and Leon, J. (1988). Stability analysis in plant breeding. Plant Breeding 101, 1–23.CrossRefGoogle Scholar
Bocianowski, J., Księżak, J. and Nowosad, K. (2019) Genotype by environment interaction for seeds yield in pea (Pisum sativum L.) using additive main effects and multiplicative interaction model. Euphytica 215, 191.CrossRefGoogle Scholar
Chairi, F., Aparicio, N., Serret, M.D. and Araus, J.L. (2020). Breeding effects on the genotype × environment interaction for yield of durum wheat grown after the Green Revolution: the case of Spain. The Crop Journal 8, 623–634.CrossRefGoogle Scholar
Ebdon, J.S. and Gauch, H.G. (2002). Additive main effect and multiplicative interaction analysis of national turf grass performance trials: I. interpretation of genotype × environment interaction. Crop Science 42, 489–496.Google Scholar
Eberhart, S.A.T. and Russell, W.A. (1966). Stability parameters for comparing varieties. Crop Science 6, 36–40.CrossRefGoogle Scholar
Finlay, K.W. and Wilkinson, G.N. (1963). Adaptation in a plant breeding programme. Australian Journal of Agricultural Research 14, 742–754.CrossRefGoogle Scholar
Francis, T.R. and 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.CrossRefGoogle Scholar
FAO. (2019). FAOSTAT 2019: FAO Statistical Databases. http://faostat.fao.org/ (accessed 23 May 2019).Google Scholar
Foulkes, M. J., Slafer, G. A., Davies, W. J., Berry, P. M., Sylvester-Bradley, R., Martre, P., Calderini, DF, Griffiths, S and Reynolds, MP. (2011). Raising yield potential of wheat. III. Optimizing partitioning to grain while maintaining lodging resistance. Journal of Experimental Botany 62, 469–486.CrossRefGoogle ScholarPubMed
Gauch, H.G. (1992). Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. Amsterdam, Netherlands: Elsevier Science Publishers.Google Scholar
Gauch, H.G., Piepho, H.P. and Annicchiarico, P. (2008). Statistical analysis of yield trials by AMMI and GGE: further considerations. Crop Science 48, 866–889.CrossRefGoogle Scholar
Gauch, H.G. (2006). Statistical analysis of yield trials by AMMI and GGE. Crop Science 46, 1488–1500.CrossRefGoogle Scholar
Gauch, H.G. (2013). A simple protocol for AMMI analysis of yield trials. Crop Science 53, 1860–1869.CrossRefGoogle Scholar
Gauch, G.H. and Zobel, R.W. (1997). Interpreting megaenvironments and targeting genotypes. Crop Science 37, 311–326.CrossRefGoogle Scholar
Gerard, G.S., Crespo-Herrera, L.A., Crossa, J., Mondal, S., Velu, G., Juliana, P., Huerta-Espino, J, Vargas, M, Rhandawa, MS, Bhavani, S and Braun, H. (2020). Grain yield genetic gains and changes in physiological related traits for CIMMYT’s High Rainfall Wheat Screening Nursery tested across international environments. Field Crops Research 249, 107742.CrossRefGoogle ScholarPubMed
Gollob, H.F. (1968). A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33, 73–155.CrossRefGoogle ScholarPubMed
Hernandez-Ochoa, I. M., Asseng, S., Kassie, B. T., Xiong, W., Robertson, R., Luz Pequeno, D. N., Sonder, K, Reynolds, M, Babar, MA, Milan, AM and Hoogenboom, G. (2018). Climate change impact on Mexico wheat production. Agricultural and Forest Meteorology 263, 373–387.CrossRefGoogle Scholar
Huehn, M. (1990). Nonparametric measures of phenotypic stability. Part 1: theory. Euphytica 47(3), 189–194.CrossRefGoogle Scholar
Iran-AgriStat. (2020) Iran Statistics for Agriculture, Ministry of Jihad-e-Agriculture, Tehran, Iran. www.amar.maj.ir Google Scholar
Katsenios, N., Sparangis, P., Leonidakis, D., Katsaros, G., Kakabouki, I., Vlachakis, D. and Efthimiadou, A. (2021) Effect of genotype × environment interaction on yield of maize hybrids in Greece using AMMI analysis. Agronomy 11(3), 479.CrossRefGoogle Scholar
Li, W., Yan, Z.H., Wei, Y.M., Lan, X.J. and Zheng, Y.L. (2006). Evaluation of genotype-environment interactions in Chinese spring wheat by the AMMI model, correlation and path analysis. Journal of Agronomy and Crop Science 192, 221–227.CrossRefGoogle Scholar
Lin, C.S. and Binns, M.R. (1988). A Superiority measure of cultivar performance for cultivar × location data. Canadian Journal of Plant Science 68(1), 193–198.CrossRefGoogle Scholar
Ludwig, F. and Asseng, S. (2010). Potential benefits of early vigor and changes in phenology in wheat to adapt to warmer and drier climates. Agricutural Systems 103, 127–136.CrossRefGoogle Scholar
Matus-Cadiz, M.A., Hucl, P., Perron, C.E. and Tyler, R.T. (2003). Genotype × environment interaction for grain color in hard white spring wheat. Crop Science 43, 219–226.CrossRefGoogle Scholar
Mohammadi, R., Armion, M., Zadhasan, E., Ahmadi, M.M. and Amri, A. (2018). The use of AMMI model for interpreting genotype × environment interaction in durum wheat. Experimental Agricultural 54, 670–683.CrossRefGoogle Scholar
Mohammadi, R., Sadeghzadeh, B., Poursiahbidi, M.M. and Ahmadi, M.M. (2021). Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat. Annals of Applied Biology 178(3), 450–465.CrossRefGoogle Scholar
Pacheco, A., Vargas, M., Alvarado, G., Rodríguez, F., Crossa, J. and Burgueño, J. (2015). GEA-R (Genotype × Environment Analysis with R for Windows), Version 4.1. Mexico: CIMMYT. http://hdl.handle.net/11529/10203 Google Scholar
Piepho, H.P. (1994). Best linear unbiased prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis. Theoretical and Applied Genetics 89, 647–654.CrossRefGoogle ScholarPubMed
Olivoto, T., Lucio, A.D., da Silva, J.A., Marchioro, V.S., de Souza, V.Q. and Jost, E. (2019). Mean performance and stability in multi-environment trials I: combining features of AMMI and BLUP techniques. Agronomy 111, 2949–2960.CrossRefGoogle Scholar
Pinthus, J.M. (1973). Estimate of genotype value: a proposed method. Euphytica 22, 121–123.CrossRefGoogle Scholar
Purchase, J.L., Hatting, H. and Van Deventer, C.S. (2000). Genotype × environment interaction of winter wheat in South Africa: II. Stability analysis of yield performance. South African Journal of Plant and Soil 17, 101–107.CrossRefGoogle Scholar
R Core Team. (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Scapim, C.A., Pacheco, C.A.P., do Amaral Junior, A.T., Vieira, R.A., Pinto, R.J.B. and Conrado, T.V. (2012). Correlations between the stability and adaptability statistics of popcorn cultivars. Euphytica 174, 209–218.CrossRefGoogle Scholar
Shiferaw, B., Prasanna, B.M., Hellin, J. and Banziger, M. (2011). Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Security 3, 307–327.CrossRefGoogle Scholar
Shukla, G.K. (1972). Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29, 237–245.CrossRefGoogle ScholarPubMed
Singh, C., Gupta, A., Gupta, V., Kumar, P., Sendhil, R., Tyagi, B.S., Singh, G., Chatrath, R. and Singh, G.P. (2019). Genotype × environment interaction analysis of multi-environment wheat trials in India using AMMI and GGE biplot models. Crop Breeding and Applied Biotechnology 19(3), 309–318.CrossRefGoogle Scholar
Temesgen, T., Keneni, G., Sefera, T. and Jarso, M. (2015). Yield stability and relationships among stability parameters in faba bean (Vicia faba L.) genotypes. The Crop Journal 3(3), 258–268.CrossRefGoogle Scholar
Wricke, G. (1962). Ubereine Methode zur Erfassung der ökologischen Streubreite in Feldversuchen. Zeitschr F Planzenz 47, 92–96.Google Scholar
Yan, W., Hunt, L., Sheng, Q. and Szlavnics, Z. (2000). Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science 40, 597–605.CrossRefGoogle Scholar
Yang, R.C., Crossa, J., Cornelius, P.L. and Burgueno, J. (2009). Biplot analysis of genotype × environment interaction: proceed with caution. Crop Science 49, 1564–1576.CrossRefGoogle Scholar
Yohane, E.N., Shimelis, H., Laing, M., Mathew, I. and Shayanowako, A. (2021). Genotype-by-environment interaction and stability analyses of grain yield in pigeonpea [Cajanus cajan (L.) Millspaugh]. Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 71(3), 145–155.Google Scholar
Supplementary material: File

Roostaei supplementary material

Roostaei supplementary material 1

Download Roostaei supplementary material(File)
File 12.8 KB
Supplementary material: File

Roostaei supplementary material

Roostaei supplementary material 2

Download Roostaei supplementary material(File)
File 12.9 KB