Identification of superior bread wheat genotypes using composite indices for cultivation in warm regions

Askari Kalestani, Ali Reza; Tabib Ghaffary, Seyed Mahmoud; Zali, Hassan; Esmaeilzadeh Moghadam, Mohsen

doi:10.22055/ppd.2024.46831.2163

Document Type : Research Paper - Plant Breeding

Authors

¹ Assistant Professor, Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Darab, Iran

² Assistant Professor, Crop and Horticultural Science Research Department, Safiabad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Desful, Iran

³ Professor, Seed and Plant Improvement Department, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

https://doi.org/10.22055/ppd.2024.46831.2163

Abstract

Introduction
Wheat (Triticum aestivum L.) is a socioeconomically important crop in Iran. Achieving genetic gain in quantitative traits through selection is essential for successful breeding programs. Identification of high-yielding genotypes with high desirable growth traits is a primary objective in wheat breeding. This study aimed to identify superior bread wheat genotypes based on grain yield and morpho-phenological traits while comparing different selection indices, including the Selection Index of Ideal Genotype (SIIG), Multi-Trait Genotype-Ideotype Distance Index (MGIDI), and ideotype design using the Best Linear Unbiased Prediction (FAI-BLUP).
Materials and Methods
The study was conducted during the 2022-2023 cropping season to identify superior bread wheat genotypes for warm and dry conditions in southern Iran (Darab in Fars province and Safiabad-Dezful). The experiment followed an augment design with six incomplete blocks and three control varieties: Sarang, Mehrgan, and Barat. Traits measured included plant height (PLH), days to heading (DHE), days to maturity (DMA), thousand kernel weight (TKW), seed filling rate (SFR), seed filling period (SFP), and grain yield (YLD). Genetic parameters were estimated using the Restricted Maximum Likelihood (REML) method. Statistical analyses were subsequently computed using SAS and R software.
Results and Discussion
The likelihood ratio test (LRT) revealed that genotype effects were significant at the 1% probability level for PLH, DHE, DMA, SFR, SFP, and YLD (excluding TKW). Heritability estimates varied, with the highest heritability observed for PLH (0.879) and the lowest for SFR (0.124). These findings, supported by the heat map of traits, highlighted substantial genetic diversity among the wheat genotypes. Heritability estimates across traits ranged from 0.124 (SFP) to 0.879 (PLH). For the Darab region, the SIIG index was the most effective, while in Dezful and across both regions, the FAI-BLUP index outperformed the other indices. The FAI-BLUP index demonstrated superior performance in selecting genotypes with multiple desirable traits and exhibited significant correlations with a larger number of traits compared to the other indices. In Darab, the SIIG index identified G5, G36 and G42 as superior genotypes. In Dezful, the FAI-BLUP index identified G26, G30 and G37 as the best genotypes, and for the combined regions, G30, G35 and G36 genotypes were identified as superior genotypes using the FAI-BLUP index.
Conclusion
Overall, the FAI-BLUP index emerged as the most effective selection tool for identifying superior bread wheat genotypes under the conditions of this study. A two-dimensional analysis of selection indices and grain yield highlighted G50, G52, and G60 as promising genotypes for preliminary trials in Darab and Dezful regions.

Keywords

Main Subjects

B: Plant Breeding

References

Barati, A., Zali, H., Marzoqian, A., Naghipour, F., Pour-Aboughadareh, A., & Kelestani, A. A. (2022). Selection of hull-less barley lines using the selection index of ideal genotype (SIIG) in Ahvaz and Darab regions. Crop Production, 15 (2), 161-181. [In Persian]

Cerón‐Rojas, J. J., & Crossa, J. (2022). The statistical theory of linear selection indices from phenotypic to genomic selection. Crop Science, 62 (2), 537-563.

Cuong, D. M., Kwon, S.-J., Nguyen, B. V., Chun, S. W., Kim, J. K., & Park, S. U. (2020). Effect of salinity stress on phenylpropanoid genes expression and related gene expression in wheat sprout. Agronomy, 10 (3), 390.

Dastfal, M., Aghaee-Sarbarzeh, M., & Zali, H. (2022). Genetic diversity and selection of durum wheat pure lines with desirable agronomy traits using SIIG index. Iranian Journal of Field Crop Science, 53 (1), 161-174. [In Persian]

Driedonks, N., Rieu, I., & Vriezen, W. H. (2016). Breeding for plant heat tolerance at vegetative and reproductive stages. Plant Reprod, 29, 67–79.

Egli, D. B. (2004). Seed-fill duration and yield of grain crops. Advances in Agronomy, 83, 243.

Farhad, M., Kumar, U., Tomar, V., Bhati, P. K., Krishnan J, N., Barek, V., Brestic, M., & Hossain, A. (2023). Heat stress in wheat: a global challenge to feed billions in the current era of the changing climate. Frontiers in Sustainable Food Systems, 7, 1203721.

Farooq, M., Bramley, H., Palta, J. A., & Siddique, K. H. M. (2011). Heat stress in wheat during reproductive and grain-filling phases, Critical Reviews in Plant Sciences, 30, 491507.

Gholizadeh, A., Ghaffari, M., & Shariati, F. (2021). Use of selection index of ideal genotype (SIIG) in order to select new high yielding sunflower hybrids with desirable agronomic characteristics. Journal of Crop Breeding, 13 (38), 116-123. [In Persian]

Holland, J. B. (2006). Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Science, 46 (2), 642-654.

Hosseini, M., Honarnejad, M., & Tarang A. R. (2005). Study of gene effects and combining ability of
quantitative characteristics and grain quality in rice. Iranian Journal of Agriculture Science. 32 (1): 21-32. [In Persian]

Jahufer, M., & Casler, M. (2015). Application of the Smith‐Hazel selection index for improving biomass yield and quality of switchgrass. Crop Science, 55 (3), 1212-1222.

Jalal Kamali, M.R., & Duveiller. E. (2008). Wheat production and research in Iran: A success story. International Symposium on Wheat Yield Potential, Challenges to International Wheat Breeding, 54 -58 pp., Mexico, CIMMYT.

Khan, A. A., & Kabir, M. R. (2014). Evaluation of spring wheat genotypes (Triticum aestivum L.) for heat stress tolerance using different stress tolerance indices, Cercetari agronomice in Moldova, 47 (4), 49–63.

Lark, T. J., Schelly, I. H., & Gibbs, H. K. (2021). Accuracy, bias, and improvements in mapping crops and cropland across the United States using the USDA cropland data layer. Remote Sensing, 13 (5), 968.

León, R., Rosero, A., García, J.-L., Morelo, J., Orozco, A., Silva, G., De la Ossa, V., Correa, E., Cordero, C., & Villalba, L. (2021). Multi-trait selection indices for identifying new cassava varieties adapted to the Caribbean region of Colombia. Agronomy, 11 (9), 1694.

Lynch, M., & Walsh, B. (1998). Genetics and analysis of quantitative traits (Vol. 1). Sinauer Sunderland, MA.

Meier, C., Marchioro, V. S., Meira, D., Olivoto, T., & Klein, L. A. (2021). Genetic parameters and multiple-trait selection in wheat genotypes. Pesquisa Agropecuária Tropical, 51, e67996.

Mishra, D., Shekhar, S., Chakraborty, S., & Chakraborty, N. (2021). High temperature stress responses and wheat: impacts and alleviation strategies. Environmental and experimental botany, 190, 104589.

Mondal, S., Singh, R., Mason, E., Huerta-Espino, J., Autrique, E., & Joshi, A. (2016). Grain yield, adaptation and progress in breeding for early-maturing and heat-tolerant wheat lines in South Asia. Field crops research, 192, 78-85.

Olivoto, T., Lúcio, A. D., da Silva, J. A., Sari, B. G., & Diel, M. I. (2019). Mean performance and stability in multi‐environment trials II: Selection based on multiple traits. Agronomy Journal, 111 (6), 2961-2969.

Olivoto, T., & Nardino, M. (2020). MGIDI: A novel multi-trait index for genotype selection in plant breeding. Agricultural and Food Sciences, Biology, 2020.2007. 2023.217778.

Olivoto, T., & Nardino, M. (2021). MGIDI: Toward an effective multivariate selection in biological experiments. Bioinformatics, 37 (10), 1383-1389.

Patterson, H. D., & Thompson, R. (1971). Recovery of inter-block information when block sizes are unequal. Biometrika, 58(3), 545-554.

Poudel, P. B., & Poudel, M. R. (2020). Heat stress effects and tolerance in wheat: A review. Journal of Biology, 9 (3), 1–6.

Pour-Aboughadareh, A., & Poczai, P. (2021). Dataset on the use of MGIDI index in screening drought-tolerant wild wheat accessions at the early growth stage. Data in Brief, 36, 107096.

Resende, M. d., Silva, F. e., & Azevedo, C. (2014). Estatística matemática, biométrica e computacional: Modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão aleatória, seleção genômica, QTL-GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa: UFV.

Rocha, J. R. d. A. S. d. C., Machado, J. C., & Carneiro, P. C. S. (2018). Multitrait index based on factor analysis and ideotype‐design: Proposal and application on elephant grass breeding for bioenergy. Global Change Biology Bioenergy, 10 (1), 52-60.

Shirzad, A., Asghari, A., Zali, H., Sofalian, O., & Chamanabad, H. M. (2022). Application of the multi-trait genotype-ideotype distance index in the selection of top barley genotypes in the warm and dry region of Darab. Journal of Crop Breeding, 14 (44), 65-76. [In Persian]

Silva, L. A., Peixoto, M. A., Peixoto, L. d. A., Romero, J. V., & Bhering, L. L. (2021). Multi-trait genomic selection indexes applied to identification of superior genotypes. Bragantia, 80, e3621.

Tadili, S., Asghari, A., Karimizadeh, R., Sofalian, O., & Chamanabad, H. M. (2020). Evaluation of drought stress tolerance in advanced lines durum wheat using the selection index of ideal genotype (SIIG). Journal of Crop Ecophysiology, 45-62. [In Persian]

Tahmasebi, S., Dastfal, M., Zali, H., & Rajaie, M. (2018). Drought tolerance evaluation of bread wheat cultivars and promising lines in warm and dry climate of the south. Cereal Research, 8 (2), 209-225. [In Persian]

Uddin, M. S., Billah, M., Afroz, R., Rahman, S., Jahan, N., Hossain, M. G., Bagum, S. A., Uddin, M. S., Khaldun, A. B. M., & Azam, M. G. (2021). Evaluation of 130 eggplant (Solanum melongena L.) genotypes for future breeding program based on qualitative and quantitative traits, and various genetic parameters. Horticulturae, 7 (10), 376.

Wahid, A., Gelani, S., Ashraf, M., & Foolad. M. R. (2007). Heat tolerance in plants: an overview. Environmental and Experimental Botany, 61 (3): 199 -223.

Wei, C., Jiao, Q., Agathokleous, E., Liu, H., Li, G., Zhang, J., Fahad, S., & Jiang, Y. (2022). Hormetic effects of zinc on growth and antioxidant defense system of wheat plants. Science of The Total Environment, 807, 150992.

Yaghotipoor, A., Farshadfar, E. A., & Saeidi, M. (2017). Evaluation of drought tolerance in bread wheat genotypes using new mixed method. Environmental Stresses in Crop Sciences, 10 (2), 247-256.

Zali, H., Barati, A., Pour-Aboughadareh, A., Gholipour, A., Koohkan, S., Marzoghiyan, A., Bocianowski, J., Bujak, H., & Nowosad, K. (2023). Identification of superior barley genotypes using selection index of ideal genotype (SIIG). Plants, 12 (9), 1843.

Zali, H., & Pour-Aboughadareh, A. (2023). Identification of superior genotypes of barley for cultivation the south regions of Fars province using MGIDI and FAI-BLUP indices. Plant Productions, 46(3), 335-351. [In Persian]

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-An International Journal, 7 (2): 703-711.

Zine, ZAF. Hannachi, F., & Benmahammed, A. ( 2021). Utilization of Multi-Trait Genotype-Ideotype Distance Index (MGIDI) increases expected genetic gains for simultaneous improvement of wheat ttaits: Conference: BGRI 2021 Technical Workshop October 6-8.

Identification of superior bread wheat genotypes using composite indices for cultivation in warm regions

References

References

Volume 47, Issue 4January 2025Pages 491-510

Volume 47, Issue 4
January 2025
Pages 491-510