[Home ] [Archive]   [ فارسی ]  
:: About :: Main :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..



 
..
:: Volume 10, Issue 1 (2023) ::
pgr 2023, 10(1): 1-28 Back to browse issues page
Selection Indices for Improving Maize Grain Yield under Normal and Salt Stress Conditions
Maryam Ebrahimi , Reza Darvishzadeh * , Amir Fayaz Moghaddam
Department of Plant Production and Genetics, Urmia University, Urmia, Iran , r.darvishzadeh@urmia.ac.ir
Abstract:   (2627 Views)
Protection of food security is one of the basic priorities of any country, which is achieved through the development and introduction of new, high-yielding and stress-resistant crop varieties. Considering the wide range of usage; human nutrition, livestock and poultry nutrition as well as use in industrial products production, maize is of special importance in agricultural development programs. To improve a trait with complex behavior and low heritability, indirect selection by other traits or a suitable index developed based on several traits can be used. In this research, 86 maize genotypes were cultivated in the form of randomized complete block design with three replications in the field in the Faculty of Agriculture, Urmia University under two normal and salt stress conditions. The measurement of the traits was done from the tassel appearance to kernel physiological maturity. In order to speed up genotype selection and increase the acuracy of selecting high yielding genotypes, four selection indices including Smith- Hazel, Pasek- Baker, Brim and Robinson were used and calculated. The results of present study revealed that selection based on the Smith- Hazel index with the highest selection efficiency (∆H) will increase the grain yield in normal and grain yield and plant height in salt stress conditions. This index, with its high correlation with the breeding value is introduced as a superior index. Based on this index, R59 and 6*/88 genotypes were introduced as the superior genotypes under normal and salt stress conditions, respectively. Nonetheless, these genotypes were recognized as the best genotypes considering the results of all other investigated indices. Identifying and introducing genotypes tolerant to salinity stress is of particular importance due to the expansion of saline lands and the limitation of access to water suitable for irrigation. Based on the above results, 6*/88 genotype is recommended for the development of promising hybrids for cultivation in areas with water or saline soil.
Keywords: Indirect selection, Maize, Multi traits selection, NaCl stress, Response to selection
Full-Text [PDF 1262 kb]   (1067 Downloads)    
Type of Study: Research | Subject: Plant improvement
References
1. Amaral Júnior, A.T., Freitas Júnior, S.P., Rangel, R.M., Pena, G.F., Ribeiro, R.M., Morais, R.C., and Schuelter, A.R. (2010). Improvement of a popcorn population using selection indexes from a fourth cycle of recurrent selection program carried out in two different environments. Genetics and Molecular Research, 9: 340-347. [DOI:10.4238/vol9-1gmr702]
2. Andrade, A.C.B., Silva, A.J., Ferraudo, A.S., Unêda-Trevisoli, S.H. and Mauro, A.S. (2016). Strategies for selecting soybean genotypes using mixed models and multivariate approach. African Journal of Agricutural Research, 11: 23-31.
3. Asghar, M.J. and Mehdi, S.S. (2010). Selection indices for yield and quality traits in sweet corn. Pakistan Journal of Botany, 42(2): 775-789.
4. Arzangh, S., Darvishzadeh, R. and Alipuor, H. (2021). Evaluation of genetic diversity of maize lines (Zea mays L.) under normal and salinity stress conditions. Cereal Research, 11(3): 243-268 (In Persian).
5. Baker, R.J. (1986). Selection Indices in Plant Breeding. CRC Press. Boca Raton, Florida, USA.
6. Boyles, R.E., Cooper, E.A., Myers, M.T., Brenton, Z., Rauh, B.L., Morris, G.P. and Kresovich, S. (2016). Genome‐wide association studies of grain yield components in diverse sorghum germplasm. The Plant Genome, 9(2): 2015-09. [DOI:10.3835/plantgenome2015.09.0091]
7. Brim, C.A., Johnson H.W. and Cockerham, C.C. (1959). Multiple selection criteria in soybeans 1. Agronomy Journal, 51(1): 42-46. [DOI:10.2134/agronj1959.00021962005100010015x]
8. Carvalho, A.D.F., Nogueira, M.T.M., Silva, G.O., Luz, J.M.Q., Maciel, G.M. and Rabelo, P.G. (2017). Seleção de genótipos de cenoura para caracteres fenotípicos de raiz. Horticultura Brasileira, 35: 97-102. [DOI:10.1590/s0102-053620170115]
9. Candido, W.D.S., Silva, C.M., Costa, M.L., Silva, B.E.D.A., Miranda, B.L.D., Pinto, J.F.N. and Reis, E.F.D. (2020). Selection indexes in the simultaneous increment of yield components in topcross hybrids of green maize. Pesquisa Agropecuária Brasileira, 17(55): e01206. [DOI:10.1590/s1678-3921.pab2020.v55.01206]
10. Crispim‐Filho, A.J., Dos Santos, F.P., Pinto, J.F.N., Melo, P.G.S., Dos Reis, E.F. and Mendes‐Resende, M.P. (2020). Dealing with multiple traits in maize: A new approach for selecting progenies. Crop Science, 60(6): 3151-3165. [DOI:10.1002/csc2.20292]
11. Dabholkar, A. (1992). Elements of Biometrical Genetics Concept. Publishing Company, New Delhi, IND.
12. Davik, J. (1989). A selection index for population improvement in white cabbage (Brassica oleracea L. var. capitata). Hereditas, 111: 17-23. [DOI:10.1111/j.1601-5223.1989.tb00371.x]
13. Dehghan Kouhestani, R., Majidi, M.M. and Saeidi, G. (2017). Direct and indirect selection responses for seed yield improvement in safflower (Carthamus tinctorius L.). Journal of Crop Production and Processing, 7(1): 115-125 (In Persian). [DOI:10.18869/acadpub.jcpp.7.1.115]
14. De Santiago, S., de Souza Junior, C.L., Lemos, L.B. and Môro, G.V. (2019). Prediction of genetic gain using selection indices in maize lines. African Journal of. Agricultural Research, 14: 787-793.
15. Dovale, J.C., Fritsche-Neto, R., Silva, P.S.L. (2011). Índice de seleção para cultivares de milho com dupla aptidão: minimilho e milho verde. Bragantia, 70: 781-787. [DOI:10.1590/S0006-87052011000400008]
16. Erenstein, O., Jaleta, M., Sonder, K., Mottaleb, K. and Prasanna, B.M. (2022). Global maize production, consumption and trade: trends and R&D implications. Food Security, 14: 1295-1319. [DOI:10.1007/s12571-022-01288-7]
17. Falconer, D.S. and Mackay, T.F.C. (1996). Introduction to Quantitative Genetics. Pearson, Harlow, UK.
18. FAO. (2020) FAO extent of salt-affected soils. Available at: http://www.fao.org/ soils-portal/soil-management/management-of-some-problem-soils/salt-affected-soils/more-information-on-salt-affected-soils/en/ (Accessed Access April 30, 2020).
19. Fazlalipour, M., Rabiei, B., Samizadeh, H. and Rahimsouroush, H. (2007). Use of coefficient path analysis for base and optimum selection indices in rice. Journal of Agricultural Science, 17(4): 97-112 (In Persian).
20. Freitas, I.L.D.J., Amaral Junior, A.T.D., Viana, A.P., Pena, G.F., Cabral, P.D.S., Vittorazzi, C. and Silva, T.R.D.C. (2013). Ganho genético avaliado com índices de seleção ecom REML/BLUP em milho-pipoca. Pesquisa Agropecuária Brasileira, 48: 1464-1471. [DOI:10.1590/S0100-204X2013001100007]
21. Hassani, A., Azapagic, A. and Shokri, N. (2021). Global predictions of primary soil salinization under changing climate in the 21st century. Nature Communications, 12: 6663. [DOI:10.1038/s41467-021-26907-3]
22. Hazel, L.N. (1943). The genetic basis for constructing selection indexes. Genetics, 28(6): 476-49. [DOI:10.1093/genetics/28.6.476]
23. Holland, J.B., Nyquist, W. and Cervantes, C. (2003). Estimating and interpreting heritability for plant breeding. Plant Breeding Reviews, 22: 9-112. [DOI:10.1002/9780470650202.ch2]
24. Lima, V.J. De, Freitas Junior, S. De P., Souza, Y.P. De, Silva, C.S. Da, Farias, J.E.C., Souza, R.F. De, Chaves, M.M. and Feitosa, J.V. (2018). Genetic gain capitalization in the first cycle of recurrent selection in popcorn at Ceará's Cariri. Revista Brasileira de Ciências Agrárias, 13: e5556. [DOI:10.5039/agraria.v13i3a5556]
25. Khavari Khorasani, S. and Mahdi Poor, A. (2018). Genetic improvement of grain yield by determination of selection index in single cross hybrids of maize (Zea mays L.). Plant Genetic Researches, 5(1): 1-18 (In Persian). [DOI:10.29252/pgr.5.1.1]
26. Kumar, P., Choudhary, M., Halder, T., Prakash, N.R., Singh, V.V.V.T., Sheoran, S.T.R.K., Longmei, N., Rakshit, S. and Siddique, K.H.M. (2022). Salinity stress tolerance and omics approaches: revisiting the progress and achievements in major cereal crops. Heredity, 128(6): 497-518. [DOI:10.1038/s41437-022-00516-2]
27. Munns, R. and Tester, M. (2008). Mechanisms of salinity tolerance. Annual Review of Plant Biology, 59: 651-681. [DOI:10.1146/annurev.arplant.59.032607.092911]
28. Noble, C.L. and Rogers, M.E. (1992). Arguments for the use of physiological criteria for improving the salt tolerance in crops. Plant Physiology, 146: 99-107. [DOI:10.1007/BF00012001]
29. Parvaiz, A. and Satyawati, S. (2008). Salt stress and phyto-biochemical responses of plants. Plant Soil Environment, 54: 89-99 [DOI:10.17221/2774-PSE]
30. Pesek, J. and Baker, R. (1970). An application of index selection to the improvement of self-pollinated species. Canadian Journal of Plant Science, 50(3): 267-276. [DOI:10.4141/cjps70-051]
31. Rahimi, M. and Rabiei, B. (2011). The application of selection indices on improvement of grain yield in rice (Oryza sativa L.). Agronomy Journal (Pajouhesh & Sazandegi), 90: 39-49 (In Persian).
32. Robinson, H.F., Comstock R.E. and Harvey, P.H. (1951). Genotypic and phenotypic correlation and their implications in selection. Agronomy Journal, 43: 282-287. [DOI:10.2134/agronj1951.00021962004300060007x]
33. Sah, R.P., Chakraborty, M., Prasad, K., Pandit, M., Tudu, V.K., Chakravarty, M.K. and Moharana, D. (2020). Impact of water deficit stress in maize: phenology and yield components. Scientific Reports, 10(1): 2944. [DOI:10.1038/s41598-020-59689-7]
34. Salehi, M. and Saeidi, G.A. (2012). Selection indicators to improve sesame seed yield. Iranian Journal of Field Crops Research, 10(4): 667-673 (In Persian).
35. Shabala, S. (2013). Learning from halophytes: physiological basis and strategies to improve abiotic stress tolerance in crops. Annals of Botany 112: 1209-1221. [DOI:10.1093/aob/mct205]
36. Shiri, M. and Ebrahimi, L. (2018). Comprehensive SAS code for computing several selection indices. Journal of Crop Improvement, 32(2): 225-238. [DOI:10.1080/15427528.2017.1407855]
37. Smith, H.F. (1936). A discriminant function for plant selection. Annals of Eugenics, 7(3): 240-250. [DOI:10.1111/j.1469-1809.1936.tb02143.x]
38. Silva, M.F., Maciel, G.M., Finzi, R.R., Peixoto, J.V.M., Rezende, W.S. and Castoldi, R. (2020). Selection indexes for agronomic and chemical traits in segregating sweet corn populations. Horticultura Brasileira, 38: 71-77. [DOI:10.1590/s0102-053620200111]
39. Subbarao, G.V. and Johansen. C. (2002). Physiological mechanisms relevant to genetic improvement of salinity tolerance in crop plants. In: Pessarakli M (ed.) Handbook of Plant and Crop Physiology. Marcel Dekker Inc., New York, USA. [DOI:10.1201/9780203908426.ch44]
40. Tahmasbali, M., Darvishzadeh, R. and Fayaz Moghaddam, A. (2021). Evaluation of oriental tobacco (Nicotiana tabacum L.) genotypes using selection indices under the presence and absence of broomrape. Iranian Journal of Field Crop Science, 52(3): 189-20 (In Persian).
41. Tahmasebi, A., Darvishzadeh, R., Fayaz Moghadam, A., Gholinejhad, E. and Abdi, H. (2022). Using selection indices to improve seed yield in native sesame stands. Plant Genetic Researches, 8(2): 117-130 (In Persian). [DOI:10.52547/pgr.8.2.9]
42. Vieira, R.A., Rocha, R.D., Scapim, C.A. and Amaral, A.T.D. (2017). Recurrent selection of popcorn composites UEM-CO1 and UEM-CO2 based on selection indices. Crop Breeding and Applied Biotechnology, 17: 266-272. [DOI:10.1590/1984-70332017v17n3n40]
43. Yue, H., Gauch, H.G., Wei, J., Xie, J., Chen, S., Peng, H., Bu, J. and Jiang, X. (2022). Genotype by environment interaction analysis for grain yield and yield components of summer maize hybrids across the huanghuaihai region in China. Agriculture, 12(5): 602. [DOI:10.3390/agriculture12050602]
44. Zhu, M., Li, Q., Zhang, Y., Zhang, M. and Li, Z. (2022) Glycine betaine increases salt tolerance in maize (Zea mays L.) by regulating Na + homeostasis. Frontiers in Plant Science, 13: 978304. [DOI:10.3389/fpls.2022.978304]
Send email to the article author



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Ebrahimi M, Darvishzadeh R, Fayaz Moghaddam A. Selection Indices for Improving Maize Grain Yield under Normal and Salt Stress Conditions. pgr 2023; 10 (1) :1-28
URL: http://pgr.lu.ac.ir/article-1-290-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 10, Issue 1 (2023) Back to browse issues page
پژوهش های ژنتیک گیاهی Plant Genetic Researches
Persian site map - English site map - Created in 0.07 seconds with 38 queries by YEKTAWEB 4657