A New Approach to Select Doubled Haploid Rice Lines under Salinity Stress Using Indirect Selection Index
Received date: 2020-05-21
Accepted date: 2021-01-04
Online published: 2021-07-28
This study determined the indirect selection index of doubled haploid (DH) rice using a multivariate analysis approach to select lines adaptive to salinity stress, comprising three experiments. The first experiment involved the selection of good agronomic characters in a field experiment conducted at an experimental station in Bogor, Indonesia. The second experiment involved salinity tolerance screening through hydroponic cultures using 0 and 120 mmol/L NaCl, conducted at a greenhouse in Bogor. The third experiment involved the validation of the indirect adaptability selection index (IASI) through a field experiment in Sukra (saline area). Field experiments followed a randomized complete block design (RCBD), whereas an RCBD nested factorial design was used for the greenhouse experiment. The first and second experiments used 56 DH lines and four check varieties with three replications. In the second experiment, Pokkali and IR29 varieties were also added as tolerant and sensitive checks of salinity, respectively. The third experiment used 28 selected DH lines, Inpari 29 and one sensitive DH line. The good agronomic index (GAI) was 0.465 yield + 0.433 number of productive tillers + 0.31 number of filled grains. This generated 24 DH rice lines with good agronomic traits. The salinity tolerance index (SaTI) was developed through the average of standardized salinity tolerance score and salinity selection index based on discriminant analysis. This generated 34 DH rice lines with good salinity stress tolerance. The IASI (IASI = GAI - SaTI) selected 28 DH rice lines adaptive to salinity stress and it was considered effective by Sukra validation.
Fuad Anshori Muhammad, Sapta Purwoko Bambang, Saraswati Dewi Iswari, Wahyuning Ardie Sintho, Bayuardi Suwarno Willy . A New Approach to Select Doubled Haploid Rice Lines under Salinity Stress Using Indirect Selection Index[J]. Rice Science, 2021 , 28(4) : 368 -378 . DOI: 10.1016/j.rsci.2021.05.007
| [1] | Acquaah G. 2007. Principles of Plant Genetics and Breeding. Oxford: Blackwell Publishing: 121?145. |
| [2] | Akbar M R, Purwoko B S, Dewi I S, Suwarno W B, Sugiyanta.2019. Selection of doubled haploid lines of rainfed lowland rice in preliminary yield trial. Biodiversitas, 20(10): 2796-2801. |
| [3] | Akter S, Biswas B K, Azad A K, Hasanuzzaman M, Arifuzzaman M. 2010. Correlation and discriminant function analysis of some selected characters in fine rice (Oryza sativa L.) available in Bangladesh. Int J Sustain Crop Prod, 5(4): 30-35. |
| [4] | Akҫura M, Ҫeri S. 2011. Evaluation of drought tolerance indices for selection of Turkish oat (Avena sativa L.) landraces under various environmental conditions. Žemdirbystė, 98(2): 157-166. |
| [5] | Ali M N, Yeasmin L, Gantait S, Goswami R, Chakraborty S. 2014. Screening of rice landraces for salinity tolerance at seedling stage through morphological and molecular markers. Physiol Mol Biol Plants, 20(4): 411-423. |
| [6] | Al-Naggar A M M, Sabry S R S, Atta M M M, El-Aleem O M A. 2015. Effects of salinity on performance, heritability, selection gain and correlations in wheat (Triticum aestivum L.) doubled haploids. Sci Agric, 10(2): 70-83. |
| [7] | Alsabah R, Purwoko B S, Dewi I S, Wahyu Y. 2019. Selection index for selecting promising doubled haploid lines of black rice. SABRAO J Breed Genet, 51(4): 279-294. |
| [8] | Al-Sayed H M, Fateh H S, Fares W M, Attaya A S. 2012. Multivariate analysis of sugar yield factors in sugar cane. Am- Eurasian J Sustain Agric, 6(1): 44-50. |
| [9] | Anshori M F, Purwoko B S, Dewi I S, Ardie S W, Suwarno W B, Safitri H. 2018. Determination of selection criteria for screening of rice genotypes for salinity tolerance. SABRAO J Breed Genet, 50(3): 279-294. |
| [10] | Anshori M F, Purwoko B S, Dewi I S, Ardie S W, Suwarno W B. 2019. Selection index based on multivariate analysis for selecting doubled-haploid rice lines in lowland saline prone area. SABRAO J Breed Genet, 51(2): 161-174. |
| [11] | Anshori M F, Purwoko B S, Dewi I S, Ardie S W, Suwarno W B. 2020. Cluster heatmap for detection of good tolerance trait on doubled-haploid rice lines under hydroponic salinity screening. IOP Conf Ser Earth Environ Sci, 484: 012001. |
| [12] | Anshori M F. 2018. Characterization and selection of doubled haploid rice (Oryza sativa L.) lines adaptive to salinity stress. [Master Thesis]. Bogor, Indonesian: IPB University. (in Indonesian) |
| [13] | Asadi A, Zebarjadi A, Abdollahi M R, Seguí-Simarro J M. 2018. Assessment of different anther culture approaches to produce doubled haploids in cucumber (Cucumis sativus L.). Euphytica, 214(11): 216. |
| [14] | Azimi M H, Jozghasemi S, Barba-Gonzalez R. 2018. Multivariate analysis of morphological characteristics in Iris germanica hybrids. Euphytica, 214(9): 161. |
| [15] | Dallastra A, Unêda-Trevisoli S H, Ferraudo A S, Di Mauro A O D. 2014. Multivariate approach in the selection of superior soybean progeny which carry the RR gene. Rev Ciênc Agron, 45(3): 588-597. |
| [16] | De Leon T B, Linscombe S, Gregorio G, Subudhi P K. 2015. Genetic variation in Southern USA rice genotypes for seedling salinity tolerance. Front Plant Sci, 6: 374. |
| [17] | Dewi I S, Purwoko B S. 2012. Kultur antera untuk percepatan perakitan varietas padi di Indonesia. J AgroBiogen, 8(2): 78-88. (in Indonesian with English abstract). |
| [18] | Egdane J A, Vispo N A, Mohammadi R, Amas J, Katimbang M L, Platten J D, Ismail A, Gregorio G B. 2003. Phenotyping Protocols for Salinity and Other Problem Soils. Los Banos, the Philippines: International Rice Research Institute: 7?8. |
| [19] | Fellahi Z E A, Hannachi A, Bouzerzour H. 2018. Analysis of direct and indirect selection and indices in bread wheat (Triticum aestivum L.) segregating progeny. Int J Agron, 2018: 8312857. |
| [20] | Gerona M E B, Deocampo M P, Egdane J A, Ismail A M, Dionisio- Sese M L. 2019. Physiological responses of contrasting rice genotypes to salt stress at reproductive stage. Rice Sci, 26(4): 207-219. |
| [21] | Ghosh B, Ali Md N, Saikat G. 2016. Response of rice under salinity stress: A review update. J Rice Res, 4(2): 1000167. |
| [22] | Godshalk E B, Timothy D H. 1988. Factor and principal component analyses as alternatives to index selection. Theor Appl Genet, 76(3): 352-360. |
| [23] | Hasan R, Akand M, Alam N, Bashar A, Huque A K M M. 2016. Genetic association analysis and selection indices for yield attributing traits in available chilli (Capsicum annum L.) genotypes. Mol Plant Breed, 7(19): 1-9. |
| [24] | Hidayatullah A, Purwoko B S, Dewi I S, Suwarno W B. 2018. Agronomic performance and yield of doubled haploid rice lines in advanced yield trial. SABRAO J Breed Genet, 50(3): 242-253. |
| [25] | Ilin A, Raiko T. 2010. Practical approaches to principal component analysis in the presence of missing values. J Mach Learn Res, 11: 1957?2000. |
| [26] | Islam M R, Kayess M O, Hasanuzzaman M, Rahman M W, Uddin M J, Zaman M R. 2017. Selection index for genetic improvement of wheat (Triticum aestivum L.). J Chem Biol Phys Sci, 7(1): 1-8. |
| [27] | Ismail A M, Platten J D, Miro B. 2013. Physiological bases of tolerance of abiotic stresses in rice and mechanisms of adaptation. Oryza, 50(2): 91-99. |
| [28] | Janmohammadi M, Movahedi Z, Sabaghnia N. 2014. Multivariate statistical analysis of some traits of bread wheat for breeding under rainfed conditions. J Agric Sci, 59(1): 1-14. |
| [29] | Jolliffe I T. 2002. Principal Component Analysis. 2nd Edition. New York: Springer-Verlag: 167?169. |
| [30] | Kose A, Onder O, Bilir O, Kosar F. 2018. Application of multivariate statistical analysis for breeding strategies of spring safflower (Carthamus tinctorius L.). Turk J Field Crops, 23(1): 12-19. |
| [31] | Kumar N, Paul S. 2016. Selection criteria of linseed genotypes for seed yield traits through correlation, path coefficient and principal component analysis. J Anim Plant Sci, 26(6): 1688-1695. |
| [32] | Lorencetti C, de Carvalho F I F, de Oliveira A C, Valério I P, Hartwig I, Benin G, Schmidt D A M. 2006. Applicability of phenotypic and canonic correlations and path coefficients in the selection of oat genotypes. Sci Agric, 3(1): 11-19. |
| [33] | Manjunatha G A, Kumar M S, Jayashree M. 2017. Character association and path analysis in rice (Oryza sativa L.) genotypes evaluated under organic management. J Pharmacogn Phytochem, 6(6): 1053-1058. |
| [34] | Mattjik A A, Sumertajaya I M. 2011. Multivariate Analysis Using SAS. Bogor, Indonesia: Statistika F-MIPA IPB: 119-128/223?237/334?335. (in Indonesian) |
| [35] | Mohamadi S F, Bagheri N, Kiani G, Jelodar N B. 2017. Evaluation of different rice genotypes in response to salinity stress. Biol Forum, 9(1): 174-182. |
| [36] | Ojulong H F, Labuschagne M T, Herselman L, Fregene M. 2010. Yield traits as selection indices in seedling populations of cassava. Crop Breed Appl Biotechnol, 10(3): 191-196. |
| [37] | Olivoto T, de Souza V Q, Nardino M, Carvalho I R, Ferrari M, de Pelegrin A J, Szareski V J, Schmidt D. 2017. Multicollinearity in path analysis: A simple method to reduce its effects. Agron J, 109(1): 131-142. |
| [38] | Peternelli L A, Moreira É F A, Nascimento M, Cruz C D. 2017. Artificial neural networks and linear discriminant analysis in early selection among sugarcane families. Crop Breed Appl Biotechnol, 17(4): 299-305. |
| [39] | Rajamani S, Sreekanth M, Naik V S, Ratnam M. 2016. Selection indices for yield attributing characters improvement in pigeon pea (Cajanus cajan L. Millspugh). Int J Life Sci Scienti Res, 2(2): 127-129. |
| [40] | Rawlings J O, Pantula S G, Dickey D A. 1998. Applied Regression Analysis: A Research Tool. 2nd Edition. New York, USA: Springer- Verlag: 213?214. |
| [41] | Rezaei Z, Khadivi A, ValizadehKaji B, Abbasifar A. 2018. The selection of superior walnut (Juglans regia L.) genotypes as revealed by morphological characterization. Euphytica, 214(4): 69. |
| [42] | Sabouri H, Rabiei B, Fazlalipour M. 2008. Use of selection indices based on multivariate analysis for improving grain yield in rice. Rice Sci, 15(4): 303-310. |
| [43] | Sadeghi S M. 2011. Heritability, phenotypic correlation, and path coefficient studies for some agronomic characters in landrace rice varieties. World Appl Sci J, 13(5): 1229-1233. |
| [44] | Saed-Moucheshi A, Pessarakli M, Heidari B. 2013. Comparing relationships among yield and its related traits in mycorrhizal and nonmycorrhizal inoculated wheat cultivars under different water regimes using multivariate statistics. Int J Agron, 14: 682781. |
| [45] | Safitri H, Purwoko B S, Dewi I S, Ardie S W. 2016. Anther culture to obtain rice lines tolerant to salinity. J Agron Indonesia, 44(3): 221-227. |
| [46] | Safitri H. 2016. Development of salinity tolerant rice through anther culture. [PhD Thesis]. Bogor, Indonesia: IPB University. (in Indonesian) |
| [47] | Singh R K, Chaudhary B D. 2007. Biometrical Methods in Quantitative Genetic Analysis. New Delhi, India: Kalyani Publisher: 69?78. |
| [48] | Spearman C. 2010. The proof and measurement of association between two things. Int J Epidemiol, 39(5): 1137-1150. |
| [49] | Sultana N, Ikeda T, Itoh R. 1999. Effect of NaCl salinity on photosynthesis and dry matter accumulation in developing rice grains. Environ Exper Bot, 42(3): 211-220. |
| [50] | Suwarno, Lubis E, Hairmansis A, Santoso.2009. Development of a package of 20 varieties for blast management on upland rice. In: Wang G L, Valent B. Advances in Genetics, Genomics and Control Rice Blast Disease. Dordrecht, Netherlands: Springer: 347?357. |
| [51] | Türkan I, Demiral T. 2009. Recent developments in understanding salinity tolerance. Environ Exp Bot, 67(1): 2-9. |
| [52] | Vaisi H, Golpavar A R. 2013. Determination of the best indirect selection criteria to improve grain yield and seed weight in oat (Avena sativa L.) genotypes. Int J Farm Alli Sci, 2(19): 747-750. |
| [53] | Varthini N V, Sudhakar D, Raveendran M, Rajeswari S, Manonmani S, Tannidi S, Aravindhan P B, Ponniah G, Gunasekaran K, Robin S. 2017. Rice diversity panel evaluated for agro-morphological diversity by multivariate analysis. Int J Curr Microbiol App Sci, 6(11): 3887-3901. |
| [54] | Yamamoto A, Sawada H, Shim I S, Usui K, Fujihara S. 2011. Effect of salt on physiological response and leaves polyamine content in NERICA rice seedling. Plant Soil Environ, 57(12): 571-576. |
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