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Plant Science Today (PST)

Research Articles

Vol. 12 No. sp3 (2025): Advances in Plant Health Improvement for Sustainable Agriculture

Efficiency of honeycomb selection design and validation of molecular markers in early generation selection in rice

DOI
https://doi.org/10.14719/pst.10454
Submitted
4 July 2025
Published
18-12-2025

Abstract

Breeding for salt tolerance is complex and tedious due to its genetics and environmental interaction. Honeycomb Selection Design (HSD), a paradigm for conventional breeding, provides an opportunity to breed density-independent cultivars under nil competition. The present study was attempted to study the effectiveness of HSD in selecting salt-tolerant genotypes in F2 and F3 populations of ADT (R) 45 × Nona Bokra rice cross raised in unreplicated honeycomb design (UN-1) in F2 generation and replicated honeycomb design (R-37) in F3 generation respectively at wider spacing of 100 cm x 90 cm. The F2 plants were selected based on plant index and F3 plants were selected based on plant and stability index. Plant index-based selection in F2 population identified 35 best performing genotypes accounting for 4.13 % selection intensity and they were forwarded to raise F3 population. In F3 population, 28 plants were selected based on plant index and stability index accounting for 3.63 % selection intensity. Such low selection intensity in HSD leads to rapid fixation of additive alleles that are indispensable for advancing through selection. In F2, 35 selected plants showed an increase of 213.86 % over the total mean of F2. Yield of F3 progenies significantly improved over the respective selected F2 plants showing positive genetic gain. In addition, the best performing 28 plants selected in F3 population were from 15 and 12 progeny lines in normal and saline condition among 35 individual entries selected in F2 population. Further, best 100 F2 and F3 plants were subjected to genotyping using nine markers linked to salt tolerance. The correlation between salt tolerance markers and observed morphological traits was weak in F2 which may be due to a lack of coverage of all the regions of salt tolerance quantitative trait loci (QTLs) and may also be due to unidentified regions of the chromosome conferring tolerance to salinity stress. Hence, selection for yield in the early generations (F₂ and F₃) of ADT (R) 45 × Nona Bokra rice cross using the HSD improves the efficiency of breeding procedure by reducing the number of genotypes to be tested in subsequent, expensive yield trials, thereby increasing genetic gain per unit cost.

References

  1. 1. Wang Z, Chen Z, Cheng J, Lai Y, Wang J, Bao Y, et al. QTL analysis of Na+ and K+ concentrations in roots and shoots under different levels of NaCl stress in rice (Oryza sativa L.). PLoS One. 2012;7(12):e51202. https://doi.org/10.1371/journal.pone.0051202
  2. 2. Sharma DK, Thimmppa K, Chinchmalatpure AR, Mandal AK, Yadav RK, Chaudhari SK, et al. Assessment of production and monetary losses from salt-affected soils in India. Technical bulletin: ICAR-CSSRI/Karnal/Tech. Bulletin/2015/05. ICAR-Central Soil Salinity Research Institute. 2015.
  3. 3. Qin H, Li Y, Huang R. Advances and challenges in the breeding of salt-tolerant rice. Int J Mol Sci. 2020;21(21):8385. https://doi.org/10.3390/ijms21218385
  4. 4. Fasoula DA. Correlations between auto-, allo- and nil-competition and their implications in plant breeding. Euphytica. 1990;50:57-62. https://doi.org/10.1007/BF00023161
  5. 5. Fasoula DA, Fasoula VA. Competitive ability and plant breeding. Plant Breed Rev. 1997;14:89-138. https://doi.org/10.1002/9780470650073.ch4
  6. 6. Fasoula VA, Fasoula DA. Honeycomb breeding: principles and applications. Plant Breed Rev. 2000;18:177-250. https://doi.org/10.1002/9780470650158.ch4
  7. 7. Fasoula VA, Fasoula DA. Principles underlying genetic improvement for high and stable crop yield potential. Field Crops Res. 2002;75:191-209. https://doi.org/10.1016/S0378-4290(02)00026-6
  8. 8. Gregorio GB, Senadhira D, Mendoza RD, Manigbas NL, Roxas JP, Guerta CQ. Progress in breeding for salinity tolerance and associated abiotic stresses in rice. Field Crops Res. 2002;78:93-8. https://doi.org/10.1016/S0378-4290(02)00031-X
  9. 9. Ashraf M, Foolad M. Crop breeding for salt tolerance in the era of molecular markers and marker-assisted selection. Plant Breed. 2013;132:10-20. https://doi.org/10.1111/pbr.12000
  10. 10. Shannon MC. Principles and strategies in breeding for higher salt tolerance. In: Pasternak D, San Pietro, editors. A Biosalinity in Action: Bioproduction with Saline Water. Developments in Plant and Soil Sciences. Springer, Dordrecht. 1985;89:227-41. https://doi.org/10.1007/978-94-009-5111-2_15
  11. 11. Tokatlidis IS. Sampling the spatial heterogeneity of the honeycomb model in maize and wheat breeding trials: Analysis of secondary data compared to popular classical designs. Exp Agric. 2016;52:371-90. https://doi.org/10.1017/S0014479715000150
  12. 12. Yang RC. When is early generation selection effective in self-pollinated crops? Crop Sci. 2009;49:2065-79. https://doi.org/10.2135/cropsci2009.01.0029
  13. 13. Fasoula DA, Ioannides IM, Omirou M. Phenotyping and plant breeding: overcoming the barriers. Front Plant Sci. 2020;10:1713. https://doi.org/10.3389/fpls.2019.01713
  14. 14. Fasoulas AC, Fasoula VA. Honeycomb selection designs. Plant Breed Rev. 1995;13:87-139. https://doi.org/10.1002/9780470650059.ch3
  15. 15. Fasoula VA, Thompson CK, Mauromoustakos A. The prognostic breeding application JMP Add-In Program. Agronomy. 2019;9(1). https://doi.org/10.3390/agronomy9010025
  16. 16. Fasoula VA. A novel equation paves the way for an everlasting revolution with cultivars characterized by high and stable crop yield and quality. In: Proc. 11th Congress of the Hellenic Society for the Genetic Improvement of Plants; Orestiada, Greece. 2006. p. 7-14.
  17. 17. Fasoula VA. Two novel whole-plant field phenotyping equations maximize selection efficiency. In: Prohens J, Badenes ML, editors. Modern variety breeding for present and future needs. Proceedings 18th Eucarpia General Congress. 2008. p. 361-65.
  18. 18. Fasoula VA. Prognostic breeding: a new paradigm for crop improvement. Plant Breed Rev. 2013;37:297-347. https://doi.org/10.1002/9781118497869.ch6
  19. 19. Duvick DN. Plant breeding, an evolutionary concept. Crop Sci. 1996;36(3):539. https://doi.org/10.2135/cropsci1996.0011183X003600030001x
  20. 20. Batzios D, Roupakias D, Kechagia U, Sendouca SG. Comparative efficiency of honeycomb and conventional pedigree methods of selection for yield and fiber quality in cotton (Gossypium spp.). Euphytica. 2001;122:203-11. https://doi.org/10.1023/A:1012718715149
  21. 21. Yoglakshmi C, Pearl RI, Fasoula VA, Karthick J, Thirumeni S. Efficiency of honeycomb selection design in early segregating generation in ADT45 x Nona Bokra cross under salt stress. Ind J Exp Biol. 2022;60:463-70. https://doi.org/10.56042/ijeb.v60i07.64067
  22. 22. Ntanos D, Roupakias DG. Effectiveness of three cycles of honeycomb pedigree selection for yield and four quality characters in an F2 rice population (Oryza sativa L.). In: Clément G, Cocking EC, editors. FAO MedNet Rice: Breeding and Biotechnology Groups: Proceedings of the Workshops. Montpellier: CIHEAM. 1994. p. 39-42.
  23. 23. Ntanos DA, Roupakias DG. Comparative efficiency of two breeding methods for yield and quality in rice. Crop Sci. 2001;41:345-50. https://doi.org/10.2135/cropsci2001.412345x
  24. 24. Mitchell JW, Baker RJ, Knott DR. Evaluation of honey-comb selection for single plant yield in durum wheat. Crop Sci. 1982;22:840-43. https://doi.org/10.2135/cropsci1982.0011183X002200040033x
  25. 25. Lungu DM, Kaltsikes PJ, Larter EN. Honeycomb selection for yield in early generations of spring wheat. Euphytica. 1987;36:831-39. https://doi.org/10.1007/BF00051867
  26. 26. Kyriakou DT, Fasoulas AC. Effects of competition and selection pressure on yield response in winter rye (Secale cereale L.). Euphytica. 1985;34:883-95. https://doi.org/10.1007/BF00035428
  27. 27. Bussemakers A, Bos I. The effect of interplant distance on the effectiveness of honeycomb selection in spring rye. III. Accumulated results of five selection cycles. Euphytica. 1998;105:229-37. https://doi.org/10.1023/A:1003418123013
  28. 28. Robertson LD, Frey KJ. Honeycomb design for selection among homozygous oat lines. Crop Sci. 1987;27(6):1105-08. https://doi.org/10.2135/cropsci1987.0011183X002700060003x
  29. 29. Gill JS, Verma MM, Gumber RK, Brar JS. Comparative efficiency of four selection methods for deriving high-yielding lines in mungbean (Vigna radiata (L.) Wilczek). Theor Appl Genet. 1995;90:554-60. https://doi.org/10.1007/BF00222003
  30. 30. Roupakias D, Zesopoulou A, Kazolea S, Dalkalitses G, Mavromatis A, Lazaridou T. Effectiveness of early generation selection under two plant densities in faba bean (Vicia faba L.). Euphytica. 1997;93:63-70. https://doi.org/10.1023/A:1002960208864
  31. 31. Batzios D, Roupakias D, Kechagia U, Galanopoulou-Sendouca S. Comparative efficiency of three selection methods for yield and quality of cotton (Gossypium hirsutum L.). Proceedings of the World Cotton Research Conference-2; Athens, Greece. 1998. p. 165-68.
  32. 32. Onenanyoli AHA, Fasoulas AC. Yield response to honeycomb selection in maize. Euphytica. 1989;40:43-8. https://doi.org/10.1007/BF00023295
  33. 33. Greveniotis V, Fasoula VA. Application of prognostic breeding in maize. Crop Pasture Sci. 2016;67:605-20. https://doi.org/10.1071/CP15206

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