Advertisement

Euphytica

, Volume 207, Issue 2, pp 353–365 | Cite as

Genotype by environment interactions and agronomic performance of doubled haploids testcross maize (Zea mays L.) hybrids

  • Julius Pyton Sserumaga
  • Sylvester O. Oikeh
  • Stephen Mugo
  • Godfrey Asea
  • Michael Otim
  • Yoseph Beyene
  • Grace Abalo
  • Joseph Kikafunda
Article

Abstract

In vivo production of maternal haploid plants and advancement in chromosome doubling technology has led to rapid production of doubled haploid homozygous lines. These in turn have boosted rapid advancement in most breeding programs. This has resulted in production of a large number of maize hybrids which need testing across production environments to select the most suitable hybrids for release and cultivation. The objective of this study was to assess the genotype × environment interactions (GE) for grain yield and other agronomic traits and evaluate the performance of 44 recently developed doubled haploids (DH) testcross hybrids along with six checks across five locations in Uganda. Significant mean squares for environment (E), genotype (G) and GE were observed for all studied traits. Environment explained 46.5 % of the total variance, while G and GE contributed 13.2 and 7.2 %, respectively. Genetic correlations among locations were high (0.999), suggesting little GE among environments. The 10 best testcross hybrids had a 49.2 % average grain yield advantage over the six checks at all locations. DH hybrids CKHDHH0887, CKDHH0878, CKDHH0859, WM1210, CKDHH0858, and WM1214 were the most stable, across locations. The DH testcross hybrids produced higher grain yield and possessed acceptable agronomic traits compared to the commercial hybrids developed earlier. Use of the best DH testcross hybrids, well targeted to the production environments, could boost maize production among farmers.

Keywords

Doubled haploids East Africa Genotype × environment Grain yield Maize Stability 

Notes

Acknowledgments

This research was supported by the Bill and Melinda Gates and the Howard G. Buffet Foundations, and the United States Agency for International Development through the Water Efficient Maize for Africa project. We appreciate all the Zonal Agricultural Research Development Institutes (ZARDI) and Mobuku Irrigation Scheme for making their facilities available for this study. We thank Dr. Dan Makumbi for helpful comments and suggestions on the manuscript. We also appreciate the constructive comments of anonymous reviewers, who helped to improve the manuscript. Also the authors would like to thank Ochen Stephen, Solomon Kaboyo, Annet Nakayima, Majid Walusimbi, Moses Ebellu, Late Stephen Okanya, Fred Ssemazzi and Jane Alupo for data collection at the various experimental sites.

Supplementary material

10681_2015_1549_MOESM1_ESM.xls (123 kb)
Supplementary material 1 (xls 123 kb)
10681_2015_1549_MOESM2_ESM.xls (45 kb)
Supplementary material 2 (xls 45 kb)

References

  1. Badu-Apraku B, Oyekunle M, Obeng-Antwi K, Osuman A, Ado S, Coulibaly N, Yallou C, Abdulai M, Boakyewaa G, Didjeira A (2012) Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis. J Agric Sci Camb 150:473–483CrossRefGoogle Scholar
  2. Baker RJ (1988) Tests for crossover genotype × environment interactions. Can J Plant Sci 68:405–410CrossRefGoogle Scholar
  3. Beyene Y, Mugo S, Pillay K, Tefera TSA, Njoka S, Karaya H, Gakunga J (2011) Testcross performance of doubled haploid maize lines derived from tropical adapted backcross populations. Maydica 56:351–358Google Scholar
  4. Beyene Y, Tarekegne A, Gakunga J, Mugo S, Tefera T, Karaya H, Semagn K, Gethi J, Chavangi A, Asea G, Kiula B, Trevisan W (2013) Genetic distance among doubled haploid maize lines and their testcross performance under drought stress and non-stress conditions. Euphytica 192:379–392CrossRefGoogle Scholar
  5. Bordes J, Charmet G, Dumas De Vaulx R, Pollacsek M, Beckert M, Gallais A, Lapierre A (2007) Doubled-haploid versus single-seed descent and S1-family variation for testcross performance in a maize population. Euphytica 154:41–51CrossRefGoogle Scholar
  6. Burdon RD (1977) Genetic correlation as a concept for studying genotype-environment interaction in forest tree breeding. Silvae Genet. 26:168–175Google Scholar
  7. Butron A, Widstrom N, Snook M, Wiseman B (2002) Recurrent selection for corn earworm (Lepidoptera: Noctuidae) resistance in three closely related corn southern synthetics. J Econ Entomol 95:458–462CrossRefGoogle Scholar
  8. Cooper M, Delacy IH (1994) Relationships among analytical methods used to study genotypic variation and genotype-by environment interaction in plant breeding multi-environment experiments. Theor Appl Genet 88:561–572CrossRefGoogle Scholar
  9. Cooper M, Delacy IH, Basford KE (1996) Relationships among analytical methods used to analyze genotypic adaptation in multi-environment trials. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB Int, Wallingford, pp 193–224Google Scholar
  10. Eisen EJ, Saxton AM (1983) Genotype by environment interactions and genetic correlations involving two environmental factors. Theor Appl Genet 67:75–86CrossRefGoogle Scholar
  11. Epinat-Le S, Dousse S, Lorgeou J, Denis J, Bon-Homme R, Carolo P, Charcosset A (2001) Interpretation of genotype × environment interactions for early maize hybrids over 12 years. Crop Sci 41:663–669CrossRefGoogle Scholar
  12. Falconer D (1952) The problem of environment and selection. Am Nat 86:293–298CrossRefGoogle Scholar
  13. Falconer D, Mackay T (1996) Introduction to quantitative genetics. Longman, LondonGoogle Scholar
  14. Frutos E, Galindo MP, Leiva V (2014) An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stoch Environ Res Risk Assess 28:1629–1641CrossRefGoogle Scholar
  15. Gauch HG (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Sci 46:1488–1500CrossRefGoogle Scholar
  16. Hallauer A, Miranda J (1981) Quantitative genetics in maize breeding. Iowa State University Press, AmesGoogle Scholar
  17. Hallauer AR, Carena M, Miranda Filho JB (2010) Quantitative genetics in maize breeding, 3rd edn. Iowa State University Press, AmesGoogle Scholar
  18. Kang MS (1993) Simultaneous selection for yield and stability in crop performance: consequences for growers. Agron J 85:754–757CrossRefGoogle Scholar
  19. Kassa Y, Asea G, Demissew AK, Ligeyo D, Demewoz N, Saina E, Sserumaga JP, Twumais-Afriyie S, Opio F, Rwomushana I, Gelase N, Gudeta N, Wondimu F, Solomon A, Habtamu Z, Andualem WBA, Habte J, Muduruma Z (2013) Stability in performance of normal and nutritionally enhanced highland maize hybrid genotypes in Eastern Africa. Asian J Plant Sci 12:51–60CrossRefGoogle Scholar
  20. Makumbi D, Diallo A, Kanampiu K, Mugo S, Karaya H (2015) Agronomic performance and genotype x environment interaction of herbicide-resistant maize varieties in Eastern Africa. Crop Sci 55:540–555CrossRefGoogle Scholar
  21. Malla S, Ibrahim AMH, Little R, Kalsbeck S, Glover KD, Ren C (2010) Comparison of shifted multiplicative model, rank correlation, and biplot analysis for clustering winter wheat production environments. Euphytica 174:357–370CrossRefGoogle Scholar
  22. Munyiri S, Pathak R, Tabu I, Gemenet D (2010) Effects of moisture stress at flowering on phenotypic characters of selected local maize landraces in Kenya. J Anim Plant Sci 8:892–899Google Scholar
  23. Odiyo O, Njoroge K, Chemining’wa G, Beyene Y (2014) Performance and adaptability of doubled haploid maize testcross hybrids under drought stress and non-stress conditions. Int Res J Agric Sci Soil Sci 4:150–158CrossRefGoogle Scholar
  24. Oikeh SO, Menkir A, Maziya-Dixon B, Welch RM, Glahn RP, Gauch JRG (2004) Environmental stability of iron and zinc concentrations in grain of elite early-maturing tropical maize genotypes grown under field conditions. J Agric Sci 142:543–551CrossRefGoogle Scholar
  25. Pixley K, Bjarnason M (2002) Stability of grain yield, endosperm modification, and protein quality of hybrid and open-pollinated quality protein maize (QPM) cultivars. Crop Sci 42:1882–1890CrossRefGoogle Scholar
  26. Robinson P (1963) Heritability: a second look. In: Hanson WD, Robinson HF (eds) Statistical genetics and plant breeding. Publ. 982. National Academy of Science. National Research Council, Washington, DC, p 609–614Google Scholar
  27. SAS Institute (2008) SAS/STAT user’s guide. SAS Institute, CaryGoogle Scholar
  28. Thomas W, Forster B, Gertsson B (2003) Doubled haploids in breeding. In: Maluszynski M et al. (eds) Doubled haploid production in crop plants. A manual. Kluwer Academic Publishers, Dordrecht, pp 337–349CrossRefGoogle Scholar
  29. Tukamuhabwa P, Assiimwe M, Nabasirye M, Kabayi P, Maphosa M (2012) Genotype by environment interaction of advanced generation soybean lines for grain yield in Uganda. Afric Crop Sci J 20:107–115Google Scholar
  30. Van Eeuwijk F, Keizer L, Bekker J (1995) Linear and bilinear models for the analysis of multi-environment trials: II. An application to data from the Dutch Maize Variety Trials. Euphytica 84:9–22CrossRefGoogle Scholar
  31. Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244. doi: 10.1080/01621459.1963.10500845 CrossRefGoogle Scholar
  32. Yan W (2001) GGE biplot: A windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron J 93:1111–1118CrossRefGoogle Scholar
  33. Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: principles and applications. Can J Plant Sci 86:623–645CrossRefGoogle Scholar
  34. Yan W, Hunt L, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci 40:597–605CrossRefGoogle Scholar
  35. Yan W, Kang MS, Ma B, Woods S, Cornelius P (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47:643–655CrossRefGoogle Scholar
  36. Zobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of yield trial. Agron J 80:388–393CrossRefGoogle Scholar

Copyright information

© The Author(s) 2015
corrected publication September 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Julius Pyton Sserumaga
    • 1
  • Sylvester O. Oikeh
    • 2
  • Stephen Mugo
    • 3
  • Godfrey Asea
    • 1
  • Michael Otim
    • 1
  • Yoseph Beyene
    • 3
  • Grace Abalo
    • 1
  • Joseph Kikafunda
    • 1
  1. 1.Cereals ProgramNational Crops Resources Research Institute (NaCRRI), National Agricultural Research Organization (NARO)KampalaUganda
  2. 2.African Agricultural Technology Foundation (AATF)NairobiKenya
  3. 3.International Maize and Wheat Improvement Center (CIMMYT), ICRAF HouseNairobiKenya

Personalised recommendations