Advertisement

Euphytica

, Volume 209, Issue 3, pp 757–769 | Cite as

Genotype-by-environment interaction and yield stability of quality protein maize hybrids developed from tropical-highland adapted inbred lines

  • Demissew Abakemal
  • Hussein Shimelis
  • John Derera
Article

Abstract

Maize (Zea mays L.) yields are significantly lower in the tropical-highlands than other environments predominantly due to the lack of well-adapted and improved cultivars and due to genotype by environment (G × E) interaction. The objectives of this study were to determine G × E interaction and yield stability of quality protein maize (QPM) single-cross hybrids recently developed from tropical-highland adapted inbred lines, and to identify promising genotypes and representative test and seed production environments. The study was conducted at seven environments representing the tropical-highland sub-humid maize growing agro-ecology in Ethiopia. Sixty-six QPM hybrids and two commercial check hybrids were evaluated using a 4 × 17 alpha lattice design. Data were analysed using the additive main effects and multiplicative interaction (AMMI) and genotype and genotype by environment (GGE) biplot methods. Using AMMI analysis, four promising QPM hybrids were identified compared to the commercial checks and designated as hybrid 10 (KIT32 × 142-1-eQ), hybrid 66 (142-1-eQ × CML144), hybrid 59 (FS60 × 142-1-eQ), and hybrid 38 (FS67 × CML144) with grain yields of 10.3, 9.6, 9.4 and 8.9 t ha−1, respectively. The same hybrids were identified as the best performers for being close to the ideal cultivar using the GGE biplot analysis. The GGE analysis delineated the test environments into two mega-environments useful for targeted evaluation of genotypes and effective maize breeding and seed production. Kulumsa site during 2013 (KUL13) was the most suitable environment in discriminating the QPM hybrids and being a representative test environment.

Keywords

Additive main effects and multiplicative interaction (AMMI) GGE biplot Genotype adaptation Multi-environment trails QPM 

Notes

Acknowledgments

The Alliance for a Green Revolution in Africa (AGRA) is sincerely thanked for financial support of the study. The Ethiopian Institute of Agricultural Research (EIAR) is acknowledged for providing the first author’s leave of absence and for hosting the field research.

Compliance with Ethical Standards

Conflict of interest

The authors have not declared any conflict of interest.

References

  1. Banziger M, Diallo AO (2000) Stress tolerant maize for farmers in sub-Saharan Africa. Maize research highlights. CIMMYT, Mexico, DF, pp 1–8Google Scholar
  2. Bigirwa G, Pixley K, Asea G, Lipps P, Gordon S, Pratt R (2003) Uses of IPM in the control of multiple diseases in maize: strategies for selection of host resistance. Afr Crop Sci J 3:89–198Google Scholar
  3. CIMMYT (1999) CIMMYT 1997/1998 world maize facts and trends. CIMMYT, Mexico, DFGoogle Scholar
  4. Cooper M, DeLacy IH, Basford KE (1996) Relationships among analytical methods used to analyse genotypic adaptation in multi-environment trials. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 193–224Google Scholar
  5. Cooper M, Stucker RE, DeLacy IH, Harch BD (1997) Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Sci 37:1168–1176CrossRefGoogle Scholar
  6. Demissew A, Hussein S, Derera J, Laing MD (2013) Farmers’ perceptions of maize production systems and breeding priorities, and their implications for the adoption of new varieties in selected areas of the highland agro-ecology of Ethiopia. J Agr Sci 5:159–172Google Scholar
  7. Demissew A, Shimelis H, Derera J, Kassa S (2015) Genetic purity and patterns of relationships among tropical highland adapted quality protein and normal maize inbred lines using microsatellite markers. Euphytica 204:49–61CrossRefGoogle Scholar
  8. Derera J, Tongoona P, Pixley KV, Vivek B, Laing MD, Rij NC (2008) Gene action controlling grey leaf spot resistance in Southern African maize germplasm. Crop Sci 48:93–98CrossRefGoogle Scholar
  9. DeVries J, Toenniessen G (2001) Securing the harvest: Biotechnology, breeding and seed systems for African crops. CABI Publishing, WallingfordGoogle Scholar
  10. FAOSTAT (2014) The Food and Agricultural Organization of the United Nations: the statistical database. http://faostat.fao.org. FAO, Rome
  11. Frashadfar E, Safari H, Jamshidi B (2012) GGE biplot analysis of adaptation in wheat substitution lines. Int J Agric Crop Sci 4:877–881Google Scholar
  12. Gabriel KR (1971) The biplot graphic display of matrices with application to principal component analysis. Biometrika 58:453–467CrossRefGoogle Scholar
  13. Gauch HG, Zobel RW (1996) AMMI analyses of yield trials. In: Kang MS, Gauch HG (eds) Genotype by environment interaction. CRC Press, Boca Raton, pp 85–122CrossRefGoogle Scholar
  14. Gauch HG, Zobel RW (1997) Identifying mega-environments and targeting genotypes. Crop Sci 37:311–326CrossRefGoogle Scholar
  15. Hallauer AR, Carena MJ, Miranda JB (2010) Quantitative genetics in maize breeding. Springer Science + Business Media, New YorkGoogle Scholar
  16. Kandus M, Almora D, Ronceros RB, Salenro JC (2010) Statistical models for evaluating the genotype-environment interaction in maize (Zea mays L.). Phyton 79:39–46Google Scholar
  17. Kassa Y, Asea G, Demissew A, Ligeyo D, Demewoz N, Saina E, Serumaga J, Afriyie ST, Opio F, Rwomushana I, Gelase N, Gudeta N, Wondimu F, Solomon A, Habtamu Z, Andualem WB, Habte J, Mduruma 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
  18. Krivanek AF, De-Groote H, Gunaratna NS, Diallo AO, Friesen D (2007) Breeding and disseminating quality protein maize (QPM) for Africa. Afr J Biotechnol 6:312–324Google Scholar
  19. Menkir A, Ayodele AM (2005) Registration of 20 tropical mid-altitude maize line sources with resistance to gray leaf spot. Crop Sci 45:803–804CrossRefGoogle Scholar
  20. Nzuve F, Githiri S, Mukunya DM, Gethi J (2013) Analysis of genotype × environment interaction for grain yield in maize hybrids. J Agric Sci 5:75–85Google Scholar
  21. Patterson HD, Williams ER (1976) A new class of resolvable incomplete block designs. Biometrika 63:83–89CrossRefGoogle Scholar
  22. Payne RW, Harding SA, Murray DA, Soutar DM, Baird DB, Glaser AI, Welham SJ, Gilmour AR, Thompson R, Webster R (2007) GenStat® release 14 statistical software for windows. VSN International Ltd, HemelGoogle Scholar
  23. Pixley KV, Bjarnason MS (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
  24. Purchase JL (1997) Parametric analysis to describe G × E interaction and yield stability in winter wheat. PhD Thesis, University of the Orange Free State, Bloemfontein, South AfricaGoogle Scholar
  25. SAS (2002) The SAS system for Windows, release 9.3. SAS Institute Inc, CaryGoogle Scholar
  26. Sibiya J, Tongoona P, Derera J, Rij N (2012) Genetic analysis and genotype by environment (G × E) for grey leaf spot disease resistance in elite African maize (Zea mays L.) germplasm. Euphytica 185:349–362CrossRefGoogle Scholar
  27. Taya G, Getachew T, Bejiga G (2000) AMMI adjustment for yield estimate and classification of genotypes and environments in field pea (Pisum sativum L.). J Genet Breed 54:183–191Google Scholar
  28. Thorne PJ, Thornton PK, Kruska R, Reynolds L, Waddington SR, Rutherford AS, Othero AN (2002) Maize as food, feed and fertilizer in intensifying crop livestock systems in East and southern Africa: an ex ante impact assessment of technology interventions to improve smallholder welfare. In: ILRI Impact Assessment Series 11, International Livestock Research Institute (ILRI), Nairobi, Kenya, p 123Google Scholar
  29. Twumasi AS, Zelleke H, Yihun K, Assefa B, Tariku S (2002) Development and Improvement of Highland Maize in Ethiopia. In: Nigusse M, Tanner D (eds) Proceedings of the second national maize workshop of Ethiopia, EARO (Ethiopian Agricultural Research Organization) and CIMMYT, 12–16 November 2001, Addis Ababa, Ethiopia, pp 31–38Google Scholar
  30. 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
  31. Yan W (2002) Singular-value partitioning in biplot analysis of multi-environmental trial data. Agron J 94:990–996CrossRefGoogle Scholar
  32. Yan W, Holland JB (2010) A heritability-adjusted GGE biplot for test environment evaluation. Euphytica 171:355–369CrossRefGoogle Scholar
  33. Yan W, Kang MS (2003) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press, New YorkGoogle Scholar
  34. Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Sci 40:597–605CrossRefGoogle Scholar
  35. Yan W, Kang MS, Ma B, Woods S, Cornelius PL (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

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Demissew Abakemal
    • 3
  • Hussein Shimelis
    • 1
  • John Derera
    • 2
  1. 1.African Center for Crop ImprovementUniversity of KwaZulu-NatalPietermaritzburgSouth Africa
  2. 2.Seed-Co, Rattray Arnold Research StationHarareZimbabwe
  3. 3.Plant Protection Research CenterEthiopian Institute of Agricultural ResearchAmboEthiopia

Personalised recommendations