Skip to main content
SpringerLink
Log in

Identification of quantitative trait loci for nitrogen use efficiency in maize

  • Published:
Molecular Breeding Aims and scope Submit manuscript

Abstract

Intensively managed crop systems are normally dependent on nitrogen input to maximize yield potential. Improvements in nitrogen- use efficiency (NUE) in crop plants may support the development of cropping systems that are more economically efficient and environment friendly. The objective of this study was to map and characterize quantitative trait loci (QTL) for NUE in a maize population. In preliminary experiments, inbred lines contrasting for NUE were identified and were used to generate populations of F2:3 families for genetic study. A total of 214 F2:3 families were evaluated in replicated trials under high nitrogen (280 kg/ha) and low nitrogen (30 kg/ha) conditions in 1996 and 1997. Analysis of ear-leaf area, plant height, grain yield, ears per plant, kernels number per ear, and kernel weight indicated significant genetic variation among F2:3 families. The heritability of these traits was found to be high (h2=0.57–0.81). The mapping population were genotyped using a set of 99 restriction fragment length polymorphism (RFLP) markers. A linkage map of these markers was developed and used to identify QTL. Between two and six loci were found to be associated with each trait. The correspondence of several genomic regions with traits measured under nitrogen limited conditions suggests the presence of QTL associated with NUE. QTLs will help breeders to improve their maize ideotype of a low-nitrogen efficiency by identifying those constitutive and adaptive traits involved in the expression of traits significantly correlated with yield, such as ear leaf area and number of ears per plant.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Agrama HAS: Sequential path analysis of grain yield and its components in maize. Plant Breeding. 115: 343–346 (1996).

    Article  Google Scholar 

  2. Agrama HAS, Moussa ME: Mapping QTLs in breeding for drought tolerance in maize (Zea mays L.). Euphytica 91: 89–97 (1996).

    CAS  Google Scholar 

  3. Allan A, Darrah LL: 1978. Effects of three cycles of reciprocal recurrent selection on N and plant population responses of two maize hybrids in Kenya. Crop Sci 18: 112–114 (1978).

    Article  CAS  Google Scholar 

  4. Azanza F, Tadmor Y, Klein BP, Rocheford TR, Juvik JA: Quantitative trait loci influencing chemical and sensory characteristics of eating quality in sweet corn. Genome 39: 40–50 (1996).

    CAS  PubMed  Google Scholar 

  5. Beauchamp EG, Kannenberg LW, Hunter RB: Nitrogen accumulation and translocation in corn genotypes following silking. Agron J 68: 418–422 (1976).

    Article  CAS  Google Scholar 

  6. Beavis WD, Grant D, Albertsen M, Fincher R: Quantitative trait loci for plant height in four maize populations and their associations with qualitative genetic loci. Theor Appl Genet 83: 141–145 (1991).

    Article  Google Scholar 

  7. Beavis WD, Keim P: Identification of quantitative trait loci that are affected by environment. In: Kang MS, Gauch HG (eds) Genotype-by-Environment Interaction, pp. 123–174. CRC Press, Boca Raton, FL (1996).

    Google Scholar 

  8. Blum A: Plant Breeding for Stress Environments. CRC Press, Boca Raton, FL (1988).

    Google Scholar 

  9. Edmeades GO, Lafitte HR, Bolaños J: Selection for abiotic stress tolerance in maize. In: De-Leon CG, Granados G, Read MD (eds) Proceedings Fourth Asian Regional Maize Workshop (Islamabad, Pakistan), pp. 230–268. CIMMYT, México (1990).

    Google Scholar 

  10. Edmeades GO, Lafitte HR, Bolaños J, Chapman S, Bänziger M, Deutsch J: Developing maize that tolerates drought or low nitrogen conditions. In: Edmeades G, Deutsch J (eds) Stress Tolerance Breeding, pp. 21–84. CIMMYT, México (1994).

    Google Scholar 

  11. Eghball B, Maranville JW: Interactive effects of water and nitrogen stresses on nitrogen utilization efficiency, leaf water status and yield of corn genotypes. Commun Soil Sci Plant Anal 22: 1367–1382 (1991).

    Article  Google Scholar 

  12. Feil B, Thiraporn R, Geisler G, Stamp P: Root traits of maize seedlings – indicators of nitrogen efficiency? In: El-Bassam N, Dambroth M, Loughman BC (eds) Genetic Aspects of Plant Mineral Nutrition, pp. 97–102. Kluwer Academic Publishers, Dordrecht, Netherlands. (1990).

    Google Scholar 

  13. Fischer KS, Edmeades GO, Johnson EC: Selection for the improvement of maize yield under moisture-deficits. Field Crops Res 22: 227–243 (1989).

    Article  Google Scholar 

  14. Gardiner J, Coe E, Hancock MS, Hoisington D, Chao S: Development of core RFLP map in maize using an immortalized F2 population. Genetics 134: 917–930 (1993).

    PubMed  CAS  Google Scholar 

  15. Gruneberg H: An analysis of the ‘pleiotropic’ effects of a new lethal mutation in rat (Mus norvegicus). Proc R Soc Lond B 125: 123–144 (1938).

    Article  Google Scholar 

  16. Guei RG, Wassom CE: Inheritance of some drought adaptive traits in maize. I. Interrelationships between yield, flowering, and ears per plant. Maydica 37: 157–164 (1992).

    Google Scholar 

  17. Harvey PH: Hereditary variation in plant nutrition. Genetics 24: 437–461 (1939).

    PubMed  CAS  Google Scholar 

  18. Helentjaris T: A genetic linkage map for maize based on RFLPs. Trends Gent 3: 217–221 (1987).

    Article  CAS  Google Scholar 

  19. Helentjaris T, King G, Slocum M, Siedenstrang C, Wegman S: Restriction fragment polymorphisms as probes for plant diversity and their development as tools for applied plant breeding. Plant Mol Biol 5: 109–118 (1985).

    Article  CAS  Google Scholar 

  20. Hoisington TG, Coe EH: Mapping in maize using RFLPs. In: Gustafson JP (ed) Gene Manipulation in Plant Improvement, vol. 2, pp. 331–352. Plenum Press, New York (1990).

    Google Scholar 

  21. Kamprath EJ, Moll RH, Rodriguez N: Effects of nitrogen fertilization and recurrent selection on performance of hybrid population of Zea mays L. Agron J 74: 955–958 (1982).

    Article  Google Scholar 

  22. Kosambi DD: The estimation of map distances from recombination values. Ann Eugen 12: 172–175 (1944).

    Google Scholar 

  23. Lafitte HR, Edmeades GO: Improvement for tolerance to low soil nitrogen in tropical maize. I. Selection criteria. Field Crops Res 39: 1–14 (1994).

    Article  Google Scholar 

  24. Lafitte HR, Edmeades GO: Improvement for tolerance to low soil nitrogen in tropical maize. II. Grain yield, biomass production, and N accumulation. Field Crops Res 39: 15–25 (1994).

    Article  Google Scholar 

  25. Lafitte HR, Edmeades GO: Improvement for tolerance to low soil nitrogen in tropical maize. III. Variation in yield across environments. Field Crops Res 39: 27–38 (1994).

    Article  Google Scholar 

  26. Lander ES, Botstein D: Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121: 185–199 (1989).

    PubMed  CAS  Google Scholar 

  27. Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, Newburg L: MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1: 174–181 (1987).

    Article  PubMed  CAS  Google Scholar 

  28. Lebreton C, Lazic-Jancic V, Steed A, Pekic S, Quarrie SA: Identification of QTL for drought responses in maize and their use in testing causal relationships between traits. J Exp Bot 46: 853–865 (1995).

    CAS  Google Scholar 

  29. Mahon JD: Limitations to use of physiological variability in plant breeding. Can J Plant Sci 63: 11–18 (1983).

    Article  Google Scholar 

  30. Mather K, Jinks JL: Biometrical Genetics. Cornell University Press, Ithaca, NY (1971).

    Google Scholar 

  31. Moll RH, Kamprath EJ, Jackson WA: Analysis and interpretation of factors which contribute to efficiency of nitrogen utilization. Agron J 74: 562–564 (1982).

    Article  Google Scholar 

  32. Mollaretti G, Bosio M, Gentinetta E, Motto M: Genotypic variability for N-related traits in maize. Identification of inbred lines with high or low levels on NO3-N in the stalks. Maydica 32: 309–323 (1987).

    Google Scholar 

  33. Murali BI, Paulsen GM: Improvement of nitrogen use efficiency and its relationship to other traits in maize. Maydica 26: 63–73 (1981).

    Google Scholar 

  34. Ottaviano E, Sari-Gorla M, Pe E, Frova C: Molecular markers (RFLP and HSPs) for genetic dissection of thormotolerance in maize. Theor Appl Gent 81: 713–719 (1991).

    CAS  Google Scholar 

  35. Paterson AH, Damon S, Hewitt JD, Zamir D, Rabinowitch HD, Lincoln SE, Lander ES, Tanksley SD: Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments. Genetics 127: 181–197 (1991).

    PubMed  CAS  Google Scholar 

  36. Pollmer WG, Eberhard D, Klein D, Dhillon BS: Genetic control of nitrogen uptake and translocation in maize. Crop Sci 19: 82–96 (1979).

    Article  CAS  Google Scholar 

  37. Prioul J, Quarrie S, Causse M, de Vienne D: Disssecting complex physiological functions through the use of molecular quantitative genetics. J Exp Bot 48: 1151–1163 (1997).

    CAS  Google Scholar 

  38. Quarrie SA, Laurie DA, Zhu J, Lebreton C, Semikhodskii A, Steed A, Witsenboer H, Calestani C: QTL analysis to study the association between leaf size and abscisic acid accumulation in droughted rice leaves and comparisons cereals. Plant Mol Biol 35: 155–165 (1997).

    Article  PubMed  CAS  Google Scholar 

  39. Reiter RS, Coors JG, Sussman MR, Gabelman WH: Genetic analysis of tolerance to low-phosphorus stress in maize using restriction fragment length polymorphisms. Theor Appl Genet 82: 561–568 (1991).

    Article  CAS  Google Scholar 

  40. Ribaut JM, Hoisington DH, Deutsch JA, Jiang C, Gonzalezde-Leon D: Identification of quantitative trait loci under drought conditions in tropical maize. 1. Flowering parameters and the anthesis-silking interval. Theor Appl Genet 92: 905–914 (1996).

    Article  CAS  Google Scholar 

  41. Ribaut JM, Hoisington DA, Deutsch JA, Jiang C, Gonzalezde-Leon D: Identification of quantitative trait loci under drought conditions in tropical maize. 2. Yield components and marker-assisted selection strategies. Theor Appl Genet 94: 887–896 (1997).

    Article  Google Scholar 

  42. Saghai-Mahoof M, Soliman K, Jorgensens R, Allard R: Ribosomal DNA spacer length polymorphisms in barley: Mendelian inheritance, chromosomal location and population dynamics. Proc Natl Acad Sci USA 81: 8014–8018 (1984).

    Article  Google Scholar 

  43. SAS Institute: SAS language guide for person computers, Edition 6.03. SAS Institute, Cary, NC (1988).

  44. Schepers JS, Below FE: Influence of corn hybrids on nitrogen uptake and utilization efficiency. In: Proceedings 42nd Annual Corn and Sorghum conference, pp. 172–186 (1987).

  45. Schön CC, Lee M, Melchinger AE, Guthrie WD, Woodman WL: Mapping and characterization of quantitative trait loci affecting resistance against-second generation European corn borer in maize with aid of RFLPs. Heredity 70: 646–659 (1993).

    Google Scholar 

  46. Smith N: Response of inbred lines and crosses in maize to variation of nitrogen and phosphorus supplied as nutrients. J Agron 26: 785–804 (1934).

    Article  CAS  Google Scholar 

  47. Springfield GH, Salter RM: Differential response of corn varieties to fertility levels and to seasons. J Agric Res 49: 991–1000 (1934).

    Google Scholar 

  48. Stuber CW, Lincoln SE, Wolff DW, Helentjaris T, Lander ES: Identification of genetic factors contributing to heterosis in a hybrid from two elite maize inbred lines using molecular markers. Genetics 132: 823–839 (1992).

    PubMed  CAS  Google Scholar 

  49. Ta CT, Weiland RT: Nitrogen partitioning in maize during ear development. Crop Sci 32: 443–451 (1992).

    Article  CAS  Google Scholar 

  50. Teyker RH, Moll RH, Jackson WA: Divergent selection among maize seedlings for nitrate uptake. Crop Sci 29: 879–884 (1989).

    Article  Google Scholar 

  51. Torres GA, Parentoni SN, Lopes MA, Paiva E: Use of RFLPs to identify genes for aluminum tolerance in maize. In: International Symposium on Use of Induced Mutations and Molecular Techniques for Crop Improvement (Vienna, 19–23 Jun 1995), pp. 227–236 (1995).

  52. Veldboom, LR, Lee M: Molecular marker facilitated studies of morphological traits in maize. II. Determination of QTLs for grain yield and yield components. Theor Appl Genet 89: 451–458 (1994).

    Article  CAS  Google Scholar 

Download references

Author information

Author notes

  1. H.A.S. Agrama

    Present address: Kansas State University, 2004 Throckmorton, Manhattan, KS, 66506-5501, USA. Email

Authors and Affiliations

  1. Applied Molecular Genetics Laboratory, Agricultural Research Center, Memphis 42, Camp Caesar, Alexandria, 21321, Egypt

    H.A.S. Agrama & A.G. Zakaria

  2. Alexandria Univ. Experimental Station., Alexandria University, Egypt

    F.B. Said

  3. Kansas State University, 2004 Throckmorton, Manhattan, KS, 66506-5501, USA

    M. Tuinstra

Authors
  1. H.A.S. Agrama
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A.G. Zakaria
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F.B. Said
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Tuinstra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H.A.S. Agrama.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Agrama, H., Zakaria, A., Said, F. et al. Identification of quantitative trait loci for nitrogen use efficiency in maize. Molecular Breeding 5, 187–195 (1999). https://doi.org/10.1023/A:1009669507144

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1009669507144

Search

Navigation