, Volume 155, Issue 1–2, pp 35–45 | Cite as

Establishment of a pearl millet [Pennisetum glaucum (L.) R. Br.] core collection based on geographical distribution and quantitative traits

  • Ranjana Bhattacharjee
  • I. S. Khairwal
  • Paula J. Bramel
  • K. N. Reddy


ICRISAT conserves a large collection of pearl millet [Pennisetum glaucum L. R. (Br.)] comprising of 21,392 accessions. This includes landraces, cultivars, genetic stocks, breeding lines, and wild relatives from 50 countries. However, only a small fraction of this huge collection has been exhaustively used in the pearl millet improvement program. The objective of our research was to develop a core collection of pearl millet to enhance utilization of genetic resources in improvement programs and simplify their management. For this purpose, accessions were initially stratified according to geographical distribution followed by hierarchical clustering on 11 quantitative traits using Ward’s method. This resulted in 25 distinct groups. Approximately 10% accessions were then randomly selected from each of these 25 distinct groups to form a core collection of 1,600 accessions. Different statistical methods like comparison of mean using Newman–Keuls test, variance using Levene’s test, frequency distributions using Chi-square test, and Wilcoxon’s rank-sum non-parametric test for the traits validated that the variation present in entire collection had been preserved in the core collection. The important phenotypic correlations among different traits that may be under the control of co-adapted gene complexes were also preserved in the core collection. The diversity represented in the core collection will therefore, be a guideline to breeders for a wider use of the pearl millet genetic resources available in the genebank.


Pennisetum glaucum Core collection Genetic resources Landraces Phenotypic diversity 



The lead author gratefully acknowledges ICRISAT for granting a research fellowship during her doctoral program and also for funding the project.


  1. Appa Rao S, Mengesha MH, Sibale PK, Rajagopal Reddy C (1986) Collection and evaluation of pearl millet (Pennisetum) germplasm from Malawi. Eco Bot 40(1):27–37Google Scholar
  2. Basigalup DH, Barnes DK, Stucker RE (1995) Development of a core collection for perennial Medicago plant introductions. Crop Sci 35:1163–1168CrossRefGoogle Scholar
  3. Bataillon TM, David JL, Schoen DJ (1996) Neutral genetic markers and conservation genetics: simulated germplasm collections. Genetics 144:409–417PubMedGoogle Scholar
  4. Brown AHD, Grace JP, Speer SS (1987) Designation of a core collection of perennial Glycine. Soybean Genet Newslett 14:59–70Google Scholar
  5. Brown AHD (1989a) Core collections: a practical approach to genetic resources management. Genome 31:818–824Google Scholar
  6. Brown AHD (1989b) The case for core collections. In: Brown AHD, Frankel OH, Marshall DR, Williams JT (eds) The use of plant genetic resources. Cambridge University Press, Cambridge, UK, pp 136–156Google Scholar
  7. Crossa J (1989) Methodologies for estimating the sample size required for genetic conservation of outbreeding crops. Theor Appl Genet 77:153–161Google Scholar
  8. Dalrymple DG (1986) Development and spread of high-yielding wheat varieties in developing countries, 7th edn. US Agency for International Development, Washington DCGoogle Scholar
  9. Diwan N, Bauchan GR, McIntosh MS (1994) A core collection for the United States annual Medicago germplasm collections. Crop Sci 34:279–285CrossRefGoogle Scholar
  10. Dowswell CR, Paliwal RL, Cantrell RL (1996) Maize in the third world. Westview Press, Boulder, USAGoogle Scholar
  11. Erskine WA, Muehlbauer FJ (1991) Allozyme and morphological variability, outcrossing rate and core collectionformation in lentil germplasm. Theor Appl Genet 83:119–125CrossRefGoogle Scholar
  12. FAOSTAT (2005) Scholar
  13. Frankel OH (1984) Genetic perspective of germplasm conservation. In: Arber W, Llimensee K, Peacock WJ, Starlinger P (eds) Genetic manipulations: impact on man and society. Cambridge University Press, Cambridge, England, pp 161–170Google Scholar
  14. Frankel OH, Brown AHD (1984) Plant genetic resources today: a critical appraisal. In: Holden JHW, Williams JT (eds) Crop genetic resources: conservation and evaluation. George Allen and Unwin, London, UK, pp 249–257Google Scholar
  15. Grenier C, Bramel-cox PJ, Hamon P (2001) Core collection of sorghum: I. Stratification based on eco-geographic data. Crop Sci 41:234–240CrossRefGoogle Scholar
  16. Harlan JR, de Wet JMJ (1971) Towards a relational classification of cultivated plants. Taxon 20(4):509–517CrossRefGoogle Scholar
  17. Holbrook CC, Anderson WF, Pittman RN (1993) Selection of a core collection from the U.S. germplasm collection of peanut. Crop Sci 33:859–861CrossRefGoogle Scholar
  18. Huaman Z, Aguilar C, Ortiz R (1999) Selecting a Peruvian sweetpotato core collection on the basis of morphological, eco-geographical, disease and pest reaction data. Theor Appl Genet 98:840–844CrossRefGoogle Scholar
  19. Huaman Z, Ortiz R, Zhang D, Rodriguez F (2000) Isozyme analysis of entire and core collections of Solanum tuberosum subsp. andigena potato cultivars. Crop Sci 40:273–276CrossRefGoogle Scholar
  20. Jiang HF, Duan NX (1998) Utilization of groundnut germplasm resources in breeding programme. Crop Genet Resour 2:24–25Google Scholar
  21. Keuls M (1952) The use of the “Studentized range” in connection with an analysis of variance. Euphytica 1:112–122CrossRefGoogle Scholar
  22. Khairwal IS, Rai KN, Andrews DJ, Harinarayana G (1999) Pearl millet breeding. Oxford & IBH publishing Co. Pvt. Ltd., New Delhi, 506 ppGoogle Scholar
  23. Levene H (1960) Robust tests for equality of variances. In: Olkin I (ed) Contributions to probability and statistics: essays in honor of Harold Hotelling. Stanford University Press, Stanford, pp 278–292Google Scholar
  24. Mahajan RK, Bisht IS, Agarwal RC, Rana RS (1996) Studies on South Asian okra collection: methodology for establishing a representative core set using characterization data. Genet Res Crop Evol 43:249–255CrossRefGoogle Scholar
  25. Malosetti M, Abadie T (2001) Sampling strategy to develop a core collection of Uruguayan maize landraces based on morphological traits. Genet Res Crop Evol 48(4):381–390CrossRefGoogle Scholar
  26. Milligan GW, Cooper M (1985) An examination of procedures for determining the number of clusters in a data set. Psychometrika 50(2):159–179CrossRefGoogle Scholar
  27. Newman D (1939) The distribution of range in samples from a normal population expressed in terms of an independent estimate of standard deviation. Biometrika 31:20–30Google Scholar
  28. Ortiz R, Ruia-Tapia EN, Mijica-Sanchez A (1998) Sampling strategy for a core collection of Peruvian Quinoa germplasm. Theor Appl Genet 96:475–483CrossRefGoogle Scholar
  29. Ortiz R, Madsen S, Ruiz-Tapia EN, Jacobsen SE, Mujica-Sanchez A, Christiansen JL, Stolen O (1999) Validating a core collection of Peruvian Quinoa germplasm. Genet Res Crop Evol 46:285–290CrossRefGoogle Scholar
  30. Prasada Rao KE, Ramanath Rao V (1995) The use of characterization data in developing a core collection of sorghum. In: Hodgkin T, Brown AHD, van Hintum MJL, Morales EAV (eds) Core collections of plant genetic resources. A Wiley-Sayee Publications, Chichester, UK, pp 109–116Google Scholar
  31. Reddy LJ, Upadhyaya HD, Gowda CLL, Singh S (2005) Development of core collection in pigeonpea [Cajanus cajan (L.) Millspaugh] using geographic and qualitative descriptors. Genet Res Crop Evol 52:1049–1056CrossRefGoogle Scholar
  32. Romesburg HC (1984) Cluster analysis for researchers. Lifetime Learning Publ., Belmont, CAGoogle Scholar
  33. SAS Institute (1989) SAS user’s guide: statistics. State University Press, Cary, NC, USA Google Scholar
  34. Schoen DJ, Brown AHD (1993) Conservation of allelic richness in wild crop relatives is aided by assessment of genetic markers. Proc Natl Acad Sci USA 90:10623–10627PubMedCrossRefGoogle Scholar
  35. Shannon CE, Weaver W (1949) The mathematical theory of communication. University of Illinois Press, Urbana, USAGoogle Scholar
  36. Shiv Kumar S, Gupta S, Chandra S, Singh BB (2004) How wide is genetic base of pulse crops? In: Ali M, Singh BB, Shiv Kumar S, Dhar V (eds) Pulses in new perspective. Proceedings of the National Symposium on Crop Diversification and Natural Resources Management, 20–22 December 2003. Indian Society of Pulses Research and Development, Indian Institute of Pulses Research, Kanpur, pp 211–221Google Scholar
  37. Skinner DZ, Bauchan GR, Auricht G, Hughes S (1999) Developing core collections for a large annual Medicago collection. In: Johnson RC, Hodgkin T (eds), Core collections for today and tomorrow. International Plant Genetic Resources Institute, Rome, ItalyGoogle Scholar
  38. Spagnoletti-Zeuli PL, Qualset CO (1993) Evaluation of five strategies for obtaining a core subset from a large genetic resource collection of durum wheat. Theor Appl Genet 87:295–304CrossRefGoogle Scholar
  39. Taba S, Diaz J, Franco J, Crossa J (1998) Evaluation of Caribbean maize accessions to develop a core subset. Crop Sci 38:1378–1386CrossRefGoogle Scholar
  40. Tohme J, Jones P, Beebe S, Iwanga M (1995) The combined use of agro-ecological and characterization data to establish the CIAT Phaseolus vulgaris core collection. In: Hodgkin T, Brown AHD, van Hintum MJL, Morales EAV (eds) Core collections of plant genetic resources. A Wiley-Sayee Publications, Chichester, UK, pp 95–107Google Scholar
  41. Upadhyaya HD, Bramel PJ, Singh S (2001) Development of a chickpea core subset using geographical distribution and quantitative traits. Crop Sci 41:206–210CrossRefGoogle Scholar
  42. Upadhyaya HD, Ortiz R, Bramel PJ, Singh S (2003) Development of a groundnut core collection using taxonomical, geographical, and morphological descriptors. Genet Resour Crop Evol 50:139–148CrossRefGoogle Scholar
  43. Upadhyaya HD, Furman BJ, Dwivedi SL, Udupa SM, Gowda CLL, Baum M, Crouch JH, Buhariwalla HK, Singh S (2006) Development of a composite collection for mining germplasm possessing allelic variation for beneficial traits in chickpea. Plant Genet Resour 4:13–19CrossRefGoogle Scholar
  44. Vellve R (1992) Saving the seed: genetic diversity and European agriculture. Earthscan Publications, LondonGoogle Scholar
  45. Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236CrossRefGoogle Scholar
  46. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1:80–83CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  • Ranjana Bhattacharjee
    • 1
  • I. S. Khairwal
    • 2
  • Paula J. Bramel
    • 1
  • K. N. Reddy
    • 1
  1. 1.Genetic Resources UnitInternational Crops Research Institute for the Semi-Arid Tropics (ICRISAT)HyderabadIndia
  2. 2.Chaudhary Charan Singh Haryana Agricultural UniversityHisarIndia

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