Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-27T16:42:58.207Z Has data issue: false hasContentIssue false
  • English
  • Français

Establishment of a cassava (Manihot esculenta Crantz) core collection based on agro-morphological descriptors

Published online by Cambridge University Press:  05 July 2012

Ranjana Bhattacharjee ,
Dominique Dumet ,
Paul Ilona ,
Soyode Folarin  and
Jorge Franco
Ranjana Bhattacharjee*
Affiliation:
International Institute of Tropical Agriculture, PMB 5320, Ibadan, Nigeria
Dominique Dumet
Affiliation:
International Institute of Tropical Agriculture, PMB 5320, Ibadan, Nigeria
Paul Ilona
Affiliation:
International Institute of Tropical Agriculture, PMB 5320, Ibadan, Nigeria
Soyode Folarin
Affiliation:
International Institute of Tropical Agriculture, PMB 5320, Ibadan, Nigeria
Jorge Franco*
Affiliation:
Biometrics Unit, International Institute of Tropical Agriculture, PMB 5320, Ibadan, Nigeria
*
*Corresponding authors. E-mail: [email protected]; [email protected]
*Corresponding authors. E-mail: [email protected]; [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

International Institute of Tropical Agriculture maintains 2544 cassava accessions (Manihot esculenta Crantz) from 28 countries in its field bank. Being vegetatively propagated, this poses challenges in maintenance in terms of cost as well as in labour requirements. A core collection representing the range of phenotypic diversity present in the entire collection would enhance the conservation aspects and increase the potential for its exploitation in crop improvement programmes. The present study aimed to establish a core collection using 40 agro-morphological traits evaluated at two locations using a different number of accessions in each location. To meet the challenges generated by the types of variables and include maximum diversity in the core collection, a sequential strategy based on five major concepts was used: hierarchical multiple factor analysis allowing the mixture of variables of different kinds; three-way analysis that included the effect of genotype × environment interaction in the clustering process; linear discriminant function to assign all those individuals who were included in one location but not in the other to the groups that were generated from the common number of accessions evaluated in both locations; and D-allocation method to select samples from each cluster. The representativeness of the core subset to the entire collection was further estimated by comparing means and variances, range, and distances between accessions. The established cassava core collection consisted of 428 accessions that conserved 15% higher phenotypic diversity with no redundancies. The phenotypic diversity represented in this core collection will be a guide to users of cassava germplasm in their crop improvement programmes.

Keywords

agro-morphological traitscassavacore collectionphenotypic diversity
Type
Research Article
Information
Plant Genetic Resources , Volume 10 , Issue 2 , August 2012 , pp. 119 - 127
Copyright
Copyright © NIAB 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Basford, K and McLachlan, GJ (1985) The mixture method of clustering applied to three-way data. Journal of Classification 2: 109–125.CrossRefGoogle Scholar
Bhattacharjee, R, Khairwal, IS, Bramel, PJ and Reddy, KN (2007) Establishment of a pearl millet [Pennisetum glaucum (L.) R. Br.] core collection based on geographical distribution and quantitative traits. Euphytica 155: 35–45.CrossRefGoogle Scholar
Brown, AHD (1989a) Core collections: a practical approach to genetic resources management. Genome 31: 818–824.CrossRefGoogle Scholar
Brown, AHD (1989b) The case for core collections. In: Brown, AHD, Frankel, OH, Marshall, DR and Williams, JT (eds) The Use of Plant Genetic Resources. Cambridge: Cambridge University Press, pp. 136–156.Google Scholar
Brown, AHD (1995) The core collection at the crossroads. In: Hodgkin, T, Brown, AHD, van Hintum, TJL and Morales, EAV (eds) Core Collections of Plant Genetic Resources. Chichester: John Wiley and Sons, pp. 3–19.Google Scholar
Cochran, WG (1977) Sampling Techniques. 3rd edn. New York, NY: John Wiley and Sons.Google Scholar
Conover, WJ (1971) Practical Non-parametric Statistics. New York, NY: John Wiley and Sons, pp. 97–104.Google Scholar
Cordeiro, CMT, Morales, EAV, Ferreira, P, Rocha, DMS, Costa, IRS, Valois, ACC and Silva, S (1995) Towards a Brazilian core collection of cassava. In: Hodgkin, T, Brown, AHD, van Hintum, TJL and Morales, EAV (eds) Core Collections of Plant Genetic Resources. Chichester: John Wiley and Sons, pp. 155–168.Google Scholar
Dixon, AGO, Whyte, JBA, Mahungu, NM and Ng, SYC (2001) Tackling the cassava mosaic disease (CMD) challenge in sub-Saharan Africa: the role of host-plant resistance and germplasm deployment. In: Taylor, N, Ogbe, F and Fauquet, CM (eds) Cassava, An Ancient Crop for Modern Times: Food, Health, Culture. St. Louis, MO: Donald Danforth Plant Sciences Center, pp. S8–05.Google Scholar
Dowswell, CR, Paliwal, RL and Cantrell, RL (1996) Maize in the Third World. Boulder, CO: Westview Press.Google Scholar
FAO(2009) FAOSTAT database. Rome: Food and Agriculture Organization of the United Nations. Available at http://faostat.fao.org.Google Scholar
Franco, J, Crossa, J and Dehpsande, S (2010) Hierarchical multiple factor analysis for classifying genotypes and forming core subsets based on phenotypic and genetic data. Crop Science 50: 105–117.CrossRefGoogle Scholar
Franco, J, Crossa, J, Taba, S and Shands, H (2003) A multivariate method for classifying cultivars and studying group × environment × trait interaction. Crop Science 43: 1249–1258.CrossRefGoogle Scholar
Franco, J, Crossa, J, Taba, S and Shands, H (2005) A sampling strategy for conserving genetic diversity when forming core subsets. Crop Science 45: 1035–1044.CrossRefGoogle Scholar
Franco, J, Crossa, J, Villasenor, J, Castillo, A, Taba, S and Eberhart, SA (1999) A two-stage, three-way method for classifying genetic resources in multiple environments. Crop Science 39: 259–267.CrossRefGoogle Scholar
Franco, J, Crossa, J, Warburton, M and Taba, S (2006) Sampling strategy for conserving Maize diversity when forming core subsets using genetic markers. Crop Science 46: 854–864.CrossRefGoogle Scholar
Frankel, OH (1984) Genetic perspective of germplasm conservation. In: Arber, W, Llimensee, K, Peacock, WJ and Starlinger, P (eds) Genetic Manipulations: Impact on Man and Society. Cambridge: Cambridge University Press, pp. 161–170.Google Scholar
Frankel, OH and Brown, AHD (1984) Current plant genetic resources, a critical appraisal, in genetics: new frontiers. In: Chopra, VL, Joshi, BC, Sharma, RP and Banasai, HC (eds) XV International Congress of Genetics. vol. IV. New Delhi: Oxford and IBH Publishing Co., pp. 3–13.Google Scholar
Fukuda, WM, Guevara, C, Kawuki, RS and Ferguson, M (2010) Selected Morphological and Agronomic Descriptors for Characterization of Cassava. Descriptors Manual. IITA publications, IITA, Ibadan, Nigeria. p. 25.Google Scholar
Gower, JC (1971) A general coefficient of similarity and some of its properties. Biometrics 27: 857–871.CrossRefGoogle Scholar
Harlan, JR (1972) Genetic resources in sorghum. In: Rao, NGP and House, LR (eds) Sorghum in the Seventies. New Delhi: Oxford and IBH Publishing Company, pp. 1–13.Google Scholar
Hershey, C, Iglesias, C, Iwanaga, M and Tohme, J (1994) Definition of a Core Collection for Cassava. Report for the First Meeting of the International Network for Cassava Genetic Resources, Aug. 1992. Cali: CIAT, pp. 145–156.Google Scholar
IPGRI/IITA(1997) Descriptors for Yam (Dioscorea spp.). Ibadan/Rome: International Institute of Tropical Agriculture/International Plant Genetic Resources Institute.Google Scholar
Jiang, HF and Duan, NX (1998) Utilization of groundnut germplasm resources in breeding program. Crop Genetic Resources 2: 24–25.Google Scholar
Lebot, V (2009) Cassava in ‘tropical root and tuber crops cassava, sweet potatoes, yams and aroids’. Crop Production Science in Horticulture Series 17: 2–87.Google Scholar
Le Dien, S and Pagès, J (2003) Analyse factorielle multiple hiérarchique. Revue de. Statistique Appliquée 51: 47–73.Google Scholar
Mardia, KV, Kent, JT and Bibby, JM (1979) Multivariate Analysis. London: Academic Press.Google Scholar
Olsen, KM and Schaal, BA (2001) Microsatellite variation in cassava (Manihot esculenta, Euphorbiaceae) and its wild relatives: further evidence for a southern Amazonian origin of domestication. American Journal of Botany 88: 131–142.CrossRefGoogle ScholarPubMed
Srinivas, T and Anantharuman, M (2000) Status of Cassava Production. Processing and Marketing in Andhra Pradesh. Sreekariyam: Central Tubers Crops Research Institute, p. 7.Google Scholar
Sriroth, K, Chollakup, R, Chotineeranat, S, Piyachomkwan, K and Ostes, CG (2000) Processing of cassava waste for improved biomass utilization. Bio-Resource Technology 71: 63–69.CrossRefGoogle Scholar
Tonukari, NJ (2004) Cassava and the future of starch. Electronic Journal of Biotechnology (www.ejbiotechnology.info//content/vol7/issue1/issues/2/).CrossRefGoogle Scholar
Upadhyaya, HD, Furman, BJ, Dwivedi, SL, Udupa, SM, Gowda, CLL, Baum, M, Crouch, JH, Buhariwalla, HK and Singh, S (2006) Development of a composite collection for mining germplasm possessing allelic variation for beneficial traits in chickpea. Plant Genetic Resources 4: 13–19.CrossRefGoogle Scholar
Supplementary material: File

Bhattacharjee supplementary material

Bhattacharjee supplementary material

Download Bhattacharjee supplementary material(File)
File 86 KB