• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Untapped potential of genetic diversity of cassava in the great lakes region of Africa
 

Untapped potential of genetic diversity of cassava in the great lakes region of Africa

on

  • 1,783 views

 

Statistics

Views

Total Views
1,783
Views on SlideShare
1,776
Embed Views
7

Actions

Likes
0
Downloads
0
Comments
0

3 Embeds 7

http://www.slideshare.net 5
http://www.health.medicbd.com 1
http://unjobs.org 1

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Untapped potential of genetic diversity of cassava in the great lakes region of Africa Untapped potential of genetic diversity of cassava in the great lakes region of Africa Presentation Transcript

    • Untapped potential of genetic diversity of cassava in the great lakes region of Africa Anthony Pariyo National Crops Resources Research Institute P. O. Box, 7084 Kampala, Uganda P O B K l U d Chusa Gi é / Veronique Mera Memorial Workshop Ch Ginés V i M M i lW k h held at Cali, Colombia, May 12 – 13, 2010
    • Where is Uganda? Eastern Africa
    • National Crops Resources Research Institute NaCRRI, NaCRRI UGANDA (AFRICA) Administration block Old Cassava Molecular lab New Mol., Biochem & Biosafety II screen house New TC Lab Pathology lab
    • Presentation outline Introduction / Background to the study Materials and Methods Key findings / discussion Conclusion Perspectives: breeding & germplasm conservation Acknowledgement
    • Introduction
    • Why cassava? y Food and Beverage Paper P Starch Wood CASSAVA Deg. Deg Plastics Glue Textile Animal Feed Ethanol
    • Low productivity compared to genetic potential of 80 – 90 t/ha Country Productivity / Yield (t/ha) Uganda 12.0 Rwanda R d 6.4 64 Kenya 7.4 Tanzania 9.7 Congo (DRC) 8.1 Nigeria g 11.2 Brazil 14.0 Colombia 10.8 Viet Nam 16.4 Thailand 22.9 FAOSTAT, 2007
    • Biotic stresses: mainly viral diseases y Cassava brown streak disease Cassava Mosaic Disease (CMD on local landrace - Ebwanateraka
    • Common whitefly species in E.A. region as vectors of most viral diseases Aleurodicus dispersus (spiralling whitefly) B. tabaci B. afer
    • CASSAVA MOSAIC DISEASE 1894: First reported (Warburg): Tanzania 1920s: Spreading in Ghana/Nigeria 1930s - Severe epidemic Madagascar 1988 - Epidemic Akwa Ibom Nigeria 1988 - Epidemic Uganda 1992 - Epidemic Cape Verde Islands 1995 - Epidemic Western Kenya 1998 - Epidemic NW Tanzania 2000 - E id i Rwanda Epidemic R d 2003 - Epidemic Burundi
    • OTHER PATHOGENS/ PESTS 1971 – African root and tuber scale: Cameroon (Stictococcus vayssierei) 1971 – Cassava green mite*: Uganda (Mononychellus tanajoa) 1973 – Cassava bacterial blight : Nigeria blight*: (Xanthomonas campestris pv manihotis) 1973 – Cassava mealybug*: DRC (Phenacoccus manihoti) 1992 – Spiralling whitefly*: Nigeria (Aleurodicus dispersus)
    • COSCA WORKING PAPER 10 (NWEKE et al. 1994) al Decade Introduced Abandoned varieties varieties 1901 10 1901-10 5 0 1911-20 4 0 1921 30 1921-30 5 5 1931-40 20 23 1941 50 1941-50 25 41 1951-60 56 83 1961 70 1961-70 57 118 1971-80 143 110
    • Variation over years in varieties released to the total Survey Districts Varieties % n= R/Total Resistant Plantings 1990-92 1990 92 21 0/67 0 1994 19 3/69 3 1997 16 3/64 21 2003 21 19/149 32 UGANDA FIELD SURVEYS 1990-2003
    • Cassava yield gap in Africa Long t L term yields i ld 80 Genetic Yield/ha 70 potential Ideal pattern of 60 IITA variety genetic gain in yield Local 50 40 30 Yield gap 20 = 700% 10 0 9 12 15 3 3 6 6 9 12 15 18 18 Ye Year ar Source: IITA, computed from FAOStat and IITA data (Dr. A. Dixon, 2008)
    • POTENTIAL THREATS Cassava horn worm (Erinnyis ello) Cassava mealybug (Phenacoccus herreni) Cassava burrowing bug (C t C b i b Cyrtomenus b i) bergi Whiteflies (five species) Cassava green mottle nepovirus: Pacific Cassava X potexvirus: Neotropics p p Cassava vein mosaic virus: Neotropics Cassava frogskin ‘virus’: Neotropics
    • Justification Understanding the amount of genetic diversity in a crop provides a basis for selection of genetic materials for further improvement
    • Study objectives To estimate genetic diversity of cassava landraces in the great lakes region of Africa using SSR markers To document farmers perspectives on p p cassava variety selection in the great lakes region of Africa
    • Materials and Methods
    • Study scope y p Sa p e o g eat a es eg o Sample of great lakes region Uganda Rwanda W. Kenya 287 farmers / fields 220 villages in Uganda 30 villages in Rwanda 35 villages in W. Kenya g y DNA analyses done at BecA, Nairobi hub 466 cassava clones assayed with 5 SSR markers 48 core collection analysed using 26 SSR markers
    • Farmers interview (287)Germplasm Collection form doc form.doc Data collected • Major cultivars grown • Trait selection criteria • Source of collection • Genotype category Ruhango district, Rwanda (Dec 2007)
    • DNA Analyses Data analyses Capillary Leaf sample collection sequencer / ABI3730 (Rwanda 2007) DNA extraction PCR and quality testing
    • Cultivar characterization in farmers fields Root quality attributes R li ib Plant architecture Flowering potential
    • Stem cuttings collected for field establishment in Uganda only? Tororo district, Uganda
    • Key Results
    • Farmers maintain a wide diversity genotypes (3 – 4) on their fields thei No. of No. of No. of No. of plants No. genotype No. genotype Country Districts villages farmers examined / village / farmer Rwanda 9 29 30 100 3.4 3.3 Kenya 4 34 35 125 3.7 3.6 Uganda 22 218 222 694 3.2 3.1
    • Differential preference in culinary qualities of cassava e ists cassa a exists in great lakes region of Africa g eat egion Af ica Cassava V i t selection by farmers based on culinary qualities C Variety l ti b f b d li liti 40 35 30 25 % of farmers 20 U Uganda 15 10 5 0 Kenya 1 2 3 4 5 6 Rwanda Traits 1 = Sweetness, 2 = Brewing, 3 = Chips, 4 = Bread (Flour), S t B i Chi B d (Fl ) 5 = Vegetable and 6 = Boiling
    • Input traits predominate farmer decision on variety selection Farmers preference based on plant archtecture and biological fitness 50 40 % reason for 30 Y choice 20 10 Uganda 0 Kenya Y PR NV EM AV ST Rwanda Y = Yi ld PR = Pest Resistance, NV = New variety, Yield, P tR i t N i t EM = Early Maturity, AV = Availability and ST = Storability
    • Flowering potential of most genotypes is known by farmers k b f Proportion of genotypes with knowledge of flowering by farmers 120 96.9 96 9 100 81.3 82.2 % of genotypes 80 60 f 40 18.7 17.8 20 3.1 0 Rwanda Kenya Uganda Country Known potential Unknown potential
    • Majority of farmers give names to their varieties Farmers interest in naming varieties 90 78.3 80 73.2 70 otypes 59.2 60 Names given / adopted % of geno 50 40.8 40 8 40 Names not given 26.8 30 21.7 20 10 0 Rwanda Kenya Uganda Country
    • Illustration of allelic frequencies in core collection and original collection as assayed by 5 markers with high PIC SSR Markers assayed SSRY102 SSRY21 SSRY38 SSRY59 SSRY69 Total No of alleles in the total collection 2.0 5.0 5.0 3.0 8.0 23.0 No of alleles in the selected genotypes 2.0 5.0 4.0 3.0 7.0 21.0 % No. of alleles in the selected genotypes 100.0 100.0 80.0 100.0 87.5 91.3
    • Highest number of alleles were observed in Rwandan collection Locus Number of alleles Total alleles Kenya y Rwanda Uganda g Total ll l T t l alleles 98 109 103 121 Mean number of alleles 3.77 4.19 3.96 4.65
    • Wide genetic structure exists between Rwandan genotypes and the Ugandan and Kenyan genotypes Kenya Rwanda Uganda Kenya - Rwanda 0.0783 - Uganda 0.0396 0 0396 0.0508 0 0508 -
    • Country level cluster shows country level genetic differentiation diffe entiation 0.01 Uganda Kenya Rwanda
    • Conclusions G eate genetic diversity exists Greater ge et c d e s ty e sts in Rwanda t a in a da than Uganda and Kenya Genotypes in Uganda and Kenya are more closely related than those from Rwanda Farmers select for output traits more strongly compared to input traits Farmers maintain a wide range of genotypes on their fields each one for a particular purpose
    • Perspectives: plant breeding and germplasm conservation • Need for a more comprehensive study to characterize these genotypes • Initiate a comprehensive germplasm conservation plan to minimize genetic erosion •E h Enhancement of participatory plant b di by t f ti i t l t breeding b the National programs will improve adoption • A comprehensive analyses of African and Latin American germplasm to enhance breeding
    • Acknowledgement g Hannington Obiero KARI / Kagamega / Kenya Gervis Gashaka ISAR / Rwanda Martin Fregene Danforthcenter / USA Morag Ferguson IITA / IRRI / Kenya Inosters Njuki IITA / IRRI / Kenya Cassava Research team Uganda Ginés - Mera IDRC Fellowship
    • Ashanti Sana Thank you Obrigado Gracias