12 Enid Katungi Objective1 Common Bean

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12 Enid Katungi Objective1 Common Bean

  1. 1. TL2 Objective 1: Common bean November 2009 Research team KARI CIAT David Karanja EIAR Enid Katungi Setegn Gebeyehu Tarcisius Mutuoki Andrew Farrow Kidane Tumsa Daniel Mulwa Monic Mutheu Fitsum Alamayehu TLII Second Annual Review Meeting: November 16-20, 2009
  2. 2. Presentation outline:   Aims   Study approach   Key findings   Situation and outlook   Household surveys   Markets   Lessons learnt   Scaling up/out
  3. 3. Aims:   Better inform targeting and priority setting for bean improvement, institutional innovations and policy   Provide information base for monitoring project progress during implementation and after completion   Contribute capacity building in NARS
  4. 4. Study Approach: 2. More detailed investigation: 1.  Broader view of the situation_TL2 countries: Tanzania, Ethiopia, Malawi and Kenya: Source of data: •  Reports, •  supplemented by time series data from FAOSTAT (1970-2004) 3. Spatial targeting
  5. 5. Situation and outlook in ESA
  6. 6. Production distribution: Area & output
  7. 7. Common bean production Environment in Africa A: Agro-ecological environment ALTITUDE Area % produced under % produced on share >400mm of rainfall Soils with pH C. Mainly produced (%) >5.5 by small-scale >1500masl 51.8 80 64 farmers, mainly 1000-1500masl 42.7 79 89 women <1000masl 5.6 NA* NA* Source: Modified from Wortmann et al., 1998; *Data not available D: Three situations of production Context B: Multiple cropping system: 1.  Highly commercial (i.e Central Rift Valley, few farms in Tanzania and Malawi 2.  Semi subsistence (most common) 3.  Highly subsistence (e.g Eastern Except in central rift valley of Ethiopia Kenya
  8. 8. Trends in bean production in the four selected countries, between 1970-2007 Area (000Ha) Yields (tons/ha) Source: FAOSTAT 2007
  9. 9. Baseline Selected results
  10. 10. Common beans: Eastern Kenya and Ethiopia 2: Yield and its distribution 3: Where is it higher or lower? Percentage of households Source: Survey data
  11. 11. Utilization of harvest Source: Survey data
  12. 12. Average weighted rank of production constraints Source: survey data *Highest rank=8; lowest=1
  13. 13. Drought typologies & its effect Eastern Kenya •  he effect on T common can be as high as 70% Ethiopia Source: Survey data *Highest rank=4 & Lowest=0
  14. 14. Country level Available varieties Kenya Variety Line Code Year of Release Varieties GLPs 1970s & 1980s Varieties 1990s New Rosecoco E8 2008 Chelalang Lyamungu 85 2008 Kenya Umoja AFR 708 2008 Super Rosecoco M22 2008 Kenya Red Kidney M18 2008 Kabete Super L36 2008 Kenya Wonder L41 2008 Miezi Mbili E2 2008 Kenya Early E4 2008 Kenya Sugar bean E7 2008 Kenya Safi MAC 13 2008 Kenya Mavuno MAC 64-1 2008 Kenya Safi MAC 13-3 2008 Kenya Tamu MAC 34-5 2008
  15. 15. Ethiopia Year of Release Average area share (%) Variety local Name (s) Omo 95 RWR 719 2003 Naser DICTA 105 2003 Dimtu DOR 554 2003 MAM 48 MAM 48 2003 Wedo MAM 41 2003 Mam 48 Mam 48 2003 Wedo MAM 41 2003 Batagonia RWV 482 2004 Argane AR04GY 2005 TAO4 JI TAO4- JI 2005 Chercher STTT-165-96 2006 Chore STTT-165-92 2006 Hirna STTT-165-95 2006 Melka Dima XAN 310 2006 Melka Dima XAN 310 2006 Dinknesh RAB 484 2006 ACOS Red - 2007 Cranscope Kranskop 2007
  16. 16. Varieties used in study area: Eastern Kenya Year of Release/ Household share Average area Origin (%) share (%) Variety local Name (s) Eastern Kenya GLP2 Large red mottled Early 1980s 71.5 25.56 Amini 4.9 1.75 Rosecoco Early 1980s 13.8 2.25 Nyayo short, saitoti or short maina 1980s 17.9 4.84 Kakunzu local 8.9 0.05 Early 1980s (Kenyan 7.3 1.57 Mwezimoja land race) Early 1980s (Kenyan 87 48.4 GLPx92 land race) Wairimu, Katune or Kamusina Early 1980s 12.2 2.99 Kitui Pre-released 1993 14.6 2.76 Kayellow, Kathika, or Ka-green Pre-released 1985 34.6 8.12 Ikoso, Ngoloso or itulenge Local 15.5 1.86 Kamwithiokya Local 0.01
  17. 17. Varieties in study areas: Ethiopia Variety Name Year of Release % Area share occupied Central Rift valley Mex-142 1972 50.17 Awash –1 1990 10.43 Unknown Improved 4.63 Awash melka 1999 10.43 AR04GY 2005 11.59 Bora 4.63 Roba -1 1990 4.63 Red wolaita 1974 3.48 SNNPR Mex-142 1972 2.93 Awash –1 1990 8.02 Red wolaita 1974 69.52 Naser 2003 1.07 Ibado 0.8 Unknown red varieties 0.53 Unkown white varieties 2.67 Logoma Local 1.07 Wakadima Local 13.37
  18. 18. Variety rating by farmers
  19. 19. Preferred traits Traders Consumers Farmers Eastern Kenya Kenya •  leanliness C •  ed/red mottled R •  rought tolerant D •  ot damaged by pests N •  arge size L •  igh yielding H •  eavy seeded H •  ast cooking F •  pward growth U •  ature with uniform colour M •  ow flatulence L Central Rift valley SNNPR •  hite W •  ize can be small S •  val shaped O
  20. 20. Gender issues •  ean plots are jointly owned & Managed B •  Separate plots for men and women rare •  ender specific activities e.g in Kenya, seed related activities are G dominated by women and Vice versa in Ethiopia •  verage labour input per hectare by Gender A
  21. 21. Seed related issues
  22. 22. Sources of new variety seed and information Source: Survey data
  23. 23. Costs of farmer produced seed •  here was generally more drought effects in Naivasha than in Nyanza T
  24. 24. Revenue Gross margin analysis
  25. 25. Grain market •  5 % of villages have weekly 7 open air markets •  imited value addition at farm L level_ incl. post harvest handling Source: Survey data •  n Ethiopia women only participate in retail I •  n Kenya gender in market is balanced I Source: Survey data
  26. 26. Sources of beans on Kenyan markets: March 2008
  27. 27. Lessons learnt   Yielding increasing as well as yield stabilizing is important   Breeding: Diversification in breeding targets   Enhanced agronomic management to complement varieties is crucial   Several constraints affecting the common value chain which in turn affect farm gate price   Decentralized seed models:_ Agribusiness skills and resource endowment is important for farmer’s success as producer of other quality seeds   Drought: There is more to be learnt about the farmer coping strategies & their interaction with bean technology   There are very few agricultural economists within NARS that the design of phase 2 need to take into account
  28. 28. Spatial targeting •  chievements A •  hallenges C •  essons learned L •  raining T
  29. 29. Compilation of Poverty Assessments
  30. 30. Livelihood strategies
  31. 31. Why is Poverty important: Baseline results   Capacity to manage crop   Resources to manage   Information to manage   Risk aversion (e.g. GLPX92 vs. GLP2)   Transport & resources to access seeds
  32. 32. Drought typology: distribution
  33. 33. Socio-ecological niches for targeting R&D Drought Poverty
  34. 34. Drought •  utput marketing O •  rought-tolerant D •  arket variety M variety (yield stability) •  rocessing P •  gronomic capacity A •  ccess to information A •  oil fertility S •  P&DM I Poverty
  35. 35. Socio- ecological niches for targeting R&D
  36. 36. Nodes of Growth project Kirinyaga Makueni (Nzaui)
  37. 37. Challenges   Data quality – poverty   Modelling capacity – drought   Recording Location   Limited Representativity of baseline Lessons learned   Validation of poverty data   Improved ‘failed seasons’ models   Institutionalisation of Mapping

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