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. Presentation outline:
 Aims
 Study approach
 Key findings
 Situation and outlook
 Household surveys
 Markets
 Lessons learnt
 Scaling up/out

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. 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. Situation and outlook in ESA

6. Production distribution: Area & output

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. Trends in bean production in the four selected countries,
between 1970-2007
Area (000Ha) Yields (tons/ha)
Source: FAOSTAT 2007

9. Baseline Selected results

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. Utilization of harvest
Source: Survey data

12. Average weighted rank of production constraints
Source: survey data *Highest rank=8; lowest=1

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. 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. 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. 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. 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. Variety rating by farmers

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. 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. Seed related issues

22. Sources of new variety seed and information
Source: Survey data

23. Costs of farmer produced seed
• here was generally more drought effects in Naivasha than in Nyanza
T

24. Revenue
Gross margin analysis

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. Sources of beans on Kenyan markets: March 2008

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. Spatial targeting
• chievements
A
• hallenges
C
• essons learned
L
• raining
T

29. Compilation
of Poverty
Assessments

30. Livelihood strategies

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. Drought typology: distribution

33. Socio-ecological niches for
targeting R&D
Drought
Poverty

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. Socio-
ecological
niches for
targeting
R&D

36. Nodes of
Growth
project
Kirinyaga
Makueni (Nzaui)

38. 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