12 Enid Katungi Objective1 Common Bean
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Transcript of 12 Enid Katungi Objective1 Common Bean
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TL2 Objective 1: Common bean November 2009
CIAT Enid Katungi Andrew Farrow
KARI David Karanja Tarcisius Mutuoki Daniel Mulwa Monic Mutheu
EIAR Setegn Gebeyehu Kidane Tumsa Fitsum Alamayehu
Research team
TLII Second Annual Review Meeting: November 16-20, 2009
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Presentation outline: Aims Study approach Key findings
Situation and outlook Household surveys Markets
Lessons learnt Scaling up/out
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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
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Study Approach:
Tanzania, Ethiopia, Malawi and Kenya:
Source of data: • Reports, • supplemented by time series
data from FAOSTAT (1970-2004)
2. More detailed investigation:
3. Spatial targeting
1. Broader view of the situation_TL2 countries:
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Situation and outlook in ESA
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Production distribution: Area & output
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Common bean production Environment in Africa
A: Agro-ecological environment ALTITUDE Area
share (%)
% produced under >400mm of rainfall
% produced on Soils with pH
>5.5
>1500masl 51.8 80 64 1000-1500masl 42.7 79 89
<1000masl 5.6 NA* NA*
Source: Modified from Wortmann et al., 1998; *Data not available
B: Multiple cropping system:
Except in central rift valley of Ethiopia
D: Three situations of production Context
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
Kenya
C. Mainly produced by small-scale farmers, mainly women
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Trends in bean production in the four selected countries, between 1970-2007
Source: FAOSTAT 2007
Area (000Ha) Yields (tons/ha)
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Baseline Selected results
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Common beans: Eastern Kenya and Ethiopia
2: Yield and its distribution 3: Where is it higher or lower?
Source: Survey data
Percentage of households
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Utilization of harvest
Source: Survey data
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Average weighted rank of production constraints
Source: survey data *Highest rank=8; lowest=1
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Drought typologies & its effect
Source: Survey data *Highest rank=4 & Lowest=0
Eastern Kenya
Ethiopia
• The effect on common can be as high as 70%
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Country level Available varieties
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
Kenya
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Ethiopia
Variety local Name (s) Year of Release Average area share (%)
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
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Varieties used in study area: Eastern Kenya
Variety local Name (s) Year of Release/
Origin Household share
(%) Average area
share (%)
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
Mwezimoja Early 1980s (Kenyan
land race) 7.3 1.57
GLPx92 Early 1980s (Kenyan
land race) 87 48.4
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
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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
Varieties in study areas: Ethiopia
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Variety rating by farmers
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Preferred traits
Farmers Traders Consumers
Eastern Kenya • Drought tolerant • High yielding • Upward growth
Central Rift valley • White • Oval shaped
• Cleanliness • Not damaged by pests • Heavy seeded • Mature with uniform colour
Kenya • Red/red mottled • Large size • Fast cooking • Low flatulence
SNNPR • Size can be small
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Gender issues
• Average labour input per hectare by Gender
• Gender specific activities e.g in Kenya, seed related activities are dominated by women and Vice versa in Ethiopia
• Bean plots are jointly owned & Managed • Separate plots for men and women rare
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Seed related issues
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Sources of new variety seed and information
Source: Survey data
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Costs of farmer produced seed
• There was generally more drought effects in Naivasha than in Nyanza
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Revenue
Gross margin analysis
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Grain market
Source: Survey data
• 75 % of villages have weekly open air markets
• Limited value addition at farm level_ incl. post harvest handling
Source: Survey data
• In Ethiopia women only participate in retail • In Kenya gender in market is balanced
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Sources of beans on Kenyan markets: March 2008
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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
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Spatial targeting • Achievements
• Challenges
• Lessons learned
• Training
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Compilation of Poverty
Assessments
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Livelihood strategies
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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
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Drought typology: distribution
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Dro
ught
Poverty
Socio-ecological niches for targeting R&D
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Dro
ught
Poverty
• Drought-tolerant variety (yield stability)
• Output marketing
• Market variety
• Processing • Agronomic capacity
• Access to information
• Soil fertility
• IP&DM
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Socio-ecological niches for targeting
R&D
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Nodes of Growth project Kirinyaga
Makueni (Nzaui)
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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