12 enid katungi objective1 common bean

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

Presentation outline:   Aims   Study approach   Key findings

  Situation and outlook   Household surveys   Markets

  Lessons learnt   Scaling up/out

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

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:

Situation and outlook in ESA

Production distribution: Area & output

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

Trends in bean production in the four selected countries, between 1970-2007

Source: FAOSTAT 2007

Area (000Ha) Yields (tons/ha)

Baseline Selected results

Common beans: Eastern Kenya and Ethiopia

2: Yield and its distribution 3: Where is it higher or lower?

Source: Survey data

Percentage of households

Utilization of harvest

Source: Survey data

Average weighted rank of production constraints

Source: survey data *Highest rank=8; lowest=1

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%

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

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

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

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

Variety rating by farmers

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

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

Seed related issues

Sources of new variety seed and information

Source: Survey data

Costs of farmer produced seed

• There was generally more drought effects in Naivasha than in Nyanza

Revenue

Gross margin analysis

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

Sources of beans on Kenyan markets: March 2008

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

Spatial targeting • Achievements

• Challenges

• Lessons learned

• Training

Compilation of Poverty

Assessments

Livelihood strategies

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

Drought typology: distribution

Dro

ught

Poverty

Socio-ecological niches for targeting R&D

Dro

ught

Poverty

• Drought-tolerant variety (yield stability)

• Output marketing

• Market variety

• Processing • Agronomic capacity

• Access to information

• Soil fertility

• IP&DM

Socio-ecological niches for targeting

R&D

Nodes of Growth project Kirinyaga

Makueni (Nzaui)

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