agriculture and livelihoods in east africa:an overview from an economics perspective

International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org

Agriculture and livelihoods in East Africa:

An overview from an economics perspective

Steffen Abele

April 2, 2008

IITA Headquarters, Ibadan, Nigeria


Contents • Challenges in Eastern Africa

• Food security

• Agricultural commercialization

• Pests and diseases

• Impact of CMD and bxw

• CMD adoption study in Uganda

• Bxw impact study Uganda

• Cassava processing business in Tanzania

• C3P: Diseases, food security and GIS

• Other activities (CIALCA, impact in WA,

agronomy)

• Outlook: Activities in the coming years


Challenges

in Eastern

Africa

Food security

Three categories of countries in Eastern Africa

Stably food secure (>2,100 kcal/cap/day):

Uganda (2,360)

Food secure but unstable (around 2,100

kcal/cap/day and slightly below):

Rwanda (2,100), Kenya (1,880), Tanzania

(1,960)

Food insecure (significantly below 2,100

Kcal/cap/day): Burundi (1,700), DRC (1,600)


Challenges

in Eastern

Africa

Commercialization

Increase in cassava market (raw and

processed) from 2005-2010: Uganda: 11 %,

Tanzania 48 %

1 million mt required for cassava processing in

Uganda and Tanzania by 2010

10-15 % of the population could benefit from

cassava commercialization (raw and

processed) in Uganda and Tanzania


Challenges

in Eastern

Africa

Pests and diseases

Banana xanthomonas wilt:

55 % production losses over a decade if

uncontained, major outbreaks in Uganda and

spreading westwards

Cassava mosaic disease:

Uganda and Western Kenya: CMD outbreak in

late eighties/early nineties with 80 percent

production losses, CMD spreading further

south-west

CBSD: Recent outbreaks, similar threats


Variety Central

(%

farmers

adopting)

Eastern

(%)

Northern

(%)

N.Western

(%)

NASE 1 6 6 6 3

NASE 2 6 8 12 3

NASE 3 77 75 46 75

NASE 4 7 6 18 13

NASE 10 2 2 0 0

NASE 12 2 3 18 6

Adoption of

cassava

varieties in

Uganda

NASE 3 has in general lower yields than the other

varieties, but dominates through short maturity periods,

market demand and flour quality. Relatively lower

cyanide content and limited use seem to be of a lesser

influence


Adoption of

cassava

varieties in

Uganda

Determinants of speed of adoption of

NASE 3

Variable Coefficient Std error Z stat

Distance 0.00091 0.00077 1.19

Age of Head -0.0076 0.0045 -1.69*

Educ of Head 0.00085 0.00067 1.28

Farm size -0.010 0.028 -0.37

H/hold size -0.0034 0.0010 -3.37***

FT Labor -0.0030 0.0024 -1.23

No. of hoes 0.0024 0.00087 2.83***

Ext. advice -0.0038 0.0022 -1.72*

Constant 4.36 0.33 13.00***

No. of obs = 216, LR chi2(8) = 26.20, Prob > chi2 = 0.0010, Pseudo R2 = 0.0158,

Log likelihood = -813.58


Ex-ante

impact

assessment

of bxw in

Uganda

Price developments without and with bacterial

wilt

0

50

100

150

200

250

300

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

Ug

sh

s/k

g m

ato

ok

e

Baserun

Wilt


Ex-ante

impact

assessment

of bxw in

Uganda

Potential economic losses through bxw

-350,000,000

-300,000,000

-250,000,000

-200,000,000

-150,000,000

-100,000,000

-50,000,000

0

50,000,000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Year

US

$

Total change Consumer welfare changes Producer welfare changes


•Welfare losses of 200 million $ p.a. (about

3 % of GDP) – a serious threat to economic

growth (which is 7 % p.a.)

•Most of the losses on the consumer side

•BXW is a macro-economic threat

•Threat could easily scale out to Burundi,

Rwanda, Eastern DRC – it becomes a

regional threat with possibly similar effects

Ex-ante

impact

assessment

of bxw in

Uganda


Business

planning

• Comparison of profits in the initial setup

stages of processing sites

Zogowale

(flour)

Chisegu

(flour)

Mtimbwani

(starch)

Bungu

(chips)

Profits -1,640 1,876 6,448 2,212


Business

planning

Bottlenecks to small scale businesses

• Low and erratic inflow of raw material (daily,

seasonal)

• Inefficient use of inputs (e.g. water), indicated

by volatile costs per unit processed

• Unstable demand at the beginning, project

members as “brokers”

• Difficult finance schemes (processors/farmers

want cash transactions, clients want bank

transactions)

• Diseconomies of small scale (see next slide)


Business

planning

• Economies of scale

0

200

400

600

800

1,000

1,200

Status quo Full press capacity Full grater capacity Full mill capacity

Technology setup

Perf

orm

ance

Investment (100 $) Fixed costs (100 $) Costs of production (100 $) Revenues (100 $) Profits (100 $)


Business

planning


C3P Objectives of the C3P food security

assessment

Link food security to crop diseases (bxw and

CMD)

Support GIS to target food insecure and

disease threatened areas

Support targeting across social strata

Shed some light on the economics of food

security

Create tools that allow short term surveys

on/assessments of food security (< 1 year)


C3P


C3PValidation of map indicators

Correlation coefficients

(significance levels)

Calorie production Maize equivalent of

income

Temporary and

permanent food

insecurity

-0.452

(.000)

-.179

(.109)

Regression

Variable

Coefficient t-value Significance level

Temporary and permanent

food-insecure people (%)

DEP

Calorie production -0.00892 -4.452 .000

Income square -0.00183 -1.837 .070

Constant 65.93800 16.276 .000

Adj. R2 0.218, n = 81. Source: Own data


C3P

Factors determining food production and

indicators for social targeting

Country Uganda Kenya Tanzania Burundi Rwanda DRC

Variable Sign

(t-value)

Sign

(t-value)

Sign

(t-value)

Sign

(t-value)

Sign

(t-value)

Sign

(t-value)

EXP +

(2.10)

+

(1.79)

+

(2.13)

+

(4.30)

+

(11.31)

+

(2.69)

EXP2 -

(-1.97)

-

(-1.86)

-

(-1.95)

-

-4.32

-

(-2.25)

-

(-2.32)

EDUCHEAD -

(-0.61)

+

(1.85)

-

(-1.03)

+

2.63

0.003

(0.16)

+

(17.80)

AGEHEAD -

(-0.41)

+

(0.87)

-

(-0.96)

+

4.11

+

(2.38)

+

(14.60)

HHSIZE -0.79

(-1.53)

-0.73

(-0.23)

-

-5.05

+

(0.88)

CASSLOSS -

(-3.63)

-

(-1.73)

-

(-1.65)

-

-2.06

-

(-2.35)

-

(-1.66)

SEXHEAD +

(2.78)

+

(0.54)

-

(-0.24)

+

3.94

-

(-3.17)

FARMLAB +

(0.39)

+

(7.83)

+

(2.90)

+

0.06

-

(-0.59)

LANDOWN +

(4.81)

-

(-0.14)

+

1.26

+

(0.13)


C3P –food

security

economics

Impact of cassava losses on food

expenditures

Variable Coefficient p-value

Per capita monthly hh food exp.

dependent

EXP 0.54 0.005

Caloric consumption 0.001 0.589

hdd 0.05 0.854

EDUCHEAD 0.05 0.613

AGEHEAD -0.11 0.296

HHSIZE -4.32 0.048

Cmdloss 0.01 0.097

SEXHEAD 0.11 0.543

FARMLAB 0.18 0.924

LANDOWN -0.44 0.042

Ky 55.48 0.007

Tz 39.68 0.04

Bu 25.03 0.030

Rw 44.00 0.000

DRC 126.9 0.000

CONSTANT 18.3 0.614


Other

activities

• DGDC/CIALCA baseline surveys: 2,600 farm

datasets in Central Africa (Rw, Bu, DRC)

• Co-authoring papers on:

•Ex ante impact assessment of ag-research

in Nigeria

•Adoption meta study

• Some basic cassava agronomics in Kenya

and Uganda (as PhD supervisor)


Outlook • Final econometrics and publication of C3P

results

• Publication of a synthesis of cassava impact

in Eastern Africa

• Publication of business planning study in

Tanzania

• Publication of Marketing Unit studies in

Southern Africa

• Backstopping DGDC CIALCA economics

• Impact assessment of Market Information

Systems in Uganda

• Continue food security and impact studies in

GLCI: Depict impact pathways

agriculture and livelihoods in east africa:an overview from an economics perspective

Technology

uganda nase

uganda age of head

eastern drc

food secure

adoption ofadoption

eastern africaafrica

food insecure

bxw cmd adoption study