Agriculture and livelihoods in East Africa:An overview from an economics perspective

Agriculture and livelihoods in East Africa:
An overview from an economics perspective

Steffen Abele
April 2, 2008
IITA Headquarters, Ibadan, Nigeria

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

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 Food security
in Eastern
Three categories of countries in Eastern Africa
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 Commercialization
in Eastern
Increase in cassava market (raw and
Africa 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 Pests and diseases
in Eastern
Banana xanthomonas wilt:
Africa 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 Eastern Northern N.Western
Adoption of (% (%) (%) (%)
farmers
cassava adopting)
varieties in NASE 1 6 6 6 3
Uganda 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

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


Determinants of speed of adoption of
Adoption of NASE 3
cassava Variable Coefficient Std error Z stat

varieties in Distance 0.00091 0.00077 1.19

Uganda 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


Price developments without and with bacterial
wilt
Ex-ante 300

impact
assessment 250

of bxw in 200

Uganda
Ugshs/kg matooke

Baserun
150
Wilt

100

50

0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Year


Potential economic losses through bxw

Ex-ante
Total change Consumer welfare changes Producer welfare changes
impact 50,000,000

assessment 0

of bxw in
20

20

20

20

20

20

20

20

20

20
01

02

03

04

05

06

07

08

09

10
-50,000,000
Uganda
-100,000,000
US $

-150,000,000

-200,000,000

-250,000,000

-300,000,000

-350,000,000
Year


Ex-ante •Welfare losses of 200 million $ p.a. (about
3 % of GDP) – a serious threat to economic
impact growth (which is 7 % p.a.)
assessment
of bxw in •Most of the losses on the consumer side
Uganda
•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


Business • Comparison of profits in the initial setup
planning stages of processing sites

Zogowale Chisegu Mtimbwani Bungu
(flour) (flour) (starch) (chips)

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


Business Bottlenecks to small scale businesses
planning
• 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)


• Economies of scale

1,200

Business 1,000

planning
800
Performance

600

400

200

0
Status quo Full press capacity Full grater capacity Full mill capacity
Technology setup

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


Validation of map indicators
C3P Correlation coefficients Calorie production Maize equivalent of
(significance levels) income

Temporary and -0.452 -.179
permanent food (.000) (.109)
insecurity

Regression Coefficient t-value Significance level
Variable
Temporary and permanent DEP
food-insecure people (%)
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


Factors determining food production and
indicators for social targeting
C3P Country Uganda Kenya Tanzania Burundi Rwanda DRC
Variable Sign Sign Sign Sign Sign Sign
(t-value) (t-value) (t-value) (t-value) (t-value) (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 - + - + 0.003 +
(-0.61) (1.85) (-1.03) 2.63 (0.16) (17.80)
AGEHEAD - + - + + +
(-0.41) (0.87) (-0.96) 4.11 (2.38) (14.60)
HHSIZE -0.79 -0.73 - +
(-1.53) (-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)


Impact of cassava losses on food
expenditures
Variable Coefficient p-value
Per capita monthly hh food dependent
C3P –food exp.
EXP 0.54 0.005
security Caloric consumption 0.001 0.589
economics 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 • DGDC/CIALCA baseline surveys: 2,600 farm
activities 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

Recommended

Recommended

More Related Content

Viewers also liked

Viewers also liked (19)

Similar to Agriculture and livelihoods in East Africa:An overview from an economics perspective

Similar to Agriculture and livelihoods in East Africa:An overview from an economics perspective (20)

More from International Institute of Tropical Agriculture

More from International Institute of Tropical Agriculture (20)

Recently uploaded

Recently uploaded (20)

Agriculture and livelihoods in East Africa:An overview from an economics perspective