1) The document discusses a study on agricultural productivity in Africa conducted by ReSAKSS and IFPRI's HarvestChoice program, as well as progress establishing country-level SAKSS organizations.
2) The study examines trends in land and labor productivity across Africa, finding that labor productivity has increased faster than land productivity. It also analyzes factors driving productivity through case studies and typologies of agricultural systems and households.
3) Preliminary findings indicate problem identification and targeting were generally well done in projects, but gender and sustainability issues were often not adequately addressed, threatening projects' longevity once donor funding ended.
1. ReSAKSS
2011 ATOR, Country SAKSS Progress
Report, and 2012 Plans
Sam Benin
The CAADP 8th PP MEETING
Hilton Hotel, Nairobi
3–4 May 2012
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2. Outline
• Agricultural productivity study: feature topic
of 2011 annual trends and outlook report
(ATOR): in collaboration with IFPRI’s
HarvestChoice program
• Progress with establishment/strengthening
of country SAKSS
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3. Agricultural Productivity Study
• How to raise and maintain high agricultural
productivity across different parts of Africa?
– fundamental and conceptual issues on the
definition and measurement of agricultural
productivity (temporal and spatial analysis)
– more sophisticated analysis on understanding
the determinants and drivers of agricultural
productivity
– seemingly-easy, but methodological-
challenging case analysis of successful and
failed agricultural productivity programs
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4. Overview of Agricultural Productivity Study:
Framework and Sequence
A. Regional B. Key System
Spatial Typologies for
Characterization focusing
of Agricultural productivity efforts
Productivity (e.g. country x
Opportunities & farming system)
Challenges
Focus Geographies/Systems
Strategic
Opportunities for
Productivity D. Case Study Analysis
C. Representative Farm
of Factors Affecting the
Enhancing Policies Analysis of
Scale and
Productivity Enhancing
& Investments Options
Sustainability of
Productivity Growth
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5. Measures of Productivity
• Partial factor productivity (land and labor)
• Total factor productivity and decomposition
– efficiency arising from reallocation of
productive factors
– technical change arising from things that do
not directly relate to the factors of
production or the productivity of the factors
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6. Trends and Spatial Patterns in
Land and Labor Productivity
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7. Land and labor productivity in SSA
and sub-regions (1961-2009)
Land productivity (2004-06 US$
Eastern &
Central SSA
Western
(2004
PPP)
Southern
Labor productivity (2004-06 US$ PPP)
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8. Land and labor productivity in
selected countries (1961-2009)
Land productivity (2004-06 US$
Ethiopia,
1993-2009
Nigeria
(2004
Kenya
PPP)
South Africa
Labor productivity (2004-06 US$ PPP)
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9. Summary of Trends
• Labor productivity has risen much faster than land
productivity in Africa as a whole
– particularly in the northern region a trend that is
driven by Egypt
• In SSA and many other countries, land productivity
has risen much faster than labor productivity
• In the southern Africa and in Morocco both
measures have risen at about the same rate
• General slowdown in the increase in both land and
labor productivity in the 1990s than in preceding or
subsequent sub-periods.
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10. Spatial Patterns (annual avg. 2005-07)
Land Labor
• Land productivity
• Closer for ECA ($690/ha) and SA ($756/ha); significantly
higher in WA ($1300/ha)
• In WA, rising from semi-arid Agro-Pastoral systems of
the Sahel ($700/ha), through the higher rainfall Cereal-
Root Crop system ($1293/ha) and Root Crop system
($2129/ha), to the sub-humid and humid Coastal
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11. Trends in Total Factor
Productivity (TFP)
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12. Share (%) in Africa’s total AgGDP
(annual average 2003-2010)
Nigeria
Egypt
Morocco
• Drivers of trends
Algeria
Sudan
Kenya
South Africa
Ethiopia
at Africa-wide
Tanzania
Côte d'Ivoire
Cameroon
Ghana
level (top 9)
Tunisia
Congo, Dem. Rep.
Uganda
Libya
Mali
– Nigeria
Mozambique
Madagascar
Zimbabwe
Benin
– Egypt
Burkina Faso
Guinea
Niger
Rwanda
Senegal
– Morocco
Angola
Zambia
Chad
Malawi – Algeria
Central African Republic
Togo
Sierra Leone
Namibia
Liberia
– Sudan*
Gabon
Mauritius
Mauritania
Burundi
Congo, Rep. of
– Kenya
Swaziland
Botswana
Gambia, The
Equatorial Guinea
– South Africa
Guinea-Bissau
Comoros
Eritrea
Lesotho
Cape Verde
– Ethiopia
Djibouti
Seychelles
Somalia
Sao Tome and Principe – Tanzania
Mayote
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13. TFP in SSA (1961=1)
1.4
1.0
0.6
0.2
1961 1971 1981 1991 2001
TFP Eff Tech
• Slight improvement in 1960s followed by a rapid
deterioration in TFP and efficiency till mid-1980s
and then recovery starting in 1984-1985
• Very little technical change
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14. Major Drivers of the trends in SSA:
Nigeria and South Africa
3 Nigeria
2
1 • Nigeria exerts
0 downward
1961 1971 1981 1991 2001
TFP Eff Tech
pressure
3 South Africa • South Africa
2 exerts upward
1 pressure
0
1961 1971 1981 1991 2001
TFP Eff Tech
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15. Annual Average Growth Rate in TFP
by Region (%, 1985-2005)
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
SSA Central Eastern Southern Western LI-1 LI-2 LI-3 MI
SSA Geograpic Location Economic Classification
Technical change Efficiency
• High TFP growth in western, but little technical change
• Southern Africa outperforms in technical change
• Technical change in the central region was also high
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16. -8
-6
-4
-2
0
2
4
6
8
10
Lesotho
Senegal
Swaziland
Madagascar
Gambia
Zimbabwe
Mauritania
Mali
Guinea
Kenya
Zambia
Ethiopia
Cote d'Ivoire
Burkina Faso
Guinea Bissau
Technical change
Cameroon
Togo
Sudan
Mozambique
Chad
Tanzania
Sierra Leone
by country (%, 1985-2005)
Benin
performance for Big 9 agricultural economies
South Africa
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• Except South Africa, average or below average
Efficiency
Gabon
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Malawi
Annual Average Growth Rate in TFP
Nigeria
Ghana
Angola
17. Factors Affecting Productivity
• Typology of agricultural production (IFPRI
spatial allocation model, several secondary
and GIS data, and cluster analysis )
• Typology of rural households (household
survey data and cluster analysis)
• Farm profit maximization analysis
(household survey data and data
envelopment analysis)
• Case study analysis (22 cases out of 120
potential) PARTNERSHIPS
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18. Typology of Production and
Rural Households
• Agricultural production (IFPRI spatial
allocation model and data)
– Farming systems (Dixon et al. 2001)
– Normalized Difference Vegetation Index
(NDVI) for agricultural potential
– Market access
– Population density
• Typology of rural households (household
survey data)
– Human capital
– Physical capital
– Financial capital PARTNERSHIPS
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19. Typology of Ag. Production in SSA
Farming System Sub-system
Tree-root crop Cassava+cocoa; Roots+cattle;
Livestock
Highlands Pulse+cassava+banana+cattle;
Maize+ cattle; Cattle; Sheep/Goats
Cereal-Root Crop Cattle; Sorghum/Millet+groundnut+
cattle; Roots
Maize Mixed Roots; Maize+tobacco+cattle;
Livestock; Sugarcane+cattle
Pastoral/Agro-pastoral Sorghum/Millet+groundnut; Rice+
livestock; Sorghum/Millet+livestock;
Livestock; Maize+cattle
Irrigated
Large commercial and
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20. Characteristics of the tree-root crop
farming system and subsystems
Tree-Root Crop Farming System
Cassava + Roots + cattle Livestock
cocoa
Share of total agricultural value in subsystem (%)
Rice 5.2 6.7 1.2
Maize 4.1 9.3 2.4
Sweet potato 5.0 10.3 1.0
Cassava 10.8 16.5 3.8
Groundnut 1.7 6.0 1.2
Banana 8.5 8.3 1.9
Coffee 1.9 2.7 0.6
Cocoa 48.2 1.4 0.1
Cattle 1.7 10.1 43.0
Sheep/goat 2.2 4.3 29.6
Share of total in farming system (%)
Population 61.3 34.1 4.6
Crop area 55.0 40.7 4.3
Production environment
Pop. density high highPARTNERSHIPS high
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NDVI high high CAADP
OF med
21. Rural households in tree-root crop
farming system and subsystems:
case of Ghana
Sub- Hhd type Physical Capital Financial Capital Main crops
system
Area & Input Machine Hired Access Income per
assets intensity labor to loans capita
1 (TC1) +++ +++ - +++ ++++ ++++ Cassava, maize
2 (TC2) + ++++ - +++ ++++ ++++ Cassava, plantain,
Tree maize
Crop
3 (TC3) +++++ +++++ + ++ +++++ +++++ Cassava/Yam, maize,
cocoa
1 (CR1) +++++ ++ - ++ +++ +++ Sorghum/millet,
Cereal- maize, groundnuts,
Root rice
Crop 2 (CR2) ++++ + - ++ ++ + Sorghum/millet,
maize, groundnuts
1 (RC1) + + - +++++ +++ ++ Maize, groundnuts,
roots
2 (RC2) + + - +++++ + +++ Yam, cassava
3 (RC3) + + - +++++ + ++++ Yam
Root
4 (RC4) ++ + - ++++ ++ + Sorghum, maize
Crop
5 (RC5) + +++ - + ++ ++ Maize, groundnuts
6 (RC6) ++ ++++ - ++ ++ +++ Maize, groundnuts,
cassava
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7 (RC7) +++ ++ +++ +++ +++ + Groundnuts, maize
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22. Ghana Farm Analysis Results I
Subsystem Profit Land Labor Land and
Hhd type eff. oriented oriented labor oriented
profit eff. profit eff. profit eff.
Tree crop 0.23 0.22 0.63 0.64
Cereal-root crop 0.34 0.14 0.43 0.43
Tree crop
Type 1 0.23 0.23 0.60 0.62
Type2 0.23 0.23 0.66 0.67
Type 3 0.22 0.22 0.63 0.64
Cereal-root crop
Type 1 0.35 0.15 0.42 0.41
Type 2 0.33 0.14 0.43 0.43
Profit efficiency in labor-direction measure is
much higher than other efficiency measures
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23. Ghana Farm Analysis Results II
• Labor is the most limiting resource across
all three subsystems and all household
types
– Shadow price of labor is much larger than
that of land
• Higher yields are related to more intensive
use of labor than to input use
• Thus, technical change and greater use of
chemical inputs more likely to occur if
channeled as part of a labor-saving
technology package
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24. Case Studies: conceptual framework
5. Conditioning and 1. Problem identification
cross-cutting factors • Is the problem correctly diagnosed?
• Participation or
involvement of 2. Design and targeting
• Right solution to the problem/socioeconomic
beneficiaries (including conditions of an area?
gender considerations) • Right area? Where the poor are located
• Funding/Financial • Right enterprise (suitability, community needs)
• Right beneficiaries (SHF)
Resources
• Complementary
3. Implementation
interventions • Appropriate strategy
• Necessary partnerships • Clarity of the intervention logic/result based?
• Supporting • Adaptive Management? / Learning from M&E?
Infrastructure
4. Sustainability
• Supporting • Natural Resource Management (soil, water)
policies, policy • Financing/ resource after (e.g. project end),
instruments, legislation Maintenance costs
• Beneficiaries motivated? Ownership and
• Capacity building to the
responsibility to sustain the success
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25. Case Study Findings I
• Problem identification, targeting, and choice of
commodity were generally well done in both
successful and failed interventions
– most of the interventions seem to be based on
good needs assessment as well as local
knowledge
• Gender consideration and sustainability issues
were problematic and not adequately incorporated
in most of the reviewed case studies
• With sustainability, main issue was little
complementary funding to that provided by donors,
and so many of the activities were not carried on
once the projected ended
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26. Unsuccessful Case Study Findings I
• Conceptualization and design phase:
– Imposed plans and top-down approaches that take no
consideration of local community beliefs, preferences
and perceptions;
– Poorly defined or unrealistic scope of operation with
no clearly defined objectives and time lines.
• Start-up phase:
– Limited coordination among stakeholders;
– Poor implementation capacity of beneficiaries
especially at the sub-national levels;
– Lack of ownership and responsibility of the
intervention by the recipient
– Delays in project start up (release of funding and
procurement of goods and services)
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27. Unsuccessful Case Study Findings II
• Project implementation and follow-up phase:
– Lack of financial support to maintain the program e.g.
no system to cater for the maintenance costs of
irrigation infrastructure, cannot afford money to
maintain boreholes, farmers cannot afford the high
costs of fertilizers at the end of a subsidy program;
– Farmer mistrust of programs due to past
disappointments;
– Leadership and management challenges—e.g. who
should be in-charge of what remains at the end of the
project period
– Imported technologies with little or no local
maintenance and spare parts.
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28. Conclusions and Implications:
raising and maintaining high
agricultural productivity in Africa
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29. Conclusions and Implications
• Agricultural productivity growth in Africa, and
particularly in SSA, has been impressive since the
mid-1980s
• But the performance represents a mere catching
up with the levels achieved in the early 1960s, and
there has been very little technical change
• Sustaining growth in labor productivity faces
challenge of population growth and slowdown in
land availability
• This will require policy improvements and
significant investments in agricultural R&D an other
investments that accelerate the expansion of
Africa’s technical frontier
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30. • AgR&D infrastructure and capacities have eroded
over time through years of neglect, primarily from
lack of public funding for agR&D.
• Growth in spending on agR&D and number of
researchers have only recently picked up; reflects the
trends in agricultural productivity growth
annual average growth rate (%)
6
5
4
3
2
1
0
1971-1981
1981-1991
1991-2001
2001-2008
1971-1981
1981-1991
1991-2001
2001-2008
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Source: Beintema and Stads (2011) OF CAADP
31. 0
5
10
15
20
25
0
5
10
15
20
25
30
Angola Angola
Benin Benin
Botswana Botswana
Burkina Faso Burkina Faso
Burundi Burundi
Cameroon Cameroon
Central African… Central African…
Chad Chad
Comoros Comoros
Congo, Dem. Rep. Congo, Dem. Rep.
Congo, Rep. Congo, Rep.
Côte d'Ivoire Côte d'Ivoire
Djibouti Djibouti
Egypt Egypt
Ethiopia Ethiopia
Gambia Gambia
Ghana Ghana
Guinea Guinea
Guinea-Bissau Guinea-Bissau
Kenya Kenya
Lesotho Lesotho
Liberia Liberia
Madagascar Madagascar
Malawi Malawi
Mali Mali
Mauritania Mauritania
Mauritius Mauritius
Morocco Morocco
Mozambique Mozambique
Namibia Namibia
Niger Niger
Annual Average (1995-2003)
Annual Average (2003-2010)
Nigeria Nigeria
Rwanda Rwanda
STP STP
Senegal Senegal
Seychelles Seychelles
Sierra Leone Sierra Leone
Sudan Sudan
Swaziland Swaziland
Tanzania Tanzania
Except Ethiopia, none of Big 9 has achieved target
Togo Togo
Meeting the Maputo 10% target
Tunisia Tunisia
Uganda Uganda
CAADP
Zambia Zambia
CAADP
10% target
10% target
Zimbabwe Zimbabwe
32. How much is spent on agR&D?
AgR&D spending as a share
of agGDP (%), 2008
Source: Beintema and Stads (2011)
• Only 8 of the 31 countries studied met the NEPAD 1% target
• Except Kenya and South Africa, the other big agricultural economies
spent less than 0.5 percent
• The other high performers
(Botswana, Burundi, Mauritania, Mauritius, Namibia, and Uganda)
together account for only 3.2 percent of Africa’s total agGDP; little impact
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33. How has the increase in agR&D
expenditure been allocated?
Ghana Tanzania
Nigeria Uganda
Source: Beintema and Stads (2011)
• Ghana: mostly salaries
• Tanzania: capital investments in 2002-2004 and
operating costs in following years
• Uganda: operating costs
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34. What types of investment are
needed?
• Those that deliver location-specific technologies
and account for diversity of potentials in and
constraints faced by farmers
– But many small economies and limited
capacities and resources for developing
effective agR&D systems
– Regional agricultural R&D strategy can help fill
these gaps and facilitate scale economies.
– African centers of excellence initiatives are
laudable
– Need complementary polices and extension
systems that enhances and maximizes the
technology spillovers from centers to all places
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36. SAKSS: Broker of Strategic
Analysis/Knowledge
Broker
Demand Supply
Parliament, PS,
Policy Think Statistics
Analysis Tanks, Centra Bureaus, Universiti
FBOs, Donors,
Units l Bank es, FBOs
Directors
SAKSS SAKSS SAKSS Network
Oversight Body Node
•Identify and sensitize •Express interest and
• Credence of SAKSS in
knowledge gaps buy into vision
CAADP process
•Synthesize knowledge •Align knowledge
• Governance
•Mobilize and coordinate generation activities
• Channel knowledge
knowledge generation •Receive funding and
and evidence to policy
•Facilitate training training
makers
•… •…
•…
37. Country SAKSS Approach
• Group countries
– SAKSS-ready: Benin, DRC, Ethiopia, Ghana, Kenya,
Malawi, Mali, Niger, Nigeria, Senegal, Tanzania, Togo (15)
– SAKSS-sensitized: Burkina Faso, Burundi, Cape Verde,
Central Africa Republic, Côte d’Ivoire, Gambia, Guinea,
Guinea Bissau, Liberia, Mauritania, Seychelles, Sierra
Leone, Swaziland, and Zambia (14)
– SAKSS-beginning: remaining countries
• Regional Workshop: SAKSS concepts and launch
capacity needs assessment work (2 done, 3 to go)
• Conduct capacity needs assessments: individual
country reports and synthesis (complete by end
June)
• Develop and implement capacity strengthening
strategy (start in July) PARTNERSHIPS
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38. SAKSS: capacity strengthening activities
Parliament, PS,
Policy Think Statistics
Analysis Tanks, Centra Bureaus, Universiti
FBOs, Donors,
Units l Bank es, FBOs
Directors
OB Node Network
Level 100
Rationale and
Concepts
(CAADP; Policy Level 200
Analysis) Concepts and
Application
(Policy Analysis; Level 300 …
Report Writing) Application and
Modeling
(CGE, Econometrics,
Data Work)