ReSAKSS at 8th CAADP

ReSAKSSReSAKSSReSAKSSReSAKSS

2011 ATOR, Country SAKSS Progress 2011 ATOR, Country SAKSS Progress 2011 ATOR, Country SAKSS Progress 2011 ATOR, Country SAKSS Progress

Report, and 2012 PlansReport, and 2012 PlansReport, and 2012 PlansReport, and 2012 Plans

Sam BeninSam BeninSam BeninSam Benin

PARTNERSHIPS IN SUPPORT OF CAADP

The CAADP 8The CAADP 8The CAADP 8The CAADP 8The CAADP 8The CAADP 8The CAADP 8The CAADP 8thththththththth PP MEETINGPP MEETINGPP MEETINGPP MEETINGPP MEETINGPP MEETINGPP MEETINGPP MEETING

Hilton Hotel, NairobiHilton Hotel, NairobiHilton Hotel, NairobiHilton Hotel, Nairobi

33333333––––––––4 May 20124 May 20124 May 20124 May 20124 May 20124 May 20124 May 20124 May 2012

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

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

A. Regional Spatial

Characterization of Agricultural Productivity

Opportunities & Challenges

B. Key System Typologies for

focusing productivity efforts

(e.g. country x farming system)

Focus Geographies/Systems

Overview Overview Overview Overview of Agricultural Productivity Study:of Agricultural Productivity Study:of Agricultural Productivity Study:of Agricultural Productivity Study:

Framework Framework Framework Framework and Sequenceand Sequenceand Sequenceand Sequence


C. Representative Farm Analysis of

Productivity Enhancing Options

D. Case Study Analysis of Factors Affecting the

Scale and Sustainability of

Productivity Growth

Strategic Opportunities for

Productivity Enhancing Policies

& Investments

Focus Geographies/Systems

Measures of Productivity

• Partial factor productivity (land and labor)

• Total factor productivity and decomposition

– efficiency arising from reallocation of


– 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

Trends and Spatial Patterns in Land and Labor Productivity


Land and Labor Productivity

Land and labor productivity in SSA and sub-regions (1961-2009)

Lan

d p

rod

ucti

vit

y (

2004-0

6 U

S$

PP

P)

Western

SSAEastern &

Central


Labor productivity (2004-06 US$ PPP)

Lan

d p

rod

ucti

vit

y (

2004

PP

P)

Southern

Land and labor productivity in selected countries (1961-2009)

Lan

d p

rod

ucti

vit

y (

2004-0

6 U

S$

PP

P)

Nigeria

Ethiopia,1993-2009

Kenya


Labor productivity (2004-06 US$ PPP)

Lan

d p

rod

ucti

vit

y (

2004

PP

P)

South Africa

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


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.

Spatial Patterns (annual avg. 2005-07)

LaborLand


• 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 Artisanal Fishing system ($2143/ha)

Trends in Total Factor Productivity (TFP)


Productivity (TFP)

• Drivers of trends at Africa-wide level (top 9)

– Nigeria

– Egypt

– MoroccoSenegalRwanda

NigerGuinea

Burkina FasoBenin

ZimbabweMadagascar

MozambiqueMali

LibyaUganda

Congo, Dem. Rep. TunisiaGhana

CameroonCôte d'Ivoire

TanzaniaEthiopia

South AfricaKenyaSudan

AlgeriaMorocco

EgyptNigeria

Share (%) in Africa’s total AgGDP(annual average 2003-2010)


– Morocco

– Algeria

– Sudan*

– Kenya

– South Africa

– Ethiopia

– Tanzania

0 5 10 15 20 25

MayoteSao Tome and Principe

SomaliaSeychelles

DjiboutiCape Verde

LesothoEritrea

ComorosGuinea-Bissau

Equatorial GuineaGambia, The

BotswanaSwaziland

Congo, Rep. of Burundi

MauritaniaMauritius

GabonLiberia

NamibiaSierra Leone

TogoCentral African Republic

MalawiChad

ZambiaAngola

SenegalRwanda

TFP in SSA (1961=1)

0.2

0.6

1.0

1.4


0.21961 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

Major Drivers of the trends in SSA: Nigeria and South Africa

0

1

2

3

1961 1971 1981 1991 2001

Nigeria

TFP Eff Tech

• Nigeria exerts downward pressure


TFP Eff Tech

0

1

2

3

1961 1971 1981 1991 2001

South Africa

TFP Eff Tech

pressure

• South Africa exerts upward pressure

Annual Average Growth Rate in TFP by Region (%, 1985-2005)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5


0.0SSA 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

Annual Average Growth Rate in TFP by country (%, 1985-2005)

-8-6-4-202468

10

Lesoth

o

Senegal

Sw

azila

nd

Madagascar

Gam

bia

Zim

babw

e

Maurita

nia

Mali

Guin

ea

Kenya

Zam

bia

Eth

iopia

Cote

d'Iv

oire

Burk

ina F

aso

Guin

ea B

issau

Cam

ero

on

Togo

Sudan

Mozam

biq

ue

Chad

Tanzania

Sie

rra L

eone

Benin

South

Afr

ica

Gabon

Mala

wi

Nig

eria

Ghana

Angola

Technical change Efficiency


• Except South Africa, average or below average performance for Big 9 agricultural economies

Madagascar

Cote

d'Iv

oire

Burk

ina F

aso

Guin

ea B

issau

Mozam

biq

ue

Sie

rra L

eone

South

Afr

ica

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)


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)

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


– Market access

– Population density

• Typology of rural households (household survey data)

– Human capital

– Physical capital

– Financial capital

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


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 smallholder

Characteristics of the tree-root crop farming system and subsystems

Tree-Root Crop Farming System

Cassava +

cocoa

Roots + cattle Livestock

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


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 high high

NDVI high high med

Rural households in tree-root crop farming system and subsystems:

case of GhanaSub-

system

Hhd type Physical Capital Financial Capital Main crops

Area &

assets

Input

intensity

Machine Hired

labor

Access

to loans

Income per

capita

Tree

Crop

1 (TC1) +++ +++ - +++ ++++ ++++ Cassava, maize

2 (TC2) + ++++ - +++ ++++ ++++ Cassava, plantain,

maize

3 (TC3) +++++ +++++ + ++ +++++ +++++ Cassava/Yam, maize,

cocoa


cocoa

Cereal-

Root

Crop

1 (CR1) +++++ ++ - ++ +++ +++ Sorghum/millet,

maize, groundnuts,

rice

2 (CR2) ++++ + - ++ ++ + Sorghum/millet,

maize, groundnuts

Root

Crop

1 (RC1) + + - +++++ +++ ++ Maize, groundnuts,

roots

2 (RC2) + + - +++++ + +++ Yam, cassava

3 (RC3) + + - +++++ + ++++ Yam

4 (RC4) ++ + - ++++ ++ + Sorghum, maize

5 (RC5) + +++ - + ++ ++ Maize, groundnuts

6 (RC6) ++ ++++ - ++ ++ +++ Maize, groundnuts,

cassava

7 (RC7) +++ ++ +++ +++ +++ + Groundnuts, maize

Ghana Farm Analysis Results ISubsystem

Hhd type

Profit

eff.

Land

oriented

profit eff.

Labor

oriented

profit eff.

Land and

labor oriented

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

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

Case Studies: conceptual framework5. Conditioning and

cross-cutting factors

• Participation or

involvement of

beneficiaries (including

gender considerations)

• Funding/Financial

Resources

• Complementary

2. Design and targeting

• Right solution to the problem/socioeconomic

conditions of an area?

• Right area? Where the poor are located

• Right enterprise (suitability, community needs)

• Right beneficiaries (SHF)

1. Problem identification

• Is the problem correctly diagnosed?


• Complementary

interventions

• Necessary partnerships

• Supporting

Infrastructure

• Supporting

policies, policy

instruments, legislation

• Capacity building to the

recipients

4. Sustainability

• Natural Resource Management (soil, water)

• Financing/ resource after (e.g. project end),

Maintenance costs

• Beneficiaries motivated? Ownership and

responsibility to sustain the success

3. Implementation

• Appropriate strategy

• Clarity of the intervention logic/result based?

• Adaptive Management? / Learning from M&E?

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

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)

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


– 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.

Conclusions and Implications:

raising and maintaining high


raising and maintaining high agricultural productivity in Africa

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


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

456

annual average growth rate (%)

• 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


01234

1971-1

981

1981-1

991

1991-2

001

2001-2

008

1971-1

981

1981-1

991

1991-2

001

2001-2

008

Source: Beintema and Stads (2011)

Meeting the Maputo 10% target

0

5

10

15

20

25

30

Angola

Benin

Bots

wana

Burk

ina F

aso

Buru

ndi

Cam

ero

on

Centr

al A

fric

an …

Chad

Com

oro

s

Congo,

Dem

. R

ep.

Congo,

Rep.

Côte

d'Iv

oire

Djib

outi

Egyp

t

Eth

iopia

Gam

bia

Ghana

Guin

ea

Guin

ea-B

issau

Kenya

Lesoth

o

Lib

eria

Madagascar

Mala

wi

Mali

Maurita

nia

Mauritiu

s

Moro

cco

Moza

mbiq

ue

Nam

ibia

Nig

er

Nig

eria

Rw

anda

ST

P

Senegal

Seyc

helle

s

Sie

rra L

eone

Sudan

Sw

azi

lan

d

Tanza

nia

Togo

Tunis

ia

Uganda

Zam

bia

Zim

babw

e

Annual Average (1995-2003) CAADP

10% target

Centr

al A

fric

an

Congo,

Dem

. R

ep.

0

5

10

15

20

25

Angola

Benin

Bots

wana

Burk

ina F

aso

Buru

ndi

Cam

ero

on

Centr

al A

fric

an …

Chad

Com

oro

s

Congo,

Dem

. R

ep.

Congo,

Rep.

Côte

d'Iv

oire

Djib

outi

Egyp

t

Eth

iopia

Gam

bia

Ghana

Guin

ea

Guin

ea-B

issau

Kenya

Lesoth

o

Lib

eria

Madagascar

Mala

wi

Mali

Maurita

nia

Mauritiu

s

Moro

cco

Moza

mbiq

ue

Nam

ibia

Nig

er

Nig

eria

Rw

anda

ST

P

Senegal

Seyc

helle

s

Sie

rra L

eone

Sudan

Sw

azi

lan

d

Tanza

nia

Togo

Tunis

ia

Uganda

Zam

bia

Zim

babw

e

CAADP 10% target

Annual Average (2003-2010)

Except Ethiopia, none of Big 9 has achieved target

How much is spent on agR&D?

AgR&D spending as a share of agGDP (%), 2008



• 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 on the performance for Africa/SSA as a whole

How has the increase in agR&D expenditure been allocated?

Ghana

Nigeria Uganda

Tanzania



• Ghana: mostly salaries

• Tanzania: capital investments in 2002-2004 and operating costs in following years

• Uganda: operating costs

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


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

Country SAKSS Update


Country SAKSS Update

SAKSS: Broker of Strategic Analysis/Knowledge

Policy

Analysis

Units

Think

Tanks, Centra

l Bank

Statistics

Bureaus, Universiti

es, FBOs

Parliament, PS,

FBOs, Donors,

Directors

BrokerDemand Supply

SAKSS NetworkSAKSSSAKSS SAKSS NetworkSAKSSNode

SAKSS Oversight Body

•Identify and sensitize

knowledge gaps

•Synthesize knowledge

•Mobilize and coordinate

knowledge generation

•Facilitate training

•…

•Express interest and

buy into vision

•Align knowledge

generation activities

•Receive funding and

training

•…

• Credence of SAKSS in

CAADP process

• Governance

• Channel knowledge

and evidence to policy

makers

• …

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)

SAKSS: capacity strengthening activities

Policy

Analysis

Units

Think

Tanks, Centra

l Bank

Statistics

Bureaus, Universiti

es, FBOs

Parliament, PS,

FBOs, Donors,

Directors

NetworkNodeOBLevel 100

Rationale and ConceptsConcepts

(CAADP; Policy Analysis)

Level 200

Concepts and Application

(Policy Analysis; Report Writing)

Level 300 …

Application and Modeling

(CGE, Econometrics, Data Work)

Thank You


Thank You

ReSAKSS at 8th CAADP

Education