Strategies for Raising and Sustaining High Agricultural
Productivity in Africa
ReSAKSS Plenary session
Chair: Samuel Benin Presenters: Zhe Guo, Bingxin Yu, Alejandro Nin Pratt,
Stella Massawe
Research Team: Stan Wood, Melanie Bacou, Linden McBride, Joseph Karugia, Paul Guthiga, Maurice Ogada, Emmanuel Musaba,
Pius Chilonda, Precious Zikhali, Mbaye Yade, Manson Nwafor, Maurice, Taondyande, Claude Bizimana
1-3 November 2011 UNECA, Addis Ababa
evidence- and outcome-based planning and implementation of agricultural-sector policies
and strategies in Africa
Strategic Analysis and Monitoring of CAADP and Agricultural Performance in Africa
Knowledge Management, Capacity Strengthening, and Policy Communications
support review and
dialogue
ReSAKSS organized around 4 nodes of operation
Background to this Study
• CAADP provides an agriculture-led integrated framework of development priorities for reducing poverty and hunger and increasing food security
– CAADP target: 6% AgGDP growth rate per year
– Possible for many African countries
– Substantial investments required (greater than the 10% target in many cases) because of moderate and slow productivity growth
As countries enter operational phase of investment program design and execution,
Key Question: how to raise and maintain high agricultural productivity across different parts of the continent?
ReSAKSS 2011 M&E work
• Answer above question, which requires addressing several follow-up questions:
– Fundamental and conceptual: definition and measurement of agricultural productivity
– Complex: understanding the determinants and drivers of productivity
– Challenging: program design and implementation by translating the knowledge into effective action
What is “Productivity”?
• Partial Factor Productivity
– Land Productivity
Yield = Output / Harvested area
– Labor Productivity
LP = Output / Total hours worked
Useful measures but: do not measure productivity of all resources
can lead to misleading policy prescriptions
Land and Labor Productivity in SSA, 1961-2009
Labor productivity (2004-06 US$ PPP)
Lan
d p
rod
uct
ivit
y (2
00
4-0
6 U
S$
PP
P)
SSA as a whole: labor productivity >> land productivity; but land productivity increased much faster, more than tripled
As expected, different picture when consider different sub-regions of Africa
Labor productivity (2004-06 US$ PPP)
Lan
d p
rod
uct
ivit
y (2
00
4-0
6 U
S$
PP
P)
Western
SSA Eastern &
Central
Southern
Again, different picture when consider different countries
Labor productivity (2004-06 US$ PPP)
Lan
d p
rod
uct
ivit
y (2
00
4-0
6 U
S$
PP
P)
Nigeria
South Africa
Ethiopia, 1993-2009
Kenya
Total Factor Productivity • Productivity of a production unit (farm, district,
region, country, etc) is the ratio of the outputs that it produces to the inputs it uses to produce those outputs
• TFP =
• Agricultural growth in the long run depends on TFP
– Efficiency: reallocation of productive factors
– Technical change: technological advancement
Inputs
Output
Total
Total
TFP growth in SSA Two different periods: both driven more by
efficiency change than technical change
0.95
0.96
0.97
0.98
0.99
1
1.01
1970 1975 1980 1985 1990 1995 2000 2005
TFP
leve
ls 19
70=1
TFP components 1970-1984 1985-1994 1995-2009
Efficiency change -0.28 0.07 0.15
Technical change -0.03 0.05 0.10
TFP -0.32 0.12 0.25
Growth Rate (%)
Based on FAOSTAT
More workers; and Less land and inputs per worker
0.8
1
1.2
1.4
1.6
1.8
2
1970 1975 1980 1985 1990 1995 2000 2005
Ind
ex
19
70
=1
Yield Labor productivity TFP
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
1970 1975 1980 1985 1990 1995 2000 2005
Inputs/Ha Inputs/Worker HA/worker
TFP (green)
Yield (blue)
Labor productivity (red)
Inputs per hectare (brown)
Inputs per worker (yellow)
Land-labor ratio (pink)
Livestock, root crops, and oil crops explain more than 60% of output growth in 1995-2009
0%
5%
10%
15%
20%
25%
30%
Contribution to growth Share in output
Yields Labor productivity TFP
Mozambique 3.50 2.72 2.32
Angola 6.62 4.28 1.97
Rwanda 3.26 2.56 1.79
Tanzania 3.59 2.01 0.67
Ethiopia 2.49 1.87 0.65
Côte d'Ivoire 1.91 1.94 0.62
Senegal 2.39 1.01 0.43
Niger 4.53 1.99 0.40
Zambia 3.92 2.51 0.37
Ghana 2.33 3.19 0.27
Mali 1.72 3.08 0.25
Best performing countries (annual average growth rates, 1995-2009)
Why is agricultural productivity growth in SSA so low?
• Intrinsic lower productivity of natural resources?
• No technology available?
• Poor infrastructure, high transaction costs and constrained market access?
• Policy: high prices of inputs as a result of distortions?
• Underdeveloped markets, institutions?
No simple answers • Multiple factors interacting differently
– Natural resource quality – Population pressure – Infrastructure – Distance to major markets and road density – Market for outputs, inputs and services, labor markets – Policies and government interventions – Household characteristics
• This diversity suggests that spatial heterogeneity matters and that answers should be geographically focused
A. Regional Spatial Characterization of
Agricultural Productivity Opportunities &
Challenges
B. Key System Typologies for focusing productivity
efforts (e.g. country x farming system)
C. Representative Farm Analysis of Productivity
Enhancing Options
D. Case Study Analysis of Factors Affecting the Scale
and Sustainability of Productivity Growth
Focus Geographies/Systems
Overview of Session (and Study) Framework and Sequence
Spatial Dimensions of Agricultural Productivity
Zhe Guo and Stanley Wood
HarvestChoice
International Food Policy Research Institute
Regional Spatial Data/Analysis Platform • A harmonized set of spatial variables, conformed to a
standardized 10km (5 arc minute) grid covering the whole of Africa (focusing on SSA), generated by HarvestChoice.
• About 300,000 grid cell records each with 200+ gridcell attributes. Attributes range from observed, e.g. rainfall through imputed, e.g. poverty, to highly-modeled, e.g. potential maize yields under different management practices.
• Provides a basis for undertaking consistent region-wide assessment of agricultural development opportunities and constraints, such as the ReSAKSS productivity study.
• Facilitates regional targeting and prioritization of agricultural development hotspots, e.g. AGRA breadbaskets, Feed the Future Farming Systems, Gates Ag. Development Strategy, CGIAR CRPs*
* As well as the type of regionally-strategic, agroecosystem-based concentration zones for agricultural production and processing proposed by Josue Dione in his plenary address.
Spatial variables influencing productivity
• Agricultural potential
• Footprint of agriculture
• Market access
• Demographics
• Human welfare
Agricultural potential Rainfall & Length of Growth Period
Long term average of annual rainfall Length of growth period
Agricultural potential Normalized Difference Vegetation Index & Potential Yield
Simulated potential yield Long term average of NDVI
NA
0
10
20
3040
0
1
2
3
4
5
6
7
10080
6040
200
IrrigationThreshold
% of Available
Soil Water
MaizeYield
Potential
t[DM]/ha
Fertilizer Application Ratekg[N]/ha
Footprint of agriculture Crop Land & Pasture Land
Cropland density Pasture land density
Footprint of Agriculture Farming System & Crop
Farming systems Maize harvested area
Footprint of Agriculture Productivity Constraints
Aluminum toxic Drought severity
Market Access Travel time to major settlements
Travel time to market with population greater than 20,000
Travel time to market with population greater than 500,000
Market Access Travel Time to Ports
Travel time to major ports Major port command area
Demographics Population
Population density Landscan 2009 Population density (GRUMP 2000)
Human Welfare Poverty & Global Hunger Index
Absolute number of poor living under $1.25 per day
Global Hunger Index
POVERTY (1000 people)
FS_NAME E S W Total Cum %
Cereal-root crop mixed 2,764 11,811 30,570 45,145 15.5
Maize mixed 28,065 16,277 9 44,352 30.7
Root crop 14,219 2,451 27,644 44,314 45.9
Agro-pastoral millet/sorghum 384 1,868 24,729 26,981 55.1
Forest based 20,365 87 3,535 23,988 63.3
Highland perennial 23,278 23,278 71.3
Tree crop 1,569 541 17,199 19,308 77.9
MAIZE AREA (1000 ha)
FS_NAME E S W Total
Maize mixed 2,860 3,197 0 6,057 24.2
Cereal-root crop mixed 128 1,214 2,718 4,059 40.4
Large commercial_smalholder 3,440 3,440 54.1
Root crop 711 329 2,228 3,268 67.2
Tree crop 145 4 1,647 1,796 74.3 HIGH PHOSPHORUS FIXATION (SHARE OF GRID CELL AREA, %)
E S W Total
Highland perennial 34.0 34.0
Forest based 14.0 26.0 15.0 16.0
Tree crop 13.0 37.0 9.0 12.0
Highland temperate mixed 13.0 11.0 8.0 11.0
Maize mixed 17.0 6.0 6.0 11.0
TRAVEL TIME TO CLOSEST PORT (hours)
FS_NAME E S W Total
Coastal artisanal fishing 15 22 15 15
Large commercial_smalholder 19 19
Tree crop 17 16 20 19
Highland temperate mixed 26 18 19 21
Rice-Tree crop 26 26
Flexible approach to spatial aggregation and analysis
Ag. Mkt Pop
Pot. Access Density Potential Development Strategies
High High High HHH Perishable cash crops
HHH Dairy, intensive livestock
HHH Non-perishable cash crops
HHH Rural non-farm development
Low High HLH Non-perishable cash crops
HLH High input perennials
HLH Livestock intensification, improved grazing
Medium High High MHH High Input cereals
MHH Perishable cash crops
MHH Dairy, intensive livestock
MHH Rural non-farm development
Low High MLH High Input cereals
MLH Non-perishable cash crops
MLH Livestock intensification, improved grazing
Low High High LHH with irrigation investment
LHH High Input cereals
LHH Perishable Cash Crops
LHH Dairy, intensive livestock
LHH Rural non-farm development
Low Low LLL Low input cereals
LLL Limited livestock intensification
LLL Emigration
Example of Potential Regional Development Strategies
Source: ASARECA Strategy. Omamo et al. 2006
Summary • We use a region-wide, consistent, high-resolution
spatial database to underpin our efforts to; • delineate and characterize regionally-significant focus
areas • identify the nature and severity of specific productivity
constraints & opportunities
• Enables the study to take account of spatial (and spatio-temporal) heterogeneity of conditions under which we seek to raise productivity
• Provides a framework for scaling up/out the results of the farm level and case study analyses
A Typology of Agricultural Productivity Zones
Bingxin Yu International Food Policy Research Institute
A. Regional Spatial Characterization of
Agricultural Productivity Opportunities &
Challenges
B. Key System Typologies for focusing productivity
efforts (e.g. country x farming system)
C. Representative Farm Analysis of Productivity
Enhancing Options
D. Case Study Analysis of Factors Affecting the Scale
and Sustainability of Productivity Growth
Focus Geographies/Systems
Overview of Session (and Study) Framework and Sequence
Farming Systems • Spatial heterogeneity exists
• Common pattern across country border
• Concept of farming systems
• Bridge between macro (regional, national) and micro (household, pixel) analysis
• Identify pathways of technology adoption and agricultural productivity growth
• Design localized agri. development strategy and policy intervention based on sub-system
Farming Systems – cont’d
• Similarity in agricultural potential/ existing production pattern
• Definition: farmers, resources, interactions
• Biophysical, socio-economic and human elements interdependent
• Biophysical: land, water, forest, climate
• Human: demography
• Socio-economic : market access
Approach • Expand FAO definition of farming system
• Quantify factors affecting productivity of each farming system
•Agricultural activities
•Agricultural potential
•Population density
•Market access
•Nuance within each farming system
Methodology Spatial and Statistical Methods
1. Combine similar FAO farming systems
2. Sub-national spatial info
• Crop and livestock production
• Socio-economic indicators
3. Identify appropriate number of groups
4. Define groups within each farm system based on major agricultural activities
Data • Country X farming system X agricultural
potential
• Crop and livestock output value (SPAM and FAO international prices)
• Population density
• Market access
• Agricultural potential (NDVI)
6 Major Farming Systems Unique constraints and comparative advantages Farming system
Pop. density
Market access Population Crop area Livestock
per ha hours million million ha mill. coweq
Tree-root
crop 0.4 7.0 99.3 28.3 27.3
Forest based 0.1 10.5 43.1 5.1 5.5
Highlands 1.0 6.1 70.5 8.0 38.2
Cereal-root
crop 0.3 6.4 83.1 30.3 61.0
Maize mixed 0.3 7.9 91.0 16.9 46.7
Pastoral 0.2 9.6 83.2 33.0 77.4
Tree-Root Crop Farming System • Major activities • cassava
• sweet potato
• cocoa
• cattle
• banana/plantain
• rice
• maize
• groundnut
• goat/sheep
• Value share
goat/sheep groundnut maize
rice banana cattle
other cocoa sweetpotato
cassava
Tree-Root Crop Farming System West and Central Africa
• Statistics determine 3 distinctive groups
Sub-system
Dominant agri. activities
Population density
Agricultural potential
Market access
1
Maize + banana
+ cattle high medium medium
2
Rice + sweet
potato + cocoa medium high high
3 roots high high low
Forest-Based Farming System • Major activities: rice, sweet potato, cassava,
groundnut, banana/plantain, coffee, cattle, pig/chicken
Sub-system
Dominant agri. activities
Population density
Agricultural potential
Market access
1 Rice + cattle low high low
2
Cassava +
banana low high very low
3 Root + banana low high very low
4 Coffee high low very low
Highlands Farming System • Major activities: maize, pulses, sweet
potato, cassava, banana, cattle, sheep/goat
Sub-system
Dominant agri. activities
Population density
Agricultural potential
Market access
1
Maize + sweet
potato + livestock high medium medium
2
Cattle dominate
livestock very high medium medium
3 Maize + cattle high medium low
4 Roots + cattle high high medium
5
Pulse + sweet
potato + banana
extremely
high high medium
Cereal-Root Crop Farming System
• Major activities: rice, maize, sorghum/ millet, pulse, sweet potato, cassava, groundnut, cotton, cattle, sheep/goat
Sub-system
Dominant agri. activities
Population density
Agricultural potential
Market access
1 Cassava medium high medium
2 Cattle medium medium medium
3
sorghum/millet
+ groundnut +
cattle high medium medium
Pastoral Farming System • Major activities: maize, sorghum/millet,
pulse, cassava, groundnut, cattle, sheep/goat
Sub-system
Dominant agri. activities
Population density
Natural endowment (NDVI)
Market access
1 Cattle medium medium low
2
sorghum/millet +
pulse + cattle medium low high
3
Cattle dominate
livestock low medium very low
4
Maize + cassava
+ cattle low medium low
5
sheep/goat
dominant livestock
extremely
low low
extremely
low
Maize Mixed Farming System East and Southern Africa
• Major activities: maize, sorghum/millet, pulse, cassava, sugarcane, tobacco, cattle, sheep/goat
Sub-system
Dominant agri. activities
Population density
Agricultural potential
Market access
1
Maize + tobacco +
cattle medium high low
2 Tobacco + cattle medium medium medium
3 Sugarcane + cattle medium medium medium
4
Cattle dominate
livestock high medium low
Heterogeneity within a Country case of Ethiopia
• Identify comparative advantages
Farm system
Sub-system
Maize share
Sorghum / millet share
Cattle share
Sheep/ goat share Pop. den
Agricultural potential
Market access
Highlands 2 10.1 4.8 55.5 7.4 high high low
Cereal-root
crop 2 6.9 5.2 63.5 8.9 high medium
very
low
Maize
mixed 3 8.4 8.7 51.8 9.2 medium medium
very
low
Pastoral 1 9.9 13.7 46.9 7.9 medium medium low
Pastoral 5 4.0 25.3 17.4 47.5 medium high medium
Determinants of Agricultural Productivity Growth and
Economic Analysis of Alternative Strategies
Alejandro Nin Pratt
International Food Policy Research Institute
A. Regional Spatial Characterization of
Agricultural Productivity Opportunities &
Challenges
B. Key System Typologies for focusing productivity
efforts (e.g. country x farming system)
C. Representative Farm Analysis of Productivity
Enhancing Options
D. Case Study Analysis of Factors Affecting the Scale
and Sustainability of Productivity Growth
Focus Geographies/Systems
Overview of Session (and Study) Framework and Sequence
The Case of Maize
Maize-mixed,
39%
Cereal-root crop, 18%
Tree- root crop, 20%
Other, 23%
1) Identify predominant production systems grouping households with similar crops
Maize-
specializedBeans-maize
Permanent
crops-maize
Share in regional maize
production45% 10% 46%
Number of households 0.86 0.45 2.2
Share of maize in output
value77% 23% 25%
2) Identify groups of households within the previous groups that are different in their behavior and welfare under different scenarios
• Input use
• Assets
• Labor
• Sales and market access
Low
inputs
High
inputs
Low
inputs
High
inputs% over total households 18 3 47 6
Yield (Kgs/HA) 1,319 2,610 1,049 2,519
Value of inputs/HA 2.9 151 14 184
ASSETS
Area (HA) 1.3 1.5 1.86 2.44
Cow equivalents/HA 1 1.15 2.23 1.89
Value of equipment/HA 70 81 78 102
LABOR
Family work days 156 106 176 165
Hired work days 36 23 31 63
SALES
Maize sales as share of output % 18 24 11 10
Total sales/output value % 9 11 50 36
Maize-
specialized
Perm. Crops-
maize
3) Use this information in household models
• Simulate household behavior given – Available technologies for different crops and
livestock activities
– Cash constraint
– Labor constraint
– Land constraint
– Transaction costs
• Understand the importance of different constraints on household decisions
4) Link household models in an economy-wide model
• Analyze impact of different events on individual household decisions and the effect of these decisions on other households and the economy – Output prices in local, regional and national
markets
– Labor markets
– Consumption and demand
• Derive policy implications
Case Studies of Productive and Sustainable Agricultural
Investment Programs
Joseph Karugia and Stella Massawe
International Livestock Research Institute
A. Regional Spatial Characterization of
Agricultural Productivity Opportunities &
Challenges
B. Key System Typologies for focusing productivity
efforts (e.g. country x farming system)
C. Representative Farm Analysis of Productivity
Enhancing Options
D. Case Study Analysis of Factors Affecting the Scale
and Sustainability of Productivity Growth
Focus Geographies/Systems
Overview of Session (and Study) Framework and Sequence
Learning from successes and failures
• Positive or negative outcomes provide useful basis for learning.
• Incorporating lessons in the design and implementation of agricultural interventions-better quality
• How do we define success?
– Increase in yields, agricultural labour productivity, introduction of new higher-value enterprise
Framework for reviewing the case studies
SPATIAL VARIATION
Wei Wei Integrated project in Kenya • Initiated in 1987, outputs were:
– Construction of intake weir on the Wei Wei river;
– Laying of an underground steel and PVC pipeline network to distribute water through gravity-fed sprinkler irrigation units on each plot;
– Reclaiming and improving over 700 hectares of land; Setting up of a pilot farm of 50 hectares to provide logistical, equipment and other inputs support to the whole scheme;
– Developing and allocating 540 individual plots of 1 hectare each.
• The project has generated a number of benefits to the community:
– Crop yields, earnings and food security: maize and sorghum yields have increased from a paltry 0.5 tonnes/ha to 3.5 tonnes/ha and 4 tonnes/ha, respectively.
– New crops such as green grams, cow peas and okra were introduced.
Wei Wei Integrated project continued • The project has also created employment and income-generation
opportunities, either on the farms or through commerce
• Adoption of innovations, not only within the project area but also in those areas outside the project. The community members are expanding land under irrigation on their own initiative;
• Strengthening social capital through increased commercial activities. The farmers have also organized themselves into groups to negotiate for better prices for their produce.
• Lessons: Community involvement, introduced in an area with a tradition of irrigation, complementary investments, cost effectiveness of the irrigation approach used, capacity building, government support
Investment on Irrigation through ASDP in Tanzania
2006 2009
Average Rice yields in
irrigation schemes (t/ha)
1.8 to 2.0 4.0 to 5.0
Rice yields in Mbeya 1.5 2.0-2.5
Rice yields in Morogoro 1.5 5
Rice yields in Manyara 1.5 6
Maize yield in Siha 0.7 3.5-4.5
Onions From one season per year Three seasons per year.
Each season 60 bags
• Since 2006, rehabilitated old irrigation schemes and constructed some new ones
• As a result of the schemes, the area under irrigation increased from 264,388 ha in the year 2006/2007 to 317,245 ha in 2010 (20 % increase)
Factors for success: involvement of the farmers, government support, complimentary investments
Bura Irrigation Scheme in Kenya
• In Tana River District, started in 1981 production of cotton, maize and groundnuts, vegetables
• No cash crops planted for 15 years (from 1990-2005), no subsistence crops for 9 years (1994-2002): frequent breakdowns of the Nanighi Pumping Station or lack of adequate funds to operate the pumping units, lack of water
• Famine, increased poverty levels and unemployment for the Scheme farmers and community; at some point, farmers at the project were relying on famine relief food supplies.
• The irrigation canal network was heavily silted up covered by bushes
• Management challenges, several changes. In 2005, the Scheme was taken over by NIB
Hifadhi Ardhi Dodoma (HADO) in Tanzania • Soil rehabilitation in Kondoa District; very deep gullies
• The objective was to reclaim degraded lands and improve agricultural and livestock keeping productivity by primarily enabling the local farmers to adopt effective land husbandry practices.
• Specific objectives: i) Ensure self-sufficient in wood requirements; ii) Encourage communal wood-growing schemes in the region; iii) Promote communal bee keeping and other income generating activities; iv) Encourage the establishment of shelter belts, windbreaks, shade trees, avenues and fruit tree growing; v) Conserve soil and water and to reclaim depleted land.
• The approach was top-down with little real participation of the local people in planning and implementing project activities. It emphasized cattle de-stocking, soil conservation measures such as contour banking and tree planting for shelterbelts, agro forestry and village woodlots.
• In severely eroded areas, cattle were excluded, effectively evicting their owners as well.
HADO Cont’d • The HADO programme did demonstrate that restoration of
vegetative cover on some degraded semi-arid lands is possible.
• No baseline study was carried out at the beginning of the project, consequently, no basis for comparison
• Though large areas were conserved, the project was criticized for relocating people.
• Lessons: HADO project was a failure, mainly because:
– Like the earlier efforts in the colonial period, HADO was a top-down and technocratic project with little real participation by the local people in setting goals or in designing and implementing the project;
– A multi-disciplinary approach was not used, so forestry technical staff did all rehabilitation work
– Through the eviction of farmers the project exported problems elsewhere.
Key Messages • Proper targeting: correct intervention for the Farming system?
• Involvement of the local communities and appropriate partnerships
• Correct implementation strategies: Avoid extreme actions drastic measures, targeting issues
• Invest in capacity; financial, technical, managerial
• Ensure supporting policy and institutional environment
• Complementary interventions
• Conditions for sustainability
Next Steps
A. Regional Spatial Characterization of
Agricultural Productivity Opportunities &
Challenges
B. Key System Typologies for focusing productivity
efforts (e.g. country x farming system)
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
Overview of Session (and Study) Framework and Sequence
Some Discussion Points • How can we improve the analysis
implementable results? – Data, methods, …
• What are key case studies (specific agricultural productivity) investment programs to learn from – both successful and not-successful?
• …