"Strategies for Raising and Sustaining High Agricultural Productivity in Africa", presented at Agricultural Productivity and Food Security in Africa Conference, Addis Ababa,1-3 November 2011

1. 1-3 November 2011
UNECA, Addis Ababa
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

2. Strategic Analysis and Monitoring of CAADP
ReSAKSS organized around and Agricultural Performance in Africa
4 nodes of operation
Knowledge Management, Capacity
Strengthening, and Policy Communications
support
review and
dialogue
evidence- and outcome-based planning and
implementation of agricultural-sector policies
and strategies in Africa

3. 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

4. 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?

5. 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

6. 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

7. Land and Labor Productivity in SSA, 1961-2009
Land productivity (2004-06 US$
PPP)
Labor productivity (2004-06 US$ PPP)
SSA as a whole: labor productivity >> land productivity; but
land productivity increased much faster, more than tripled

8. As expected, different picture when
consider different sub-regions of Africa
Eastern &
Land productivity (2004-06 US$
Central SSA
Western
PPP)
Southern
Labor productivity (2004-06 US$ PPP)

9. Again, different picture when consider
different countries
Land productivity (2004-06 US$ Ethiopia,
1993-2009
Nigeria
Kenya
PPP)
South Africa
Labor productivity (2004-06 US$ PPP)

10. 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
Total Output
• TFP =
Total Inputs
• Agricultural growth in the long run depends on TFP
– Efficiency: reallocation of productive factors
– Technical change: technological advancement

11. TFP growth in SSA
Two different periods: both driven more by
efficiency change than technical change
1.01
1
TFP levels 1970=1
0.99
0.98
0.97
0.96
0.95
1970 1975 1980 1985 1990 1995 2000 2005
Growth Rate (%)
TFP components 1970-1984 1985-1994 1995-2009
Efficiency change -0.28 0.07 0.15
Based on Technical change -0.03 0.05 0.10
FAOSTAT TFP -0.32 0.12 0.25

12. More workers; and
Less land and inputs per worker
Yield Labor productivity TFP
2
1.8
TFP (green)
1.6
Index 1970=1
1.4 Yield (blue)
1.2
1 Labor productivity (red)
0.8
1970 1975 1980 1985 1990 1995 2000 2005
Inputs/Ha Inputs/Worker HA/worker
2
1.8
1.6 Inputs per hectare (brown)
1.4
1.2
1
Inputs per worker (yellow)
0.8
0.6 Land-labor ratio (pink)
0.4
1970 1975 1980 1985 1990 1995 2000 2005

13. Livestock, root crops, and oil crops explain
more than 60% of output growth in 1995-2009
Contribution to growth Share in output
30%
25%
20%
15%
10%
5%
0%

14. Best performing countries
(annual average growth rates, 1995-2009)
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

15. 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?

16. 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

17. Overview of Session (and Study) Framework and Sequence
A. Regional Spatial B. Key System Typologies
Characterization of for focusing productivity
Agricultural Productivity efforts (e.g. country x
Opportunities & farming system)
Challenges
Focus Geographies/Systems
D. Case Study Analysis of
C. Representative Farm
Factors Affecting the Scale
Analysis of Productivity
and Sustainability of
Enhancing Options
Productivity Growth

18. Spatial Dimensions of
Agricultural Productivity
Zhe Guo and Stanley Wood
HarvestChoice
International Food Policy Research Institute
z.guo@cgiar.org

19. 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.

20. Spatial variables influencing productivity
• Agricultural potential
• Footprint of agriculture
• Market access
• Demographics
• Human welfare

21. Agricultural potential
Rainfall & Length of Growth Period
Long term average of annual rainfall Length of growth period

22. Agricultural potential
Normalized Difference Vegetation Index & Potential Yield
7
Maize
Yield
Potential 6
t[DM]/ha
5
4
3
2
40
1 30
20
0 10
Irrigation
0
100 Threshold
80 NA % of Available
60
40
Fertilizer Application Rate 20 Soil Water
0
kg[N]/ha
Long term average of NDVI Simulated potential yield

23. Footprint of agriculture
Crop Land & Pasture Land
Cropland density Pasture land density

24. Footprint of Agriculture
Farming System & Crop
Farming systems Maize harvested area

25. Footprint of Agriculture
Productivity Constraints
Aluminum toxic Drought severity

26. Market Access
Travel time to major settlements
Travel time to market with population Travel time to market with
greater than 20,000 population greater than 500,000

27. Market Access
Travel Time to Ports
Travel time to major ports Major port command area

28. Demographics
Population
Population density (GRUMP 2000) Population density Landscan 2009

29. Human Welfare
Poverty & Global Hunger Index
Absolute number of poor Global Hunger Index
living under $1.25 per day

30. Flexible approach to spatial aggregation and analysis
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, %)
TRAVEL TIME TO CLOSEST PORT (hours) E S W Total
FS_NAME perennial
Highland E S 34.0 W Total 34.0
Forest based
Coastal artisanal fishing 15 14.0
22 26.0
15 15.0
15 16.0
Tree crop
Large commercial_smalholder 13.0
19 37.0 9.0
19 12.0
Tree crop Highland temperate mixed 17 13.0
16 11.0
20 8.0
19 11.0
Maize mixed
Highland temperate mixed 26 17.0
18 6.0
19 6.0
21 11.0
Rice-Tree crop 26 26

31. Example of Potential Regional
Development Strategies
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
Source: ASARECA Strategy. Omamo et al. 2006

32. 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

33. A Typology of
Agricultural Productivity Zones
Bingxin Yu
International Food Policy Research Institute
b.yu@cgiar.org

34. Overview of Session (and Study) Framework and Sequence
A. Regional Spatial B. Key System Typologies
Characterization of for focusing productivity
Agricultural Productivity efforts (e.g. country x
Opportunities & farming system)
Challenges
Focus Geographies/Systems
D. Case Study Analysis of
C. Representative Farm
Factors Affecting the Scale
Analysis of Productivity
and Sustainability of
Enhancing Options
Productivity Growth

35. 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

36. 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

37. 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

38. 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

39. 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)

40. 6 Major Farming Systems
Unique constraints and comparative advantages
Farming Pop. Market
system density 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

41. Tree-Root Crop Farming System
• Value share • Major activities
• cassava
• sweet potato
• cocoa
• cattle
• banana/plantain
• rice
goat/sheep groundnut maize • maize
rice banana cattle
other cocoa sweetpotato • groundnut
cassava • goat/sheep

42. Tree-Root Crop Farming System
West and Central Africa
• Statistics determine 3 distinctive groups
Sub- Dominant agri. Population Agricultural Market
system activities density potential access
Maize + banana
1 + cattle high medium medium
Rice + sweet
2 potato + cocoa medium high high
3 roots high high low

43. Forest-Based Farming System
• Major activities: rice, sweet potato, cassava,
groundnut, banana/plantain, coffee, cattle,
pig/chicken
Sub- Dominant agri. Population Agricultural Market
system activities density potential access
1 Rice + cattle low high low
Cassava +
2 banana low high very low
3 Root + banana low high very low
4 Coffee high low very low

44. Highlands Farming System
• Major activities: maize, pulses, sweet
potato, cassava, banana, cattle, sheep/goat
Sub- Dominant agri. Population Agricultural Market
system activities density potential access
Maize + sweet
1 potato + livestock high medium medium
Cattle dominate
2 livestock very high medium medium
3 Maize + cattle high medium low
4 Roots + cattle high high medium
Pulse + sweet extremely
5 potato + banana high high medium

45. Cereal-Root Crop Farming System
• Major activities: rice, maize, sorghum/
millet, pulse, sweet potato, cassava,
groundnut, cotton, cattle, sheep/goat
Sub- Dominant agri. Population Agricultural Market
system activities density potential access
1 Cassava medium high medium
2 Cattle medium medium medium
sorghum/millet
+ groundnut +
3 cattle high medium medium

46. Pastoral Farming System
• Major activities: maize, sorghum/millet,
pulse, cassava, groundnut, cattle, sheep/goat
Sub- Dominant agri. Population Natural Market
system activities density endowment (NDVI) access
1 Cattle medium medium low
sorghum/millet +
2 pulse + cattle medium low high
Cattle dominate
3 livestock low medium very low
Maize + cassava
4 + cattle low medium low
sheep/goat extremely extremely
5 dominant livestock low low low

47. Maize Mixed Farming System
East and Southern Africa
• Major activities: maize, sorghum/millet, pulse,
cassava, sugarcane, tobacco, cattle, sheep/goat
Sub- Dominant agri. Population Agricultural Market
system activities density potential access
Maize + tobacco +
1 cattle medium high low
2 Tobacco + cattle medium medium medium
3 Sugarcane + cattle medium medium medium
Cattle dominate
4 livestock high medium low

48. Heterogeneity within a Country
case of Ethiopia
• Identify comparative advantages
Sorghum Sheep/
Sub- Maize / millet Cattle goat Agricultural Market
Farm system system share share share share Pop. den potential access
Highlands 2 10.1 4.8 55.5 7.4 high high low
Cereal-root very
crop 2 6.9 5.2 63.5 8.9 high medium low
Maize very
mixed 3 8.4 8.7 51.8 9.2 medium medium 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

49. Determinants of Agricultural
Productivity Growth and
Economic Analysis of Alternative
Strategies
Alejandro Nin Pratt
International Food Policy Research Institute
a.ninpratt@cgiar.org

50. Overview of Session (and Study) Framework and Sequence
A. Regional Spatial B. Key System Typologies
Characterization of for focusing productivity
Agricultural Productivity efforts (e.g. country x
Opportunities & farming system)
Challenges
Focus Geographies/Systems
D. Case Study Analysis of
C. Representative Farm
Factors Affecting the Scale
Analysis of Productivity
and Sustainability of
Enhancing Options
Productivity Growth

51. The Case of Maize
Other, 23% Maize-
mixed,
39%
Tree- root
crop, 20%
Cereal-root
crop, 18%

52. 1) Identify predominant production
systems grouping households with
similar crops
Maize- Permanent
Beans-maize
specialized crops-maize
Share in regional maize
45% 10% 46%
production
Number of households 0.86 0.45 2.2
Share of maize in output
77% 23% 25%
value

53. 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

54. Maize- Perm. Crops-
specialized maize
Low High Low High
inputs inputs inputs 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

55. 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

56. 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

57. Case Studies of Productive
and Sustainable Agricultural
Investment Programs
Joseph Karugia and Stella Massawe
International Livestock Research Institute
s.massawe@cgiar.org

58. Overview of Session (and Study) Framework and Sequence
A. Regional Spatial B. Key System Typologies
Characterization of for focusing productivity
Agricultural Productivity efforts (e.g. country x
Opportunities & farming system)
Challenges
Focus Geographies/Systems
D. Case Study Analysis of
C. Representative Farm
Factors Affecting the Scale
Analysis of Productivity
and Sustainability of
Enhancing Options
Productivity Growth

59. 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

60. Framework for reviewing SPATIAL
VARIATION
the case studies

61. 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.

62. 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

63. Investment on Irrigation through ASDP in Tanzania
• 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)
2006 2009
Average Rice yields in 1.8 to 2.0 4.0 to 5.0
irrigation schemes (t/ha)
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
Factors for success: involvement of the farmers, government support, complimentary
investments

64. 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

65. 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.

66. 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.

67. 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

68. Next Steps

69. Overview of Session (and Study) Framework and Sequence
A. Regional Spatial B. Key System Typologies
Characterization of for focusing productivity
Agricultural Productivity efforts (e.g. country x
Opportunities & farming system)
Challenges
Focus Geographies/Systems
Strategic
Opportunities for
D. Case Study Analysis of
Productivity C. Representative Farm
Factors Affecting the Scale
Enhancing Policies & Analysis of Productivity
and Sustainability of
Enhancing Options
Investments Productivity Growth

70. 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?
• …