Scientific and Technical Partnerships in Africa: Technologies, Platforms, and Partnerships in support of the African agricultural science agenda, Abidjan, Cote d'Ivoire, April 4&5, 2017

1. Assessment of the sectoral effects of selected
CGIAR technologies
Technologies, Platforms and Partnerships in support of the African agricultural science agenda
Abidjan, Cote d’Ivoire / April 4 and 5, 2017
Mark W. Rosegrant
Director, Environment and Production Technology Division
International Food Policy Research Institute

2. Outline
 Monitoring and Evaluation Approaches
 Technology Assessment
 Modeling Systems
 Country Case Studies

3. Production levels
Farm incomes
Trade flows
Nonfarm jobs
Value-addition
etc.
Project Indicators
Value chain
 Farm households are targeted
beneficiaries
 Conduct ex-post survey
• Ideally RCTs
 Limitations:
• Once-off evaluation
• Vulnerable to external shocks (esp. if
not RCT)
• Expensive (esp. RCT)
• Does not measure AFS changes
(only farmer or VC outcomes)
Technology (e.g. fertilizer)
Infrastructure (e.g. irrigation)
Finance (e.g. micro-loans)
Investment Project
Farmer
Traditional M&E Approach

4. Production levels
Farm incomes
Trade flows
Nonfarm jobs
Value-addition
etc.
Project Indicators
National economy
Agri-food system
Value chain
Technology (e.g. fertilizer)
Infrastructure (e.g. irrigation)
Finance (e.g. micro-loans)
Investment Project
Price policy (e.g. tariffs)
Regulation (e.g. food safety)
Public investment (e.g. roads)
Project’s Policy Change
System Indicators
GDP
Employment
Poverty
Nutrition
Resource depletion
etc.
Farmer
Moving to System-Level M&E

5.  Establish a “business-as-usual” baseline
• Expected outcomes based on recent historical trends
• Continuation of past public and donor investments
• Projected world prices for agricultural outputs and inputs
• Average weather patterns
Year 0 Year 2 Year 5
Business-as-usual outcomes
Technology Assessment

6.  Estimate expected impacts under proposed GFSS program
• Project: greater use of fertilizer, seeds and irrigation; improved coverage and quality of
advisory services (extension); reduced marketing costs, etc.
• Policy: Fewer price distortions; increased public investments, etc.
• Identify a set of progress indicators to track (e.g., prices, production, etc.)
• Estimate expected program benefits/impacts
Year 0 Year 2 Year 5
Business-as-usual outcomes
Outcome with new
technology
Technology Assessment

7. Modeling System
Economywide Impacts
GLOBE/RIAPA CGE models
Agriculture and Natural
Resources
IMPACT ag water/land models
Crop yields,
profitability
Economic Outcomes
e.g., GDP, jobs, poverty,
Crop Technologies
Spatial DSSAT/DREAM models
Technology (seeds, fert.)
Infrastructure (irrigation)
Farm management
Farm Investment
Market development
Infrastructure (roads)
Price policy (subsidies)
Policy Change
Natural Resource Outcomes e.g.,
ag production, demand, prices and
trade, land and water resource
depletion, GHG emissions, etc.

8.  DSSAT crop models
• High resolution (10km x 10km)
 Information inputs
• Soil types, weather
 Options
• Fertilizers, seeds, irrigation, farm management
 Outputs
• Simulated crop yields for different permutations
of technologies
• Variation in outcomes under historical weather
patterns
• Results for zones of influence and economic
spatial units
Local seeds,
20kg N/ha
Hybrids,
40kg N/ha
Sub-optimal
management
Optimal
management
Example: Maize yields under different
technologies and farm management
Farm Investment Analysis

9. Market Accessibility
Improved road and logistics
infrastructure will be critical to
feeding the growing and increasingly
urbanized markets throughout SSA.
 98% of SSA’s population lives within one day
of travel to a city of 20K+ people, yet just
55% of the population can reach these
markets in 3 hours or less.
 50% of crop value is located beyond 3 hours
of travel.
Socio-economic constraints
Sources: Guo Z., and Cox C. (2014). Market Access. In K. Sebastian (Ed.), Atlas of African Agriculture Research & Development.
http://dx.doi.org/10.2499/9780896298460_28; Joglekar, A.B., Z. Guo and J.M. Beddow. 2016. “Travel Time to Agricultural
Markets in Sub-Saharan Africa v2: Technical Documentation”. HarvestChoice Working Paper. IFPRI and InSTePP.; Joglekar, A.B. and
P.G. Pardey. 2016. “Proximity to African Agricultural Markets, Down to the Last Kilometer.” HarvestChoice Brief. InSTePP.

10.  New Production Technologies
• Chicken (eggs and meat)
• Beans
• Rice
 IMPACT Model
 Climate Change scenario is used
 Simulation period – 15 years (2015-2030)
 Final changes in national productivity and projected total production
– based on yield increases of new technologies and adoption rates
Sectoral Impacts of Case Study Technologies

11.  Projected indicators
• Total production – shows the impact of new technology on domestic
production based on level of adoption and productivity improvement of the
technology
• Per capita production – shows whether the growth in production outstrips
growth in population
• Net trade – shows the impact of the technologies on trade situation of the
country
Results

12. Technology:
Chicken and Egg Production in Ethiopia
 Productivity increase:
• Egg Production = 20.8%
• Meat Production = 18.7%
 Adoption rate in 15 years:
• Moderate = 40%
• High = 60%

13. Ethiopia
 African chicken genetic gains
• Age at first egg (early producers)
• Egg production (egg productivity)
• Body weight (meat productivity)
• Survival (resilience against disease, environmental stresses)

14. 0
10
20
30
40
50
60
2000 2005 2010 2015 2020 2025 2030
000mt
Years
Production
Historical
Climate Change
Moderate Adoption
High Adoption
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
2000 2005 2010 2015 2020 2025 2030
kg/capita/yr
Years
Per Capita Production
Historical
Climate Change
Moderate Adoption
High Adoption
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
2010 2015 2020 2025 2030
000mt
Years
Net Trade
Historical
Climate Change
Moderate Adoption
High Adoption
Ethiopia: Egg

15. 0
20
40
60
80
100
120
140
2000 2005 2010 2015 2020 2025 2030
000mt
Years
Production
Historical
Climate Change
Moderate Adoption
High Adoption
0.00
0.20
0.40
0.60
0.80
1.00
1.20
2000 2005 2010 2015 2020 2025 2030
kg/capita/yr
Years
Per capita Production
Historical
Climate Change
Moderate Adoption
High Adoption
-25
-20
-15
-10
-5
0
2010 2015 2020 2025 2030
000mt
Years
Net Trade
Historical
Climate Change
Moderate Adoption
High Adoption
Ethiopia: Chicken meat

16. Beans in Africa
 Early maturity (for drought escape and to ‘fill the hunger
gap’)
 Marketability (for both domestic and export markets)
 Cooking time as well as taste (shorter cooking time for
beans is an important, consumer-preferred trait)

17. Technology: Bean Production
 Countries: Malawi, Rwanda
 Productivity increase:
• Moderate increase = 40%
• High increase = 50%
 Adoption rate in 15 years:
• Moderate adoption = 40%
• High adoption = 60%

18. 0
50
100
150
200
250
300
2000 2005 2010 2015 2020 2025 2030
000mt
Years
Production
Historical
Climate Change
Moderate Adoption
High Adoption
0
1
2
3
4
5
6
7
8
9
10
2000 2005 2010 2015 2020 2025 2030
kg/capita/yr
Years
Per Capita Production
Historical
Climate Change
Moderate Adoption
High Adoption
-100
-80
-60
-40
-20
0
20
40
2000 2005 2010 2015 2020 2025 2030
000mt
Years
Net Trade
Historical
Climate Change
Moderate Adoption
High Adoption
Malawi: Bean

19. 0
100
200
300
400
500
600
700
2000 2005 2010 2015 2020 2025 2030
000mt
Years
Production
Historical
Climate Change
Moderate Adoption
High Adoption
0
5
10
15
20
25
30
35
40
2000 2005 2010 2015 2020 2025 2030
kg/capita/yr
Years
Per Capita Production
Historical
Climate Change
Moderate Adoption
High Adoption
-250
-200
-150
-100
-50
0
50
2000 2005 2010 2015 2020 2025 2030
000mt
Years
Net Trade
Historical
Climate
Change
Moderate
Adoption
Rwanda: Bean

20.  High yielding and stable varieties over the years and across environments
 Disease resistance/tolerance
 Upland ecology: Early maturing, drought tolerant, weed competitiveness
 Rainfed and irrigated ecology: Early to medium maturity, iron toxicity
tolerant, submergence tolerant, extreme temperatures
 Mangrove and saline prone irrigated ecology: Early to medium
maturity, salt tolerant, submergence tolerant
 High elevation: Early to medium maturity, cold tolerant
Rice in Africa

21. Technology: Rice Production
 Countries: Cote d’Ivoire, Senegal, Tanzania
 Productivity increase = 30%
 Adoption rate in 15 years:
• Moderate adoption = 48%
• High adoption = 60%

22. 0
500
1,000
1,500
2,000
2,500
3,000
3,500
2000 2005 2010 2015 2020 2025 2030
000mt
Years
Production
Historical
Climate Change
Moderate Adoption
High Adoption
0
20
40
60
80
100
120
2000 2005 2010 2015 2020 2025 2030
Kg/capita/year
Years
Per Capita Production
Historical
Climate Change
Moderate Adoption
High Adoption
-1,200
-1,000
-800
-600
-400
-200
0
200
400
600
800
1,000
2000 2005 2010 2015 2020 2025 2030
000mt
Years
Net Trade
Historical
Climate Change
Moderate Adoption
Cote d’Ivoire: Rice

23. 0
100
200
300
400
500
600
700
800
2000 2005 2010 2015 2020 2025 2030
000mt
Years
Production
Historical
Climate Change
Moderate Adoption
High Adoption
0
5
10
15
20
25
30
35
40
45
2000 2005 2010 2015 2020 2025 2030
Kg/capita/year
Years
Per Capita Production
Historical
Climate Change
Moderate Adoption
High Adoption
-1,200
-1,000
-800
-600
-400
-200
0
2000 2005 2010 2015 2020 2025 2030
000mt
Years
Net Trade
Historical
Climate Change
Moderate Adoption
High Adoption
Senegal: Rice

24. 0
500
1,000
1,500
2,000
2,500
3,000
3,500
2000 2005 2010 2015 2020 2025 2030
000mt
Years
Production
Historical
Climate Change
Moderate Adoption
High Adoption
0
5
10
15
20
25
30
35
40
45
50
2000 2005 2010 2015 2020 2025 2030
Kg/capita/year
Years
Per Capita Production
Historical
Climate Change
Moderate Adoption
High Adoption
-400
-200
0
200
400
600
800
1,000
1,200
1,400
1,600
2000 2005 2010 2015 2020 2025 2030
000mt
Years
Net Trade
Historical
Climate Change
Moderate Adoption
High Adoption
Tanzania: Rice

25. Conclusions
 Developing an integrated framework for assessment of the
impacts of technologies and policies from farm to national
and global levels
 Can assist in prioritizing technology development and
scaling under the Science Agenda
 Results show the significant impact of the case study
technologies at the sectoral level in several countries