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Priority assessment process and linking with IDOs and business cases
1. Priority assessment, process and
linking with IDOs and business cases
RTB Annual Review and Planning Meeting 2014
29th Sept to 3rd Oct, Entebbe, Uganda
2. Task Force Permanent Members
Bioversity: Diemuth Pemsl, Charles Staver
CIAT: Bernardo Creamer, Glenn Hyman
CIP: Guy Hareau, Ulrich Kleinwechter
IITA: Tahirou Abdoulaye, Arega Alene,
Joseph Rusike, Holger Kirscht
RTB: Graham Thiele
3. Outline of presentation
• Status of RTB priority assessment (PA)
– Overview of six steps methodology
– Summary of key outputs and results
• Lessons learnt from the PA exercise
– What went/worked well? How to improve?
• Next steps: outlook on 2014/15 activities
– Communication strategy for priority assessment
– Ex post impact assessment studies
• Discussion
– Stakeholder feedback
– Data bases including RTB maps
• Linking with IDOs and business cases
• Discussion: Application of PA outputs and results
5. Key outputs of priority assessment
• Expert survey (N = 1,681) to identify key constraints
with input from diverse, international group of stakeholders
• Priority research options selected
based on expert survey results & input from RTB resource persons
• Collection of data/information for model parameters
from statistics, existing data sets and group of technical experts
• Economic surplus (ES), poverty and cost-benefit
analysis for a total of 31 research options
with harmonized assumptions and methods across all five crops
• 10 RTB working papers completed
Expert survey & ES report for each crop
• RTBMaps developed as cooperation of RTB Centers
interactive online tool providing geographic information to the
research and development community of roots, tubers and bananas
6. How many participated:
Expert survey on key constraints:
- Bananas
- Cassava
- Potato
- Sweet potato
- Yam
Research options and model parameters:
- Bananas
- Cassava
- Potato
- Sweet potato
- Yam
7. All reports uploaded to RTB webpage
Online comment function are available!
http://www.rtb.cgiar.org/category/resources/working-papers/
8. Editors’ Choice Winner
ComputerWorld 2014
Special Achievement in GIS Award
ESRI International Conference 2013
9. Potato expert survey results: top 10
Notes: Experts scored research activities on a five point scale: 1 = not important, 2 = somewhat important,
3 = important, 4 = very important, 5 = extremely important:
LAC: Latin America and Caribbean (N=140), SSA: Sub-Saharan Africa (N=79), ESEA: East and Southeast Asia (N=121),
SWCA: South, West and Central Asia (N=61);
Share of female respondents: 22%
10. Priority assessment: Summary of results
Expert survey
• Contact lists of crop experts/stakeholders
• Major constraints and opportunities by crop
• Ranking of different research options for each crop
(results can be explored on global or regional level, by ecosystem, country
or in the case of banana by cultivar group or cropping system)
• Rich data set available for further applications
Selection of research options / model parameters
Different approaches for different crops
• Banana: Kampala workshop with 45 ARI and national
banana scientists to select ROs and initial parameters
• Banana: final parameters by few CG experts
• Cassava and yam – expert consultation CG based
• Potato / sweet potato – similar options as Fuglie + CIP
consultation
11. Priority assessment: Summary of results
Economic surplus model and poverty effect
Total of 31 research options (RO) assessed; # RO by crop as follows:
Banana - 6; Cassava - 10; Potato - 6; Sweetpotato - 4, Yams - 5
• Data base of parameters (country and technology specific)
• Positive net present values (NPVs) for all RO
• IRRs of assessed ROs well beyond 10% benchmark
• Results indicate considerable poverty reduction
13. Yam results: adoption ceiling & benefits
Note: Lower adoption scenario: analysis with 50% lower adoption ceiling.
Net Present Values (NPV) calculated using a real interest rate of 10%.
14. Cassava: beneficiaries & poverty effect
Note: Lower adoption scenario: analysis with 50% lower adoption ceiling. Poverty reduction
computed using on NPV (10% interest rate), national poverty incidence, share of agriculture on GDP
(%), population, region specific elasticity (see Alene et al. 2009).
16. Extending economic surplus analysis:
estimation of DALY benefits for OFSP
Note: Lower adoption scenario: analysis with 50% lower adoption ceiling.
NPV calculated using an interest rate of 10%.
17.
18. Lessons learnt: what went well?
• Successful application of priority assessment
approach in a multi-Center and multi-crop context
(consistent methodology and same set of outputs for each crop)
• Generated valuable information to guide strategic
decision making and inform RTB target setting
(rich and detailed data set from expert surveys; data compiled for
economic surplus analysis both can be further explored)
• Participation of a global group of stakeholders (large
scale expert surveys, workshops and groups of resource persons)
• Capacity building of RTB scientists in ex ante impact
assessment approaches (crucial especially for Centers with
no previous experience in systematic priority assessment)
19. Lessons learnt: how to improve?
• Careful cross calibration of parameter estimates
e.g. to ensure same levels of optimism about scale of adoption and
magnitude of effects such as yield increase or cost reductions
• Expand depth and breadth of impact modeling
- incorporate additional methods (e.g. DALY for health impacts)
- compare different models (e.g. IMPACT) for other indicators
- develop methodology to incorporate gender aspect/impacts
• Harmonize & integrate with other RTB activities
- Link data collection from field trials and M&E with PA data needs
- Better link development of RTB flagships and PA
• Consistent/continuous involvement of stakeholders
- stakeholder feedback on parameter estimates
- future use and updating of RTBMaps and Banana mapper
- encourage involvement of RTB scientists in (next) PA
20. Next steps: Activities for 2014
• Completion of priority assessment work:
– Review of RTB working papers
http://www.rtb.cgiar.org/category/resources/working-papers
• In-house: pdfs available on RTB webpage
(online comment function will be added soon)
• Peer-review by selected independent (crop) experts
– Synthesis report
• Develop and implement PA communication strategy
together with RTB communication unit
– Sharing with and feedback from stakeholders
e.g. SROs (CORAF,ASARECA, IICA, etc.), banana networks,
RTB meetings and webpage, social media
– Publications: RTB working papers, journal papers
21. Next steps: an integrated approach
for RTB ex post impact assessment
PMU supporting impact studies for 2014/15:
– Re-engaging stakeholders: leveraging priority assessment and
network analysis for more effective outcomes and impact of
RTB research (Bioversity, cooperation with ILAC and
coordination with RTB communication strategy)
– Use of DNA finger-printing and improved measures of on-farm
yield gains to assess poverty impacts of cassava in sub-Saharan
Africa (Nigeria), Asia and Latin America (Colombia) (CIAT, IITA,
coordination with BMGF)
– Outcomes of crop germplasm improvement research: cassava,
potatoes and sweetpotatoes varietal release & adoption in Asia
(China, India, 12 other countries) (CIP, CIAT, coordination with
SIAC/MSU/SPIA)
Taskforce will develop and apply common methods & databases
22. Key points for discussion
• How to engage stakeholders:
- Appropriate communication strategy, to whom and
about what
feedback on parameters
informing research priorities and regional policy
in what languages?
- More applications/use of priority assessment
outputs by stakeholders
• RTB crop maps and other data bases – link to other
uses and build for future use
23. Linking with IDOs and business cases
Assessed banana research options and links to RTB flagships
24. RTB impact pathway and links with PA
Research
products Research
outcomes
Flagships and
linked products Next Users
IDOs
First level
development
outcomes
End Users
Intermediate
development
outcomes
System level
development
outcomes
RTB – Impact pathways
Adoptable
innovations
( = outputs) of
research options
Adoption
estimates
Estimates of:
economic surplus,
poverty reduction;
nutrition and
health benefits
Benefit estimates:
Changes in costs,
productivity,
income, health,
etc.
RTB Priority Assessment
25. Key points for discussion
• What will be the role of priority assessment in the
development of the second phase?
• When do we do the next priority assessment?
• What are key areas for improvement for the next
time and how to we build the data bases?
Shall we highlight names of people who have left? And/or include Ricardo?
Introduce the “success indicators” NPV and IRR, as well as adoption ceiling or number of beneficiaries reached
Results can be used to “test viability” of a research project (positive returns?) and to compare different alternatives if resources are limited.
Results obviously determined by parameters used (garbage in – garbage out?!) BUT: once model structure exists, parameters can be modified if more/better information/data become available. Parameter tables need to be provided together with results so that stakeholders can see what assumptions went into the assesssment. Results are “to the best of our current knowledge” – improvement over less structured or ad hoc decisions for allocation of research funds.
Estimated number of households and persons who will benefit from each of the research options is based on adoption ceilings and assumptions on average crop area per HH and number of persons per HH (following assumptions used in RTB proposal; roughly 0.2 to 1 ha of banana area and some 4-6 family members – different for each country included in the assessment). These figures are determined by the adoption ceiling in each of the countries, the number of countries included, and the production area within those countries. Similar to the NPV results, this information should be interpreted with respect to the different magnitude of the investments required/assumed across research options.
For the calculation of the estimated poverty reduction effects of the different research options we followed the methodology described in Arega et al. (2009), which predicts poverty reduction based on estimated growth in the agricultural sector of a given country. To this end, the NPV of the benefits generated by the surplus model is interpreted as agricultural growth. The poverty model uses the country-specific poverty level, the relative size of the agricultural sector (% GDP from agriculture), and the total national population as inputs. Country-level data were obtained from the World Bank’s World Development Indicators database
Estimated number of households and persons who will benefit from each of the research options is based on adoption ceilings and assumptions on average crop area per HH and number of persons per HH (following assumptions used in RTB proposal; roughly 0.2 to 1 ha of banana area and some 4-6 family members – different for each country included in the assessment). These figures are determined by the adoption ceiling in each of the countries, the number of countries included, and the production area within those countries. Similar to the NPV results, this information should be interpreted with respect to the different magnitude of the investments required/assumed across research options.
For the calculation of the estimated poverty reduction effects of the different research options we followed the methodology described in Arega et al. (2009), which predicts poverty reduction based on estimated growth in the agricultural sector of a given country. To this end, the NPV of the benefits generated by the surplus model is interpreted as agricultural growth. The poverty model uses the country-specific poverty level, the relative size of the agricultural sector (% GDP from agriculture), and the total national population as inputs. Country-level data were obtained from the World Bank’s World Development Indicators database
Estimated number of households and persons who will benefit from each of the research options is based on adoption ceilings and assumptions on average crop area per HH and number of persons per HH (following assumptions used in RTB proposal; roughly 0.2 to 1 ha of banana area and some 4-6 family members – different for each country included in the assessment). These figures are determined by the adoption ceiling in each of the countries, the number of countries included, and the production area within those countries. Similar to the NPV results, this information should be interpreted with respect to the different magnitude of the investments required/assumed across research options.
For the calculation of the estimated poverty reduction effects of the different research options we followed the methodology described in Arega et al. (2009), which predicts poverty reduction based on estimated growth in the agricultural sector of a given country. To this end, the NPV of the benefits generated by the surplus model is interpreted as agricultural growth. The poverty model uses the country-specific poverty level, the relative size of the agricultural sector (% GDP from agriculture), and the total national population as inputs. Country-level data were obtained from the World Bank’s World Development Indicators database