Humid Tropics: How Humid Tropics approaches regional research and selects research sites - Kwesi Atta Krah
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Humid Tropics: How Humid Tropics approaches regional research and selects research sites - Kwesi Atta Krah
- 1. Humidtropics Regions and Sites Selection
- 2. Outline 1. Systems Research - Humidtropics 2. Action Areas and Flagship Projects 3. Regions & Site Selection Process 4. Extrapolation and scaling 5. Concluding Comments
- 4. Systems Research • Involves whole-system analysis and interactions – multi cropping systems, – integrated crop – livestock, – NRM – productivity-markets/institutions interactions – gender and generational issues, etc. • The blending of local and technical knowledge • Matching of technological options to the biophysical and socio-economic contexts • Participatory multi-stakeholder engagement and partnerships. Livelihood enhancement, Sustainable Intensification and NR integrity
- 5. Systems-CRPs • Core business: Integrated systems approaches • The “melting pot” dimension: integration across CRPs operating within AEZ • Strategic partnership with selected CRPs • Elements exist within other CRPs
- 6. 10 Elements of “Systemness” 1. Systems mindset – Boundaries – Components 2. Inter-”disciplinary” Teams – Partnerships – New competencies 3. Stakeholder engagement – R4D, Innovation Platforms 4. Integrated systems: interactions and trade-offs 5. Innovation: hardware, software and ‘orgware’ 6. Scale dimension: -scaling up/out 7. Gender research: -women and youth 8. Capacity building -across “actors” 9. Development orientation - IDOs 10. Learning - mechanisms and strategies
- 7. 100% 0% Transitioning Towards Increased ‘Systemness’ Systems Research - Situation analysis - R4D Platforms - Partnerships - ETC. Ongoing Research of Centres • Various domains • Progressive alignment towards systems and IDOs 2012 2020
- 8. Theory of Change B) C) Systems Innovations Poverty Destitute Status (SLO 2) Wealthy Humidtropics Strategic Objectives All Four SLOs High NR integrity High productivity Effective Institutions High NR Integrity High Productivity A) A2 Ineffective Institutions Low NR Integrity Low Productivity A1 Degraded Ecosystem Integrity (SLO 4) Healthy
- 9. Program Framework Better livelihood opportunities in a sustainable environment West Africa humid lowlands East and Central Africa humid highlands Central Mekong Central America and Caribbean Cross-cutting Themes Tier 1 Sustainable Intensification Systems Innovation Women & Youth Empowerment Livelihoods Improvement Productivity + Environment Gender Income + Nutrition + Youth Innovation Flagship Projects IDOs SOs Tier 2 West Africa Moist Savanna Southern Africa Moist Savanna Northern Andes Transect Indonesian Humid Lowlands SRTs Systems Analysis and Global Synthesis Integrated Systems Improvement Productivity x NRM x Institutions Scaling and Institutional Innovation
- 11. Implementing Humidtropics 1: 2012 2: 2017 3: 2020 Action Areas Flagship Regions
- 12. Flagship Projects Instruments for research structuring, organization and implementation. Two types: 1. Crosscutting Flagship 2. Area-Based Flagships • East and Central Africa • West Africa • Central America and the Caribbean • Central Mekong
- 13. Flagship Projects Portfolio - …The Hamburger Model Crosscutting Flagship Area-based Flagships E C A H W A H L C A C C M e k o n g Crosscutting Crosscutting Gender Capacity Development Monitoring and Evaltn. Global Synthesis Situation Analysis Tools and Methods Int. Systems Res. • Nutrition Innovation
- 14. Crosscutting Flagship - Emerging Research Areas Strengthening scientific coherence - in relation to our Theory of Change and Program Hypotheses 1. Foresight (in partnership with others, CRPs) 2. Integrated soil fertility mgt. X Productivity X Markets 3. Social science and policy dimensions ??? How do these areas interact with the regional Flagships? …… … and with other CRPs???
- 15. Site Selection Spatial analysis – ‘Hard’ criteria • Use the 3 key variable site selection process, based on: • Poverty • Market access • Risk of degradation • Combinations of High and Low for each of these leads to 8 categories (HHH, HLH, HLL, etc.) • Those categories which are the dominant features of the Action Site should be included to some degree in the Field Sites
- 16. Site Selection ‘Soft’ criteria - Dialogue with partners and local experts • Local knowledge • Local partnerships and views of local experts with better grounded knowledge and experience may bring in other ground considerations influencing choice of Field Sites • Institutional, political, security considerations, etc. • Developmental opportunities
- 17. Site Selection
- 18. Site Selection Development Domains in the ECA Flagship • Site selection guidelines produced • Three key variables are used (scaled as hi or lo) • All combinations are mapped to encompass the range of social, political, and environmental heterogeneity • Spatial analysis is subjected to stakeholder consultation for final selection Poverty Level Risk of Degradation (HANNP) Market Access
- 19. Site Selection
- 20. West Africa Flagship • Southern part of four selected countries: Nigeria, Cameroun, Ghana, Cote d’Ivoire • About 28% of population live with less than US$1.25/day • Average market access is about 3 hours • 58% of land area is estimated to be degraded
- 21. West Africa Flagship • West Africa Flagship Project launched in June 2013
- 22. West Africa Flagship • West Africa Flagship: 4 Action Sites • Two operational: Nigeria and Cameroon
- 23. Cameroon Sites Selection Criteria i) population density ii) potential for poverty reduction iii) potential to reduce land degradation and social conflicts iv) action to protect agro-biodiversity hotspots v) enhanced social learning for multi-cultural communities vi) potential for cross-border trade and market chains vii) national interests in collaboration Combination of hard and soft criteria
- 24. Data Sources for Site Selection • Population: Global Rural-Urban Mapping Project*, Version 1 data: • Poverty Reduction: HarvestChoice Poverty maps, commissioned by the CGIAR SRF Team • Land degradation: Human Appropriation of Net Primary Productivity (HANPP)* • Trade and market chains: Market Access and Influence data* • ETC., ETC.
- 25. Domains Poverty, Market Access and Land Degradation - in that order -
- 26. Domains Population Density, Market Access and Land Degradation - in that order -
- 27. Joint Planning and Platforms Action Area meeting, Bukavu, May 2013
- 28. Scaling Out Action Site Rural Pop. HH Size Rural HH Arable Land % Forest Cover % Burundi 7,488,513 4.70 1,652,533 102,646 52 44,058 22 DRC 3,644,162 5.16 837,084 31,657 10 212,784 69 Western Ethiopia 17,236,800 5.13 3,373,150 358,860 28 348,553 28 Western Kenya 10,133,030 4.85 2,110,209 111,137 53 44,864 21 Rwanda 10,394,860 4.55 2,394,017 142,127 56 54,136 21 Uganda 20,000,890 5.06 4,381,667 403,864 44 311,957 34 ECA Total 68,898,255 4.91 14,748,660 1,150,291 41 1,016,352 33 Central/Western Cameroon 10,059,010 5.26 2,429,774 192,892 13 860,275 59 Côte d’Ivoire 10,345,100 5.22 2,757,167 579,607 35 677,658 41 Southern Ghana 15,641,130 3.80 3,782,939 463,446 33 470,344 33 Southwest Nigeria 61,249,060 4.84 16,315,948 619,560 28 604,453 27 WA Total 97,294,300 4.78 25,285,829 1,855,505 27 2,612,730 40 Development Triangle 7,220,079 4.91 7,587,514 131,375 11 671,741 54 Golden Triangle 4,287,239 4.47 4,449,623 77,463 9 526,509 59 Green Triangle 6,340,461 4.22 6,661,121 130,476 15 352,278 41 CM Total 17,847,779 4.53 18,698,257 339,314 11 1,550,528 51 Dominican Republic 2,161,645 3.94 567,760 41,550 26 65,400 41 El Salvador 2,729,246 6.00 597,357 24,043 50 15,442 32 Guatemala 7,780,990 5.65 1,597,361 72,128 27 124,210 47 Haiti 2,615,173 4.61 570,622 40,994 46 14,048 16 Honduras 4,052,513 4.90 882,406 46,125 15 167,831 54 Central-Northern 2,369,737 6.05 395,380 90,434 20 244,377 55 Nicaragua CAC Total 21,709,304 5.19 4,610,887 315,273 31 631,309 41
- 29. AfriPop Population Rural Population Agricultural population Poor farmers Scaling Out
- 30. Similarity Mapping Area of interest Log Pop Accessibility LGP
- 31. Similarity Mapping • Map variables of relevance to a given issue • Environmental suitability for particular farming systems • Access to specialised markets • Chose area of interest • Source area (AA, AS, FS) • Target area • Run similarity model • Chose variables • Chose statistical method • Evaluate outputs • Select similarity threshold • Quantify relevant variables in target area – rural population, poor farmers, land degradation
- 32. Collaboration with CRPs Systems CRPs • Regular consultations among Directors • Joint events: Enhancing coherence in systems research – S-CRPs workshop on “Capacity to Innovate” as an IDO, Amsterdam, March, 2014 – IFSA/CGIAR Symposium on Global Partnerships in systems research and innovation, Berlin, April, 2014. – Integrated Systems Conference for Sustainable Intensification: 3-6 March, 2015, IITA, Ibadan, Nigeria Other CRPs Co-location, Coordination, Collaboration – I. RTB, A4NH, L&F – II Maize, FTA, PIM, CCAFS – Humidtropics Focal Point for Cross-CRP interaction
- 33. Humidtropics: Diverse agricultural systems Food security and cash crops RTB Banana Beans Cassava Maize Coffee Natural resource status Rwanda East-DR Congo Livestock Potato
- 34. Humidtropics and RTB team up - Co-Location - Potato lines (RTB-CIP) that produce tubers at warmer temperatures are being tested in Western Kenya action area as part of the systems intensification research of Humidtropics Humidtropics integration of varieties into system: best fit, trade-offs and interactions RTB-CIP screening potato varieties, for possible fit into the humid tropics. Zone of convergence Humidtropics R4D Platform
- 35. Conclusion Humidtropics is making progress …. …. within reasonable limits!! • Increased coherence and trust building: • Combining crosscutting with regional Flagships • Site selection links to extrapolation domains • Make partnerships count for good – Regional organizations – R4D Platforms • More efforts in CRPs collaboration – Co-location, Coordination, Collaboration • Overall, positive feel! Great Expectations!! Focus on DELIVERY!
Notas del editor
- These ‘Hard’ i.e. data-driven selection criteria were as described in the site selection guidelines
- In addition to the ‘hard’ spatial criteria described on the previous slide there are ‘soft’ criteria to be accounted for in site selection – the spatial analysis should inform this discussion.
- Maps of the four Flagship project Areas showing the different Action Sites in each. These are overlain on the global Ruminant production systems maps (Robinson et al 2011) which show livestock only areas (LG*), mixed rainfed areas (MR*), and mixed irrigated areas (MI*) …. For each of which is distinguished hyper-arid (**Y), arid and semi-arid (**A), humid and sub-humid (**H;) and temperate and tropical highlands (**T). (other is largely forested areas). The Humidtropics should fall within **H and **T.
- This is based on the descriptions provided in the site selection paper (Duncan et al .. ) LLL means low poverty; low market access and low risk of degradation, for example. The idea proposed is that different combinations of these define ‘development domains’ and that the selected field sites should embrace the diversity of the development domains in a given Action Site (and ultimately, Action Area).
- This is an example for W. Kenya where there were two dominant development domains identified so two (initial) field sites were selected from each (though Kisumu is more variable, apparently).
- An important part of the Global synthesis work (FP cluster 1.1) is to quantify the Humidtropics areas and to that end we are geo-referencing all of our Action Areas and Action Sites. This is essential if we are to know who and how many are the direct beneficiaries of the research in the Action Sites and, as research outputs are scaled through various platforms and partnerships, who are the potential beneficiaries in the broader Humidtropics agro-ecological zone. Important layers for quantification include rural population, arable land, forest resources, livestock numbers, and such-like (Field Sites will also be included in the future but we don[t have very detailed and complete descriptions of these at the moment)
- In order to estimate the number of poor farmers and farming families that the Humidtropics programme can impact directly (in Action Sites) and indirectly, through scaling out, we start from the human population – for which very detailed maps now exist (AfriPop, for example, in this example for Western Kenya. Work is on-going under Global Synthesis (FP Cluster 1.1) to move from estimates of total population, to rural population (masking out urban areas); to Agricultural population (by estimating the % of the rural population engaged in Agriculture); and thence to poor farmers of various types (e.g. poor mixed, crop-livestock farmers) based on combinations of farming systems estimates and of poverty. This is how we estimate the potential reach of the Humidtropics programme.
- Linked to the above we are developing a ‘Similarity Mapping’ tool to estimate the extent of areas similar to those within our Action Areas, Action Sites and field sites. This slide shows the delineation of an area of interest (the red hashed area) and a ‘training site’ the W. Kenya Action Site in this example. It also shows three examples of the type of data layer that can be used to define similarity – in this case demography (human population density), infrastructure (market access), and agro-ecology (length of growing period). The next slide goes through the process.
- The process for similarity mapping is outlined on the right hand side and the map shows a similarity map for Eastern Africa – based on the W. Kenya Action Site as the ‘training site’ and the data layers shown in the previous slide: (1) demography (human population density), (2) infrastructure (market access), and (3) agro-ecology (length of growing period). Four different statistical methods are available in the programme - this example uses Euclidean similarity, which transforms the covariates prior to analysis using a Principal Components Analysis (PCA). Models can be run globally (taking account of all Humidtropics Action Sites) or regionally (AS) or for smaller areas (FS) – for which more specific data layers may be available. It would be appropriate to produce different models for different types of intervention – e.g. one could include variables that determine suitability for a particular crop. The idea is that this will show the extent of areas that are ‘similar’ to our area of research (AA, AS, FS) - by setting a threshold we can then quantify that area (how many poor farmers are there, for example) and that is the potential reach of the research in question. The actual reach then depends on scaling, which is determined by partnerships, platforms and uptake rates. This tool is a nice example of something that can promote cross-SRT action: this SRT1 (targetting) output feeds directly into SRT3 (scaling) – via the technical proposal emerging from SRT2 (intervention).