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Decision and Policy Analysis in CIAT - March 2013

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Presentation made to the ISPC in their meeting in CIAT, March 2013.

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Decision and Policy Analysis in CIAT - March 2013

  1. 1. Decision and Policy Analysis Andy Jarvis 23 March 2013 Since 1967 / Science to cultivate change www.ciat.cgiar.org
  2. 2. Decision and Policy Analysis• The misfits of CIAT (or the rest of CIAT are the misfits)• Cross-cutting, multi-disciplinary team who believe that better decisions can be made with the power of information• Supporting functions within CIAT, and global research leadership in specific themes
  3. 3. Decision and Policy Analysis• Focussed on delivering research outcomes in: o Climate change (CRP7) o Ecosystem Services (CRP5) o Linking Farmers to Markets (CRP2)• Through expert, disciplinary groups in: o Modelling o Gender analysis o Impact and Strategic Studies o Policy Analysis o Knowledge Management o Big Data
  4. 4. Climate change:Let’s start with our mandate crops
  5. 5. What will be the role of cassava in a climate changed world?• We know cassava is a resistant crop….• …..but will it stand up to climate change?• How does it fair up with other major staples?• What are the research challenges over the coming decades?• What has all this got to do with film stars?
  6. 6. Current suitability
  7. 7. Future suitability change
  8. 8. What will this mean for cassava?
  9. 9. The Rambo root!
  10. 10. But what about other staples?The Rambo root versus Mr. Bean
  11. 11. Cassava suitability change compared with other staples• Cassava consistently outperforms other staples in terms of changes in suitability
  12. 12. Cassava’s role as a substitution crop• Cassava as a fallback crop under an uncertain climate (risk management)• Cassava as the substitution crop for other staples more sensitive to heat and drought• Ongoing: finalizing a cassava mechanistic model to further support breeding programs
  13. 13. Getting to grips with climate adaptation: The right choices
  14. 14. Var. CariocaVar. Jamapa Var. Calima Evaluating varietal adaptation
  15. 15. Likely yield with different planting dates
  16. 16. Modelling potential losses from extreme events with different planting dates
  17. 17. Benefits of potential adaptation options: conservation agriculture % yield loss % water deficit
  18. 18. Playing out transformative climate smartadaptation in CCAFS benchmark sites inEast Africa: When, where, how and withwhom?
  19. 19. Where do we work?CCAFS sites Main crops Main livestock (forages) Maize Beans Wheat Beef cattle GoatsBorana(ET) (96.6%) (86.4%) (33.1%) (93.2%) (77.8%) Maize Sorghum Beans Goats Chicken/hensNyando (KE) (99.2%) (73.3%) (34.4%) (66.9%) (61.2%) Maize Beans Tomatoes Chicken/hens Dairy cowsUsambara (TZ) (87.1%) (75%) (29%) (82.1%) (56.4%) SweetAlbertine Cassava Beans Chicken/hens potatoes Pigs (63.1%)Rift (UG) (78.6%) (68.4%) (82.5%) (59.8%)
  20. 20. Climate smart agriculture: tackling adoption head onRash model (Campell, 1963): Attitude towards change = number + difficulty of change made
  21. 21. Gender Dimensions• Why consider gender? o To develop appropriate adaptation strategies for both male and female farmers (to ensure inclusion of female farmers)• Findings (Context Specific) o Gender division of labor o Decision-making o Control and Access of Resources
  22. 22. Relations with the Host Country: CIAT-Ministry of Agriculture Agreement onIntegrative analysis of production systems in Colombia for adaptation to climate
  23. 23. Objective of the AgreementUnite effort, resources and capacity between the ministry andCIAT to strengthen the agricultural and livestock sector to adaptto climate change, and improve the resource use-efficiency inprioritised production systems• US$8m, 18 months, 11 national partners, 3 international partners• “CCAFS Colombia”, 4 themes• Improved crop models, seasonal climate and crop forecasting, carbon and water footprints, varietal evaluation across climate gradients• Direct input into National Adaptation Plan for the Agricultural Sector, and the national mitigation plan for the agricultural sector
  24. 24. Rewarding for Ecosystem Services in watersheds
  25. 25. Different groups want different things• Downstream o Urban dwellers want clean, reliable water supplies o Lowland farmers want cheap, reliable irrigation water o Tourists want clean, attractive water• Midstream o Hydropower companies want reliable low-silt water without having to invest in large storage reservoirs• Upstream o Highland communities want to live better o Citizens want to preserve highland ecosystem services
  26. 26. Peruvian case study, Canete River watershed – Current situation Ecosystemanduse (m3/s) River flow land uses Water service provision(4000-5800Upper basin(4000-5800(4000-5800Upper (Water yield (mm)) Extensive degrading0 grazing, subsistence agriculture 1111-1507 (mostly from springs) Hydropower company 51-256Middle basin(350 – 4000(350 – 4000(350 – 4000 Shrimp growers 250, 64 Urban dwellers 0-50(0-350)(0-350)Lower basin(0-350) Water inefficient commercial agriculture Tourists (rafting)
  27. 27. Desired situation Investment in (4000-5800 Upper basin productive alternatives Middle basin (350 – 4000 Watershed’s Transfer part socioeconomic of their asymmetries might benefits be balanced by this benefit-sharing mechanism (0-350) Lower basin
  28. 28. Research outputs and intermediate project outcomes• Conceptual approach: Adopted by MINAM … Is not only about paying for improving the delivery of the ESS but also about rewarding for ESS already being delivered (positive externalities) Recently presented by Vice-Ministry of Environment (Nov, 2012)
  29. 29. Where we are right now: Putting research into use• Participating in drafting national Ecosystem Services Law that draws on Cañete experience: Final version of ESS Law before Congress for approval• Other case study catchments (6 others) contributing to a systematic review of potential for benefit sharing schemes in Andes
  30. 30. Linking Farmers to MarketsUnder what conditions can market linkages be an effective tool for rural poverty reduction for gender and socially differentiated actors?Iterative process of design, testing and documentation of approaches for inclusive business models, R4D platforms and public policies in Latin America, E. Africa and S.E. Asia
  31. 31. AMBITIOUS DESTINATIONS, FEW ROADSDonors, business and civil society are in broad consensus onbenefits of linking smallholders to markets.• Many islands of success but few cases of sustained, transformational change that benefit women, minorities and the rural poor.• The concept is clear but HOW to achieve beneficial and sustained market access is not.• Need to understand appropriate roles for public, private and civil society actors
  32. 32. Supply chain policies in Colombia CUADRO 4 10 ORGANIZACIONES DE LAS CADENAS PRODUCTIVAS: ANALISIS DE FOCALIZACIÓN FOCALIZACIÓN Aguacate Arroz Cacao Caucho Cítricos GEOGRÁFICA Y Yha NBI Y Yha NBI Y Yha NBI Y Yha NBI Y Yha NBI % Población con NBI           % Población Rural con               NBI  Índice Desarrollo              Humano Índice Gini de Tierras          2009   Índice Gini de      Propietarios 2009     Núm. Intervenciones USAID (Programas             MIDAS y ADAM) Núm. Intervenciones MADR (Oportunidades              Rurales y Alianzas Productivas) FOCALIZACIÓN Fique Fruticola Guayaba Mango Platano GEOGRÁFICA Y Yha NBI Y Yha NBI Y Yha NBI Y Yha NBI Y Yha NBI % Población con NBI         % Población Rural con                NBI  Índice Desarrollo              Humano     Índice Gini de Tierras                 2009    Índice Gini de                  Propietarios 2009    Núm. Intervenciones USAID (Programas                      MIDAS y ADAM) Núm. Intervenciones MADR (Oportunidades                      Rurales y Alianzas Productivas) Policy density (# chains) by Department Social performance by supply chain Quantitative macro analysis I R (policies & development outcomes) n e c s Qualitative meso analysis i e (why does the policy work /fail?) d a e r n c Household level surveys c h (what does it mean for the poor?) ePolicy performance by Department
  33. 33. Thinking through impact Strategy & Results Framework: Performance management….
  34. 34. Examples of Impacts• Over 5.3 million rural households in sub- Saharan Africa have adopted modern bean varieties over the last 17 years, generating benefits worth nearly US$200 million• Adoption of improved cassava varieties in Thailand and Vietnam has nearly reached 90%, creating benefits worth almost $12 billion over the last 20 years• Improved forages now cover an area estimated at 25.4 million hectares in tropical America, generating huge benefits through improved livestock production – estimated at $1 billion in Colombia, for example• Nearly 60% of Latin America’s rice area is planted to improved rice, with benefits valued at $860 million from 1967 to 1995 alone
  35. 35. LAC Foresight: CIAT, IADB and IICA • Presented at GCARD2 • Resulted from IADB Workshop March 2012, and CIAT hosted expert meeting October 2012 • First concrete step towards a longer term combined effort – a regional platform?
  36. 36. Big Data: The engine behind it all• Great climate data• Improved soil information• Crop distribution and yield data• Land-use data• Capacity to manage and analyze it: o Infrastructure o Geeks
  37. 37. CCAFS Climate: 40,000 users in 18 months
  38. 38. Agtrials http://agtrials.org/ Public data! 4296 trials 20000 varieties/races• Calibration, validation of crop models• Exploration and testing of adaptation options o Genetic improvement o On-farm management practices• Assess technology transfer options• Build “adaptation packages”
  39. 39. Loss detections Jan 2004 Results Oct 2012 Latin America
  40. 40. What is Terra- Terra-i is a system of habitat changes monitoring that uses different mathematical models that combine vegetation data (MODIS NDVI) and precipitation data (TRMM) to detect deviations from the natural cycle of the vegetation over time and thus antrophogenic impacts on natural ecosystemsIt has maps of habitat loss every 16 days at the continental level with 250 meters of spatial resolution
  41. 41. Case study: Sierra de Rio Tinto National Forest, Sanguijuelosa ForestSanguijuelosa Forest Photo, GreenWood, Feb 2013
  42. 42. Visualization tool- Explore, visualize and get Terra-i data for Latin-America - http://www.terra-i.org/
  43. 43. In conclusion….• Science informed laws on benefit sharing• Better national plans and policies for dealing with climate change• Breeders breeding for the right traits• Farmers and their organizations making the right choices in a dynamic climate• Countries tackling deforestation head on with REDD+ (e.g. Bolivia)• Some very motivated and dedicated geeky misfits• And much more….
  44. 44. CIAT: Science to Cultivate ChangeWebsite: www.ciat.cgiar.org Follow us: http://twitter.com/ciat_Blog: www.ciatnews.cgiar.org/en/ http://www.facebook.com/ciat.ecoefficient

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