IFPRI              Setting Priorities of Regional Agricultural R&D in             Africa: Incorporating R&D Spillovers and...
Current Study Objective1.       To review a number of methods that have been applied in         the African context and at...
Motivations for the 3 studies   Responding to increased demand and recognition for regional    agricultural R&D strategy,...
Goals across all 3 studies   To assess the critical investment and policy alternatives for    regional Ag R&D strategy  ...
Key Questions Addressed      What investment and policy options, and in which key commodity       areas, offer the best p...
Context – The Big Picture Challenge                                    SADC (w/out SA) / World Ag Exports                 ...
Trends in Cereal Yields, 1961-2009        2.5                                                     SSA (without S. Africa)M...
Performance has been mixed across regions...1.41.2                                                                        ...
.. within regions, and not only agriculture, but overalleconomies can be quite diverse               Middle              I...
Analytical Framework and Methods             Spatial Analysis of underlying                                               ...
Spatial Analysis – mapping out diversity in resource endowments and socio-economic environment (DD) - ASARECA             ...
SADC Region         Crop Production Potential        (Length of Growing Period)IFPRI
SADC Region        Population DensityIFPRI
SADC Region                      Market Access        (Travel time to local rural, urban, national                   & reg...
CORAF Region - Agricultural suitability                                                                        low        ...
CORAF Region - Population density                                                                      low                ...
CORAF Region - Market access                                                                         low                  ...
Spatial analysis illustrated potential for spillovers.. Challenge ismeasuring the spillover potential..    Approach taken...
For ASARECA.. Identifying source of spillover     countries (R&D capacities by commodity)60                               ...
Spillover potential influenced by developmentdomains - ASARECA                   LH = “Low Ag Potential”                  ...
Potential gross welfare gains from                  agricultural R&D spillovers in ASARECA              9Million US$      ...
For CORAF.. Also potentially large                                      12/8/2011 – Page 22IFPRI
For SADC – construction of spillover matrices           Final spillover matrices (e.g. Maize)           Spillout (columns)...
Potential maize technology spillovers (aggregated at country)                                                             ...
 Analysis      of alternative future growth        scenarios.. Identifying key drivers of        growth – with welfare go...
Not all countries can achieve 6% CAADP target - SADCNotes:            Source; The EMM model simulation results(a) 6% of ag...
Key drivers of agricultural growth (2009-2015)                                     For middle incomes                     ...
...But varies by country - SADC    12                 Fishery   Livestock    High-value                 O.food    Roots   ...
For CORAF, only few can meet 6% target from         closing yield gaps (2006-2015 average)         Others              Liv...
Measures of the gains from potential  spillovers under the „targeted‟ growth  scenario can be quite significant           ...
Low income countries benefit most from spillovers –esp. for Cereals (SADC)   IFPRI
Beneficiaries of spillovers (or spill-in) by commodity                                                (US$ million, 2009-2...
Developing criteria for ranking priorities using scoring method (e.g. from results of analysis)Commodity or    Contributio...
Key Findings across all 3 studies   Each region has lots of similarities across countries, and therefore    development d...
Implications   There also needs to be strong national agricultural research    systems for regional efforts to succeed. ...
Summary Conclusion     Increasing productivity growth where there is high potential for      growth, spillovers, and dema...
Key Lessons       Improving methods as integration is not always easy – but        strengthening use of simpler models an...
Thank You                    12/8/2011 – Page 38IFPRI
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Setting Priorities of Regional Agricultural R&D Investments in Africa: Incorporating R&D Spillovers and Economy-wide Effects

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By Michael Johnson, Sam Benin, Xinshen Diao, and Liangzhi You.
Presented at the ASTI-FARA conference Agricultural R&D: Investing in Africa's Future: Analyzing Trends, Challenges, and Opportunities - Accra, Ghana on December 5-7, 2011. http://www.asti.cgiar.org/2011conf

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Setting Priorities of Regional Agricultural R&D Investments in Africa: Incorporating R&D Spillovers and Economy-wide Effects

  1. 1. IFPRI Setting Priorities of Regional Agricultural R&D in Africa: Incorporating R&D Spillovers and Economy- wide Effects Michael Johnson International Food Policy Research Institute ASTI-FARA Conference, Accra, Ghana December 5th – 7th, 2011INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 12/8/2011
  2. 2. Current Study Objective1. To review a number of methods that have been applied in the African context and at the regional level (based on 3 past studies). 1. ASARECA in East and Central Africa (2003-2004), 2. CORAF in West and Central Africa (2006-2007), and 3. SADC/FANR in Southern Africa (2010-2011)2. By comparing and contrasting all 3 studies, this offers an opportunity to: 1. Review the methods used across them 2. Data needs and limitations 3. Review the results and policy implications, and 4. Their translation into policy action within each region. 12/8/2011 – Page 2IFPRI
  3. 3. Motivations for the 3 studies Responding to increased demand and recognition for regional agricultural R&D strategy, that... » There are scale economies to be had from regional cooperation given many small countries (& many that are landlocked) » Given similar resource endowments and constraints, investments in one country have the potential to generate externalities (spillovers) in neighboring countries, and » Many cross-cutting issues extend beyond national boundaries » Diverse economies in the region offer great potential to spur growth through greater trade linkages Regional cooperation in R&D is already strong - FARA, SROs (ASARECA, CORAF/WECARD, SADC/FANR) BUT, little empirical work done in helping to identify what the regional priorities should be – especially in a rigorous fashion IFPRI
  4. 4. Goals across all 3 studies To assess the critical investment and policy alternatives for regional Ag R&D strategy Undertake an ex-ante impact evaluation of alternative future growth options and potential welfare benefits from increased Ag R&D investments. Taking the first important step in empirically investigating alternative entry points for investments needed to achieve regional growth and welfare targets (CAADP, RISDP, MDG) – other analyses would be needed to elaborate specific interventions within each strategic area. IFPRI
  5. 5. Key Questions Addressed  What investment and policy options, and in which key commodity areas, offer the best potential for accelerating agricultural growth in order to achieve regionally set goals?  Among the key commodity areas, which ones would be most suitable for a regional R&D program based on the degree to which they have greater potential for wider adaptation across countries?  Finally, what kinds of constraints and other complementary or crosscutting issues are important to consider in the context of enhancing productivity growth in the region? IFPRI
  6. 6. Context – The Big Picture Challenge SADC (w/out SA) / World Ag Exports 3.5 SADC (w/out SA) / World Cereal Imports 3.0 2.5Percent share (%) 2.0 1.5 1.0 0.5 0.0 IFPRI
  7. 7. Trends in Cereal Yields, 1961-2009 2.5 SSA (without S. Africa)MT/ha 2 South Asia 1.5 1 The Potential 0.5 is there 0 1967 1970 1976 1979 1985 1988 1991 1997 2000 2006 2009 2018 1961 1964 1973 1982 1994 2003 2012 2015 12/8/2011 – Page 7 IFPRI
  8. 8. Performance has been mixed across regions...1.41.2 5 per. Mov. Avg. (CORAF) 10.8 5 per. Mov. Avg. (ASARECA)0.6 5 per. Mov. Avg. (SADC0.4 (without S. Africa))0.2 0 1964 1967 1973 1976 1982 1985 1991 2000 2006 2009 1961 1970 1979 1988 1994 1997 2003 12/8/2011 – Page 8 IFPRI
  9. 9. .. within regions, and not only agriculture, but overalleconomies can be quite diverse Middle Income Low Income & post civil Low Income conflict IFPRI
  10. 10. Analytical Framework and Methods Spatial Analysis of underlying Expert conditions & potential (DD) Surveys & Consultations Agricultural Potential Market Access / Pop Density Regional & economy-wide analysis of Analysis of economic returns to future growth scenarios due to R&D regional R&D and spillovers –(Economy-wide Multi-Market - EMM) commodity-based economic Prices Growth Prices Income surplus method (e.g. DREAM) Rankings among key sub-sectors & Potential welfare effects -- on activities, implications for future food security and poverty investments & policies outcomes IFPRI
  11. 11. Spatial Analysis – mapping out diversity in resource endowments and socio-economic environment (DD) - ASARECA Population density Production potential Market access hours to mkt LGP (days)persons/sq.km 0-40 > 10 41-81 10-20 82-121 20-50 122-162 163-202 50-100 203-243 > 100 244-283 284-324 IFPRI 325-365
  12. 12. SADC Region Crop Production Potential (Length of Growing Period)IFPRI
  13. 13. SADC Region Population DensityIFPRI
  14. 14. SADC Region Market Access (Travel time to local rural, urban, national & regional markets) Local urban, national & regional markets Local Rural MarketsIFPRI
  15. 15. CORAF Region - Agricultural suitability low medium highIdeally, this should includeclimatic and soil information.Due to the lack of good spatialdatasets and the heterogeneity ofsoils in the region, we relied on climatic data: Length of GrowingPeriod IFPRI
  16. 16. CORAF Region - Population density low medium highThresholds of <30 (low), 30-100 (medium) and>100 (high) persons per km2 were used to identifyrural population density regimes of relevance foragriculture IFPRI
  17. 17. CORAF Region - Market access low medium high High: <4 hrs to major seaports or large cities ofAccess to<2 hrs to towns of 100,000+; <1 hr to 500,000+; input & outputmarkets depends on distance, quality local mkts (10,000+)and quantity of transportMedium: <6 hrs to large cities (500,000+); <4 hrs tonetworks, the<2 hrs of local markets (10,000+)towns (100,000+); size of markets, and thenature of the land surface (landcover, topography) areas Low: all other IFPRI
  18. 18. Spatial analysis illustrated potential for spillovers.. Challenge ismeasuring the spillover potential.. Approach taken, Ad Hoc assumptions for ASARECA – construction of spillover matrix for SADC For SADC – 3 dimensions used.. » Capture similar production environments across countries using DD analysis » Estimate yield gaps across countries to proxy knowledge and/or technology stocks and determine the potential direction of transfer of technologies, and » Add a capacity for R&D dimension to capture the probability for adoption through successful adaptive research (using evidence of past Rate of Return Studies) IFPRI
  19. 19. For ASARECA.. Identifying source of spillover countries (R&D capacities by commodity)60 Kenya50 Ethiopia Kenya Madagascar40 Sudan Uganda Uganda Ethiopia Tanzania Tanzania30 Ethiopia Ethiopia Kenya Tanzania Tanzania Madagascar Uganda Sudan20 Burundi100 cassava coffee cotton maize plantain potato rice sorghum 1st rank 2nd rank 3rd rank 12/8/2011 – Page 19 IFPRI
  20. 20. Spillover potential influenced by developmentdomains - ASARECA LH = “Low Ag Potential” & “High Land Density” 12/8/2011 – Page 20 IFPRI
  21. 21. Potential gross welfare gains from agricultural R&D spillovers in ASARECA 9Million US$ 8 Additional gains with 7 spillovers 6 Total regional gains 5 without spillovers 4 3 2 1 0 12/8/2011 – Page 21 IFPRI
  22. 22. For CORAF.. Also potentially large 12/8/2011 – Page 22IFPRI
  23. 23. For SADC – construction of spillover matrices Final spillover matrices (e.g. Maize) Spillout (columns)and Spillins (rows) Ave spill-in ANG BOT DRC LES MAD MAL MOZ NAM SAF SWZ TNZ ZAM ZIM effect ANG 1.00 0.00 0.00 0.04 0.23 0.04 0.13 0.30 0.54 0.05 0.11 0.21 0.08 0.25 BOT 0.15 1.00 0.14 0.12 0.40 0.09 0.17 0.45 0.39 0.16 0.14 0.21 0.21 0.25 DRC 0.00 0.00 1.00 0.00 0.16 0.09 0.10 0.06 0.18 0.17 0.00 0.14 0.13 0.10 LES 0.00 0.00 0.00 1.00 0.02 0.02 0.00 0.00 0.01 0.02 0.00 0.01 0.01 0.01 MAD 0.00 0.00 0.00 0.00 1.00 0.05 0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.04 MAL 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.01 MOZ 0.00 0.00 0.00 0.00 0.14 0.13 1.00 0.08 0.26 0.16 0.00 0.20 0.00 0.12 NAM 0.00 0.00 0.00 0.00 0.00 0.01 0.00 1.00 0.16 0.00 0.00 0.00 0.00 0.05 SAF 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 SWZ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 1.00 0.00 0.00 0.00 0.02 TNZ 0.00 0.00 0.00 0.00 0.19 0.29 0.00 0.21 0.43 0.28 1.00 0.19 0.11 0.20 ZAM 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.12 0.00 0.00 1.00 0.00 0.04 ZIM 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.11 0.20 0.00 0.10 1.00 0.05Ave spill-out 12/8/2011 – Page 23 0.00 0.00 0.00 0.00 0.07 0.08 0.02 0.06 0.16 0.10 0.01 0.09 0.03 effect IFPRI
  24. 24. Potential maize technology spillovers (aggregated at country) Spill-out (the sources): This is the effect on Swaziland, 0.02 Spillin average productivity in the rest of SADC Zambia, 0.04 Malawi, 0.01 region due to adoption of technologies Madagascar, Lesotho, 0.01 generated in country Y (the source 0.04 Namibia, 0.05 country), relative to the average own Angola, 0.25 productivity effects in the other countries Zimbabwe, 0.05 CongoDemRep, 0.10 Spillout Botswana, 0.25 Mozambique, Mozambique, 0.02 Tanzania, 0.01 0.12 Zimbabwe, 0.03 Tanzania, 0.20 Namibia, 0.06 SouthAfrica, 0.16 Spill-ins (the beneficiaries): This is average effect on productivity in Country Madagascar, 0.07 X due to adoption of technologies generated in other SADC countries, and Swaziland, 0.10 relative to the productivity effect Malawi, 0.08 associated with adoption of country Xs Zambia, 0.09 own technologies.
  25. 25.  Analysis of alternative future growth scenarios.. Identifying key drivers of growth – with welfare goals in mind 12/8/2011 – Page 25IFPRI
  26. 26. Not all countries can achieve 6% CAADP target - SADCNotes: Source; The EMM model simulation results(a) 6% of agricultural growth is achieved in 8 countries – under accelerated growth (targeted)(b) But such growth may be unrealistic in some countries where the current growth rate is very low (e.g. Zimbabwe). IFPRI
  27. 27. Key drivers of agricultural growth (2009-2015) For middle incomes countries, almost 50% of potential growth would come from Livestock and Fisheries For low incomes countries, almost 60% of potential growth would come from Cereals & Grains and RootsIFPRI
  28. 28. ...But varies by country - SADC 12 Fishery Livestock High-value O.food Roots Cereals 10 8 6% 4 2 0 12/8/2011 – Page 28 IFPRI
  29. 29. For CORAF, only few can meet 6% target from closing yield gaps (2006-2015 average) Others Livestock O.high value % Cotton Cocoa Vegetables & fruits8 Pulses & oilseeds Roots Cereals7 Sahel Coastal Central Africa6543210 12/8/2011 – Page 29 IFPRI
  30. 30. Measures of the gains from potential spillovers under the „targeted‟ growth scenario can be quite significant 12/8/2011 – Page 30IFPRI
  31. 31. Low income countries benefit most from spillovers –esp. for Cereals (SADC) IFPRI
  32. 32. Beneficiaries of spillovers (or spill-in) by commodity (US$ million, 2009-2015) - SADC 1,200 1,100 Zimbabwe, 391.9 1,000 900Cumulative, US million (2009 - 2015) 800 Tanzania, 207.3 700 600 Mozambique, 167.5 500 400 Tanzania, 117 Congo, DR, 100.1 300 Mozambique, 109.7 200 Tanzania, 175.6 Zimbabwe, 28.2 Angola, 293.3 Zimbabwe, 55.3 Madagascar, 122.2 Mozambique, 253.8 Tanzania, 59.1 100 Tanzania, 55.2 Mozambique, 18.2 Madagascar, 47.4 Mozambique, 63 Congo, DR, 29.3 Congo, DR, 52.8 Angola, 26.6 Angola, 24.3 0 Maize Rice Cattle Cassava Sorghum Beans 12/8/2011 – Page 32 IFPRI
  33. 33. Developing criteria for ranking priorities using scoring method (e.g. from results of analysis)Commodity or Contribution to Degree of Contributio Contribution Gender Total FinalSector Ag growth* regional n to poverty to intra- considerati Weighted rank spillover reduction regional ons Score potential** tradeWeights 0.5 0.5 0.0 0.0 0.0 1Maize 25.6 39.1 32.4 1Cassava 14.5 8.4 11.5 2Rice 8.9 13.6 11.3 3Cattle 5 9.4 7.2 4Fruit & Veg 10.2 - 5.5 5Beans 3.2 5.9 4.5 6Fisheries 7.3 - 4.1 7Sorghum 1.6 6.1 3.8 8Wheat 4.3 2.3 3.3 9Potato 3.5 2.5 3 10Chicken 2.9 2.5 2.7 11Milk & Egg 4.2 - 2.5 12Groundnuts 2.3 2.6 2.4 13Pigs 2.1 1.1 1.6 14Sheep & Goats 1.6 1.5 1.5 15Cotton 1.2 1.3 1.2 16Sugar 1.1 0.3 0.7 17Millet 0.5 0.8 0.7 18 Total 100.00 100.00 100.00 100.00 100.00 100.00
  34. 34. Key Findings across all 3 studies Each region has lots of similarities across countries, and therefore development domains, that can be exploited for targeting R&D and technology development There are also clear differences (areas of comparative advantage), which offer opportunities for specialization Smaller and lower income countries stand to benefit more from focusing attention on adaptive R&D Final rankings according to commodities that offer the greatest potential for driving sector growth and greater technology spillovers – emphasize food staples (crops and livestock) – either though seeds, farming practices, or NRM IFPRI
  35. 35. Implications There also needs to be strong national agricultural research systems for regional efforts to succeed. Regional efforts have a lot to gain from taking advantage of strong NARS -- as a source for existing knowledge and expertise – especially when they have a high potential for adaptation in neighboring countries. Analysis only partial, » Other criteria may also matter (e.g. poverty reduction, contribution intra-regional trade, climate change, gender) » Other analyses still needed to elaborate specific interventions within each strategic area identified IFPRI
  36. 36. Summary Conclusion Increasing productivity growth where there is high potential for growth, spillovers, and demand Strengthening links with markets: » Domestic urban markets and intra-regional trade linkages (regional common markets – inputs and outputs) » Focusing on entire value chain (e.g. links with agro-industry) Exploiting opportunities for greater cooperation in R&D (incentives) Strengthening NARs and their capacities for adaptive research and extension (including impact assessment and priority setting) Translating evidence into strategies, policies, and programs through bottom up – and following through with implementation 12/8/2011 – Page 36 IFPRI
  37. 37. Key Lessons Improving methods as integration is not always easy – but strengthening use of simpler models and approaches (e.g. DREAM) Second, the availability of sufficient and quality data: » Agricultural and socioeconomic data at lower administrative units » Research resource capacities, time frames, and expenditures by commodity or research discipline and type (especially unit costs) » Yield performance data by technology and location (actual and on-farm trials, expert opinion) » Information on past observations of adoption, diffusion, time frames, and simple returns to agricultural investments (ROR) On the policy front, in order to enjoy the benefits from greater cross-country cooperation in R&D, » Finding cost-effective ways to collaborate based on cross-country similarities and comparative advantage » Dominance by donors – maybe changing if linked closer to RECs and CAADP 12/8/2011 – Page 37IFPRI
  38. 38. Thank You 12/8/2011 – Page 38IFPRI

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