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A Technology Platform for Africa

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Information sharing on the development of a Science Agenda for Agriculture in Africa With inputs for CAADP-CGIAR alignment …

Information sharing on the development of a Science Agenda for Agriculture in Africa With inputs for CAADP-CGIAR alignment
April 13, 2013
Dublin, Ireland

Published in: Education, Business

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  • 1. A Technology Platform for AfricaCreating a common vision of the opportunity:Rationale?Criteria for relevance/success?Core functions/services?Implementation phases?
  • 2. ReSAKSS/SAKSS• Policy analysis• CapacitystrengtheningPotted History/ContextCGIAR ReformAU CAADP Planning(-> Implementation)(NAIPs)Dublin ProcessAU(NEPAD, FARA, SROs)/CGIAR/DP(G8) New Alliance forFS&NCountry/DPsPrivate SectorTPs (CG, AGRA, FARA)• Roadmap• AU-CGIAR MoU• Science Agenda• G8 Tech. Platform support• Investment Mapping& AlignmentPrototype(themes, FS, commodities,AEZs, partners, investment,FTE’s, location mapping,bibliography, contextualknowledge)• CRPs• New commitmentto partnerships• Open access policy40 African countries(at varying pace)Global (at varying pace)3 => 6 => 10 countries(at varying pace)All SSA Regions andseveral countries(at varying pace)• G8 pledges & coordination• National policy reform• Increased private investment• Mutual accountability framework• Focus crops/Yield targets• Technology Platform• Targeted tech. options• Scaled implementation• ICT/Open Data
  • 3. The Emergence of a Technology Platform (Phase I)NAIPs, CPPs… & other locally-owned goals/plansTechnology Inventory(structured, open access, knowledgebase (e.g., databases, videos) oftechnologies and practices; types, benefits, costs, access, sources,service/scaling partners)
  • 4. Sample Inventory of Technologies (and Practices)Several existing examples to draw from; varieties, soil fertility, small scale irrigation, etc tobe pooled as a core component of the Technology Platform (simple variety example shown)
  • 5. NAIPs, CPPs… other locally-owned goals/plansTechnology Inventory(structured, open access, knowledgebase (e.g., databases, videos) oftechnologies and practices; types, benefits, costs, access, sources,service/scaling partners)Commodity Prioritization & ProductivityGrowth TargetsData/tools for prioritizing commodities and setting yield goalsThe Emergence of a Technology Platform (Phase I)
  • 6. G8 New Alliance - Technology Platform National Target Worksheet:Ghanaf(∆nutrem, ∆watrem)Base year: 2012 Statistics Trials/Fiel Calculated HH Surve Expert/UserCalculated f(∆cal, ∆prot) f(seed sales, share marketed)Price AreaAv.YieldAchiev.YieldAbs.YieldTarget DIVA GLSS5Adoptionafter 10yearsYieldIncreaseNationalAv. YieldLevel ∆VoP∆Grossreturn ∆Calorie ∆ProteinEconomicImpactShareto Poor NutritionSustain-abilityPrivateSectorcontribution OVERALL(P) (A) (Y) (Yp) (Yp) (%) (%) (a10) (∆Y10) (Yt) (∆VoP) (∆GR) (∆Cal) (∆Prot)$/T 1000ha t/ha t/ha % % % % % t/ha $M/year $/ha % Bkcal (1000t) 0.40 0.20 0.15 0.15 0.10 1.00CerealsMaize 117 991.7 1.70 6.00 253 57 28 57 144.2 4.2 283.90 286.3 54 8,653 230.91 1.75 1.33 5.35 -0.50 1.49 1.84Millet 162 176.6 1.30 2.00 54 28 56 30.2 1.7 10.3 63.6 71 216 6.17 0.06 1.75 0.13 -1.41 0.65 0.25Sorghum 158 252.6 1.30 2.00 54 28 56 30.2 1.7 15.6 61.9 72 340 10.00 0.10 1.77 0.21 -1.04 0.65 0.33Rice (Paddy) 249 181.2 2.40 6.50 171 28 56 95.7 4.7 103.8 572.7 54 1,165 24.97 0.64 1.31 0.72 -0.59 1.35 0.67Cassava 54 875.0 13.80 48.70 253 36 30 60 61.2 22.2 396.0 452.5 31 8,055 66.51 2.44 0.75 4.91 -0.35 0.70 1.88Cocoyam 122 205.3 6.70 8.00 19 30 60 11.6 7.5 19.5 95.2 26 138 2.40 0.12 0.65 0.08 -0.36 0.33 0.17Yam 122 384.9 15.30 49.00 220 30 60 132.2 35.5 947.4 2461.2 46 7,861 101.19 5.84 1.12 4.79 -0.28 0.60 3.30Sweet potato 416 73.4 8.00 24.00 200 30 60 120.0 17.6 293.1 3993.6 44 648 4.93 1.81 1.08 0.40 -0.29 0.00 0.95Plantain 133 328.0 11 20.00 82 30 60 49.1 16.4 235.2 717.1 26 1,328 14.17 1.45 0.64 0.81 -0.65 0.84 0.82LegumesCowpeas 685 167.0 1.30 3.10 138 82 28 82 113.5 2.8 168.9 1011.8 64 843 57.67 1.04 1.56 0.60 -1.50 1.40 0.73Soybean 250 76.2 1.50 2.30 53 94 28 94 50.1 2.3 14.3 188.0 52 192 21.78 0.09 1.28 0.18 -1.25 0.00 0.13Groundnut 366 353.4 1.5 2.50 67 28 56 37.3 2.1 72.5 205.1 63 819 37.01 0.45 1.54 0.53 -1.86 2.00 0.49OthersCocoa 3011 160.0 0.4 1.00 150 57 26 52 78.0 0.7 150.3 939.4 18 207 2.00 0.93 0.45 0.25 -3.59 3.72 0.33Pawpaw 330 1.0 45.00 75.00 67 26 52 34.7 60.6 5.0 5148.0 26 4 0.06 0.03 0.65 0.00 -0.97 0.14 0.01Pineapple 276 12.3 50.00 72.00 44 26 52 22.9 61.4 38.8 3151.7 15 37 0.28 0.24 0.36 0.02 -0.91 0.42 0.08Tomato (rainf) 411 30.0 7.50 15.00 100 26 52 52.0 11.4 48.0 1601.2 28 20 0.94 0.30 0.70 0.01 -1.02 1.07 0.21Tomato (irrig) 411 30.0 30.00 65.00 117 26 52 60.7 48.2 224.2 7472.9 28 93 4.37 1.38 0.70 0.06 -1.02 1.07 0.66Garden eggs 283 3.6 8.00 15.00 88 26 52 45.5 11.6 3.7 1030.1 18 3 0.12 0.02 0.45 0.00 -0.94 0.84 0.04Pepper 686 5.4 6.50 32.30 397 26 52 206.4 19.9 49.7 9203.4 37 200 7.75 0.31 0.91 0.13 -2.05 0.74 0.09Data sources:Roots and TubersNEW ALLIANCE COMMODITY PRIORTIY RATINGSNationalPriority Crops10 YR TARGETS OUTCOME INDICATORSSharetoPoorest40%PRODUCTION BASEBASELINEADOPTIONATTAINABLEYIELDCommodity/Value Chain Prioritization:Ghana exampleSpreadsheet model: Populated by for each country based on available secondary datasources. Individual NA teams will adapt as needed and validate or replace data sourcesPrioritizationcriteria andweighting
  • 7. Average and achievable yield of CAADP prioritycrops under rainfed conditions: GhanaNational Priority Crops AverageyieldAchievableyieldPerformance YieldTargets(av/achievable) (% overaverage)*(Mt / ha) (Mt / ha) (%) (%)CerealsMaize 1.7 6.0 28.3 253Millet 1.3 2.0 65.0 54Sorghum 1.3 2.0 65.0 54Rice (Paddy) 2.4 6.5 36.9 171Roots and TubersCassava 13.8 48.7 28.3 253Cocoyam 6.7 8.0 83.8 19Yam 15.3 49.0 31.2 220Sweet potato 8.0 24.0 33.3 200Plantain 11.0 20.0 55.0 82LegumesCowpea 1.3 2.6 50.0 100Groundnut 1.5 2.3 65.2 53Soybean 1.5 2.5 60.0 67OthersCashew 0.8 1.8 44.4 125Cocoa 0.4 1.0 40.0 150Pawpaw 45.0 75.0 60.0 67Pineapple 50.0 72.0 69.4 44Tomato 7.5 15.0 50.0 100Garden eggs 8.0 15.0 53.3 88Pepper 6.5 32.3 20.1 397Source: Ghana MoFA (2010) and Agriculture in Ghana: Facts and Figures (2011)
  • 8. Potential Focus Commodities/Value ChainsNational PriorityCropsG 8 Investment CountriesEthiopia Ghana Tanzania MozambiqueBurkinaFasoCoteDIvoireCerealsMaizeMilletSorghumRice (Paddy)TeffWheatRoots and TubersCassavaPotatoSweet potatoYamLegumesBeansChickpeasCowpeasFababeanGroundnutSoybeanOthersCocoaCoffeeCottonSugarcane        
  • 9. Investment/Activity Mapping (past, on-going,planned). Opportunities for coordination, collaboration and co-location (alignment, gaps & duplication)NAIPs, CPPs… other locally-owned goals/plansTechnology Inventory(structured, open access, knowledgebase (e.g., databases, videos) oftechnologies and practices; types, benefits, costs, access, sources,service/scaling partners)Commodity Prioritization & ProductivityGrowth TargetsData/tools for prioritizing commodities and setting yield goalsThe Emergence of a Technology Platform (Phase I)
  • 10. Locate Investments/Activities(Gates, WB, AGRA, AfDB, USAID,…..)
  • 11. Investment Locations and Contextual Variables(live link into Funder and HarvestChoice database)
  • 12. Report Alignment by Agroecosystem (AES)
  • 13. Investment/Activity Mapping (past, on-going,planned). Opportunities for coordination, collaboration and co-location (alignment, gaps & duplication)NAIPs, CPPs… other locally-owned goals/plansTechnology Inventory(structured, open access, knowledgebase (e.g., databases, videos) oftechnologies and practices; types, benefits, costs, access, sources,service/scaling partners)Technology EvaluationData and tools for what if scenarios of technology use by type,location, and human capacity to drive models and analysisCommodity Prioritization & ProductivityGrowth TargetsData/tools for prioritizing commodities and setting yield goalsThe Emergence of a Technology Platform (Phase I)
  • 14. MANAGEMENT• Planting window• Planting density• Irrigation• Inorganic fertilizer• Organic manure• Tillage• ResidueCULTIVAR• Phenology• Max # of kernels• Kernel filling rate*DSSAT Cropping System Model Ver. 4.0.2.000 May 21, 2009; 16:32:33*RUN 1 : RAINFED LOW NITROGENMODEL : MZCER040 - MAIZEEXPERIMENT : UFGA8201 MZ NIT X IRR, GAINESVILLE 2N*3ITREATMENT 1 : RAINFED LOW NITROGENCROP : MAIZE CULTIVAR : McCurdy 84aa ECOTYPE :IB0002STARTING DATE : FEB 25 1982PLANTING DATE : FEB 26 1982 PLANTS/m2 : 7.2 ROW SPACING : 61.cmWEATHER : UFGA 1982SOIL : IBMZ910014 TEXTURE : - Millhopper Fine SandSOIL INITIAL C : DEPTH:180cm EXTR. H2O:160.9mm NO3: 2.5kg/ha NH4: 12.9kg/haWATER BALANCE : IRRIGATE ON REPORTED DATE(S)IRRIGATION : 13 mm IN 1 APPLICATIONSNITROGEN BAL. : SOIL-N & N-UPTAKE SIMULATION; NO N-FIXATIONN-FERTILIZER : 116 kg/ha IN 3 APPLICATIONSRESIDUE/MANURE : INITIAL : 1000 kg/ha ; 0 kg/ha IN 0 APPLICATIONSENVIRONM. OPT. : DAYL= 0.00 SRAD= 0.00 TMAX= 0.00 TMIN= 0.00RAIN= 0.00 CO2 = R330.00 DEW = 0.00 WIND= 0.00SIMULATION OPT : WATER :Y NITROGEN:Y N-FIX:N PHOSPH :N PESTS :NPHOTO :C ET :R INFIL:S HYDROL :R SOM :GMANAGEMENT OPT : PLANTING:R IRRIG :R FERT :R RESIDUE:N HARVEST:M WTH:M*SUMMARY OF SOIL AND GENETIC INPUT PARAMETERSSOIL LOWER UPPER SAT EXTR INIT ROOT BULK pH NO3 NH4 ORGDEPTH LIMIT LIMIT SW SW SW DIST DENS Ccm cm3/cm3 cm3/cm3 cm3/cm3 g/cm3 ugN/g ugN/g %-------------------------------------------------------------------------------0- 5 0.026 0.096 0.230 0.070 0.086 1.00 1.30 7.00 0.10 0.50 2.005- 15 0.025 0.086 0.230 0.061 0.086 1.00 1.30 7.00 0.10 0.50 1.0015- 30 0.025 0.086 0.230 0.061 0.086 0.70 1.40 7.00 0.10 0.50 1.0030- 45 0.025 0.086 0.230 0.061 0.086 0.30 1.40 7.00 0.10 0.50 0.5045- 60 0.025 0.086 0.230 0.061 0.086 0.30 1.40 7.00 0.10 0.50 0.5060- 90 0.028 0.090 0.230 0.062 0.076 0.05 1.45 7.00 0.10 0.60 0.1090-120 0.028 0.090 0.230 0.062 0.076 0.03 1.45 7.00 0.10 0.50 0.10120-150 0.029 0.130 0.230 0.101 0.130 0.00 1.45 7.00 0.10 0.50 0.04150-180 0.070 0.258 0.360 0.188 0.258 0.00 1.20 7.00 0.10 0.50 0.24TOT-180 6.2 22.2 45.3 16.1 21.4 <--cm - kg/ha--> 2.5 12.9 87080SOIL ALBEDO : 0.18 EVAPORATION LIMIT : 2.00 MIN. FACTOR : 1.00RUNOFF CURVE # :60.00 DRAINAGE RATE : 0.65 FERT. FACTOR : 0.80MAIZE CULTIVAR :IB0035-McCurdy 84aa ECOTYPE :IB0002P1 : 265.00 P2 : 0.3000 P5 : 920.00G2 : 990.00 G3 : 8.500 PHINT : 39.000*SIMULATED CROP AND SOIL STATUS AT MAIN DEVELOPMENT STAGESRUN NO. 1 RAINFED LOW NITROGENCROP GROWTH BIOMASS CROP N STRESSDATE AGE STAGE kg/ha LAI kg/ha % H2O N------ --- ---------- ----- ----- --- --- ---- ----25 FEB 0 Start Sim 0 0.00 0 0.0 0.00 0.0026 FEB 0 Sowing 0 0.00 0 0.0 0.00 0.0027 FEB 1 Germinate 0 0.00 0 0.0 0.00 0.009 MAR 11 Emergence 29 0.00 1 4.4 0.00 0.0027 MAR 29 End Juveni 251 0.43 4 1.6 0.00 0.091 APR 34 Floral Ini 304 0.44 4 1.5 0.00 0.50*DSSAT Cropping System Model Ver. 4.0.2.000 May 21, 2009; 16:32:33*RUN 1 : RAINFED LOW NITROGENMODEL : MZCER040 - MAIZEEXPERIMENT : UFGA8201 MZ NIT X IRR, GAINESVILLE 2N*3ITREATMENT 1 : RAINFED LOW NITROGENCROP : MAIZE CULTIVAR : McCurdy 84aa ECOTYPE :IB0002STARTING DATE : FEB 25 1982PLANTING DATE : FEB 26 1982 PLANTS/m2 : 7.2 ROW SPACING : 61.cmWEATHER : UFGA 1982SOIL : IBMZ910014 TEXTURE : - Millhopper Fine SandSOIL INITIAL C : DEPTH:180cm EXTR. H2O:160.9mm NO3: 2.5kg/ha NH4: 12.9kg/haWATER BALANCE : IRRIGATE ON REPORTED DATE(S)IRRIGATION : 13 mm IN 1 APPLICATIONSNITROGEN BAL. : SOIL-N & N-UPTAKE SIMULATION; NO N-FIXATIONN-FERTILIZER : 116 kg/ha IN 3 APPLICATIONSRESIDUE/MANURE : INITIAL : 1000 kg/ha ; 0 kg/ha IN 0 APPLICATIONSENVIRONM. OPT. : DAYL= 0.00 SRAD= 0.00 TMAX= 0.00 TMIN= 0.00RAIN= 0.00 CO2 = R330.00 DEW = 0.00 WIND= 0.00SIMULATION OPT : WATER :Y NITROGEN:Y N-FIX:N PHOSPH :N PESTS :NPHOTO :C ET :R INFIL:S HYDROL :R SOM :GMANAGEMENT OPT : PLANTING:R IRRIG :R FERT :R RESIDUE:N HARVEST:M WTH:M*SUMMARY OF SOIL AND GENETIC INPUT PARAMETERSSOIL LOWER UPPER SAT EXTR INIT ROOT BULK pH NO3 NH4 ORGDEPTH LIMIT LIMIT SW SW SW DIST DENS Ccm cm3/cm3 cm3/cm3 cm3/cm3 g/cm3 ugN/g ugN/g %-------------------------------------------------------------------------------0- 5 0.026 0.096 0.230 0.070 0.086 1.00 1.30 7.00 0.10 0.50 2.005- 15 0.025 0.086 0.230 0.061 0.086 1.00 1.30 7.00 0.10 0.50 1.0015- 30 0.025 0.086 0.230 0.061 0.086 0.70 1.40 7.00 0.10 0.50 1.0030- 45 0.025 0.086 0.230 0.061 0.086 0.30 1.40 7.00 0.10 0.50 0.5045- 60 0.025 0.086 0.230 0.061 0.086 0.30 1.40 7.00 0.10 0.50 0.5060- 90 0.028 0.090 0.230 0.062 0.076 0.05 1.45 7.00 0.10 0.60 0.1090-120 0.028 0.090 0.230 0.062 0.076 0.03 1.45 7.00 0.10 0.50 0.10120-150 0.029 0.130 0.230 0.101 0.130 0.00 1.45 7.00 0.10 0.50 0.04150-180 0.070 0.258 0.360 0.188 0.258 0.00 1.20 7.00 0.10 0.50 0.24TOT-180 6.2 22.2 45.3 16.1 21.4 <--cm - kg/ha--> 2.5 12.9 87080SOIL ALBEDO : 0.18 EVAPORATION LIMIT : 2.00 MIN. FACTOR : 1.00RUNOFF CURVE # :60.00 DRAINAGE RATE : 0.65 FERT. FACTOR : 0.80MAIZE CULTIVAR :IB0035-McCurdy 84aa ECOTYPE :IB0002P1 : 265.00 P2 : 0.3000 P5 : 920.00G2 : 990.00 G3 : 8.500 PHINT : 39.000*SIMULATED CROP AND SOIL STATUS AT MAIN DEVELOPMENT STAGESRUN NO. 1 RAINFED LOW NITROGENCROP GROWTH BIOMASS CROP N STRESSDATE AGE STAGE kg/ha LAI kg/ha % H2O N------ --- ---------- ----- ----- --- --- ---- ----25 FEB 0 Start Sim 0 0.00 0 0.0 0.00 0.0026 FEB 0 Sowing 0 0.00 0 0.0 0.00 0.0027 FEB 1 Germinate 0 0.00 0 0.0 0.00 0.009 MAR 11 Emergence 29 0.00 1 4.4 0.00 0.0027 MAR 29 End Juveni 251 0.43 4 1.6 0.00 0.091 APR 34 Floral Ini 304 0.44 4 1.5 0.00 0.50*DSSAT Cropping System Model Ver. 4.0.2.000 May 21, 2009; 16:32:33*RUN 1 : RAINFED LOW NITROGENMODEL : MZCER040 - MAIZEEXPERIMENT : UFGA8201 MZ NIT X IRR, GAINESVILLE 2N*3ITREATMENT 1 : RAINFED LOW NITROGENCROP : MAIZE CULTIVAR : McCurdy 84aa ECOTYPE :IB0002STARTING DATE : FEB 25 1982PLANTING DATE : FEB 26 1982 PLANTS/m2 : 7.2 ROW SPACING : 61.cmWEATHER : UFGA 1982SOIL : IBMZ910014 TEXTURE : - Millhopper Fine SandSOIL INITIAL C : DEPTH:180cm EXTR. H2O:160.9mm NO3: 2.5kg/ha NH4: 12.9kg/haWATER BALANCE : IRRIGATE ON REPORTED DATE(S)IRRIGATION : 13 mm IN 1 APPLICATIONSNITROGEN BAL. : SOIL-N & N-UPTAKE SIMULATION; NO N-FIXATIONN-FERTILIZER : 116 kg/ha IN 3 APPLICATIONSRESIDUE/MANURE : INITIAL : 1000 kg/ha ; 0 kg/ha IN 0 APPLICATIONSENVIRONM. OPT. : DAYL= 0.00 SRAD= 0.00 TMAX= 0.00 TMIN= 0.00RAIN= 0.00 CO2 = R330.00 DEW = 0.00 WIND= 0.00SIMULATION OPT : WATER :Y NITROGEN:Y N-FIX:N PHOSPH :N PESTS :NPHOTO :C ET :R INFIL:S HYDROL :R SOM :GMANAGEMENT OPT : PLANTING:R IRRIG :R FERT :R RESIDUE:N HARVEST:M WTH:M*SUMMARY OF SOIL AND GENETIC INPUT PARAMETERSSOIL LOWER UPPER SAT EXTR INIT ROOT BULK pH NO3 NH4 ORGDEPTH LIMIT LIMIT SW SW SW DIST DENS Ccm cm3/cm3 cm3/cm3 cm3/cm3 g/cm3 ugN/g ugN/g %-------------------------------------------------------------------------------0- 5 0.026 0.096 0.230 0.070 0.086 1.00 1.30 7.00 0.10 0.50 2.005- 15 0.025 0.086 0.230 0.061 0.086 1.00 1.30 7.00 0.10 0.50 1.0015- 30 0.025 0.086 0.230 0.061 0.086 0.70 1.40 7.00 0.10 0.50 1.0030- 45 0.025 0.086 0.230 0.061 0.086 0.30 1.40 7.00 0.10 0.50 0.5045- 60 0.025 0.086 0.230 0.061 0.086 0.30 1.40 7.00 0.10 0.50 0.5060- 90 0.028 0.090 0.230 0.062 0.076 0.05 1.45 7.00 0.10 0.60 0.1090-120 0.028 0.090 0.230 0.062 0.076 0.03 1.45 7.00 0.10 0.50 0.10120-150 0.029 0.130 0.230 0.101 0.130 0.00 1.45 7.00 0.10 0.50 0.04150-180 0.070 0.258 0.360 0.188 0.258 0.00 1.20 7.00 0.10 0.50 0.24TOT-180 6.2 22.2 45.3 16.1 21.4 <--cm - kg/ha--> 2.5 12.9 87080SOIL ALBEDO : 0.18 EVAPORATION LIMIT : 2.00 MIN. FACTOR : 1.00RUNOFF CURVE # :60.00 DRAINAGE RATE : 0.65 FERT. FACTOR : 0.80MAIZE CULTIVAR :IB0035-McCurdy 84aa ECOTYPE :IB0002P1 : 265.00 P2 : 0.3000 P5 : 920.00G2 : 990.00 G3 : 8.500 PHINT : 39.000*SIMULATED CROP AND SOIL STATUS AT MAIN DEVELOPMENT STAGESRUN NO. 1 RAINFED LOW NITROGENCROP GROWTH BIOMASS CROP N STRESSDATE AGE STAGE kg/ha LAI kg/ha % H2O N------ --- ---------- ----- ----- --- --- ---- ----25 FEB 0 Start Sim 0 0.00 0 0.0 0.00 0.0026 FEB 0 Sowing 0 0.00 0 0.0 0.00 0.0027 FEB 1 Germinate 0 0.00 0 0.0 0.00 0.009 MAR 11 Emergence 29 0.00 1 4.4 0.00 0.0027 MAR 29 End Juveni 251 0.43 4 1.6 0.00 0.091 APR 34 Floral Ini 304 0.44 4 1.5 0.00 0.50OUTPUTPhenologyflowering, grain/seed/tuber,maturityYield componentgrain/seed/tuber, biomass, LAIGrowthgrain/seed/tuber, biomass, LAISoilnitrogen balance, water balance,carbon balance0123456789100 50 100 150 200Yield(t/ha)Fertilizer (kg[N]/ha)Supplementinglimited empiricaldata by assessingpotential yieldresponses forspecific locationsand technologies
  • 15. OPV, ---100% 200%300%100%200%300%% Diff. Yie100%200%300%% Diff. Yie100%200%300%% Diff. Yie100%200%300%% Diff. Yie100%200%300%% Diff. Yie100%200%300%% Diff. Yie100%200%300%% Diff. Yie100%200%300%% Diff. Yie100%200%300%% Diff. Yie131% 116%73%66%6%0%Variation in potentialyields and in theefficacy of differenttechnology packagesby regionHelps assess more realistic localyield targets (higher or lowerthan national target), as well asinforming identification ofregion-specific technology andinput use recommendationsand packages
  • 16. Maize DrylandRiceDrylandWetland0%10%20%30%0%10%20%% Diff. Yield0%10%20%% Diff. Yield0%20%40%% Diff. Yield10%5%4%0%Comparing the likely effectiveness of specifictechnologies sub-nationallyAdministrativeunit or AEZ-baseddisaggregation ofpotentialtechnologyimpacts
  • 17. Investment/Activity Mapping (past, on-going,planned). Opportunities for coordination, collaboration and co-location (alignment, gaps & duplication)NAIPs, CPPs… other locally-owned goals/plansTechnology Inventory(structured, open access, knowledgebase (e.g., databases, videos) oftechnologies and practices; types, benefits, costs, access, sources,service/scaling partners)Technology EvaluationData and tools for what if scenarios of technology use by type,location, and human capacity to drive models and analysisCommodity Prioritization & ProductivityGrowth TargetsData/tools for prioritizing commodities and setting yield goalsTechnology use and effectivenessStatus and trends of the uptake, effectiveness andsustainability of specific technologies/practicesThe Emergence of a Technology Platform (Phase I)
  • 18. Evidence of improved varietal adoption*Adoption (percent of planted area)Ethiopia Ghana Tanzania MozambiqueBurkinaFasoCoteDIvoireCerealsMaize 57 72 54Rice (Paddy) 42 92Roots and TubersCassava 36 30 19 20LegumesCowpeas 82 31 11 10Soybean 94 79 100Source: DIVA (in publication)Note: Based on secondary data and expert consultation in countries by CG centers
  • 19. Economically important improvedvarieties in Ghana: MaizeVariety Release year Adoption (% area)Obatanpa 1992 26Etubi 2007 11Mamaba (GH 110) 1996 11Okomasa 1988 5Dorke-SR 1992 3Kawandzie 1984 1All MVs ( national ) 57Source: Diffusion of Improved Crop Varieties in Africa. The Effectiveness of Crop Improvement Programs in Sub-Saharan Africa fromthe Perspectives of Varietal Output and Adoption: The Case of Cassava, Cowpea, Maize, and Soybean. Objective 1 Technical ReportArega D. Alene and Jonas Mwalughali IITA, PO Box 30258, Lilongwe, Malawi. July 2012.
  • 20. CRP Projects &ScientistsInvestment/Activity Mapping (past, on-going,planned). Opportunities for coordination, collaboration and co-location (alignment, gaps & duplication).• Open AccessPolicies*• Growing role ofICTs & RemoteSensing• Infrastructure• HostingNAIPs, CPPs… other locally-owned goals/plansTechnology Inventory(structured, open access, knowledgebase (e.g., databases, videos) oftechnologies and practices; types, benefits, costs, access, sources,service/scaling partners)Technology EvaluationData and tools for what if scenarios (e.g., CA with CC) by type,location, and local capacity to drive models and analysisCommodity Prioritization & ProductivityGrowth TargetsData/tools for prioritizing commodities and setting yield goalsNationalProductivity TeamsGov agencies &scientistsUniversitiesPrivate SectorR&D PartnersScaling PartnersTechnology use and effectivenessStatus and trends of the uptake, effectiveness andsustainability of specific technologies/practicesProductivity Progress & Performance DashboardData and tools for what if scenarios of technology use by type,location, and human capacity to drive models and analysisSRO’s(regional priorities,Spillover, capacity building..AgMIPGAEZ Farming SystemsAssessmentAfSISLSMSCRSPsCIMSANS…………IBM ASTIDIVA Agribenchmark ReSAKSS/SAKSSAGRODEPThe Emergence of a Technology Platform (Phase I)
  • 21. Some Technology Platform Attributes• Initial focus on structured documentation of technologies andpractices, assessing potential in local production contexts, &actual uptake and effectiveness in sustainable productivitygrowth. Reach back into R&D over time• Many TP components already exist, potential partners (publicand private) willing to participate and good indication of donorsupport. Country experiments / SRO consolidation• Importance of CGIAR and CAADP investments using commontaxonomy/ coding/ ontologies of investment themes,agroecological zones, commodities, partners, etc…• Build common taxonomies into all investmentplanning/reporting• Scope and reliability (hence utility) of data and services willgrow over time in response to use/ feedback/ learning
  • 22. Some Questions/Challenges• Process for improving existing concept note?e.g., broadening participation in shapingtechnical and institutional context and settingout overall scope of ambition while taking arealistic phased approach to implementation• How to integrate strengths and potential rolesof both national and regional capacities?
  • 23. A critical Technology Platform service will be readyaccess to on-line, interoperable suites of “best bet”and promising practices both maintained andproviding benefit to NA partners (governmentagencies, private sector, CGIAR, SROs/NARs, AGRA,NGOs, farmer organizations, etc)Organizing and Sharing Knowledge onthe Existence, Accessibility,Performance, and Sourcing ofInnovations (Technologies & Practices)
  • 24. What yields can specific technologiesand technology packages achieve?Can we tune yield targets andtechnology packages to different sub-national (e.g. agrecosystem) contexts?This section describes the application of cropping systems data and modeling tools tohelp augment national empirical data and inform national technical discussions aboutspecific geographic and technology targeting design and implementation choices
  • 25. Technology & Innovation CommitmentsNew Alliance communique (May 18, 2012) called forenabling actions to take innovation to scale by;• Determining 10 year targets for sustainable yields andadoption of new technologies that will increase food security,resilience, and nutrition outcomes• Launching a Technology Platform to assess availability of andshare knowledge about improved technologies and practices• Identifying current constraints to adoption• Launching a technology scaling initiative with AGRA• Share data with G8 and African partners and launch ICTinnovation challenge
  • 26. Questions to be Addressed - 1• Which CAADP commodities/value chains tofocus on?• What 10 year yield targets are achievable?• What existing technologies are available toachieve yield targets?• How best to tune yield targets and technologiesto different sub-national conditions (e.g. majoragroecosystems)?
  • 27. Questions to be Addressed - 2• What policies, strategies, and services areneeded to deliver the most appropriatetechnologies at scale and increase theprobability of their sustainable adoption?• How best to facilitate cross-country learning andknowledge spillover (e.g. “Virtual” TechnologyPlatform facilitated by technical support partners– FARA, CGIAR and AGRA)?
  • 28. Which CAADP commodities/valuechains to focus on?
  • 29. Commodity/Value Chain Prioritization• Goal: To identify 3-4 focus commodities/value chains for each country from withinCAADP priorities (typically 8-13 commodities)• Method: Spreadsheet prioritization model developed (HarvestChoice/IFPRI) forrefinement and validation with individual NA country teams• Criteria: Commodity prioritization derived as overall ranking based on user-definedweighting of the following criteria:– Size of sub-sector 2010 (National Value of Production - FAOSTAT)– Projected demand growth 2010-2030 (National demand growth – IFPRIIMPACT/Global Projections)– Exploitable yield gap (Current national yields vs Best bet technology performancewith best farmers * Expected national adoption within 10 years with NA focus &investments – Published trials/Expert opinion/Household data)– Share of benefits to poor (Share of bottom 40% of households who (a) produce &(b) consume the commodity – HarvestChoice HH survey collection )– Nutrition outcomes (Increased in national calorie and protein availability ifexploitable yield gap closed by expected amount in 10 year horizon – ∆Production *Calorie of protein per unit of production – Published coefficients)– NR sustainability (increased extraction of net (a) total nutrients (NPK) and (b)water consumption based on 10 year production increase – HarvestChoice data andPublished coefficients)– Attractiveness to the private sector (input market potential: adoption of improvedseeds, output market potential: share of output marketed – Published data, CGIAR,AGRA-PASS, HarvestChoice HH survey data collection)