[Day 2] Center Presentation: IFPRI
Upcoming SlideShare
Loading in...5
×
 

[Day 2] Center Presentation: IFPRI

on

  • 1,072 views

Presented by Jawoo Koo, Zhe Guo, and Stanley Wood at the CGIAR-CSI Annual Meeting 2009: Mapping Our Future. March 31 - April 4, 2009, ILRI Campus, Nairobi, Kenya

Presented by Jawoo Koo, Zhe Guo, and Stanley Wood at the CGIAR-CSI Annual Meeting 2009: Mapping Our Future. March 31 - April 4, 2009, ILRI Campus, Nairobi, Kenya

Statistics

Views

Total Views
1,072
Views on SlideShare
995
Embed Views
77

Actions

Likes
0
Downloads
13
Comments
0

1 Embed 77

http://www.cgiar-csi.org 77

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    [Day 2] Center Presentation: IFPRI [Day 2] Center Presentation: IFPRI Presentation Transcript

    • It turns out… believe it or not… IFPRI … IS SPATIAL TOO! crop fertilizer final remarks introduction profitability modeling JAWOO KOO, ZHE GUO, AND STANLEY WOOD INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE CGIAR-CSI 2009, ILRI, Nairobi, Kenya (1 APRIL 2009)
    • SChEF ECONOMIC EVALUTION dialogue with CHANGES stakeholders/user groups on nutrient & water scenarios, evaluation, and managements, technology-scale evaluation germplasm adoption, climate change, etc. BASELINE characterization, Ingredients potential productivity,  SPAM profitability  Production systems characterization  SIMPLR  Price modeling  DREAM  Web-based data query and visualization tools SPATIAL CHARATERIZATION & EVALUATION FRAMEWORK SChEF
    •  ASSESSMENT OF PRODUCTIVITY RESPONSES TO DETERMINANTS AND THEIR INTERACTIONS  Variety  Nutrient management  Water management  Biotic constraints  Abiotic constraints  Climate change  DYNAMIC CROP SYSTEMS MODELS  DSSAT  APSIM  WOFOST  ORYZA IFPRI HPC (80 CPU’s) GRID-BASED REGIONAL-SCALE ASSESSMENT OF CROP RESPONSES TO CHANGES DYNAMIC CROP SYSTEMS MODELS
    •  Climatology  Soils  Cropping Patterns  Crop Variety  Planting Window  Planting Density  Water Management  Nutrient Management (N and P*)  Residue Management*  Biotic Stresses*  Soil Constraints and Interventions (P and pH)* *Coming Soooooooon! CROP SYSTEMS MODEL APPLICATIONS SIMPLR MODELING TEMPLATE
    • Global / Monthly mean of 1950-2000 Tmax,Tmin,Rain / 1km Global / Daily 1997-recent Srad,Tmax,Tmin,Rain / 1 degree* *WorldWeather downscales to 15’ HISTORIC CLIMATOLOGY DAILY WEATHER WorldClim NASA-POWER
    • Continent Count Africa 1300 Asia 975 Australia 31 Europe 370 North America 291 Oceania 49 South America 388 Total 3404 GLOBAL SOIL PROFILES ISRIC WISE 1.1 DATABASE
    • GENERIC SOIL PROFILES (HWSD+WISE1.1) HC27: Fertility x Texture x Depth
    •  Crop: MAIZE  Model: DSSAT-CSM v4  Region: SSA  Resolution: 5 ARC-MIN  Climate: NASA-POWER (1998-2007)  Soils: HC27  Managements:  Monthly planting window  Rainfed/Irrigation  Three cultivars (long, medium, and short maturity)  Supplemental N fertilizer applications (0, 20, …, 100 kg*N+/ha) Latest Dataset Release (Ver.SIM09064) SPATIAL EVALUATION OF MAIZE PERFORMANCE
    •  Cassava, Chickpea, Cowpea, Fababean, Maize, Millet, Mungbean, Potato, Sorghum, Soybean, Sweet Potato, Wheat, Rice  WOFOST & ORYZA  Global, 1-degree MAIZE | Water limited, med. maturity, coarse soil texture  NASA-POWER daily weather (1997-2007)  Options:  Biweekly planting window  Water-limit vs. potential  Sandy or clayey soil texture  Three maturity classes MAIZE | Potential with no limitation REFERENCE LAYERS GLOBAL YIELD POTENTIAL
    • Scenario A2A (2050-2000) kg/ha < -3,000 MAIZE -2,999 - -2,000 -1,999 - -1,000 -999 - -500 -499 - 500 501 - 1,000 1,001 - 2,000 > 2,001 Scenario A2A (2050-2000) Maize: Climate change hotspots (red) kg/ha GROUNDNUT < -3,000 -2,999 - -2,000 -1,999 - -1,000 -999 - -500 -499 - 500 501 - 1,000 1,001 - 2,000 > 2,001 HOTSPOT ANALYSIS CLIMATE CHANGE IMPACTS
    • Crop model- + SPATIAL Yield ANN generated ASPATIAL Climate, Soil impacts training datasets REDUCED FORM OF CROP SYSTEMS MODEL CLIMATE CHANGE IMPACTS
    • Beyond Nitrogen: 1. GIS: What is the location and severity of key soil productivity constraints in SSA? What crop areas are affected? 2. SIMPLR: How much yield and production is “currently” being lost because of these constraints? What is the annual costs of those losses? 3. SIMPLR: To what extent can the impact of these constraints be mitigated by improved inputs/management interventions? 4. DREAM: What is the potential size and distribution of economic benefits of mitigation/intervention hbo.com NEW THEME 2009 ABIOTIC CONSTRAINTS TO CROP PRODUCTIVITY AND PRODUCTION
    • Key Factors Influencing Fertilizer Adoption & Profitability Farmgate Output Markets Location & Production Prices System Specific Fertilizer Responses (Climate/Weather Farmgate Transport Soils, Management) Fertilizer Costs Prices (on & off road) Policy Experiments for a more enabling environment (a) negotiated urea import discounts Fertilizer Import Costs (b) reduced unit transport costs (c) reduce border crossing costs
    • Maize price Port/border processing fee Y2008 Y2006 Y2004 U.S. $/Mt 395.9841 224.3687 219 Migori : KE U.S.$/kg Stevedore Handling Removal Charge storage Terminal handling 270.7492 153.4093 153.3523 Kitale : KE Kenya 0.008 0.006 0.002 0.0005 0.008 310.3333 191.3333 206.6667 Eldoret : KE Uganda 0.008 0.006 0.002 0.0005 0.008 267.4 169.6667 219 Nakuru : KE 293.6 225 219.25 Nairobi : KE Tanzania 0.005 0.004 0.004 0.0005 0.008 395.3333 224 223.9167 Kisumu : KE Rwanda 0.005 0.004 0.004 0.0005 0.008 291.4 217.3333 210.75 Mombasa : KE Burundi 0.005 0.004 0.004 0.0005 0.008 347.6927 220.6128 245 Kitui : KE 265.5 168.875 138 Busia : KE Source: www.tradeafrica.biz 283.8 270.25 184.2 Kigali : RW A Guide For Maize Traders on Regulatory Requirements For Import and Export Maize in East African Community 259.5 240.1667 191 Ruhengeri : RW 306.6 187.8333 164.6667 Dar es salaam : TZ 276.6981 156.78 187.6364 Arusha : TZ 231.8831 146.615 114.25 Mbeya : TZ 260.012 205.1448 186.9 Mwanza : TZ 224.5972 153.7778 108.5 Songea : TZ 211.7557 133.8889 118 Sumbawanga : TZ 246.4771 196.1111 176.3333 Tanga : TZ 277.8545 219.2222 206 Bukoba : TZ 252.2355 155.375 133 Iganga : UG 237.6648 175.9224 168.3636 Kabale : UG 245.2 181.5 172.1667 Kampala : UG 200.5419 181 153.3333 Kasese : UG 205.2909 151.9588 150.1818 masindi : UG 303 159.75 165.3333 Mbale : UG 254 171.25 150.6667 Lira : UG Source: Regional Agricultural trade intelligence network
    • Policy Scenarios Scenario:1 Reduced Procurement Cost Negotiated decrease in import price of Urea (50%) Scenario 2: Road Transport Cost Reduce road transport costs by 20% per ton per km Scenario 3: Reduce Border Cost Border-crossing cost reduces 50% Baseline
    • Maize Markets & Market Prices 40 maize 40 towns & cities reporting 2005 av. maize prices in “marketsheds” maize prices each marketshed
    • Maize transport costs from Net maize “farmgate” price farmgate to target market
    • VCRs: Baseline and policy scenarios 1 & 2 (estimated at 35kg N/ha) < 0.5 0.5-1.0 Baseline 2: 20% lower road transport 1: 50% lower urea 1.1-2.0 cost procurement price 2.1-4.0 >4.0
    • VCR By Country & Market Access (early 2008) VCR by Market Access Class Country High Med. Low Average Burundi 2.5 2 2 2.25 Kenya 2.75 2.25 1.5 2.25 Rwanda 2 1.5 1.5 1.75 Tanzania 3.25 2.75 1.25 2.5 Uganda 3 2 1.75 2 All 2.75 2.25 1.5 2.25
    • WHERE TO INVEST? AG MARKET FINDER | marketfinder.info
    • Demonstration* http://marketfinder.info *Please, FREE the network for a moment! WHERE TO INVEST? AG MARKET FINDER | marketfinder.info
    •  SPAM* UPDATE  2000 ver.3  2005  AEZ UPDATE FOR SSA  HARVESTCHOICE DOMAIN  VISUALIZATIONS: GKS-2-HMNS *NO name change! THINGS KEEP US BUSY UPDATES
    • Mashup Ingredients  Map data (e.g., Shapefile)  Map tiles using GMapCreator  Google Maps API  Base map  Interface  Marker  Reverse Geocoding  Overlaying tiles  Ajaxing using jQuery  Apache Web Server  Data tables in MySQL  Programming in PHP (CodeIgniter)  Google Chart API  Lots of Coffee DATA EXPLORATION AND VISUALIZATION DROPPR