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Julian R - Using the EcoCrop model and database to forecast impacts of cc


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Preliminary results on the assessment of global food security issues under changing climates. Presented at Tyndall Centre, Norwich, UK, by Julian Ramirez

Published in: Technology

Julian R - Using the EcoCrop model and database to forecast impacts of cc

  1. 1. Using the EcoCrop niche model to forecast impacts of climate change on global crop production Julián Ramírez and Andy Jarvis International Centre for Tropical Agriculture (CIAT) Bioversity International Cali, Colombia
  2. 2. Impact assessment: methods and data <ul><li>The model: EcoCrop </li></ul><ul><ul><li>Based on expert knowledge </li></ul></ul><ul><ul><li>A simple algorithm to look at the broad niche of each species </li></ul></ul><ul><ul><li>Ten growing parameters to set up the model </li></ul></ul><ul><ul><ul><li>Absolute rainfall interval </li></ul></ul></ul><ul><ul><ul><li>Absolute temperature interval </li></ul></ul></ul><ul><ul><ul><li>Optimum rainfall interval </li></ul></ul></ul><ul><ul><ul><li>Optimum temperature interval </li></ul></ul></ul><ul><ul><ul><li>Lenght of the growing season </li></ul></ul></ul><ul><ul><ul><li>Crop freezing temperature </li></ul></ul></ul>
  3. 3. The Model: EcoCrop <ul><li>So, how does it work? </li></ul>It evaluates on monthly basis if there are adequate climatic conditions within a growing season for temperature and precipitation… … and calculates the climatic suitability of the resulting interaction between rainfall and temperature…
  4. 4. Choosing target crops <ul><li>50 target crops selected based on area harvested in FAOSTAT </li></ul>
  5. 5. Playing around with all this… <ul><li>Changes in suitability and confidence for all crops… most impacted regions… </li></ul>
  6. 6. More regional level impacts Percent of crops with significant gains /losses in each region… bad news for North and Sub-Saharan Africa, Latin America, the Caribbean and the Pacific… Maximum positive and negative changes per region… Europe: the big winner…
  7. 7. And a bit more… <ul><li>Globally… the most negatively impacted is wheat, specially in North America… </li></ul><ul><li>Staples to be affected significantly… </li></ul>
  8. 8. Crop-based results <ul><li>Global suitability change of all crops. Bubble size is coefficient of variation </li></ul>
  9. 9. Crop-based results <ul><li>Relative importance versus percent of impacted lands </li></ul>Contrasting responses on the same industry
  10. 10. Regional and crop-based results: contrasts EUROPE NORTH AFRICA SUB-SAHARAN AFRICA LATIN AMERICA
  11. 11. Country based results <ul><li>Change in suitability for all world countries for all crops, versus percent of area with loss. Bubble size is the percent of rural population within the country. </li></ul>
  12. 12. Country based results Current suitability and predicted changes to 2050s for geographic regions. Note the high variability in SSA and Asia, while relatively low variability in changes within Latin America Current suitability and number of crops with significant negative changes. Bubble size is Infant Mortality Rate
  13. 13. Average change in suitability for all crops in 2050s
  14. 14. Winners and losers Number of crops with more than 5% loss Number of crops with more than 5% gain
  15. 15. Next steps <ul><li>Validate the model and adjust the parameters </li></ul><ul><li>Downscale suitability predictions to yields and… producers’ income </li></ul><ul><li>Evaluate the economic impacts of adaptation strategies. Where, when, how to adapt? </li></ul><ul><li>Use the model to evaluate likelihood of crop substitution </li></ul><ul><li>More regionally oriented results (incl. RCM) </li></ul>
  16. 16. Next steps: validate and adjust the parameters <ul><li>Expert based model… expert based validation and re-parameterisation </li></ul><ul><li>GoogleEarth KMLs and web-based plugin </li></ul>Suitability prediction Visual validation Survey to gather validation data