The Implications of Climate Change on Mesoamerican Agriculture and Small-farmers Coffee Livelihoods Tropentag 7th of Octob...
CONTENT <ul><li>Why Coffee </li></ul><ul><li>The facts about climate change </li></ul><ul><li>Methodology </li></ul><ul><l...
WHY COFFEE <ul><li>One of the most important crop in terms of national agricultural GDP in countries of the tropics </li><...
THE FACTS
METHODOLOGY WorldClim (Hijmans et al, 2005) Climate (Current)
METHODOLOGY WorldClim (Hijmans et al, 2005) Climate (Current)
METHODOLOGY <ul><li>“ Global climate models” (GCMs) based on atmospheric science, chemistry, physics and biology </li></ul...
METHODOLOGY Climate (Future)
METHODOLOGY Variables
METHODOLOGY <ul><li>Current Climate </li></ul><ul><li>19 bioclimatic variables (WorldClim) </li></ul><ul><li>Climate Chang...
METHODOLOGY Crop Prediction Models MAXENT (Phillips et al. 2006) ECOCROP (Hijmans et al. 2005)
RESULTS
RESULTS
RESULTS
RESULTS Source: FAOSTATS Area Harvested  (Ha) 219584 Mangifera indica L. Mango 15 9175222 Zea mays L. s. mays Maize 14 167...
RESULTS Crop prediction models
RESULTS
RESULTS
RESULTS
CONCLUSIONS <ul><li>There will be areas that don’t produce coffee any more in the future </li></ul><ul><li>There will be a...
¡Muchas gracias!  Peter Laderach (CIAT) [email_address]
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The implications of climate change for agriculture in Mesoamerica and the livelihoods of smallholder coffee farmers.

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The implications of climate change for agriculture in Mesoamerica and the livelihoods of smallholder coffee farmers.

  1. 1. The Implications of Climate Change on Mesoamerican Agriculture and Small-farmers Coffee Livelihoods Tropentag 7th of October, Hamburg, Germany Peter Laderach (CIAT) [email_address] Peter Laderach, Andy Jarvis, Julian Ramirez, Anton Eitzinger, Oriana Ovalle
  2. 2. CONTENT <ul><li>Why Coffee </li></ul><ul><li>The facts about climate change </li></ul><ul><li>Methodology </li></ul><ul><li>Results </li></ul><ul><li>Conclusions </li></ul>
  3. 3. WHY COFFEE <ul><li>One of the most important crop in terms of national agricultural GDP in countries of the tropics </li></ul><ul><li>Millions of farmers in the tropics depend directly or indirectly on coffee (production, processing, etc) </li></ul><ul><li>Coffee systems (Agro-forestry) provide goods and services to farmers and the society </li></ul><ul><li>Demand for coffee in consumer countries </li></ul>
  4. 4. THE FACTS
  5. 5. METHODOLOGY WorldClim (Hijmans et al, 2005) Climate (Current)
  6. 6. METHODOLOGY WorldClim (Hijmans et al, 2005) Climate (Current)
  7. 7. METHODOLOGY <ul><li>“ Global climate models” (GCMs) based on atmospheric science, chemistry, physics and biology </li></ul><ul><li>Runs from the past (to calibrate) and into the future </li></ul><ul><li>Uses different gas emissions scenarios </li></ul>Climate (Future)
  8. 8. METHODOLOGY Climate (Future)
  9. 9. METHODOLOGY Variables
  10. 10. METHODOLOGY <ul><li>Current Climate </li></ul><ul><li>19 bioclimatic variables (WorldClim) </li></ul><ul><li>Climate Change </li></ul><ul><li>Downscaling: Spline interpolation (same as used in WorldClim) </li></ul><ul><li>Generation of 19 bioclimatic variables </li></ul><ul><li>Future Climate </li></ul><ul><li>Current Climate + Change = Future Climate </li></ul>
  11. 11. METHODOLOGY Crop Prediction Models MAXENT (Phillips et al. 2006) ECOCROP (Hijmans et al. 2005)
  12. 12. RESULTS
  13. 13. RESULTS
  14. 14. RESULTS
  15. 15. RESULTS Source: FAOSTATS Area Harvested (Ha) 219584 Mangifera indica L. Mango 15 9175222 Zea mays L. s. mays Maize 14 16756 Lactuca sativa var. capitata L. Lettuce 13 --- Andropogon gayanus Kunth Gamba 12 2119650 Phaseolus vulgaris L. Beans, dry 11 23001 Brassica oleraceae var. Botrytis Cabbages and other brassicas 10 59574 Allium cepa L. v cepa Onions, dry 9 1287388 Saccharum officinarum L. Sugarcane 8 82592 Theobroma cacao L. Cocoa beans 7 --- Brachiaria mutica Stapf. Brachiaria 6 207175 Musa acuminata Colla. Bananas 5 321764 Oriza sativa L. s. japonica Rice paddy 4 113414 Gossypium hirsutum L. Cotton, seed 3 19065 Agave sisalana americana Agaves Otras 2 189804 Elaeis guineensis Jacq. Oil palm 1 Scientific name Crops N Area Harvested (Ha) Scientific name Crops N 83054 Arachis hypogaea L. Maní con cáscara 16 422754 citrus Sinensis (L.) Osbeck Oranges 17 199595 Cocos nucifera L. Coconuts 18 90850 Solanum tuberosum L. Batata 19 26528 Carica papaya L. Papaya 20 66614 Ananas comosus Pineapples 21 57872 Musa balbisiana Colla Platains 22 --- Brassica oleraceae L.v capi. Repollo 23 104700 Sesamum indicum L. Sesame seed 24 78726 Glycine max Soybean 25 2026824 Sorghum bicolor L. Moench Sorghum 26 24713 Nicotiana tabacum L. Tabacco 27 130812 Lycopersicon esculentum M. Tomatoes 28 38322 Manihot sculenta Crantz. Cassava 29 17073 Daucus carota L. Carrot 30
  16. 16. RESULTS Crop prediction models
  17. 17. RESULTS
  18. 18. RESULTS
  19. 19. RESULTS
  20. 20. CONCLUSIONS <ul><li>There will be areas that don’t produce coffee any more in the future </li></ul><ul><li>There will be areas where in the future coffee can be produced under adapted agronomic management </li></ul><ul><li>There will be areas were today no coffee grows but which will be suitable in the future </li></ul><ul><li>The vulnerability of smallholder farmers to coffee and crop suitability decrease is very site-specific </li></ul><ul><li>Site specific adaptation strategies are needed </li></ul>
  21. 21. ¡Muchas gracias! Peter Laderach (CIAT) [email_address]

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