DAPA on World climate teach-in day

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DAPA on World climate teach-in day

  1. 1. The implications of climate change on agriculture and small-farmers livelihoods<br />A. Eitzinger, P. Laderach, A. Jarvis, J.Ramirez<br />CIAT - (International Center of Tropical Agriculture)<br />Presentation in the context of the „World Climate Teach-In Day“<br />
  2. 2. OUTLINE<br />Climate change: demanding information for agriculture<br />Estimate the impact using crop prediction models<br />Translate results on livelihoods<br />Upstream supply chain adaptation by participation<br />Conclusions<br />Some questions<br />
  3. 3. Climate change: demanding information for agriculture<br />Agriculture is a niche industry - high resolution of IPCC prediction models are needed; Downscaling techniques for 1 - 5 km resolution<br />Climate baseline; www.worldclim.org database (Hijmans et al, 2005).<br />Timeseries of future climate data; monthly data until 2030 (2050) a relevant for making decisions now <br />Certainty of prediction; Measurement of agreement between models<br />Biologically meaningful variables for crop caracterization<br />Modelling geographic distribution; crop-niche modeling <br />
  4. 4. Climate baseline WorldClim<br />Data from mayor climate db (more than 47.000)<br />SRTM elevation database as input<br />Interpolated by using thin plate smoothing splines<br />21 „global climate models“ GCMs<br />based on atmospheric science, chemistry, physics, biology<br />Run from the past through to the present and into the future<br />Use different scenarios for emissions<br />Downscaled by CIAT to 1 km resolution<br />
  5. 5. Bioclimatic variables<br />19 variables derived from monthly temperature and rainfall<br />Represent annual trends<br />Seasonality and extreme or limiting environmental factors<br />Examples: Annual mean temperature, Annual Precipitation, Maximum temperature of warmest month, Precipitation of Driest Month, Mean Temperature of Driest Quarter, Precipitation of Wettest Quarter, …<br />
  6. 6. Generation of future climate<br />Current climate from worldclim (1km resolution)<br />Prec, temp min/mean/max, 19 bioclims<br />Future climate<br />Calculate anomaly (future – current)<br />Downscale (spline interpolation)<br />Add to current climate (worldclim)<br />Calculate 19 bioclimatic variables for future climate<br />Current climate + Climate-Change = Future climate<br />
  7. 7. Estimate the impact using crop prediction models<br />EcoCrop - developed by the FAO(http://ecocrop.fao.org/ecocrop/srv/en/home)<br />MaxEnt - Maximum Entropy modelling of species geographic distributionhttp://www.cs.princeton.edu/~schapire/maxent/ <br />CaNaSTA – Crop Niche Selection for Tropical Agriculture http://csusap.csu.edu.au/~robrien/canasta/index.htm<br />AquaCrop - Crop Water Productivity Modelhttp://www.fao.org/nr/water/aquacrop.html<br />
  8. 8. Suitability of crops: MaxEnt principle<br />Example: Coffea arabica in Nicaragua<br />Input: Crop evidence (5.000 GPS points)<br />19 bioclimatic variables of<br />current (worldclim) & future climate (18 GCM)<br />Output:<br />Crop suitability (%)<br />
  9. 9. Relation between crop-suitability and altitude for current climates, and predicted for 2050<br />
  10. 10. Importance of different climatic drivers (of 19 bioclims)by stepwise regression<br />
  11. 11. Crop prediction models: Suitability of crops: Ecocrop model<br />Evaluates on monthly basis if there are adequate climatic conditions within a growing season for temperature and precipitation…<br />… and calculates the climatic suitability of the resulting interaction between rainfall and temperature…<br />
  12. 12. Example: department of Guatemala<br />Suitability change of 14 mayor crops<br />by the year 2050<br />Most crops are loosingsuitability between 20-40%<br />Few are gaining<br />
  13. 13. Translate results on livelihoods<br />Impact on production, pest and desease of crops in supply chain <br />Definition Systemvulnerability to climate change<br />Exposition of system<br />Sensibility of system on climate<br />Capacity to adapt<br />Searching for socio-economic indicators depending on climate-change by participatory analysis (Focus groups)<br />Use identified indicators for socio-econometric models to transalte results to livelihoods<br />
  14. 14. Data collection by surveys<br />Questions based on indicators ofThe Five Capitals Model of sustainable development<br />natural<br />human<br />social<br />financial<br />manufacture<br />
  15. 15. Methodology<br />Current and future crop Suitability<br />Socio-economic indicators<br />Economtetric models<br />System Vulnerability<br />econometric<br />models<br />
  16. 16. Upstream supply chain adaptation by participation<br />Workshops with case-studies<br />Include local expert-knowledge<br />Sharing experience<br />Best practise examples and adaptation strategies<br />Identify crop/production alternatives<br />
  17. 17. Conclusions <br />changes in crop suitability a site-specific because of its own very specific environmental conditions.<br />Solution is site-specific management<br />Climate change will bring not only bad news but also a lot of new potential.<br />The winners will be those who are prepared for change and know how to adapt.<br />
  18. 18. Some guiding questions<br />Where will crop grow in the future?<br />Where will crop not grow any more?<br />Where can crop still grow with adapted mgt?<br />What are crop prediction models?<br />What are the decisive climate factors to manage?<br />Which livelihoods are most affected?<br />How to translate to livelihoods?<br />How can we design value-chain up-streaming adaptation strategies?<br />What means participate analysis on climate change?<br />
  19. 19. What can we do?<br /><ul><li>investigate
  20. 20. share
  21. 21. compare
  22. 22. discuss</li></ul>…. existing information on climate-change impacts on agriculture<br />CIAT - DAPA Blog on climate change<br />http://gisweb.ciat.cgiar.org/dapablogs/ <br />
  23. 23. THANK YOU VERY MUCH<br />DAPA – “Decision and Policy Analysis” Program of CIAT<br />P. Laderach, A. Eitzinger, J.Ramirez, A. Jarvis <br />

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