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Mer F - Use of climate predictions for impact studies, Nairobi Aug 2012


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Mer F - Use of climate predictions for impact studies, Nairobi Aug 2012

  1. Using climate predictions for impact studies Nairobi, August 2012 Flora Mer
  2. The World is changing…. Population growth Industrial revolution Non-environmentally friendly technologies/practices LEAD TO GREENHOUSE GASES EMISSIONS INCREASING
  3. Temperature is increasing…
  4. Rainfall is changing…
  5. Changes in climates affect crops we grow... There will be winners… Number of crops with more than 5% gain…But muchmore losers indevelopingcountries Number of crops with more than 5% loss
  6. We need models to quantifyimpacts and design effective adaptation options
  7. GCMs Statistical Downscaling MarkSim Dynamical downscaling: Regional Climate Model DSSAT Statistical Downscaling GLAM EcoCrop Effective MaxEnt adaptation options Bias correction Any model
  8. Crop Models ProbabilityEcoCrop Environmental gradientMaxEnt Models based on crop nichesDSSAT Models based on processesGLAM
  9. The Model: EcoCrop– A simple algorithm to look at the broad niche of each species only based on climate data– Ten growing parameters to set up the model • Absolute rainfall interval • Absolute temperature interval • Optimum rainfall interval • Optimum temperature interval • Length of the growing season • Crop freezing temperature– Use of climate data • Statistical downscaling of GCMs (IPCC4) • Present-day climates from WorldClim = Interpolations of observed data, representative of 1950-2000 • 24 different climate models (GCMs) to sample uncertainties
  10. The Model: EcoCrop • So, how does it work?It evaluates on monthly basis if thereare adequate climatic conditionswithin a growing season for …and calculates the climatic suitability of thetemperature and precipitation… resulting interaction between rainfall and temperature…
  11. Common Bean Current SuitabilityKiling temperature (°C) 0 Growing season (days) 90Minimum absolute temperature (°C) 13.55 Minimum absolute rainfall (mm) 200.0Minimum optimum temperature (°C) 17.45 Minimum optimum rainfall (mm) 362.5Maximum optimum temperature (°C) 23.05 Maximum optimum rainfall (mm) 449.5Maximum absolute temperature (°C) 25.63 Maximum absolute rainfall (mm) 710.0
  12. Common Bean Future Suitability and Change 2030s SRES-A1B 2030s SRES-A1B
  13. Bean regional impacts
  14. Average change in suitability for 50 food crops in 2050s
  15. Suitability changesCrop Comparison in Africa
  16. MaxEnt Maximum Entropy Modelling• Model predicting the potential distribution of a species• Statistical dowscaling method apply to climate data.• Many modellers use the set of the bioclimatic variables Maxent use the principle of the maximum entropy Maxent use only presence point of specific species and environmental variables• One of the most accurate model for the prediction of shifts in suitable growth ranges of species
  17. MaxEnt Application on Kenyan coffee Main coffee-producing areas in Kenya are located in two areas: - the central region around Mount Kanya - in the Rift Valley in the west - The most suitable areas: in the higher areas of Bungoma, Embu, Kericho, Kiambu, Kirinyaga, Kisii, Machakos, Meru, Muranga, Nithi, Nyamira, Nyeri and Trans-NzoiaNew marketsManagement Alternatives to tea
  18. DSSAT Decision Support System for Agrotechnology Transfer• Based on crop processes• Integrate the interaction of weather, soil, management and genetic factors• Prediction of yields, plant phenologic stages, plant weight,harverst date, water soil quantity, N quantity…• Current & Future predictions• Need precise and daily data
  19. DSSAT predicts yields Jones and Thornton, 2003Maize Yield negatively impacted by CC in most areas in Africa Need effective adaptation options
  20. DSSAT predicts yields In 2055: Maize Yield would be negatively impacted by CC in most areas in Ethiopia Need to develop adaptation strategies Jones and Thornton, 2003
  21. GLAM - General Large Area Model Challinor et al. (2004) • Designed at climate model scale to capitalize on known large-scale relationships between climate and crop yield, thus avoiding over-parameterization. Uses grid-scaled agricultural statistics To simulate yields at to simulate yields climate model scale Large-area models are able to reproduce large-scale historical yield responses to climate and inter-annual variabilityObserved peanut yields (kg/ha) Rate of simulated to observed yields
  22. Conclusions• Different models exist for evaluating the impact of CC on crops• The application of each model depends on what information we have and what we want to know: • Daily/Monthly data • Crop suitability • Yield • Agricultural management practices• Impact studies at agricultural level benefit from having climate data of higher resolution• Inform adaptation to stakeholders: policy makers, donors, other researchers, but also farmers.• Gaps: Need more research to understand better crop responses to climate change  To decrease uncertainties
  23. Future research plans• Input climate data quality and its effects on impact predictions• Analysis of trial data to better understand crop responses to environment (soil, nutrients, CO2, heat stress, drought stress, and their interactions)• Expansion of crop model parameterisation, including multi-Ag- Model ensembles• Impacts of future climate change and future variability on crop yields• Uncertainty quantification (crop and climate)• Design of crop genotypic adaptation strategies (“ideotypes”) and link with analogues to find useful germplasm• Scale up adaptation strategies to national/ international level (policy-making)
  24. Thank you ! Any questions?