Yield & Climate Variability: Learning from Time Series & GCM

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Yield & Climate Variability: Learning from Time Series & GCM

Presented by Amer Ahmed at the AGRODEP Workshop on Analytical Tools for Climate Change Analysis

June 6-7, 2011 • Dakar, Senegal

For more information on the workshop or to see the latest version of this presentation visit: http://www.agrodep.org/first-annual-workshop

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Yield & Climate Variability: Learning from Time Series & GCM

  1. 1. Yield & ClimateVariability:Learning fromTime Series & GCM Presented by: Amer Ahmed, World Bank www.agrodep.org AGRODEP Workshop on Analytical Tools for Climate Change Analysis June 6-7, 2011 • Dakar, Senegal Please check the latest version of this presentation on: http://agrodep.cgxchange.org/first-annual-workshop
  2. 2. Yield & Climate Variability:Learning from Time Series & GCM Amer Ahmed World Bank June 7, 2011 AGRODEP Members’ Meeting and Workshop Dakar, Senegal
  3. 3. Outline• Statistical models of crop response & climate – Focus on time series and panel data approaches – Ricardian approach of Mendelsohn et al. not covered• Illustrations & insights from recent research – Long run forecasts – Volatilities and extremes• Implications of methodology 2
  4. 4. General: Statistical Modeling Literature• Strengths: – Abstracts away from biophysical processes – Statistical measures of accuracy (e.g. model fit) – Does not need calibration• International time series production/yield data• Several publications in recent years (e.g. Lobell et al., 2008, 2011 in Science) – Generally negative impacts in LDCs, and tropics – Temperature often more important than precipitation 3
  5. 5. Climate and CO2 Changes Agricultural productivity Average Global Yields vs. Temperatures, 1961-2002Yield Change (%) Temperature Change (ºC) 4 Lobell and Field (2007)
  6. 6. Africa : Schlenker-Lobell (2010, ERL)• Data: – for estimation: 1961-2002 data from FAO, National Centers of Environmental Prediction, CRU; – for prediction: climate data from 16 GCMs (2046–2065)• Model – Four specifications: Average weather; Quadratic in average weather; Degree days; Degree day categories• Predicted impacts distribution 5
  7. 7. In all cases, except cassava – 95% probability that damages > 7% – 5% probability that damages > 27% 6
  8. 8. Changing Climate Volatility• Extreme outcomes may be particularly important for agriculture (White et al, 2006; Mendelsohn et al, 2007)• Climate volatility is already changing (Easterling et al, 2000) – Higher temperature and precipitation extremes in the future (IPCC, 2007) 7
  9. 9. US Maize Yield Response to Temperature 8 Schlenker and Roberts (2008)
  10. 10. 9Ahmed et al. (2009)
  11. 11. Illustration: Sensitivity of Tanzanian Grain to Climate Volatility• Ahmed, Diffenbaugh, Hertel, Lobell, Ramankutty, Rios, & Rowhani (GEC, 2011)• Econometric estimation to explain interannual change in yields using panel data from 17 administrative regions: 1992-2005 – Maize, rice, and sorghum yields (tonnes/ha) – Temperature (growing season mean in degrees C) – Precipitation (growing season mean in mm/month)• Use yield equation to translate historical and future climate into output changes 10
  12. 12. 11
  13. 13. Distribution of Interannual % Changes in Tanzanian Grains Yield due to ClimateMass of distributionshifting left median yield change(more/larger outcomeswith % decline in yieldfrom previous year) Outcomes within box represent 75% of all predicted outcomes 12 Ahmed et al. (2011)
  14. 14. Take Homes• Panel data approach can be powerful tool – Temperature more important than precipitation• African staples yields likely to decline• Climate change includes change in volatility – Extreme outcomes can be more important than mean changes• Need to account for heterogeneity & uncertainty due to GCMs – Bounded envelopes – Bias-correction 13

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