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Effects of Weather Change on Agricultural, Food Production & the Developing World Wolfram Schlenker Columbia University and NBER New School - November 18, 2013 Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New

  1. Effects of Weather Change on Agricultural, Food Production & the Developing World Wolfram Schlenker Columbia University and NBER New School - November 18, 2013 Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 1 / 35

  2. Outline Motivation for Statistical Studies 1 Modeling US Yields 2 Water versus Temperature 3 Modeling Yields in Africa 4 Climate Change and Global Production Trends 5 Conclusions 6 Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 2 / 35

  3. Outline Motivation for Statistical Studies 1 Modeling US Yields 2 Water versus Temperature 3 Modeling Yields in Africa 4 Climate Change and Global Production Trends 5 Conclusions 6 Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 3 / 35

  4. Statistical Approaches Statistical studies Estimated using real-world data Aggregate or field-level data Limited number of variables Usually precipitation and temperature (degree days) Agronomic models use many more variables Estimated impacts of climate change Predicted changes in yields/profits from panel Estimating adaptation using cross-section Studies regress trends in yields on trends in weather Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 4 / 35

  5. Statistical Approaches Statistical studies Estimated using real-world data Aggregate or field-level data Limited number of variables Usually precipitation and temperature (degree days) Agronomic models use many more variables Estimated impacts of climate change Predicted changes in yields/profits from panel Estimating adaptation using cross-section Studies regress trends in yields on trends in weather What key variable to use? Statistical studies have learned from crop models Degree days (non-linear transformation) Degrees above (below) threshold Statistical studies have identified key parameters E.g., negative effects of extreme heat Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 4 / 35

  6. Construction of Degree Days Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 5 / 35

  7. Construction of Degree Days Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 5 / 35

  8. Construction of Degree Days Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 5 / 35

  9. Construction of Degree Days Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 5 / 35

  10. Outline Motivation for Statistical Studies 1 Modeling US Yields 2 Water versus Temperature 3 Modeling Yields in Africa 4 Climate Change and Global Production Trends 5 Conclusions 6 Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 6 / 35

  11. Link between Temperature and US Yields Statistical Analysis Panel of county-level yields in Eastern United States Corn and Soybeans (two biggest staple commodities in US) Fine-scale weather (daily temperature / precip on 2.5mile grid) Years: 1950-2005 Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 7 / 35

  12. Link between Temperature and US Yields Statistical Analysis Panel of county-level yields in Eastern United States Corn and Soybeans (two biggest staple commodities in US) Fine-scale weather (daily temperature / precip on 2.5mile grid) Years: 1950-2005 Model accounts for Amount of time spent in each 1 ◦ C interval Quadratic in total precipitation State-specific quadratic time trends County fixed effects Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 7 / 35

  13. Results: Effect of Weather on Yields Panel of Corn and Soybean Yields Schlenker & Roberts (PNAS 2009) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 8 / 35

  14. Results: Source of Variation Corn and Soybean Yields - Various Source of Identification Schlenker & Roberts (PNAS 2009) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 9 / 35

  15. Results: Climate Impacts Climate Impacts - Uniform Scenarios Schlenker & Roberts (PNAS 2009) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 10 / 35

  16. Recent Example: 2012 Heat Wave / Drought Berry, Roberts & Schlenker (2013) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 11 / 35

  17. Recent Example: 2012 Heat Wave / Drought Berry, Roberts & Schlenker (2013) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 12 / 35

  18. Recent Example: 2012 Heat Wave / Drought Berry, Roberts & Schlenker (2013) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 13 / 35

  19. Adaptation to Trends (1980-2005) Burke & Emerick (2013) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 14 / 35

  20. Adaptation to Trends (1980-2005) Burke & Emerick (2013) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 15 / 35

  21. Outline Motivation for Statistical Studies 1 Modeling US Yields 2 Water versus Temperature 3 Modeling Yields in Africa 4 Climate Change and Global Production Trends 5 Conclusions 6 Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 16 / 35

  22. Agronomic Evidence on Mechanism Schlenker & Roberts (PNAS 2009) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 17 / 35

  23. Agronomic Evidence on Mechanism Biophysical evidence Lobell, Hammer, McLean, Messina, Roberts, Schlenker (2013) APSIM: biophysical model of crop growth Includes water balance, etc Mechanism behind EDD (Extreme degree days) Impacts water stress in two ways Reducing soil water (evaporation) Increased demand for soil water to sustain carbon uptake Precipitation only impacts soil moisture Drought is a relative concept Water requirements depend on temperature Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 17 / 35

  24. Heat versus Water Water versus Temperature: Chicago Marathon (2007) in Hot Weather Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 18 / 35

  25. Heat versus Water Water versus Temperature: Chicago Marathon (2007) Ran Out of Water Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 18 / 35

  26. Heat versus Water Lobell, Hammer, McLean, Messina, Roberts, & Schlenker (2013) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 18 / 35

  27. Outline Motivation for Statistical Studies 1 Modeling US Yields 2 Water versus Temperature 3 Modeling Yields in Africa 4 Climate Change and Global Production Trends 5 Conclusions 6 Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 19 / 35

  28. Statistical Study in Africa Lobell, B¨ anzinger, Magorokosho, and Vivek (2011) Unique data set of fiel trials 123 research stations CIMMYT Testing for drought conditions Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 20 / 35

  29. Statistical Study in Africa Lobell, B¨ anzinger, Magorokosho, and Vivek (2011) Unique data set of fiel trials 123 research stations CIMMYT Testing for drought conditions Matched with closest weather station Better than gridded weather data Authors split season into three phases (separate coefficients) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 20 / 35

  30. Statistical Study in Africa Lobell, B¨ anzinger, Magorokosho, and Vivek (2011) Unique data set of fiel trials 123 research stations CIMMYT Testing for drought conditions Matched with closest weather station Better than gridded weather data Authors split season into three phases (separate coefficients) Major results Find nonlinearity effect of temperature on yield Stronger under drought conditions Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 20 / 35

  31. Location of Field Trials Lobell, B¨ anzinger, Magorokosho, & Vivek (2011) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 21 / 35

  32. Regression Coefficients for Temperature Lobell, B¨ anzinger, Magorokosho, & Vivek (2011) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 22 / 35

  33. Simulating 1 ◦ C Warming Lobell, B¨ anzinger, Magorokosho, & Vivek (2011) Wolfram Schlenker (Columbia and NBER) Weather Extremes and Agriculture New School - November 18, 2013 23 / 35

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