AGRODEP Workshop on Analytical Tools for Climate

Change Analysis

June 6-7, 2011 • Dakar, Senegal

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Yield & Climate

Variability:

Learning from

Time Series & GCM

Presented by:

Amer Ahmed, World Bank

Please check the latest version of this presentation on:

http://agrodep.cgxchange.org/first-annual-workshop

Yield & Climate Variability: Learning from Time Series & GCM

Amer Ahmed

World Bank

June 7, 2011

AGRODEP Members’ Meeting and Workshop

Dakar, Senegal

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

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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

Agricultural productivity

Climate and CO2 Changes

Temperature Change (ºC)

Yiel

d C

han

ge (

%)

Average Global Yields vs. Temperatures, 1961-2002

Lobell and Field (2007)4

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

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In all cases, except cassava

– 95% probability that damages > 7%

– 5% probability that damages > 27%

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)

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US Maize Yield Response to Temperature

Schlenker and Roberts (2008)8

Ahmed et al. (2009)9

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

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Distribution of Interannual % Changes inTanzanian Grains Yield due to Climate

median yield change

Mass of distribution shifting left (more/larger outcomes with % decline in yield from previous year)

Outcomes within box represent 75% of all predicted outcomes

Ahmed et al. (2011)12

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-correction13