Prioritizing climate change adaptation needs for food security in 2030

Science

Volumn 319, Issue 5863, 2008, Pages 607-610

  Lobell, David B ^a,b   Burke, Marshall B ^a   Tebaldi, Claudia ^c   Mastrandrea, Michael D ^a   Falcon, Walter P ^a   Naylor, Rosamond L ^a  

^a STANFORD UNIVERSITY   (United States)

^b LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

^c NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGRICULTURAL DEVELOPMENT; AGRICULTURAL ECONOMICS; CLIMATE CHANGE; CLIMATE EFFECT; FOOD SECURITY; GENERAL CIRCULATION MODEL; UNCERTAINTY ANALYSIS;

AFRICA; AGRICULTURAL MANAGEMENT; ARTICLE; CLIMATE CHANGE; CROP; EVOLUTIONARY ADAPTATION; FOOD SECURITY; POPULATION; PRIORITY JOURNAL; SOUTH ASIA; STATISTICAL MODEL;

AFRICA, SOUTHERN; AGRICULTURE; ASIA; BAYES THEOREM; CLIMATE; CROPS, AGRICULTURAL; FOOD SUPPLY; MODELS, STATISTICAL; MONTE CARLO METHOD; PROBABILITY; RISK; TEMPERATURE;

AFRICA; ASIA; EURASIA; SOUTH AFRICA; SOUTH ASIA; SOUTHERN AFRICA; SUB-SAHARAN AFRICA;

EID: 38849177523     PISSN: 00368075     EISSN: 10959203     Source Type: Journal    
DOI: 10.1126/science.1152339     Document Type: Article

References (11)

1. W. Easterling et al., in Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, Cambridge, 2007), pp. 273-313.
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4. J. M. Alston, C. Chan-Kang, M. C. Marra, P. G. Pardey, T. J. Wyatt, "A meta-analysis of rates of return to agricultural R&D: Ex pede Herculem?" (International Food Policy Research Institute, Washington, DC, 2000).
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6. We used FAO data on national crop production and area, which include quantities consumed or used by the producers in addition to those sold on the market.
7. Model simulations under three SRES (Special Report on Emissions Scenarios) emission scenarios corresponding to relatively low (B1), medium (A1b), and high (A2) emission trajectories were used. Although the mean projections for the emission scenarios exhibit very small differences out to 2030, the use of three scenarios provided a larger sample of simulations with which to assess climate uncertainty. For all simulations, average monthly output for 1980-1999 was subtracted from that of 2020-2039 to compute monthly changes in temperature and precipitation.
8. J. H. Christensen et al., in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, S. Solomon et al., Eds. (Cambridge Univ. Press, Cambridge, 2007), pp. 847-940.
9. Namely, the crop regression model was fit with a bootstrap sample from the historical data, and the coefficients from the regression model were then multiplied by projected changes in average temperature and precipitation, which were randomly selected from the CMIP3 database. This process was repeated 100 times for each crop. Bootstrap resampling is a common approach to estimate uncertainty in regression coefficients, although this addresses only the component of model uncertainty that arises from a finite historical sample and not the potential uncertainty from structural errors in the model. Similarly, the representation of climate uncertainty by equally weighting all available GCMs is a common approach but could potentially be improved, such as by weighting models according to their agreement with the model consensus and with historical observations. Nonetheless, the resulting probability distributions incorporate reasonable measures of both climate and crop uncertainty, and thus should fairly reflect both the absolute and relative level of uncertainties between crops.
10. B. Smit et al., in Climate Change 2001: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, Cambridge, 2001), pp. 877-912.
11. We thank D. Battisti, C. Field, and three anonymous reviewers for helpful comments. D.B.L. was supported by a Lawrence Fellowship from LLNL. Part of this work was performed under the auspices of the U.S. Department of Energy (DOE) by LLNL under contract DE-AC52-07NA27344. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison, and the WCRP's Working Group on Coupled Modelling for their roles in making available the WCRP CMIP3 multimodel data set. Support of this data set is provided by the Office of Science, DOE.