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Title: Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management

Process-based agricultural models, applied in novel ways, can reproduce historical crop yield anomalies in the US, with median absolute deviation from observations of 6.7% at national-level and 11% at state-level. In seasons for which drought is the overriding factor, performance is further improved. Historical counterfactual scenarios for the 1988 and 2012 droughts show that changes in agricultural technologies and management have reduced system-level drought sensitivity in US maize production by about 25% in the intervening years. Finally, we estimate the economic costs of the two droughts in terms of insured and uninsured crop losses in each US county (for a total, adjusted for inflation, of 9 billion USD in 1988 and 21.6 billion USD in 2012). We compare these with cost estimates from the counterfactual scenarios and with crop indemnity data where available. Model-based measures are capable of accurately reproducing the direct agro-economic losses associated with extreme drought and can be used to characterize and compare events that occurred under very different conditions. This study suggests new approaches to modeling, monitoring, forecasting, and evaluating drought impacts on agriculture, as well as evaluating technological changes to inform adaptation strategies for future climate change and extreme events.
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [4] ;  [3] ;  [6] ;  [7]
  1. Univ. of Chicago, IL (United States). Computation Inst.; Argonne National Lab. (ANL), Lemont, IL (United States)
  2. Univ. of Chicago, IL (United States). Dept. of the Geophysical Sciences
  3. NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States)
  4. Univ. of Florida, Gainesville, FL (United States). Agricultural and Biological Engineering Dept.
  5. US Dept. of Agriculture (USDA)., Ames, IA (United States). National Lab. for Agriculture and the Environment
  6. London School of Economics, London (United Kingdom). Center for Analysis of Time Series
  7. Univ. of Chicago, IL (United States). Computation Inst.; Computation Inst.; Argonne National Lab. (ANL), Lemont, IL (United States)
Publication Date:
Grant/Contract Number:
AC02-06CH11357; SES- 0951576; 1215910; DGE-1133082; OCI-1148443; SES-0951576
Accepted Manuscript
Journal Name:
Agricultural Systems
Additional Journal Information:
Journal Volume: 159; Journal Issue: C; Journal ID: ISSN 0308-521X
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC); Univ. of Chicago, IL (United States); Agricultural Model Intercomparison and Improvement Project (AgMIP); National Science Foundation (NSF)
Country of Publication:
United States
60 APPLIED LIFE SCIENCES; 29 ENERGY PLANNING, POLICY, AND ECONOMY; 54 ENVIRONMENTAL SCIENCES; Climate extremes; Drought impacts; Agriculture; Seasonal prediction; Adaptation
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1426770