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

Abstract

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.

Authors:
 [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:
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)
OSTI Identifier:
1426663
Alternate Identifier(s):
OSTI ID: 1426770
Grant/Contract Number:  
AC02-06CH11357; SES- 0951576; 1215910; DGE-1133082; OCI-1148443; SES-0951576
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Agricultural Systems
Additional Journal Information:
Journal Volume: 159; Journal Issue: C; Journal ID: ISSN 0308-521X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 29 ENERGY PLANNING, POLICY, AND ECONOMY; 54 ENVIRONMENTAL SCIENCES; Climate extremes; Drought impacts; Agriculture; Seasonal prediction; Adaptation

Citation Formats

Elliott, Joshua, Glotter, Michael, Ruane, Alex C., Boote, Kenneth J., Hatfield, Jerry L., Jones, James W., Rosenzweig, Cynthia, Smith, Leonard A., and Foster, Ian. Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management. United States: N. p., 2018. Web. doi:10.1016/j.agsy.2017.07.012.
Elliott, Joshua, Glotter, Michael, Ruane, Alex C., Boote, Kenneth J., Hatfield, Jerry L., Jones, James W., Rosenzweig, Cynthia, Smith, Leonard A., & Foster, Ian. Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management. United States. doi:10.1016/j.agsy.2017.07.012.
Elliott, Joshua, Glotter, Michael, Ruane, Alex C., Boote, Kenneth J., Hatfield, Jerry L., Jones, James W., Rosenzweig, Cynthia, Smith, Leonard A., and Foster, Ian. Mon . "Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management". United States. doi:10.1016/j.agsy.2017.07.012. https://www.osti.gov/servlets/purl/1426663.
@article{osti_1426663,
title = {Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management},
author = {Elliott, Joshua and Glotter, Michael and Ruane, Alex C. and Boote, Kenneth J. and Hatfield, Jerry L. and Jones, James W. and Rosenzweig, Cynthia and Smith, Leonard A. and Foster, Ian},
abstractNote = {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.},
doi = {10.1016/j.agsy.2017.07.012},
journal = {Agricultural Systems},
issn = {0308-521X},
number = C,
volume = 159,
place = {United States},
year = {2018},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

Fig. 1. Fig. 1.: A) Comparison between June-August average Z-index weighted by maize production over the US Corn Belt (left axis) and the observed deviation of average maize yield from trend (right axis); and the June–August Palmer Z-Index (by US climate division) for B) 1988 and (C) 2012.

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Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.