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

Journal Article · Mon Jan 01 00:00:00 EST 2018 · Agricultural Systems

DOI:https://doi.org/10.1016/j.agsy.2017.07.012· OSTI ID:1426663

Elliott, Joshua ^[1]; Glotter, Michael ^[2]; Ruane, Alex C. ^[3]; Boote, Kenneth J. ^[4]; Hatfield, Jerry L. ^[5]; Jones, James W. ^[4]; Rosenzweig, Cynthia ^[3]; Smith, Leonard A. ^[6]; Foster, Ian ^[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)

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 in 1988 and $21.6 billion 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 work 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.

• Research Organization: Argonne National Lab. (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC); Univ. of Chicago, IL (United States); Agricultural Model Intercomparison and Improvement Project (AgMIP); National Science Foundation (NSF)
• Grant/Contract Number: AC02-06CH11357; SES- 0951576; 1215910; DGE-1133082; OCI-1148443; SES-0951576
• OSTI ID: 1426663
• Alternate ID(s): OSTI ID: 1426770
• Journal Information: Agricultural Systems, Vol. 159, Issue C; ISSN 0308-521X
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

60 APPLIED LIFE SCIENCES
29 ENERGY PLANNING, POLICY, AND ECONOMY
54 ENVIRONMENTAL SCIENCES
Climate extremes
Drought impacts
Agriculture
Seasonal prediction
Adaptation