Open AccessMaize yield loss risk under droughts in observations and crop models in the United States
Author(s) -
Guoyong Leng
Publication year - 2021
Publication title -
environmental research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.37
H-Index - 124
ISSN - 1748-9326
DOI - 10.1088/1748-9326/abd500
Subject(s) - crop yield , environmental science , yield (engineering) , evapotranspiration , probabilistic logic , crop simulation model , risk assessment , statistics , climatology , mathematics , computer science , agronomy , ecology , materials science , computer security , metallurgy , biology , geology
The negative drought impacts on crop yield are well recognized in the literature, but are evaluated mainly in a deterministic manner. Considering the randomness feature of droughts and the compounding effects of other factors, we hypothesize that droughts effects on yields are probabilistic especially for assessment in large geographical regions. Taking US maize yield as an example, we found that a moderate, severe, extreme and exceptional drought event (based on the standardized precipitation evapotranspiration index) would lead to a yield loss risk (i.e. the probability of yield reduction lower than expected value) of 64.3%, 69.9%, 73.6%, and 78.1%, respectively, with hotspots identified in Central and Southeastern US. Irrigation has reduced yield loss risk by 10%–27%, with the benefit magnitude depending on the drought intensity. Evaluations of eight process crop models indicate that they can well reproduce observed drought risks for the country as a whole, but show difficult in capturing the spatial distribution patterns. The results highlight the diverse risk pattern in response to a drought event of specific intensity, and emphasize the need for better representation of drought effects in process models at local scales. The analysis framework developed in this study is novel in that it allows for an event-based assessment of drought effects in a risk manner in both observations and process crop models. Such information is valuable not only for robust decision-makings but also for the insurance sector, which typically require the risk information rather than a single value of outcome especially given the uncertainty of drought effects.