A global-scale relationship between crop yield anomaly and multiscalar drought index based on multiple precipitation data. (6th January 2022)

• Record Type: Journal Article
• Authors: Hendrawan, Vempi Satriya Adi; Kim, Wonsik; Touge, Yoshiya; Ke, Shi; Komori, Daisuke
• Is Part Of: Environmental research letters. Volume 17:Number 1(2022)
• Publication Date: 2022-01-06
• Subjects: crop -- drought -- multisource -- standardized precipitation index -- yield loss; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/ac45b4
• Languages: English
• ISSNs: 1748-9326

Abstract

Abstract: Drought impact on crop production is well known as crop yield is strongly controlled by climate variation. Previous studies assessed the drought impact using a drought index based on a single input data set, while the variability of the drought index to the input data choice is notable. In this study, a drought index based on the standardized precipitation index with multiple timescales using several global precipitation datasets was compared with the detrended anomaly based on the global dataset of historical yield for major crops over 1981–2016. Results show that the drought index based on the ensemble precipitation dataset correlates better with the crop yield anomaly than a single dataset. Based on the drought index using ensemble datasets, global crop areas significantly affected by drought during the study period were around 23%, 8%, 30%, and 29% for maize, rice, soybean, and wheat, respectively, induced mainly by medium to longer drought timescale (5–12 months). This study indicates that most crops cultivated in dry regions were affected by droughts worldwide, while rice shows less correlation to drought as it is generally irrigated and cultivated in humid regions with less drought exposure. This study provides a valuable framework for data choices in drought index development and a better knowledge of the drought impact on agriculture using different timescales on a global scale towards understanding crop vulnerability to climate disruptions.