Projection of future drought and its impact on simulated crop yield over South Asia using ensemble machine learning approach

Foyez Ahmed Prodhan; Jiahua Zhang; Til Prasad Pangali Sharma; Lkhagvadorj Nanzad; Da Zhang; Ayalkibet M Seka; Naveed Ahmed; Shaikh Shamim Hasan; Muhammad Ziaul Hoque; Hasiba Pervin Mohana

doi:10.1016/j.scitotenv.2021.151029

Projection of future drought and its impact on simulated crop yield over South Asia using ensemble machine learning approach

Sci Total Environ. 2022 Feb 10;807(Pt 3):151029. doi: 10.1016/j.scitotenv.2021.151029. Epub 2021 Oct 18.

Affiliations

¹ Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China; Department of Agricultural Extension and Rural Development, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh.
² Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: zhangjh@radi.ac.cn.
³ Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China.
⁴ Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China; Department of Remote Sensing, Information and Research Institute of Meteorology, Hydrology and Environment, Ulaanbaatar 15160, Mongolia.
⁵ Key Laboratory of Mountain Surface Process and Ecological Regulations, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China.
⁶ Department of Agricultural Extension and Rural Development, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh.
⁷ Department of Agricultural Extension and Rural Development, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh; Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China.
⁸ China Agricultural University, Beijing 100083, China.

PMID: 34673078
DOI: 10.1016/j.scitotenv.2021.151029

Abstract

Understanding the development mechanism of drought events, characterization of future drought metrics, and its impact on crop yield is crucial to ensure food security globally, and more importantly, in South Asia. Therefore, the present study assessed the changes in future projected drought metrics and evaluated the future risk of yield reduction under drought intensity. We characterized the magnitude, intensity, and duration of future drought by means of the SPEI drought index using CMIP6 (Coupled Model Inter-comparison Phase-6) climate models. The impact of future drought on crop yield was quantified from the ISI-MP (Inter-Sectoral Impact Model Inter-comparison Project) crop model by a proposed non-linear ensemble of Random Forest (RF) and Gradient Boosting Machine (GBM). Results suggested that high drought magnitude with a longer drought duration is projected in some regions of South Asia while high drought intensity comes with a shorter duration. It was also found that Afghanistan, Pakistan, and India will experience a longer drought duration in the future. Our proposed ensemble machine learning (EML) approach had high predictive skill with a minimum value of RMSE (0.358-0.390), MAE (0.222-0.299), and a maximum value of R² (0.705-0.918) compared to the stand-alone methods of RF and GBM for yield loss risk projection. The drought-driven impact on crop yield demonstrates a high risk of yield loss under extreme drought events, which will encounter 54.15%, 29.30%, and 50.66% loss in the future for rice, wheat, and maize crops, respectively. Furthermore, drought and yield loss risk dynamics suggested a one unit decrease in SPEI value would lead to a 14.2%, 7.5%, and 10.9% decrease in yield for rice, wheat, and maize crops, respectively. This study will provide a notable direction for policy agencies to build resistance to crop production against the drought impact in the regions that are critical to climate change.

Keywords: Crop yield; Drought; Ensemble machine learning; South Asia.

MeSH terms

Afghanistan
Climate Models*
Crops, Agricultural / growth & development*
Droughts*
India
Machine Learning
Pakistan