Author:
Wei Yanqi,Jin Juliang,Li Haichao,Zhou Yuliang,Cui Yi,Commey Nii Amarquaye,Zhang Yuliang,Jiang Shangming
Abstract
AbstractClimate change can lead to and intensify drought disasters. Quantifying the vulnerability of disaster-affected elements is significant for understanding the mechanisms that transform drought intensity into eventual loss. This study proposed a growth-stage-based drought vulnerability index (GDVI) of soybean using meteorological, groundwater, land use, and field experiment data and crop growth model simulation. The CROPGRO-Soybean model was used to simulate crop growth and water deficit. Four growth stages were considered since the sensitivity of soybean to drought is strictly related to the growth stage. The GDVI was applied to the Huaibei Plain, Anhui Province, China, with the goal of quantifying the spatiotemporal characteristics of soybean drought vulnerability in typical years and growth stages. The results show that: (1) The sensitivity of leaf-related parameters exceeded that of other parameters during the vegetative growth stage, whereas the top weight and grain yield showed a higher sensitivity in the reproductive growth stage; (2) A semi-logarithmic law can describe the relationship between the drought sensitivity indicators and the GDVI during the four growth stages. The pod-filling phase is the most vulnerable stage for water deficit and with the highest loss upper limit (over 70%); (3) The 2001 and 2002 seasons were the driest time during 1997−2006. Fuyang and Huainan Cities were more vulnerable to drought than other regions on the Huaibei Plain in 2001, while Huaibei and Suzhou Cities were the most susceptible areas in 2002. The results could provide effective decision support for the categorization of areas vulnerable to droughts.
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Safety Research,Geography, Planning and Development,Global and Planetary Change
Reference45 articles.
1. Allakonon, M.G.B., S. Zakari, P.G. Tovihoudji, A.S. Fatondji, and P.B.I. Akponikpè. 2022. Grain yield, actual evapotranspiration and water productivity responses of maize crop to deficit irrigation: A global meta-analysis. Agricultural Water Management 270: Article 107746.
2. Anapalli, S.S., D.K. Fisher, K.N. Reddy, P. Wagle, P.H. Gowda, and R. Sui. 2018. Quantifying soybean evapotranspiration using an eddy covariance approach. Agricultural Water Management 209: 228–239.
3. Bai, X., Y. Wang, J. Jin, S. Ning, Y. Wang, and C. Wu. 2020. Spatio-temporal evolution analysis of drought based on cloud transformation algorithm over Northern Anhui Province. Entropy 22(1): Article 106.
4. Bai, X., S. Xu, and Y. Qi. 2013. Carrying capacity and utilization potential analysis of groundwater resources in semiarid district in Heilongjiang Province. Journal of Northeast Agricultural University (English Edition) 20(2): 77–81.
5. Buddhaboon, C., A. Jintrawet, and G. Hoogenboom. 2018. Methodology to estimate rice genetic coefficients for the CSM-CERES-Rice model using GENCALC and GLUE genetic coefficient estimators. The Journal of Agricultural Science 156(4): 482–492.
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献