Identifying influence patterns of regional agricultural drought vulnerability using a two-phased grey rough combined model
Grey Systems: Theory and Application
ISSN: 2043-9377
Article publication date: 27 November 2020
Issue publication date: 3 January 2022
Abstract
Purpose
A core challenge of assessing regional agricultural drought vulnerability (RADV) is to reveal what vulnerability factors, under which kinds of synergistic combinations and at what strengths, will lead to higher vulnerability: namely, the influence patterns of RADV.
Design/methodology/approach
A two-phased grey rough combined model is proposed to identify influence patterns of RADV from a new perspective of learning and mining historical cases. The grey entropy weight clustering with double base points is proposed to assess degrees of RADV. The simplest decision rules that reflect the complex synergistic relationships between RADV and its influencing factors are extracted using the rough set approach.
Findings
The results exemplified by China's Henan Province in the years 2008–2016 show higher degrees of RADV in the north and west regions of the province, in comparison with the south and east. In the patterns with higher RADV, the higher proportion of agricultural population appears in all decision rules as a core feature. A smaller quantity of water resources per unit of cultivated land area and a lower adaptive capacity, involving levels of irrigation technology and economic development, present a significant synergistic influence relationship that distinguishes the features of higher vulnerability from those of the lower.
Originality/value
The proposed grey rough combined model not only evaluates temporal dynamics and spatial differences of RADV but also extracts the decision rules between RADV and its influencing factors. The identified influence patterns inspire managerial implications for preventing and reducing agricultural drought through its historical evolution and formation mechanism.
Keywords
Acknowledgements
This paper is supported by the National Natural Science Foundation of China (No. 71771119; No. 51979106; No. 71701105), Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX19_0127), Key Research Project of Social Science Fund in Jiangsu Province (No. 16GLA001), Fundamental Research Funds for the Central Universities (NP2017301) and China Scholarship Council.
Citation
Sun, H., Fang, L., Dang, Y. and Mao, W. (2022), "Identifying influence patterns of regional agricultural drought vulnerability using a two-phased grey rough combined model", Grey Systems: Theory and Application, Vol. 12 No. 1, pp. 230-251. https://doi.org/10.1108/GS-07-2020-0090
Publisher
:Emerald Publishing Limited
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