Abstract
Regional agricultural drought vulnerability (RADV) is a complex nonlinear problem caused by the interaction of multiple factors, and an objective and systematic method is proposed by this paper to identify its influencing factors, which plays an important role in preventing and regulating the risks of regional agricultural drought. Firstly, to provide a reference for the evaluation problem in selecting the number of factors, the influencing factors affecting RADV are revealed by using the method of phase space reconstruction (PSR). Secondly, to rank the importance of influencing factors, a grey trend relational analysis (TGRA) method is proposed, considering the dynamic development relationship between the RADV index and the influencing factors and integrating the absolute and relative variation of sequences in each corresponding period. Finally, to reduce the collinearity between the influencing factors, a grey trend relational clustering (TGRC) analysis method is proposed. According to the above steps, the process of identifying factors based on PSR-TGRC method is formed. Taking Henan Province as an example, 14 main influencing factors and their effects on RADV are identified from all 42 factors, and the identification results which are consistent with the actual drought relief work show the rationality and practicality of PSR-TGRC method and provide theoretical support for formulating strategies of regional agricultural disaster prevention and mitigation.
Highlights
With the acceleration of global warming and the intensity of human activities, the frequency and destructiveness of drought disasters, which seriously threatens agricultural production, farmers’ property security, and livelihood security, are gradually increasing and restricting the sustainable development of regional economy and society
Henan Province is selected as the research object; the main influencing factors of Regional agricultural drought vulnerability (RADV) are identified by the phase space reconstruction (PSR)-TGRC method to reduce the degree of RADV and provide theoretical support for reducing agricultural drought losses
The annual precipitation comes from the China Meteorological Data Network from 1960 to 1998, and the annual precipitation comes from the Henan Provincial Water Resources Bulletin from 1999 to 2018, containing 19 typical meteorological stations in Henan Province (Anyang, Xinxiang, Zhengzhou, Nanyang, Xinyang, etc.). e data of the remaining factors mainly comes from the China Statistical Yearbook, the China Rural Statistical Yearbook, the China Science and Technology Statistical Yearbook, and the Henan Provincial Statistical Yearbook from 2012 to 2018
Summary
With the acceleration of global warming and the intensity of human activities, the frequency and destructiveness of drought disasters (drought for short), which seriously threatens agricultural production, farmers’ property security, and livelihood security, are gradually increasing and restricting the sustainable development of regional economy and society. Obtaining the number of influencing factors accurately can effectively reduce the subjectivity of factors’ selection in the evaluation problems. From the perspective of chaos theory, a method that quickly obtains the number of Mathematical Problems in Engineering influencing factors by reconstructing the phase space of RADV index and calculating the correlation dimensions by G-P algorithm [12, 13] was proposed. Xu et al researched the number of factors affecting the total power of agricultural machinery in Heilongjiang Province by PSR [16]. In this paper, based on RADV index [17, 18], the number of factors affecting the evaluation of RADV is excavated by PSR, the subjectivity of factor selection is reduced, and the efficiency of data collection is improved
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
