Construction of influencing factors and indicator models for regional agricultural drought resistance and disaster reduction capacity

doi:10.14088/j.cnki.issn0439-8114.2024.04.032

Abstract

Abstract: In order to prevent and control regional agricultural disasters, the influencing factors of drought were analyzed in Shandong Province. A drought warning model was constructed based on the influencing factors of drought, and the coupling relationship of various factors was analyzed. Finally, the constructed agricultural drought comprehensive evaluation model was applied to specific agricultural production environments. The results indicated that there were significant differences in drought resistance and disaster reduction capacity among different agricultural regions. The top three cities in the comprehensive assessment of agricultural drought were Yantai, Rizhao, and Qingdao, with comprehensive assessment values of 2.90, 2.87, and 2.86, respectively. The economically developed regions of Shandong Province were concentrated in coastal areas, which had sufficient technological, material, talent, and financial support, and strong agricultural drought resistance and disaster reduction capacity. For areas with poor rainfall and weak economy, agricultural drought resistance and disaster reduction capacity were relatively poor, concentrated in areas such as Dezhou City, Jining City, and Dongying City. It could be seen that in order to enhance the drought resistance and disaster reduction capabilities of these regions, it was necessary to start from financial investment, agricultural technology, emergency management, and other aspects to ensure the scientific development of regional agriculture.

Key words: region, agricultural drought resistance and disaster reduction capacity, model construction, influencing factors of drought

CLC Number:

S422

ZHAI Xiao-yan. Construction of influencing factors and indicator models for regional agricultural drought resistance and disaster reduction capacity[J]. HUBEI AGRICULTURAL SCIENCES, 2024, 63(4): 174-178.

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