关键词: 甘肃省, 农作物受灾率, 时空格局, 影响因素, 地理探测器

Abstract:

Based on the crop disaster rate data of 14 cities (prefectures) in the four sub−regions (Hexi, Longzhong, Longdong and southern Gansu) from 2009 to 2023, this study comprehensively adopted methods including cold and hot spot analysis, gravity center migration model, kernel density estimation and Dagum Gini coefficient to analyze the spatiotemporal differentiation characteristics of the crop disaster rate in the four sub-regions of Gansu province. The geodetector was used to quantify the impacts of natural and anthropogenic factors on the disaster pattern and their interaction, aiming to reveal the spatiotemporal evolution patterns and key influencing factors of the crop disaster rate in Gansu province from 2009 to 2023. The research was intended to provide references for optimizing regional agricultural layout and preventing agricultural meteorological disaster risks. The results showed that: (1) from 2009 to 2023, the overall crop disaster rate in the four sub−regions of Gansu province showed a shrinking trend, with drought being the dominant agricultural meteorological disaster. (2) The cold spots of the crop disaster rate in Gansu province showed a shrinking trend from 2009 to 2023 and completely disappeared by 2021. Since then, only hot spots existed, and by 2023, the hot spots were concentrated in Zhangye, Jinchang and Wuwei city in the Hexi region. The gravity center of the crop disaster rate in Gansu province generally migrated from Baiyin city in the Longzhong region to Wuwei city in the Hexi region in a northwest direction from 2009 to 2023, with a migration distance of 238.7km. (3) Kernel density estimation indicates that the crop disaster rate in Gansu province showed a tended to ease trend from 2009 to 2023, but spatial disparities in disaster distribution in Hexi region intensified. The disaster disparity within the Longzhong region decreased, while the Longdong and southern regions exhibited polarization with significant differences among cities (prefectures) within these sub−regions. (4) The Gini coefficient of the crop disaster rate in Gansu province fluctuated between 0.389 and 0.604, showing an overall increasing trend from 2009 to 2023, indicating relatively large overall disparities in the crop disaster rate among the regions of Gansu province. (5) The effective irrigated area (0.230), slope (0.153), and total agricultural machinery power (0.143) in Gansu province were the main factors affecting the crop disaster rate. The interaction between altitude and total agricultural machinery power (0.447) had the highest impact on the crop disaster rate in Gansu province. In conclusion, the crop disaster pattern in Gansu province is jointly constrained by both topographic conditions and the level of agricultural mechanization. In the future, emphasis should be placed on strengthening water conservancy facilities in light of local conditions, optimizing water resource allocation, and enhancing the level of agricultural mechanization to improve disaster resistance and address the bottleneck of coexisting water scarcity and inefficient utilization.

Key words: Gansu province, Crop disaster rate, Spatiotemporal pattern, Influencing factors, Geographical detector