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Grain Risk Analysis of Meteorological Disasters in Gansu Province Using Probability Statistics and Index Approaches
With global warming, agrometeorological disasters are also rising, posing a severe threat to China’s food security. Risk assessment serves as a bridge from disaster crisis management to risk management. Gansu Province is geographically crucial, so we performed a refined assessment of grain production risk for this province using multiple features of disaster loss data recorded at the county level. Analyses were performed for each district and county with a probability approach and an index system. We found that grain trend yields in each district and most counties in Gansu Province are increasing. Wuwei and Linxia districts had higher yearly growth rates, of more than 120 kg/(ha·year). However, there are considerable differences in risk levels among counties, even within the same district. Huating and Jinchang counties are high risk locations, while Cheng, Diebu, Jinta, and Xiahe counties are low risk zones. In 39.2% of counties, the fluctuation tendency rate of relative meteorological yield was positive. The average yield reduction rates of grain in the 1980s, 1990s, 2000s, and 2010s were 5.5%, 6.6%, 8.1%, and 4.2%, respectively, and the average fluctuation coefficients were 5.0%, 5.5%, 7.1%, and 3.8%, respectively. After 2010, most regions’ average yield reduction rates fell dramatically, and grain output progressively stabilized. Counties prone to heavy disasters are primarily spread along the Hexi Corridor, with the probability exceeding 8%. However, 27.9% of counties were spared from severe calamities, which were mainly distributed in southwestern Gansu Province. Crop disaster conditions significantly positively correlated with grain risk. Drought is the primary cause of grain yield decline in Gansu Province. The findings can provide essential policy advice for the government in disaster prevention.
Grain Risk Analysis of Meteorological Disasters in Gansu Province Using Probability Statistics and Index Approaches
With global warming, agrometeorological disasters are also rising, posing a severe threat to China’s food security. Risk assessment serves as a bridge from disaster crisis management to risk management. Gansu Province is geographically crucial, so we performed a refined assessment of grain production risk for this province using multiple features of disaster loss data recorded at the county level. Analyses were performed for each district and county with a probability approach and an index system. We found that grain trend yields in each district and most counties in Gansu Province are increasing. Wuwei and Linxia districts had higher yearly growth rates, of more than 120 kg/(ha·year). However, there are considerable differences in risk levels among counties, even within the same district. Huating and Jinchang counties are high risk locations, while Cheng, Diebu, Jinta, and Xiahe counties are low risk zones. In 39.2% of counties, the fluctuation tendency rate of relative meteorological yield was positive. The average yield reduction rates of grain in the 1980s, 1990s, 2000s, and 2010s were 5.5%, 6.6%, 8.1%, and 4.2%, respectively, and the average fluctuation coefficients were 5.0%, 5.5%, 7.1%, and 3.8%, respectively. After 2010, most regions’ average yield reduction rates fell dramatically, and grain output progressively stabilized. Counties prone to heavy disasters are primarily spread along the Hexi Corridor, with the probability exceeding 8%. However, 27.9% of counties were spared from severe calamities, which were mainly distributed in southwestern Gansu Province. Crop disaster conditions significantly positively correlated with grain risk. Drought is the primary cause of grain yield decline in Gansu Province. The findings can provide essential policy advice for the government in disaster prevention.
Grain Risk Analysis of Meteorological Disasters in Gansu Province Using Probability Statistics and Index Approaches
Jing Wang (author) / Feng Fang (author) / Jinsong Wang (author) / Ping Yue (author) / Suping Wang (author) / Liang Zhang (author)
2023
Article (Journal)
Electronic Resource
Unknown
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