Chinese Journal of Agrometeorology ›› 2022, Vol. 43 ›› Issue (06): 487-498.doi: 10.3969/j.issn.1000-6362.2022.06.006

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Assessment Regional Grain Yield Loss Based on Re-Examination of Disaster-Yield Model in Three Northeastern Provinces

LIU Bu-chun, LIU Yuan, ZHENG Fei-xiang, ZHU Yong-chang, GUO An-hong, CHEN Di, YANG Xiao-juan, MEI Xu-rong   

  1. 1.Institute of Environment and Sustainable Development in Agriculture, CAAS/National Engineering Laboratory of Efficient Crop Water Use and Disaster Reduction/Key Laboratory of Agricultural Environment, Ministry of Agriculture and Rural Affairs, Beijing 100081,China; 2.CMA Institute for Development and Programmer Design, China Meteorological Administration, Beijing, 100081; 3.National Meteorological Center, Beijing 100081
  • Received:2021-08-27 Online:2022-06-21 Published:2022-06-21

Abstract: The aims of this paper is to illustrate the importance of grain production in China's food security strategy based on the three Northeastern provinces (NEP) in the new period, which quantitative assessment of climate change under the background of the loss caused by meteorological disasters of regional food production is clarify. In this paper, using grain planting area, yield and agricultural disaster statistics from 1981 to 2020, comparison analysis grain production and disaster characteristics between NEP and the whole country respectively. The disaster-yield assessment model was used to estimate the disaster-yield loss and final yield of the NEP by inputting disaster data in recent 10 years, and the sensitivity and stability of the disaster-yield assessment model were re-tested. The results showed that :(1) from 1981 to 2020, the grain planting area and total output in NEP increased significantly, and the proportion of the total output of NEP increased steadily, and the proportion of the total output of the three northeast provinces reached 1/5 of the total output of the whole country by 2020. (2) The disaster situation in NEP showed a significant trend of first increasing and then decreasing. The incrasing of average grain yield during the past 40 years was 65.96kg·ha−1 and 252.5kg·ha−1 per year for NEP and the whole country, while the incrasing of average grain yield during the past 10 years was 52.6kg·ha−1 per year for NEP significantly. (3) From 2011 to 2020, the average affected area and affected area in China were 23704.5×103ha and 11204.7×103ha , respectively, and 3899.1×103ha and 1900.0×103ha, respectively. In this decade, the disasters of the whole country and the three northeastern provinces were significantly lower than those of the previous three years, making it the decade with the least severe disasters in 40 years. (4) The simulation accuracy of the disaster-yield assessment model was high. The linear regression coefficients (R2) of simulated grain yield and measured grain yield in Heilongjiang, Jilin and Liaoning provinces were 0.98, 0.90 and 0.88, respectively. The slopes were 1.05, 1.02 and 0.98 (P < 0.01), respectively. The loss rate of grain yield due to agro-disaster was 10.4%, 17.9% and 18.0% respectively in the three province, while which was more than 8.0%, 17.0% and 16.0% for 20a.years. (5) The model slightly overestimated the grain yield of Jilin and Liaoning in recent 10 years due to the overall light disaster situation. Based on the data from 1981 to 2010, the regional grain disaster loss assessment model was proved to be able to evaluate the loss of grain yield caused by meteorological disasters well, and had the performance of predicting grain yield, and has the feasibility of operational application. The impact of meteorological disasters on the grain output in northeast China is higher than the average level of disasters in the whole country. Considering that the grain output in Northeast China accounts for a high proportion of the national grain output, preventing the risk of agro-meteorological disasters in Northeast China in the new period is of great importance to guarantee the national food security.

Key words: Food security, Regional disaster, Disaster-yield loss model, Meteorological disasters, Disaster-covered area, Disaster-affected area