中国农业气象 ›› 2018, Vol. 39 ›› Issue (04): 280-291.doi: 10.3969/j.issn.1000-6362.2018.04.007

• 论文 • 上一篇    

基于农作物灾情的长江中下游地区粮食产量损失评估

张轶,刘布春,杨晓娟,刘园,白薇,董博超   

  1. 1.中国农业科学院农业环境与可持续发展研究所/作物高效用水与抗灾减损国家工程实验室/农业部农业环境重点实验室,北京 100081;2.沈阳农业大学农学院,沈阳 110866
  • 收稿日期:2017-07-21 出版日期:2018-04-20 发布日期:2018-04-17
  • 作者简介:张轶(1992-),女,硕士,主要研究方向为农业气象灾害减灾与天气指数保险。E-mail: yizhangcaas@163.com
  • 基金资助:
    农业行业专项课题“季节性干旱灾变危害评价与预警和旱灾防控预案”(201203031-02);中国农业科学院科技创新工程(CAAS-ASTIP-2014-IEDA)

Grain Yield Loss Evaluation Based on Agro-meteorological Disaster Exposure in the Middle-Lower Yangtze Plain

ZHANG Yi,LIU Bu-chun,YANG Xiao-juan,LIU Yuan,BAI Wei,DONG Bo-chao   

  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, MOA, Beijing 100081, China; 2.College of Agronomy, Shenyang Agricultural University, Shenyang 110866
  • Received:2017-07-21 Online:2018-04-20 Published:2018-04-17

摘要: 利用长江中下游地区7省(市)1949-2014年农作物灾情统计数据和粮食作物单产数据,采用多元线性回归方法,构建粮食气象减产量与农作物受灾面积、成灾面积及绝收面积的回归模型,运用主成分分析法研究长江中下游7省(市)影响粮食产量的主要农业气象灾害,对因气象灾害导致的粮食产量损失进行评估。结果表明:(1)粮食气象减产量与总灾情显著相关(P<0.05),其中除湖南省外,其它各省相关关系达极显著水平(P<0.01)。粮食气象减产量与成灾面积关系更密切,依据总灾情评估粮食产量,模型的拟合优度(R2)除上海市外均大于0.9。(2)根据粮食气象减产量与干旱、洪涝、风雹、低温、台风5种灾情的相关性,建立主要灾种粮食产量模型,仅从R2数值来看,除湖南省外,其它6省(市)主要灾种模型的R2均略高于总灾情模型,但单因素方差分析表明,两类模型的R2不存在显著差异。(3)两类模型均不能解释粮食作物结构性逐年波动变化,是模型产生模拟误差的一个主要因素。本研究建立的灾损模型和产量评估模型能较好地模拟灾情与粮食产量的关系,2015年数据试报检验表明,可以将其用作长江中下游地区不同省(市)粮食产量短期预测。

关键词: 因灾减产量, 气象灾害, 产量模拟模型, 灾损率, 灾害损失评估

Abstract: Multiple linear regression analysis was used to identify the relationship between grain climate yield loss and the covered, affected and destroyed areas of crop, based on statistical data of crop disasters and grain yield (1949-2014) in the Middle Lower Yangtze Plain. Principal component analysis (PCA) was used to evaluate the impacts of main agro-meteorological disasters on grain climate loss. The results showed that the grain climate yield loss was significantly correlated with the whole disaster exposure at the province level (P<0.05), and the correlation also reached extremely significant (P<0.01), except for Hunan province. Specifically, the affected area was more closely related to grain climate yield loss. The coefficients of determination (R2) of the whole disaster exposure of yield regression models were over 0.9 except for shanghai city. According to the correlation between grain climate yield and drought, flood, windstorm, chilling, and typhoon to establish the main disaster exposure yield model, R2 of model was higher than the whole disaster exposure yield model, except for Hunan province. However, there was no significant difference between the two models based on single factor variance analysis. Two types of model error were mainly due to the fluctuation of grain crop planting structure. The model of disaster damage and yield assessment established in this study simulated the relationship between disaster exposure and grain yield. The predication results indicated that the disaster exposure model would be used as the short-term prediction for grain yield in the Middle Lower Yangtze Plain by using the data of disaster area in 2015.

Key words: The loss caused by disasters, Meteorological disaster, Yield simulation model, Losing rate, Disaster loss evaluation