中国农业气象 ›› 2013, Vol. 34 ›› Issue (06): 720-726.doi: 10.3969/j.issn.1000-6362.2013.06.016

• 论文 • 上一篇    下一篇

基于农业灾情的东北粮食产量估算模型及灾损分析

王健,刘布春,刘园,杨晓娟,白薇   

  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北京市延庆县气象局,北京102100
  • 收稿日期:2013-04-24 出版日期:2013-12-20 发布日期:2014-05-06
  • 作者简介:王健(1987-),女,山西临汾人,硕士生,从事农业气象减灾研究。Email:wangjian_cau@163.com
  • 基金资助:

    国家自然基金面上项目“面向天气指数作物保险产品的气象灾害损失指数化研究”(41171410);国家公益性行业(农业)科研专项课题“季节性干旱灾变危害评价与预警和旱灾防控预案”(201203031-02)

Grain Yield Estimation Models and Loss Analysis Based on Agrometeorological Disaster Exposure in Northeast China

WANG Jian, LIU Bu chun, LIU Yuan, YANG Xiao juan, BAI Wei   

  1. Institute of Environment and Sustainable Development in Agriculture, CAAS/National Engineering Laboratory of Efficient Crop Water Use and Disaster Reduction, P.R.China/Key Laboratory of Agricultural Environment, MOA, Beijing 100081, China;2Yanqing Meteorological Bureau,Beijing 102100
  • Received:2013-04-24 Online:2013-12-20 Published:2014-05-06

摘要: 利用1981-2010年东北三省农业灾情统计数据和粮食产量数据,应用多元回归方法分析粮食气候减产量与灾情的关系,并构建基于灾情数据的粮食产量估算模型;在粮食因灾减产量估算的基础上,应用灰色关联法评价干旱、洪涝、低温和风雹4种灾害在受灾率、成灾率及绝收率水平上对粮食减产量的影响。结果表明,东北三省粮食气候减产量与农业灾情统计数据存在极显著(P<0.01)相关关系,回归模型决定系数(R2)分别为0.76(黑龙江省)、0.78(吉林省)和0.87(辽宁省),各模型估算的粮食产量模拟值与实际值间的平均相对误差分别为-0.06%、-0.32%和-0.20%。可见在气象灾害发生时历史农业灾情统计资料对区域粮食灾损量和粮食产量具有较强的指示作用,能为以粮食作物为主的地区提供可靠的粮食产量估算和农业气象灾害评价依据。对粮食因灾减产量与灾情的灰色关联度的分析表明,在受灾水平上,干旱的关联度在三省均为最高;在成灾、绝收水平上,风雹的关联度均位列第1;低温灾害在受灾、成灾和绝收水平上的关联度都不是最高的。由此可见,造成东北三省粮食减产的主要气象灾害是以程度轻、范围广的干旱及程度重、局地性强的风雹为主,而东北地区作为气候变暖趋势最明显的地区之一,低温不再是当地首要的农业气象灾害。

关键词: 农业气象灾害, 粮食气候减产量, 粮食因灾减产量, 估算模型

Abstract: he model for estimating grain yield was established, through analyzing the relationship between climatic grain loss and disaster exposure by using multiple regression method based on the statistical data of agro meteorological disaster exposure and grain yield in Northeast China from 1981 to 2010. Meanwhile, the impact of drought, flood, low temperature and wind-hail on grain loss was evaluated by using grey relational analysis on the slight, moderate and severe damage level respectively. The results showed that there was significant correlation between climatic grain loss and statistical data of agrometeorological disaster exposure (P<0.01). The coefficients of determination (R2) of regression models were 0.76 (Heilongjiang province), 0.78 (Jilin province) and 0.87 (Liaoning province), and their average relative errors between simulated yield and real yield were -0.06%, -0.32% and -0.20% respectively. The results indicated that statistical data of agrometeorological disaster exposure was a good indicator for predicting regional grain loss and grain yield while the meteorological disaster occurred. They were dependable basis for estimating of regional grain yield and evaluating of agrometeorological disasters. The results of grey relational analysis showed that the correlation degrees between drought and grain loss were the maximum in three provinces on the slight damage level, the correlation degrees between windhail and grain loss were the maximum in three provinces on the moderate and severe damage level, the correlation degrees between low temperature and grain loss were not the maximum. Therefore, the main meteorological disaster was light degree and wide range of drought or strong and local based wind hail. However, as one of the most obvious warming regions, low temperature was no longer the most important disaster in Northeast China.

Key words: Agrometeorological disasters, Climatic grain loss, Yield loss caused by disasters, Estimation model