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作物学报 ›› 2011, Vol. 37 ›› Issue (10): 1868-1878.doi: 10.3724/SP.J.1006.2011.01868

• 耕作栽培·生理生化 • 上一篇    下一篇

基于作物生长模型的东北玉米冷害监测预测研究

马玉平1,2,*，王石立1，李维京3   

  1. 1中国气象科学研究院，北京100081；2南京信息工程大学, 江苏南京 210044；3国家气候中心，北京100081
  • 收稿日期:2011-01-26 修回日期:2011-06-25 出版日期:2011-10-12 网络出版日期:2011-07-28
  • 通讯作者: 马玉平, E-mail: mayp@cams.cma.gov.cn
  • 基金资助:

    本研究由国家公益性行业(气象)科研专项(GYHY200906022), 中国气象科学研究院基本科研业务费专项(2009Y005)和国家科技支撑计划项目 (2006BAD04B02)资助。

Monitoring and Predicting of Maize Chilling Damage Based on Crop Growth Model in Northeast China

MA Yu-Ping,WANG Shi-Li1,LI Wei-Jing3   

  1. 1Chinese Academy of Meteorological Sciences, Beijing 100081, China; 2 Nanjing University of Information Science & Technology, Nanjing 210044, China; 3National Climate Center, Beijing 100081, China
  • Received:2011-01-26 Revised:2011-06-25 Published:2011-10-12 Published online:2011-07-28
  • Contact: 马玉平, E-mail: mayp@cams.cma.gov.cn

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摘要： 在全球气候变暖背景下，东北地区的气温明显上升，但由于存在玉米相对晚熟品种越区(北扩)种植现象，区域性和阶段性的玉米低温冷害仍时有发生。因此，开展东北玉米低温冷害监测预测研究仍十分必要。本文首先利用东北玉米生长模型(NEC_MaGM)构造冷害监测指标，然后开展玉米冷害监测预测方法研究并进行个例分析。结果显示，从不利天气条件和玉米对低温响应两方面遴选的8个单项指标中，以指标2 (DC_Tas9，抽雄期到9月底的累积热量单位与同期多年平均值的差值百分率)和指标1 (DN_Tas，当年抽雄期与多年平均抽雄期的差值)对历史玉米冷害的概括能力最强；根据冷害致灾机理、单一指标的历史概括能力及其独立性等因素，构建了由指标1、2以及指标4 (DW_GrS，水分适宜条件下模拟穗重与多年平均穗重的差值百分率)和指标7 (DW_Fro，初霜冻日时模拟穗重与模拟成熟时穗重的差值百分率)组合而成的东北玉米低温冷害综合指标；基于NEC_MaGM和冷害综合指标进行单点冷害监测，确定了若45%以上站点出现玉米冷害即为区域性冷害的标准，独立样本监测检验与实际情况一致；利用NEC_MaGM在网格尺度上的监测可以得到更详细的冷害空间分布状况，有利于开展农业气象业务服务工作；根据前期天气实况加上区域气候模式预测的气象要素数据，再结合预报时效之后的多年平均气候数据，在格点尺度利用NEC_MaGM可以实现对东北玉米低温冷害的预测。预测个例表明，该方法能够反映冷害的形成过程和严重程度，但其准确性不仅与作物模型有关，还依赖于区域气候模式的模拟能力。

关键词: 玉米冷害指标, 监测预测, 东北玉米生长模型

Abstract: Although atmospheric temperature is obviously increased in northeast China with the global warming, regional maize chilling damage still occurs due to North extension of cultivation region of relative late-maturing varieties. Therefore, the research on monitoring and predicting maize chilling damage is still necessary. In this paper, we firstly constructed chilling damage indexes based on northeast China maize growth model (NEC_MaGM), and then developed the methods of monitoring and predicting maize chilling damage. The results are as follows: (1) In the eight individual chilling damage indicators selected from adverse weather conditions and the response of maize growth to low temperature, the second one (DC_Tas9, reduction of heat units during tasseling to September 31 compared with multi-year average.) and the first one (DN_Tas, the difference of tasseling stage between this year and multi-year average) had the best historical matching capability for chilling damage. The accuracy of chilling damage simulation based on DN_Tas and DC_Tas9 was 93.0% and 81.4%, respectively. (2) Based on formation mechanism of chilling damage, historical matching capability of every individual indicator and its independence, we constructed an integrated chilling damage index, which included the first, the second, the forth (DW_GrS, the loss of WSO suffered by low temperature in growing season compared with multi-year average) and the seventh individual indicators (DW_Fro, the loss of WSO suffered by first frost). (3) Chilling damage was monitored for every site by using NEC_MaGM and indicators, and then regional maize chilling was described by calculating the proportion of the site with chilling damage. On the base of integrated index, we defined that regional chilling damage should have more than 45% of maize chilling site. Thus, the accuracy and the skill score of maize chilling damage simulation were 93.6% and 84.2%, respectively. The result of independent samples test was consistent with actual situation. (4) Monitoring of chilling damage in the grid scale could be described more detailed spatial distribution. With the constantly updated live weather data, chilling dynamic monitoring could be achieved. The causes of chilling damage in the different regions were explored by using every individual indicator. The severity and spatial distribution of chilling damage simulations in the grid scale were fairly consistent with the literature. This method of monitoring chilling damage was favorable to business development of agricultural meteorological services. (5) According to weather data measured and predicted by regional climate models, combined with climate average data, regional maize chilling damage could be predicted by using NEC_MaGM. Case study showed that the method reflected to some extent the development and severity of chilling damage, but its accuracy was not only related with crop models, but also depended on simulation ability of regional climate model. The study could provide a scientific basis for the disaster prevention and mitigation.

Key words: Maize chilling damage index, Monitoring and predicting, NEC_MaGM

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