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中国农学通报 ›› 2020, Vol. 36 ›› Issue (34): 96-99.doi: 10.11924/j.issn.1000-6850.casb20191100868

所属专题: 水稻 农业气象

• 资源·环境·生态·土壤·气象 • 上一篇    下一篇

基于相似气象年型和关键气象因子的云南一季稻动态产量预报

张加云(), 陈瑶, 朱勇, 张茂松, 余凌翔(), 范立张   

  1. 云南省气候中心,昆明 650034
  • 收稿日期:2019-11-22 修回日期:2020-07-27 出版日期:2020-12-05 发布日期:2020-12-15
  • 通讯作者: 余凌翔
  • 作者简介:张加云,女,1981年出生,云南德宏人,高工,硕士,研究方向:农业气象和农业生态。通信地址:650034 云南省昆明市西昌路77号 云南省气候中心,Tel:0871-64114865,E-mail: 36913878@qq.com
  • 基金资助:
    云南省科技厅重点项目“气候变化下主要气象对高原特色农业的影响评估及监测预报技术研究”(2018BC007);中国气象局小型基建项目“特色农业气象服务能力建设”(CMABMJS2019-02);中国气象局气候变化专项“云南山地风电场典型气象灾害影响评估研究”(CCSF201936)

Dynamic Prediction of Single Cropping Rice Yield Based on Similar Meteorological Year Type and Key Meteorological Factors in Yunnan

Zhang Jiayun(), Chen Yao, Zhu Yong, Zhang Maosong, Yu Lingxiang(), Fan Lizhang   

  1. Yunnan Climate Center, Kunming 650034
  • Received:2019-11-22 Revised:2020-07-27 Online:2020-12-05 Published:2020-12-15
  • Contact: Yu Lingxiang

摘要:

建立适用于云南的一季稻产量动态预报模型,以期为及时准确预测云南省一季稻的产量提供理论依据和决策支持。利用云南省2000—2018年一季稻产量资料和一季稻生育期内逐日平均气温、日降水量和日照时数等资料,分别采用相似气象年型法和关键气象因子法建立一季稻动态产量预报模型。应用2种模型对2010—2018年云南省一季稻单产进行动态预报。结果表明:2种模型6月1日、7月1日、8月1日和8月21日的产量预报准确率均超过92%;相似气象年型法2016—2018年6月1日和8月21日的平均预报准确率为95.4%和97.5%,而关键气象因子法2016—2018年6月1日和8月21日的平均预报准确率均为97.4%。2种模型预报准确率均较高,能够满足云南一季稻产量预报业务服务的需要。

关键词: 一季稻, 相似气象年型, 关键气象因子, 动态产量预报, 云南

Abstract:

The study established the suitable dynamic prediction of single cropping rice yield in Yunnan, aiming to provide a theoretical basis and decision support for predicting single cropping rice yield timely and accurately. Based on the data of single cropping rice yield, daily average air temperature, daily precipitation and daily sunshine hours in the growth period from 2000 to 2018 in Yunnan, dynamic prediction models were established by using methods with similar meteorological year type and with key meteorological factors. The results showed that the average prediction accuracy of the two prediction models both exceeded 92% on June 1, July 1, August 1 and August 21. The average accuracy of the similar meteorological year type model was 95.4% and 97.5% respectively on June 1 and August 21 from 2016 to 2018; and the average accuracy of the key meteorological factors model was 97.4% in the same period. The prediction accuracy of both the two dynamic prediction models is high enough to meet the needs of prediction service in Yunnan.

Key words: single cropping rice, similar meteorological year type, key meteorological factors, dynamic yield prediction, Yunnan

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