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中国农学通报 ›› 2017, Vol. 33 ›› Issue (29): 25-30.doi: 10.11924/j.issn.1000-6850.casb16080146

所属专题: 生物技术 水稻

• 农学 农业基础科学 • 上一篇    下一篇

基于生理发育时间的水稻发育期预测方法

张明达1,胡雪琼1,,朱 涯2,张加云1,何雨芩1,徐梦莹1,朱 勇1   

  1. (1云南省气候中心,昆明 650034;2云南省大气探测技术保障中心,昆明 650034。)
  • 收稿日期:2016-08-31 修回日期:2017-09-25 接受日期:2017-04-24 出版日期:2017-10-24 发布日期:2017-10-24
  • 通讯作者: 张明达
  • 基金资助:
    西南区域项目“川滇高原山地水稻盛夏低温冷害及其对策研究”(2013-2);云南省气象局预报员技术开发专项项目“云南省大宗粮食作物动态产量预报技术研究”(YB201205)。

Forecasting Method of Rice Phenology Stage Based on Physiological Development Period

Zhang Mingda1, Hu Xueqiong1,Zhu Ya2, Zhang Jiayun1, He Yuqin1, Xu Mengying1, Zhu Yong1   

  1. (1Yunnan Climate Center, Kunming 650034;2Yunnan Atmospheric Exploration Technology Security Center, Kunming 650034)
  • Received:2016-08-31 Revised:2017-09-25 Accepted:2017-04-24 Online:2017-10-24 Published:2017-10-24

摘要: 水稻发育期模型研究是开展现代农业气象服务工作的基础。基于作物生理发育时间守恒原理,综合考虑温度和日长因子对水稻发育期的影响,利用云南省12个农气观测站2011—2014年水稻发育期观测和地面气象观测资料,分别构建并验证了适用于籼稻种植区和粳稻种植区的发育期预报模型。结果表明,2套模型在全发育期和各发育阶段的预报值与观测值模拟效果总体较好,平均全发育期RMSE值为7.47,RE值为7.99%,粳稻模型和籼稻模型的RE值分别为6.49%和9.5%,粳稻区模拟效果优于籼稻区。模型生物学意义明确、参数通用性强,适用于农业气象业务服务中水稻发育期预测,具有推广应用价值。

关键词: 全红杨, 全红杨, 关键, 栽培技术, 研究

Abstract: Rice phenological models are the bases of modern agro-meteorological service. Based on the conservation principle of crop physiological development stage, with the consideration of influences of climatic variables (i.e. temperature, sunshine hours and so on) on rice phenological stage, we built and verified the phenological forecasting model which were suitable for hsien rice and japonica rice planting region, using the observational data during rice phenology stage and ground meteorological data (2011-2014) from 12 agro-meteorological stations of Yunnan. The results showed that: both of prediction and observation value hsien and japonica phonological prediction models had good performances with 7.47 in averaged root-mean-square error (RMSE) and 7.99% in relative error (RE); the relative error (RE) was higher in hsien phonological prediction model (9.5%) than in japonica phonological prediction model (6.49%), implying the better performance of model prediction for japonica. Therefore, the biologically based and universally applied crop phonological process model is applicable for agrometeorological service.

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