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培矮64S在泰国的育性气候适应性及其安全制种的播期研究
引用本文:李兆芹,姚克敏.培矮64S在泰国的育性气候适应性及其安全制种的播期研究[J].南京气象学院学报,2003,26(6):829-836.
作者姓名:李兆芹  姚克敏
作者单位:南京气象学院,应用气象学系,江苏,南京,210044
基金项目:国家高技术研究发展计划(863计划)项目(2001AA211151)
摘    要:为了推动中国加入WTO之后农业生产的国际化进程,将我国首创的两系法杂交水稻生产技术推向世界。以泰国曼谷、清迈地区1980-1997年逐日4个时次气温观测资料和NCEP/NCAR再分析资料为基础,结合两地区理论日长的计算结果和培矮64S的育性转换指标集及发育期模型,对我国目前应用最广泛的光、温敏核雄性不育系培矮64S在泰国产稻区代表地曼谷和清迈的育性气候适应性进行了分析。给出了90%、95%、100%保证率下安全制种和繁殖的最早、最晚播种期;计算了相应的风险概率,为两系杂交稻在泰国的推广应用提供可靠的农业气候依据。

关 键 词:培矮64S  杂交水稻  稳定不育期  稳定可育期  育性风险概率
文章编号:1000-2022(2003)06-0829-08
修稿时间:2002年11月28

Analysis of the Climatic Adaptability of Fertility and Determination of Sowing Period for Safe Seed Production of Pei'ai 64S in Tailand
LI Zhao-qin,YAO Ke-min.Analysis of the Climatic Adaptability of Fertility and Determination of Sowing Period for Safe Seed Production of Pei''''ai 64S in Tailand[J].Journal of Nanjing Institute of Meteorology,2003,26(6):829-836.
Authors:LI Zhao-qin  YAO Ke-min
Abstract:In order to accelerate the internationalization of Chinese agriculture after entry into WTO,and to introduce the productive technique of two-line hybrid rice initially invented in China to the world,on the basis of the temperature data of daily four times observations from 1980 to 1997 in Bangkok and Chiang Mai,Tailand,we supplement the missing observations with NCEP/NCAR re-analysis temperature statistics.In terms of calculations of theoretical daylength and fertility conversion index as well as developmental model of Pei'ai 64S,we analyze the climatic adaptability of fertility of photo-thermo-sensitivity male infertility Pei'ai 64S,which is planted most widely in China at present,in Bangkok and Chiang Mai-representative areas of rice production in Tailand,and give the earliest and latest sowing date of safe seed production under the assurance of 90,95 and 100 percent respectively,thus offering a realiable agroclimatic basis for the extention and application of two-line hybird rich technique in Tailand.
Keywords:Pei'ai 64S  steady sterile period  steady fertility period  fertility risk probability  Bangkok  Chiang Mai
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