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中国水稻科学  2009, Vol. 23 Issue (2): 172-172~178     DOI:
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CERES-Rice模型区域应用中遗传参数升尺度的一种方法
江敏1, 2,金之庆1,*
1江苏省农业科学院 农业资源与环境研究所, 江苏 南京 210014; 2福建农林大学 作物学院 农村区域发展系, 福建 福州 350002; *通讯联系人, E-mail:
A Method to Upscale the Genetic Parameters of CERES-Rice in Regional Applications
JIANG Min 1,2, JIN Zhi qing 1,*
1 Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China;2 Department of Rural Regional Development, Fujian Agriculture and Forestry University, Fuzhou 350002, China; *Corresponding author, E-mail:
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摘要 为了提升CERESRice模型中遗传参数的空间尺度,以适应区域性研究需要,以江苏省为例,在具有不同水稻品种生态类型的4个稻区各选5~6个样点,以稻区为空间尺度,利用每个样点4年(2001-2004年)县级统计水稻单产资料及同期同地的气象和土壤资料,结合江苏省各地的水稻品种区域试验资料,采用试错法对CERESRice中8个遗传参数,特别是4个与产量相关的遗传参数分别进行了调试与确定(方法1,简称稻区尺度调试法),并与传统的其他3种升尺度方法,即代表性品种单点调试法(方法2)、县级尺度调试法(方法3)和超大尺度调试法(方法4)的模拟效果进行了比较。结果显示,方法1的模拟效果较为理想,各稻区模拟产量与统计产量的相关系数均达到显著或极显著水平,均方根差值均小于9%。而其他3种方法的模拟效果均明显不如方法1。研究提出的遗传参数升尺度方法不仅适用于气候变化影响评价研究,也可为作物生长模型在其他区域研究中的应用提供方法上的借鉴。
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江敏
金之庆
关键词模拟模型   区域应用   遗传参数   升尺度     
AbstractIn order to upscale the genetic parameters of CERESRice to satisfy the requirements in its regional applications, Jiangsu Province, the second largest rice producing province in China, was taken as an example. The province was divided into four rice regions for different varietal types and five to six sites in each region were selected. Then the eight genetic parameters of CERESRice,particularly the four parameters related to yield were modified and then validated using Trial and Error Method and based on the local statistical rice yield data at a county level from 2001 to 2004, combined with the regional experiments of rice varieties in the province as well as the local meteorological and soil data (Method 1). The simulated results of Method 1 were compared with that of the other three traditional methods upscaling the genetic parameters, i.e., using onesite experimental data of a local representative rice variety (Method 2), using local longterm rice yield data at a county level after deducting the trend yield due to progress of science and technology (Method 3) and using rice yield data at a super scale, such as provincial, ecological zone, country or continent levels (Method 4). The results showed that a good fitness efficiency was obtained by using the Method 1, its correlation coefficients between the simulated yields and the statistical yields were significant at 0.05 or 0.01 statistical levels and the RMSE (root mean squared error) values were less than 9% for all the four rice regions, which were obviously better than those of the other three traditional methods. The method upscaling the genetic parameters of CERESRice presented is not only valuable for the impact studies of climate change, but also favorable to provide a methodology for reference in crop model applications to the other regional studies.
Key wordssimulation model   regional application   genetic parameter   upscaling   rice   
收稿日期: 1900-01-01;
引用本文:   
江敏,金之庆. CERES-Rice模型区域应用中遗传参数升尺度的一种方法[J]. 中国水稻科学, 2009, 23(2): 172-172~178 .
JIANG Min ,JIN Zhi qing . A Method to Upscale the Genetic Parameters of CERES-Rice in Regional Applications [J]. , 2009, 23(2): 172-172~178 .
 
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