| 黑龙江省三种水稻热量指数预测方法的对比研究 |
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| 引用本文： | 王秋京,马国忠,王 萍,赵克葳,杨晓强,于瑛楠,王 铭,姜丽霞.黑龙江省三种水稻热量指数预测方法的对比研究[J].中国农学通报,2020,36(5):1-7. |
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| 作者姓名： | 王秋京 马国忠 王 萍 赵克葳 杨晓强 于瑛楠 王 铭 姜丽霞 |
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| 作者单位： | 1. 中国气象局东北地区生态气象创新开放实验室/黑龙江省气象院士工作站/黑龙江省气象科学研究所,哈尔滨 150030;2. 黑龙江省气象台,哈尔滨 150030 |
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| 基金项目： | 国家自然科学基金项目“寒地水稻洪涝致灾机制与灾损量化综合评估方法研究(31671575);中国气象局东北地区生态气象创新开放实验 室开放研究基金项目“黑龙江冰雹灾害损失风险区划及生态减灾研究”(stqx201805);中国气象局沈阳大气环境研究所区域合作项目“东北玉米干旱 和低温冷害混合发生过程监测评估技术研究”(2018SYIAEHZ1)。 |
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| 摘 要： | 旨在选出一套适合黑龙江不同区域水稻低温冷害预测方法,为相关部门制定粮食生产和调整农作物种植结构提供科学依据。选择黑龙江省11个水稻农气观测站为研究对象,利用1971—2016年的气温资料、74类大气环流资料、水稻发育期数据,将黑龙江省划分为东、西、南3个区域,分别建立逐步回归预测模型、GM(1,1)灰色预测模型和均生函数预测模型,预测黑龙江水稻生育期总热量指数并进行对比分析。结果表明：建立的3种预测模型通过了残差检验,1971—2010年拟合平均准确率均在95%以上,结果差异不大;2011—2016年的试报准确率为85%~99%,其中GM(1,1)灰色预测模型准确率(97%~99%)高于逐步回归预测模型(91%~97%)和均生函数预测模型(85%~95%)。通过3种预测方法对比结果显示,GM(1,1)灰色预测模型模拟效果最好。
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| 关 键 词： | 水稻 热量指数 预测模型 |
| 收稿时间： | 2018/9/28 0:00:00 |
| 修稿时间： | 2020/1/14 0:00:00 |
Three Prediction Methods of Rice Heat Index in Heilongjiang Province: A Comparative Analysis |
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| Wang Qiujing,Ma Guozhong,Wang Ping,Zhao Kewei,Yang Xiaoqiang,Yu Yingnan,Wang Ming,Jiang Lixia.Three Prediction Methods of Rice Heat Index in Heilongjiang Province: A Comparative Analysis[J].Chinese Agricultural Science Bulletin,2020,36(5):1-7. |
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| Authors: | Wang Qiujing Ma Guozhong Wang Ping Zhao Kewei Yang Xiaoqiang Yu Yingnan Wang Ming Jiang Lixia |
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| Institution: | 1. Innovation and Opening Laboratory of Regional Eco-Meteorology in Northeast, China Meteorological Administration/Meteorological Academician Workstation of Heilongjiang Province/Heilongjiang Institute of Meteorological Sciences, Harbin 150030;2. Heilongjiang Meteorological Observatory, Harbin 150030 |
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| Abstract: | This study aims to select a prediction method of rice chilling damage in different regions of Heilongjiang Province and to provide references for making food production and adjusting crop- planting structure. 11 agro-meteorological observation stations were used as the test objects, Heilongjiang Province was divided into three areas, east, west and south area, by using temperature records, 74 types of atmospheric circulation characteristics and the datum of rice phenology stage from 1971 to 2016. The stepwise regression function and the GM (1, 1) forecasting model and mean regression function of heat index were established respectively in every area. Heat index was dynamically forecasted, and then the results were analyzed. The results showed that the three prediction models all passed the residue test. The average regression-calculating accuracy of these models was above 95% from 1971 to 2010, they had little difference; the average forecast accuracy was 85%-99% from 2011 to 2016 and the average forecast accuracy of the GM(1, 1) forecasting model was 97%-99%, which was better than that of the stepwise regression function (91%-97%) and the mean regression function (85%-95%). |
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| Keywords: | Rice Heat index Prediction model |
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