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Comparative Study on Main Crop Yield Separation Methods
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摘要: 对作物产量进行分离是分析气象因子对产量影响的重要步骤之一。以1985—2018年江苏省24个县（市）水稻统计产量为基础，分别采用3年滑动平均法、5年滑动平均法、五点二次平滑法、二次指数平滑法、HP滤波法和年际增量法对作物产量进行分离。从趋势产量与气象产量两方面比较6种方法的一致性与差异性，将分离出的气象产量与典型年增减产记录对比，选出更能准确捕获气象因子导致产量变异的方法，利用气象因子与产量关系的合理性对选定的方法进行检验。结果表明：就趋势产量拟合而言，前5种方法（年际增量法不能模拟趋势产量）均能较好地拟合趋势产量，与研究区域的趋势产量的一致性相关系数绝大多数为较好和极好等级范围；就气象产量而言，HP滤波法和年际增量法分离气象产量的合理性较差，标准差明显大于其他方法。综合看，3年滑动平均法与五点二次平滑法更具有普适性，可以捕获整个地区绝大多数典型年份气象因子带来的产量变化。Abstract: Crop yield separation is one of the important steps in analyzing the impact of meteorological factors on yield. Statistical rice yield data for 1985-2018 from 24 counties in Jiangsu are used to analyze the rationality of different separation methods. Six separation methods are 3-year moving mean, 5-year moving mean, five-point quadratic smoothing, quadratic exponential smoothing, HP filter and year-to-year increment. Consistencies and differences are analyzed from aspects of trend yield and meteorological yield. In order to select better methods that could accurately capture the yield variation caused by meteorological factors, the meteorological yield based on different methods are compared with the typical annual increase and decrease of rice yield records. Finally, as mentioned above, the selected methods are calibrated by the rationality of the relationship between meteorological factors and yield. Results show that the trend yield curves fitted by different methods are in line with the process of social technology development. Compared with the average trend yield, almost all the consistency correlation coefficients are greater than 0.5. It suggests that different methods do not differ much in trend fitting. Characteristics of meteorological yield separated by 3-year moving mean, 5-year moving mean, five-point quadratic smoothing and quadratic exponential smoothing in each county are simultaneously increasing or decreasing. And their standard deviation values are significantly smaller than HP filter method and year-to-year increment method. The result suggests that the rationality of separating the meteorological yields by 3-year moving mean, 5-year moving mean, five-point quadratic smoothing, and quadratic exponential smoothing is higher than the other two methods. Five-point quadratic smoothing method and 3-year moving mean method can capture almost 100% of typical annual meteorological yield changes in the whole research area. Further verification results show that the positive and negative effects of meteorological factors captured by 3-year moving mean and five-point quadratic smoothing method are more consistent with the response to meteorological factors. Overall, separation methods of five-point quadratic smoothing method and 3-year moving mean method are more suitable for this research area and match well with meteorological factors.
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Key words:
- climate change;
- rice;
- trend yield;
- meteorological yield;
- separation methods
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图 1 研究区域及气象站点分布
Fig. 1 The research region and distribution of meteorological stations
图 2 5种方法拟合的1987—2016年趋势产量
Fig. 2 Rice yield trends fitted by 5 methods from 1987 to 2016
图 3 6种产量分离方法得到的相对气象产量区域平均值及标准差序列
Fig. 3 Regional mean value and standard deviation series of relative meteorological yield seperated by 6 methods
表 1 典型年份记录
Table 1 Records of typical years
典型年型 年份 水稻生育期内对产量影明显的气象条件 减产年 1997 3月下旬—6月中旬江淮长期干旱少雨, 水稻栽秧用水严重短缺 1999 水稻分蘖期、抽穗扬花期与灌浆盛期先后出现寡照阴雨天气 2003 6月21日—7月12日连续多次大范围暴雨到大暴雨过程, 田块被淹 2007 6月19日—7月24日江淮地区降水特多, 夏秋两季发生梅雨涝灾及台风 丰产年 2002 8月6日—18日出现罕见的凉爽连续阴雨天气, 对水稻形成大穗有利 
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表 2 江苏省24个县(市)不同方法拟合的趋势产量序列与研究区域平均趋势产量序列的一致性检验
Table 2 Consistency statistics of trend yield series fitted by 5 methods at 24 counties in Jiangsu and average trend yield series of research areas
相关系数 样本量 3年滑动平均 5年滑动平均 五点二次平滑 二次指数平滑 HP滤波 0≤ρc < 0.5 1 1 1 3 2 0.5≤ρc < 0.85 12 11 16 15 10 0.85≤ρc < 1 11 12 7 6 12
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表 3 江苏省24个县(市)不同方法分离的气象产量与5个典型年记录的比较
Table 3 Comparison between the meterorological yield seperated by different methods and records of 5 typical years at 24 counties in Jiangsu
吻合比例/% 样本量 3年滑动平均 5年滑动平均 五点二次平滑 二次指数平滑 HP滤波 年际增量 100 21 12 23 5 5 13 80 3 10 1 9 14 8 60 0 2 0 10 5 3
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表 4 镇江和金湖相对气象产量模型参数
Table 4 Parameters of meteorological yield models for Zhenjiang and Jinhu
地区 分离方法 正相关 负相关 气象因子 回归系数 气象因子 回归系数 镇江 3年滑动平均 10月上旬日照时数 0.555 6月上旬最高气温
5月下旬最低气温
9月降水-0.661
-0.382
-0.269五点二次平滑 10月上旬日照时数 0.637 6月上旬最高气温
5月下旬最低气温
9月降水-0.626
-0.301
-0.267金湖 3年滑动平均 10月上旬日照时数 0.435 7月上旬降水 -0.456 五点二次平滑 10月上旬日照时数 0.491 7月上旬降水 -0.395
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