徐经纬2,
高苹1,
宋迎波3
1.江苏省气候中心 南京 210008
2.南京信息工程大学气象灾害教育部重点实验室/南京信息工程大学气象灾害预报预警与评估协同创新中心 南京 210044
3.国家气象中心 北京 100081
基金项目: 2019年国内外作物产量气象预报专项、江苏省气象局面上科研基金KM201906
国家自然科学基金项目41871053
详细信息
作者简介:徐敏, 主要从事作物产量预报方法研究。E-mail:amin0506@163.com
中图分类号:F062.2计量
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出版历程
收稿日期:2019-11-19
录用日期:2020-01-03
刊出日期:2020-03-01
Evaluation of winter wheat yield prediction ability of different statistical models—A case study of Jiangsu wheat-growing region
XU Min1,,,XU Jingwei2,
GAO Ping1,
SONG Yingbo3
1. Climate Center of Jiangsu Province, Nanjing 210008, China
2. Key Laboratory of Meteorological Disaster, Ministry of Education and Nanjing University of Information Sciences & Technology/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Sciences & Technology, Nanjing 210044, China
3. National Meteorological Center, Beijing 100081, China
Funds: the Special Found for Meteorological Forecast of Crop Yield in Domestic and Foreign in 2019, the Surface Scientific Research Fund of Jiangsu Meteorology BureauKM201906
the National Natural Science Foundation of China41871053
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Corresponding author:XU Min, E-mail: amin0506@163.com
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摘要
摘要:在多类冬小麦单产统计预报模型中筛选出预报能力强的模型，并对优选出的模型进行加权集成，以此提高产量预报准确率，对保障粮食安全具有重要意义。利用1993-2018年江苏省69个基本气象观测站逐日气象资料和冬小麦产量数据及生育期资料，在5种气象产量分离方法（线性分离、差值百分率、5年滑动平均、3年滑动平均、二次曲线）的基础上，采用准确率、标准差、相关系数、泰勒图等检验法，评估分析了丰歉相似年法、关键气象因子法、气候适宜度法与集成预报法在江苏省冬小麦单产预报中的模拟效果。结果表明：1）对于同一种预报方法，不同的产量分离法对预报精度影响较大，二次曲线分离法要好于其他4种方法；丰歉相似年预报方法中加权法的预报精度高于大概率法。1993-2013年丰歉相似年法、关键气象因子法、气候适宜度法平均准确率分别为89.67%、94.86%和94.96%。2）集成预报法近5年预报准确率在96.33%以上，高于丰歉加权模型、关键气象因子二次曲线分离模型、气候适宜度二次曲线分离模型等单个最优模型，在一定程度上可以弥补单一预报方法预报结果稳定性差的不足。3）起报时间越接近成熟期，预报因子信息越全面，则预报模型准确率越高。研究结果可为江苏省冬小麦采用合理的单产预报模型提供科学依据，同时模型筛选思路也可供外省借鉴。
关键词:冬小麦/
产量预报/
丰歉相似年法/
关键气象因子法/
气候适宜度法/
产量分离方法/
最优模型集成
Abstract:We screened for the highest performance model among several winter wheat yield predicting models. The selected model was weighted and integrated in order to improve the accuracy of prediction, as it plays a key role in ensuring food security. Daily meteorological observations, winter wheat yield data, and growth period observations were obtained from 69 basic meteorological stations in Jiangsu Province from 1993 to 2018. Then, five methods of meteorological yield and trend yield separation (linear separation, percentage difference, 5-or 3-year sliding average, and quadratic curve) were compared. On this base, by using the fitting test and hind-casting test, we evaluated and analyzed the simulation effects of yield prediction methods based on similar years with bumper or poor harvest, key meteorological factors and climate suitability, and integrated the methods for Jiangsu winter wheat yield prediction. The results revealed the following:1) For the same yield prediction method, the yield separation methods had a greater effect on prediction accuracy. The quadratic curve method was the best among the linear separation, percentage difference, 5-or 3-year sliding average and quadratic curve methods. The prediction accuracy of the weighting method was higher than the large probability method in the similar years with bumper or poor harvest prediction method. From 1993 to 2013, the average accuracy of the methods of the similar years with bumper or poor harvest prediction, key meteorological factor, and climate suitability were 89.67%, 94.86%, and 94.96%, respectively. 2) The accuracy of the integrated prediction method was more than 96.33% in the past 5 years, and it was higher than that of the similar years with bumper or poor harvest-weighting model, key factor-quadratic curve model and climate suitability-quadratic curve model. This could probably overcome the less stability of prediction accuracy of a single prediction method. 3) The closer the predicted time to the maturity period and the more comprehensive the prediction factor information, the higher the accuracy of the prediction model. These results provide a scientific basis for selecting an optimized prediction model for winter wheat yield in Jiangsu, and the methodology of model screening can also be used in other provinces.
Key words:Winter wheat/
Yield prediction/
The method of similar years with bumper or poor harvest/
The method of key meteorological factors/
The method of climate suitability/
Separation method of meteorological yield and trend yield/
Integration of optimal model result
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图11993—2013年江苏省12种冬小麦单产预报模型历史模拟值与实际值
(a:起报时间4月10日; b:起报时间5月10日)
GY为单产实测值。GB-W和GB-A分别表示丰歉相似年法中的加权法和大概率法。KMF和CS分别表示基于关键气象因子和气候适宜度的单产预测方法。L、D、M5、M3和Q分别表示线性分离法、差值百分率分离法、5年滑动平均分离法、3年滑动平均分离法和二次曲线分离法等产量分离方法。
Figure1.Winter wheat yield simulated by 12 yield prediction models and actual yield in Jiangsu Province from 1993 to 2013
(a: starting time is April 10; b: starting time is May 10)
GY is the actual grain yield per unit area. GB-W and GB-A are bumper or poor harvest methods of weighting and approximate ratio. KMF and CS are yield prediction methods based on key meteorological factors and climate suitability. L, D, M5, M3 and Q are yield separation methods of linear separation, percentage difference, 5-year sliding average, 3-year sliding average separation, and quadratic curve.
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图21993—2013年江苏省冬小麦12种单产预报模型的历史拟合平均准确率(a)和单产增减正确率(b)
GB-W和GB-A分别表示丰歉相似年法中的加权法和大概率法。KMF和CS分别表示基于关键气象因子和气候适宜度的单产预测方法。L、D、M5、M3和Q分别表示线性分离法、差值百分率分离法、5年滑动平均分离法、3年滑动平均分离法和二次曲线分离法等产量分离方法。
Figure2.Average accuracy of yield fitting (a) and average accuracy of yield increase and decrease (b) calculated by 12 yield prediction models in Jiangsu Province from 1993 to 2013
GB-W and GB-A are bumper or poor harvest methods of weighting and approximate ratio. KMF and CS are yield prediction methods based on key meteorological factors and climate suitability. L, D, M5, M3 and Q are yield separation methods of linear separation, percentage difference, 5-year sliding average, 3-year sliding average separation, and quadratic curve.
下载: 全尺寸图片幻灯片
图31993—2013年江苏省冬小麦12种单产预报模型历史模拟值与实际值的泰勒图
GY为单产实测值。GB-W和GB-A分别表示丰歉相似年法中的加权法和大概率法。KMF和CS分别表示基于关键气象因子和气候适宜度的单产预测方法。L、D、M5、M3和Q分别表示线性分离法、差值百分率分离法、5年滑动平均分离法、3年滑动平均分离法和二次曲线分离法等产量分离方法。
Figure3.Taylor diagrams for wheat yield between the observations and simulation by 12 yield prediction models in Jiangsu Province from 1993 to 2013
GY is the actual grain yield per unit area. GB-W and GB-A are bumper or poor harvest methods of weighting and approximate ratio. KMF and CS are yield prediction methods based on key meteorological factors and climate suitability. L, D, M5, M3 and Q are yield separation methods of linear separation, percentage difference, 5-year sliding average, 3-year sliding average separation, and quadratic curve.
下载: 全尺寸图片幻灯片表12014—2018年不同起报时间江苏省冬小麦最优模型和多模型集成单产预报值与实际值
Table1.Winter wheat yield simulated by optimal yield forecasting models and multiple model integration for different forecast starting time and actual yield in Jiangsu Province from 2014 to 2018
| 起报时间(月-日) Staring time (month-day) | 年份 Year | 实际单产 Actual yield (kg·hm-2) | GB-W | KMF-Q | CS-Q | 最优模型集成 Integration of optimal model | |||||||
| 预报单产 Predicted yield (kg·hm-2) | 准确率 Accuracy (%) | 预报单产 Predicted yield (kg·hm-2) | 准确率 Accuracy (%) | 预报单产 Predicted yield (kg·hm-2) | 准确率 Accuracy (%) | 预报单产 Predicted yield (kg·hm-2) | 准确率 Accuracy (%) | ||||||
| 04-10 | 2014 | 5 162 | 5 436 | 94.67 | 5 026 | 97.38 | 5 108 | 98.97 | 5 149 | 99.75 | |||
| 2015 | 5 181 | 5 301 | 97.68 | 5 157 | 99.54 | 5 037 | 97.22 | 5 176 | 99.91 | ||||
| 2016 | 5 112 | 5 359 | 95.17 | 4 843 | 94.73 | 5 308 | 96.16 | 5 230 | 97.70 | ||||
| 2017 | 5 369 | 5 237 | 97.55 | 5 067 | 94.38 | 5 151 | 95.95 | 5 177 | 96.42 | ||||
| 2018 | 5 363 | 5 347 | 99.73 | 5 037 | 93.94 | 5 338 | 99.55 | 5 282 | 98.50 | ||||
| 平均 Average | 96.96 | 95.99 | 97.57 | 98.46 | |||||||||
| 05-10 | 2014 | 5 162 | 5 316 | 97.01 | 4 974 | 96.36 | 5 150 | 99.78 | 5 165 | 99.94 | |||
| 2015 | 5 181 | 4 971 | 95.95 | 5 092 | 98.30 | 5 151 | 99.43 | 5 097 | 98.39 | ||||
| 2016 | 5 112 | 5 359 | 95.17 | 4 841 | 94.70 | 5 220 | 97.89 | 5 186 | 98.56 | ||||
| 2017 | 5 369 | 5 237 | 97.54 | 5 002 | 93.17 | 5 178 | 96.44 | 5 172 | 96.33 | ||||
| 2018 | 5 363 | 5 527 | 96.91 | 5 048 | 94.14 | 5 261 | 98.12 | 5 298 | 98.80 | ||||
| 平均 Average | 96.52 | 95.33 | 98.33 | 98.41 | |||||||||
| GB-W为丰歉相似年法中的加权法。KMF和CS分别表示基于关键气象因子和气候适宜度的单产预测方法。Q表示二次曲线产量分离法。GB-W is bumper or poor harvest methods of weighting. KMF and CS are yield prediction methods based on key meteorological factors and climate suitability. Q is yield separation method of quadratic curve. | |||||||||||||
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