中国农业气象 ›› 2021, Vol. 42 ›› Issue (02): 123-133.doi: 10.3969/j.issn.1000-6362.2021.02.004

• 农业生物气象栏目 • 上一篇    下一篇

冬小麦观测产量与统计产量的差异性分析

刘维,孟翠丽,宋迎波   

  1. 1.国家气象中心,北京 100081;2.武汉农业气象试验站,武汉 430040
  • 收稿日期:2020-07-07 出版日期:2021-02-20 发布日期:2021-02-19
  • 通讯作者: 宋迎波,研究员,研究方向为作物产量预报,E-mail:songyb@cma.gov.cn E-mail:songyb@cma.gov.cn
  • 作者简介:刘维,E-mail:rainvswindvs@163.com
  • 基金资助:
    国家气象中心预报员专项(Y201912);2019年国内外作物产量预报专项;2020年国内外作物产量预报专项

Studies on the Difference of Observed Yield and Statistical Yield of Winter Wheat

LIU Wei, MENG Cui-li, SONG Ying-bo   

  1. 1. National Meteorological Center, Beijing 100081, China; 2. Argo-meteorological Station of Wuhan, Wuhan 430040
  • Received:2020-07-07 Online:2021-02-20 Published:2021-02-19

摘要: 分析对比全国123个冬小麦农业气象观测站1991−2017年观测产量与所在县统计产量的年代际、变异系数和倾向率的差异;利用2006−2010年各县冬小麦种植面积平均值省内占比作为权重因子,将县观测产量与县统计产量加权集成为省级尺度观测产量和省级尺度统计产量,并与统计局发布的省公布产量进行对比,分析省级尺度三种不同产量的年代际变化和倾向率差异。结果表明:(1)县观测产量和县统计产量均表现为高产县数量增幅明显,低产县数量减幅明显;21世纪10年代两者均为高产年代,21世纪00年代两者差值达到峰值。(2)县尺度变异系数观测产量离散程度高于县统计产量,49个县观测产量的变异系数小于0.20,仅8个站大于0.40;72个县统计产量变异系数小于0.20,仅9个站点变异系数大于0.30。(3)各县观测产量中有73个站点倾向率呈现显著增加趋势,大多集中在河北、河南、山东、江苏、安徽等冬小麦主产省;100个县统计产量呈显著增加趋势。(4)21世纪00年代为各省观测产量和统计产量的高产年代,20世纪90年代为低产年代;山东、安徽、河北、江苏、陕西和山西等省10a观测产量的平均值在各个年代均高于省统计产量平均值。(5)除新疆和山西外其余省份省级尺度观测产量倾向率均通过显著性检验;所有省份省统计产量和公布产量倾向率均通过显著性检验,且产量增幅均为正值。总体来说,基于观测产量的冬小麦产量序列可以为产量预报提供新的数据来源。

关键词: 冬小麦, 农试站, 观测产量, 统计产量, 面积权重集成

Abstract: The difference of interdecadal variations, coefficient of variation and tendency ratio between the observed yield of winter wheat from 123 agrometeorological observation stations and the statistical yield of winter wheat at county level where the observation station was located from 1991 to 2017. The proportion of average winter wheat planting area in each county in five years(2006−2010) was used as the weight factor to integrate the observed yield and statistical yield at province level, at the same time using the announced yield at province level from National Bureau of Statistics. The interdecadal variations and tendency ration of three different yields at provincial level were compared and analyzed. The results showed that:(1) the number of high yield counties increased significantly, and low yield counties decreased significantly in both observed yield and statistical yield counties. The two yield were both high yield years in the 2010s, and the difference between the two reached peak value in the 2000s. (2) The coefficient of variation of observed yield at the county scale was higher than the statistical yield. The coefficient of variation of statistical yield in 49 counties were less than 0.20 and only 8 were greater than 0.40, while 72 statistical yield counties were less than 0.20 and only 9 were greater than 0.30. The coefficient of variation of statistical yield in all counties in Xinjiang and Shandong provinces were less than 0.30. (3) The tendency ratio of 73 observed yield counties showed a significant increase mostly concentrated in the major producing provinces such as Hebei, Henan, Shandong, Jiangsu, and Anhui; and 100 statistical yield counties showed the same significant increase. The tendency ratio of observed and statistical yield in 72 counties passed the significance test at the same time. (4) The 2000s were the high yield years for both observed and statistical yield at provinces level and 1990s were the low yield years. The average of the observed yield in every 10 years was higher than the average of the statistical yield in Shandong, Anhui, Hebei, Jiangsu, Shaanxi and Shanxi province. (5) Eight provinces had passed the significant test on tendency ration of the observed yield at the provincial level except for Xinjiang and Shanxi province. While tendency ration of the statistical yield and the announced yield in all provinces had passed the significant test and the yield growth was positive. In general, the winter wheat yield series based on the observed yield could provide a new data source for yield forecast.

Key words: Winter wheat, Agrometeorological observation stations, Observed yield, Statistical yield , Area weight factor integration