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| 粮食估产的“通道-概率模型”的验证 |
| Verification of “Channel-Probability Model” of Grain Yield Estimation |
| Received:August 11, 2015 |
| DOI：10.13254/j.jare.2015.0194 |
| 中文关键词: 粮食估产;通道-概率模型;验证 |
| 英文关键词: grain yield estimating;channel-probability model;verification |
| 基金项目:中国农业科学院科技创新工程（2014-cxgc-hyl） |
| Author Name | Affiliation | E-mail | | ZHENG Hong-yan | Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China | | | LI Jing-ya | Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China | | | LIU Shu-tian | Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China | | | HUANG Zhi-ping | Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China | | | MI Chang-hong | Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China | | | HOU Yan-lin | Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China | bjyours@sina.com | | WANG Nong | Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China | | | CAI Yan-ming | Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China | | | WANG Shuo-jin | Beijing Research Center for Information Technology in Agriculture, Beijing 100089, China | | | HOU Xian-da | Software Development and Service Center of Beijing Yours, Beijing 100089, China | |
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| 中文摘要: |
| 本文应用全国、31个省、6个典型地区和16个典型县的数据对粮食估产的“通道-概率模型”进行了系统性的验证和讨论。研究结果如下：（1）国家级估产由于地域空间尺度足够大，不同地区气象条件对产量影响的互补性强，所以估产误差小，因此国家级可以不使用小趋势修正和气候年型修正；省级、地区级和县级的估产由于同处一个气候区，因此气象条件对产量影响的互补性不强，必须使用小趋势修正和气候年型修正，县级估产还必须增加根据作物适时长势和专家经验的修正。（2）小趋势修正有两个公式：当预测误差小于10%时，使用Y×（1-K）修正；当预测误差大于10%时，使用Y/（1+K）修正。（3）估产单元气候年型可以自动划分，一般分为5级，波动大的预测单元可以使用7级，其中超丰年和超欠年的修正参数必须根据实时气象条件和作物实时长势具体确定。（4）研究表明：“通道-概率”估产理论和方法是科学的、实用的和准确的；在小趋势修正和气候年型修正基础上，如能结合作物长势调查和当地专家经验，估产误差可以达到3%以下。 |
| 英文摘要: |
| The "channel-probability model" of grain yield estimation was verified and discussed systematically by using the grain production data from 1949 to 2014 in 16 typical counties, and 6 typical districts, and 31 provinces of China. The results showed as follows:(1)Due to the geographical spatial scale was large enough, different climate zones and different meteorological conditions could compensated, and grain yield estimation error was small in the scale of nation. Therefore, it was not necessary to modify the grain yield estimation error by mirco-trend and the climate year types in the scale of nation. However, the grain yield estimation in the scale of province was located at the same of a climate zone,the scale was small, so the impact of the meteorological conditions on grain yield was less complementary than the scale of nation. While the spatial scale of districts and counties was smaller, accordingly the compensation of the impact of the meteorological conditions on grain yield was least. Therefore, it was necessary to use mrico-trend amendment and the climate year types amendment to modify the grain yield estimation in districts and counties.(2)Mirco-trend modification had two formulas, generally, when the error of grain yield estimation was less than 10%, it could be modified by Y×(1-K); while the error of grain yield estimation was more than 10%, it could be modified by Y/(1+K).(3)Generally, the grain estimation had 5 grades, and some had 7 grades because of large error fluctuation. The parameters modified of super-high yield year and super-low yield year must be depended on the real-time crop growth and the meteorological condition. (4)By plenty of demonstration analysis, it was proved that the theory and method of "channel-probability model" was scientific and practical. In order to improve the accuracy of grain yield estimation, the parameters could be modified with micro-trend amendment and the climate year types amendment. If the assessment can be further combined with the real-time crop growth survey and local expert experience, the grain estimation precision will be within 3%. |
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