文献类型： 外文期刊
作者： Zhang, Qi 1 ; Wang, Kaiyi 2 ; Han, Yanyun 2 ; Liu, Zhongqiang 2 ; Yang, Feng 2 ; Wang, Shufeng 2 ; Zhao, Xiangyu 2 ; Zhao, Chunjiang 1 ;
作者机构： 1.Northeast Agr Univ, Sch Elect & Informat, Harbin 150030, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing, Peoples R China
3.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China
关键词： Yield prediction; Meteorological factors; Ecological region; Yield data compensation; Key growth periods
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）
ISSN： 0168-1699
年卷期： 2022 年 203 卷
页码：
收录情况： SCI
摘要： Forecasting crop variety yield, selecting suitable ecological regions for designated crop varieties, and finding high-yield crop varieties for designated ecological regions are all critical aspects of crop variety popularization. A crop variety yield prediction system based on variety yield data compensation (CVYPS-VYDC) is described in this paper. This novel system is used to predict the yield of designated crop varieties using the large amount of breeding data stored in the Golden Seed Breeding Cloud Platform and meteorological data from the National Meteorological Science Data Center. The prediction system was devised to (a) forecast the yield of designated crop varieties in the target ecological regions, (b) calculate the effects of different environmental factors on the yield of different crop varieties, (c) identify the key growth periods affecting crop yield, and (d) promote crop varieties according to a comprehensive analysis of the promoted variety yield, preferences for different meteorological factors, and climatic characteristics of the target ecological regions. In addition to meteorological factors, soil factors affect crop yield. However, large-scale measurements of soil composition require a large number of sensors and considerable human resources. By using the yield data compensation method, the yield data of predicted varieties across different regions were expanded and corrected, which reduces the yield prediction error caused by soil differences among regions to some extent. In addition, the coefficient of determination (R2) between actual and predicted yield was improved to 0.82. In addition to predicting the yield of 90 maize varieties in the Huang-Huai-Hai area of China, the preferences of different maize varieties for different environmental factors were analyzed at the same time. CVYPS-VYDC identified the key environmental factors greatly impacting the maize yield, and the proportion by which different factors affected the yield prediction of different maize varieties was determined. Moreover, the growth periods that have great impact on maize yield were identified by analyzing 39 cultivation sites of various maize varieties in the Huang-Huai-Hai area of China.
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