基于COSIM模型的棉花区域产量动态预测研究——以新疆阿克苏市为例

doi:10.11963/1002-7807.hltpxb.20171206

摘要/Abstract

摘要： 【目的】提高基于作物生长模型进行产量动态预测的准确率。【方法】以新疆阿克苏市为例，采用1991―2014年的气象资料、棉花产量资料和棉花的物候资料，通过应用COSIM棉花模型分别以多年平均播种期和适宜播种区间内多个播种期作为播种期输入，比较分析两种产量预测方法的效果。【结果】结果表明：两种方法的预报准确率均在90.0%以上，在阿克苏市均有很好的适用性。但对比分析得出，在棉花生育期内按照月份进行产量动态预测时，以及预报年份实际播种期不确定时，基于适宜播种期区间的多播种期预测方法的预报准确率更高。【结论】研究结果为阿克苏市提供了两种基于作物生长模型的棉花产量预报方法，针对不同预报年的特征选择合适的预报方法可以提高预报准确率，同时可为其他棉花产区的产量动态预测提供参考和借鉴。

关键词: 棉花; COSIM模型; 播种期; 产量预测

Abstract: [Objective] Dynamic prediction of crop yield using a crop growth simulation model is the focus of increasing research attention. [Method] Based on meteorological, cotton yield, and cotton phenology data recorded at Akesu in Xinjiang from 1991 to 2014, this study aimed to improve the accuracy of crop yield prediction by the COSIM model. The average sowing date for each study year, as well as multiple sowing dates during the suitable sowing period, was imported into the COSIM model, and the two yield prediction methods were compared and analyzed. [Result] The accuracy of both yield prediction methods was higher than 90.0%, indicating that the two methods showed good applicability at Akesu. However, the method using multiple sowing dates during the suitable sowing period showed higher prediction accuracy when cotton yield was dynamically predicted in each month and the actual sowing date was uncertain. [Conclusion] The two prediction methods based on the crop growth simulation model are suitable for prediction of cotton yield at Akesu. In addition, according to the characteristics of different forecast years, the appropriate forecasting method can be used to improve the accuracy of prediction. The results also provide a reference for dynamic prediction of cotton yield in other cotton-producing areas.

Key words: cotton; COSIM model; sowing date; yield prediction

中图分类号:

S562：S165+.27

胡莉婷, 潘学标, 王雪姣, 胡琦. 基于COSIM模型的棉花区域产量动态预测研究——以新疆阿克苏市为例[J]. 棉花学报, 2018, 30(1): 83-91.

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链接本文: https://journal.cricaas.com.cn/Jweb_mhxb/CN/10.11963/1002-7807.hltpxb.20171206

https://journal.cricaas.com.cn/Jweb_mhxb/CN/Y2018/V30/I1/83

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