中国农业气象

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基于花粉量的作物产量预测模型研究进展

单琨;刘布春;李茂松;武永峰;   

  1. 中国农业科学院农业环境与可持续发展研究所/农业部农业环境与气候变化重点开放试验室/农业部旱作节水农业重点开放实验室;
  • 出版日期:2010-04-10 发布日期:2010-04-10
  • 基金资助:
    农业部“农村脆弱地区天气指数农业保险国际合作”;; 中央级公益性科研院所基本科研业务费专项资金项目“应用于农业保险的气象灾害损失评估模型的研究”

Research Progress of Pollen Variable Models for Forecasting Crop Yield

SHAN Kun,LIU Bu-chun,LI Mao-song,WU Yong-feng( Institute of Environment and Sustainable Development of Agriculture,Chinese Academy of Agricultural Sciences/Key Laboratory of Agro-Environment & Climate Change/Key Laboratory of Dryland Agriculture,Ministry of Agriculture,Beijing 100081,China)   

  • Online:2010-04-10 Published:2010-04-10

摘要: 为了探究以花粉量为主要因子的作物产量预测模型的应用前景,综述了国内外构建花粉量产量预测模型的相关研究进展,发现在传统的气象产量预测统计模型中,引入花粉量因子,可以提高产量预测的准确性;基于花粉量监测技术的限制,花粉量产量预测模型更适用于同一生长季内单一作物,面积超过一定规模的种植区域的产量预测,有较好的应用前景;对于同一生长季,作物种类多、面积小、空间分散的种植区域来说,花粉量产量预测模型应用效果的好坏将主要取决于通过花粉量监测技术获得的花粉量数据的质量和数量。要使花粉量产量预测模型在中国得到很好的应用,需要建设完备的花粉监测网,提高花粉识别技术并推广应用,积累足够的花粉量数据以构建稳定而全面的产量预测模型。

关键词: 花粉密度, 花粉通量, 花粉指数, 作物产量, 预测模型

Abstract: In order to explore the potential of models introduced with pollen variable for forecasting harvest yields,progress in the forecast model research was summarized. It was indicated that the accuracy of the forecasting results could be enhanced by introducing pollen variable in traditional statistical models which were composed only of meteorological variables. Due to the limitation in the monitoring technologies,the forecast model was more applicable to the single crop in the same growing period. In the area where various crops were separated in small size fields in the same growing period,the forecasting accuracy of this kind of model was largely depended on the quality and quantity of the pollen data obtained by using the relevant monitoring technologies. In order to improve the application potential of pollen variable models in China.It is necessary to build the complete monitoring network,improve and develop the pollen recognition technology,and also accumulate enough pollen variable data for a stable and comprehensive yield forecast model.

Key words: Pollen density, Pollen density, Pollen flux, Pollen index, Crop yield, Forecast model