Chinese Journal of Agrometeorology ›› 2018, Vol. 39 ›› Issue (11): 725-738.doi: 10.3969/j.issn.1000-6362.2018.11.004

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A Comparative Study on Forecast Model for Soybean Yield by Using Different Statistic Methods in Liaoning Province

WANG He-ran, ZHANG Hui, WANG Ying, LI Jing, MI Na, WANG Ruo-nan, LI Lin-lin, DONG Wei, ZHANG Qi, SU Hang   

  1. 1.Institute of Atmospheric Environment, China meteorological Administration, Shenyang 110166, China; 2.Liaoning Institute of Meteorological Sciences, Shenyang 110166; 3.Jinzhou Ecology and Agriculture Meteorological Center, Jinzhou 121001; 4.Liaoning Meteorological Equipment Support Center, Shenyang 110166; 5.Liaoning Centre, China Meteorological Administration Training Centre, Shenyang 110166; 6.Shenyang Central Meteorological Observatory, Shenyang 110166
  • Online:2018-11-20 Published:2018-11-13

Abstract:

Two different dynamic forecast methods for soybean yield based on key meteorological factors and climatic suitability were established in this study. The key meteorological factors and climatic suitable index were calculated by daily meteorological data from 56 meteorological stations, soybean growth stage records of 5 key agrometeorological stations and yield data in Liaoning province from 1992 to 2016. The results showed that the key meteorological factors-based forecast model was suitable to forecast soybean yield on June 16, July 21, July 26, August 1, August 26 and September 16 (P<0.05), and the climatic suitability-based forecast model could be used for every 5 or 6 days from August 16 to October 1 (P<0.05) in Liaoning province. The average accuracies of return test of both models were higher than 83.0%. Compared with the key meteorological factors-based model, the climatic suitability-based model was stable, which indicated by smaller variation of average verified accuracy and average forecast test accuracy. From 1997 to 2016, both models could be applied in soybean yield tendency forecast, according to scoring criteria, core above zero reach up to 60% by the two models. In conclusion, the yield forecast models based on key meteorological factors and climatic suitability could meet the basic needs for agrometeorological services in Liaoning province. For soybean yield tendency forecasting, the method based on key meteorological factors should be preferred. In the normal years without severe meteorological disaster, the method on climatic suitability could be given priority to yield quantitative forecasting to reduce forecast times.

Key words: Soybean, Key meteorological factors, Climatic suitability, Yield forecast, Liaoning