Abstract:

This paper used the meteorological data of lycium barbarum growing period from 2011 to 2020, and meteorological factors such as temperature, precipitation, sunshine and lycium barbarum production data from 1991 to 2019 in the main production area, and applied statistical methods such as stepwise regression, probability statistics, and chart analysis to study the forecasting model of lycium barbarum production based on key meteorological factors. The results showed that: (1) the growing periods which affected lycium barbarum production were leaf expansion, old eye branch flowering, spring branch flowering, old eye branch fruit ripening, autumn branch flowering, summer fruit ripening and autumn fruit ripening. (2) According to the soil moisture in the field, the first layer of water should be irrigated as early as possible to promote leaf expansion; when the daily maximum temperature exceeded 25℃, flowering and pollination would be adversely affected, and the rate of falling flowers increased; strong sunshine and long sunshine were conducive to fruit ripening, while strong wind and many windy days were easy to cause fruit falling; continuous overcast and rainy weather could easily induce black fruit disease and reduce the yield and quality of wolfberry; sunny day was good for harvesting and drying. (3) The prediction model of lycium barbarum established by 0.01 significant tests of meteorological factors and yield was used to test the yield in 2019, the accuracy rate reached 91.8%, and the relative error was 8.2%. It fully showed that the selected meteorological factors could accurately predict the yield of lycium barbarum in the region, with good forecasting accuracy, and could be applied in actual forecasting operations. (4) During the forecasting service of lycium barbarum yield, if there were significant changes in other influencing factors other than meteorological factors, the significant changes in trend yield should be considered, and the forecast results of the model should be adjusted in combination with the field survey to further improve the forecast accuracy.

Key words: growing period of Lycium barbarum, key meteorological factors, lycium barbarum production, prediction model research