Abstract:

To ensure safe wheat production, based on the incidence data of spring wheat stripe rust in Anding District, Dingxi City from 2008 to 2017, combined with concurrent meteorological and agro-meteorological observation data of spring wheat, the occurrence and development characteristics of stripe rust and its relationship with meteorological factors were analyzed by statistical methods. A prediction model of spring wheat stripe rust was established using stepwise regression analysis. The results showed that in recent years, the timing of typical growth stages of spring wheat varied considerably, with a difference of 36 days between the earliest and latest emergence dates, followed by a 19-day difference for the milk-ripening stage. The onset time of spring wheat stripe rust showed no significant correlation with the typical growth stages. Over the past decade, the incidence of spring wheat stripe rust exhibited a decreasing trend, with a linear change rate of -0.25/10 a (P > 0.05). The incidence was negatively correlated with the mean air temperature in summer, autumn, and winter, but positively correlated with spring temperature. A positive correlation was generally observed with precipitation, with the strongest correlation occurring in winter (r = 0.663, P < 0.05). The incidence was mainly negatively correlated with sunshine duration; increased sunshine was associated with lower disease incidence. The strongest correlation was with sunshine duration in May (r = -0.599), although it did not reach statistical significance. Correlations between air humidity and disease incidence varied across different periods. A climate prediction model for spring wheat stripe rust was established using temperature, precipitation, sunshine duration, and humidity data, and it performed well in practical applications. These findings provide an important basis for forecasting the occurrence and development of stripe rust, predicting meteorological risk levels, and supporting scientific decision-making in disease control.

Key words: spring wheat, growth stage, spring wheat stripe rust, climatic element, forecast model, growing season, stripe rust affected area