Optimal Water And Fertilizer Management Of Winter Wheat In North China Based On Different Forecast Periods

Winter wheat is one of the main grain crops in North China.Its stable yield and increasing income play a decisive role in national food security.Drought and irrational application of chemical fertilizer have resulted in low yield of winter wheat in this region and even caused a series of ecological and environmental problems.Therefore,optimizing the irrigation and fertilization management system in this region has a very important practical significance for improving the utilization efficiency of water and fertilizer for winter wheat and guaranteeing the sustainable development of agriculture in this irrigation area.In this study,AquaCrop model was used to investigate the effects of different water and nitrogen stress on winter wheat growth in North China.The AquaCrop model was verified by field measurement data,and the key parameters of the model were obtained.The pressure coefficients related to fertilizer stress under different nitrogen application levels were quantified,and the parameterization scheme of semi-quantitative method was proposed.The effects of irrigation amount on the growth of winter wheat were studied by setting different irrigation systems.On this basis,the ET₀ prediction model with high evaluation accuracy in different prediction periods was selected and coupled with AquaCrop model to dynamically monitor soil water content and winter wheat growth index,so as to realize the optimal management of water and fertilizer in winter wheat.The main conclusions are as follows:（1）The AquaCrop model can simulate the canopy cover,soil moisture,yield,and biomass of winter wheat under different water and nitrogen stresses in North China with high accuracy.（2）The semi-quantitative method of the model was used to explore the relationship between nitrogen application in the field and fertilizer stress parameters,and the parameter values related to fertilizer stress at any nitrogen application level were quantified,which broke through the research on nitrogen application optimization management using semi-quantitative methods.（3）Multi-scenario simulations were performed at different nitrogen application levels based on the semi-quantitative method of the model.The simulated results and the quadratic function relationship between nitrogen application and yield were established.The yield of winter wheat increased with the input of nitrogen in the nitrogen application range of 0～0.355 t?hm^-2.Once it exceeds this range,the yield of winter wheat begins to decrease,which is consistent with the results of field experiments.Excessive nitrogen application will also reduce nitrogen fertilizer use efficiency.（4）The evaluation accuracy of weather forecast data and ET₀ forecast model met the requirements,and PT model was selected for 7-day,15-day and 30-day ET₀forecast models.（5）The ET₀ prediction model and AquaCrop model in different prediction periods were coupled to simulate the growth of winter wheat.The combined simulation results in different prediction periods were compared with the observed values in the field.It was found that the R² and d between the simulated values and the observed values of canopy coverage,soil moisture content and biomass were all greater than0.504 and 0.449,respectively.The NRMSE were all less than 34.827%,indicating that the ET₀ prediction model and AquaCrop model could be coupled to optimize the water and fertilizer management of winter wheat.（6）With the increase of irrigation amount,the yield and water use efficiency of winter wheat increased first and then decreased.When the irrigation amount exceeded300 mm,the yield increase effect was not obvious,but the water use efficiency decreased obviously.Under the condition of the same irrigation amount,winter wheat yield and water use efficiency could be improved within a certain range by setting the irrigation lower limit in different growth periods.There was no significant difference in winter wheat yield and water use efficiency under different forecast periods.