Study On Irrigation Forecast Of Winterwheatbasedon Weather Forecast Messages

In this study, the winter wheat field trials were conducted from 2012 to 2014 in Xinxiang. The high precision reference crop evapotranspiration ET0 prediction model was identifited based on the daily weather forecast information andthe measured weather data combined with the the winter wheat field trials data.Using the temperature forecast data, crop coefficient simulation modelwas build based on accumulated temperature.Coupling the crop coefficient simulation model and the above builted ET0 prediction modelto the water balance equation, the future periodssoil moisturecan be forecasted. By simulating the soil moisture during the 2012-2014 winter wheatusing the SIMDual_Kc crop coefficient model, the model is better andcan be applited in Xinxiang, and when measured data is missing, the combined datacan be interpolated.The user-oriented visual programming interface was established based on.NET Framework4.5, which can provide intuitive visual decision basis for irrigation management and decision-makers, so that timely and appropriate guidance irrigation irrigation.The main conclusions are as follows:(1) By statistical analysis of weather forecast forecast accuracy, the forecast accuracyof maximum, minimum temperature and sunshine hours steadily declined with the increasedforecast period, and in the same forecast period, the accuracy of the minimum temperature was slightly higher than the highest temperature.(2) Among the ET0 prediction model based on the weather forecast information analysis, PMT1 modelaccuracywas the highest in the short-term forecasting models, and in the medium term forecast, forecast values of P-T and HG-M models can meet the accuracy requirement, and simplify the calculation procedure, so we can selecte the ET0 prediction models depending on the precision of forecast and forecast period.Compared the winter wheat reference crop evapotranspiraton comparison based on the random prediction model of BP neural network and BP neural network and PM formula, the principal component analysis neural network has a higher prediction accuracy, strong identify ability and high stability to the forecasting sample outside the training sample, so the BP neural network was better in the selection of the reference crop evapotranspiration forecasting model.(3) The winter wheatheight and LAI simulation modelwere built based on the sowing days, the model was6 degree polynomial,the overall fit was better, fitting curve can better reflect the strain trends of height and leaf area index, but fitting curve is not goodfor some larger or smaller data.Meanwhile, a six degree polynomial simulation model was built based on normalized the leaf area index and accumulated effective temperature.Logistic model with modified simulated based on the relative effective accumulated relatively LAI, the Logistic was preferredfor LAI prediction model.LAI was linear correlation with crop coefficients in winter wheat growing period. Based on logistic LAI simulation model, a crop coefficient simulation model based on effective accumulated temperature was built. The winter wheat crop coefficient measured in 2013- 2014 was used to verify the model, and the daily temperature were used to correct the crop coefficient prediction value, compared with the forecast value of Kc, in the corrected prediction value Kc, where r was increased 1.1%, d was increased up 1.1%, showing that the corrected prediction value Kc can enhance the prediction accuracy.(4) The real-time forecasting of winter wheatwas conducted in 2013-2014, which was based on the water balance equationsoil coupledwith the screened three ET0 prediction model and crop coefficient prediction model.The winter wheat soil moisture in 2012-2014 was simulated using SIMDual_Kc dual crop coefficient model, with the model parameters calibration using the real measured soil moisture in 2012-2013, and the model validation using the real measured soil moisture in 2013-2014.Statistics show that, the SIMDual_Kc dual crop coefficient model can be used to simulate the winter wheat soil moisture in Xinxiang area, which has a of a high simulation precision and can be applited in Xinxiang.(5) The irrigation forecasting system was set up based on.NETFramework 4.5, which establish background database and Access Database Engine using the Access, developed for, the forecasting was user-friendly and convenient, and forecasting, weather information can be automatically obtained from the Internet, the system can provide the fundamental basis for the irrigation decision guidance.