There are many econometric methods for forecasting by different economic variables in the future, recently, the procedures of dynamic forecasting either for Univariate or multivariate models were available for estimation on the software packages, i,e,, EViews, The research problem of the study, concerned with the different types of such dynamic models, with respect to, estimation, choosing the best fit model for forecasting by the economic variables, i,e,, price on the agricultural and national level, So the objective study,  is to concentration and determination the best forecasting model among Univariate and multivariate dynamic time series models, The time series data on the farm gate price of sugar cane and sugar beet were collected from the ministry of agriculture during the period (1998-2013), The methodology framework discussed the theoretical and mathematical approach for the dynamic Univariate models, i.e. autoregressive integrated moving average (ARIMA). The dynamic models contain four stages that have, identification, i.e. Stationarity and Co integration tests, model selection criteria for determination the lag length, Granger causality test, and choosing the techniques of estimation, also estimation stage, diagnostic stage for model accuracy, and finally forecasting stage, The study estimated the dynamic models by (ARIMA) models, (during the period (1998-2013), and forecasting by price of the two crops through the period (2015-2020), The estimation and forecasting results, indicated that the price will increase at increasing rate. Finally the study recommended by more projection studies in the field of agriculture with its different resources , and encouragement the investment in projects that have high returns , the expanding in cultivating new lands and national projects, also increasing the price of these crops that reflect the high productivity.