Time series has vital importance in various fields mainly, business, economics, finance, industry, agriculture statistics, banking, medical, research, many more real life problems etc. In the context of agriculture statistics various Time series models are used to analyze the trends and forecast of area under crops, production and productivity of crops. Trends and forecast of crops helps any government to plan various policies and agricultural programs in the country. This includes minimum support price, prices of seeds, fertilizers, pesticides etc. Forecast of various agricultural crops helps the government to release the buffer stock at their hands. Time series analysis plays important role in framing the agriculture policy in the country. There are number of time series models to analyze trends and forecasts with respect various agricultural factors viz. Yield, production, productivity, prices etc. This chapter deals with the applications of various time series model in agricultural fields. Additive and multiplicative models of Time series models are explained. Analysis of Trends and forecasts will be made using linear, quadratic, cubic, exponential and S shape trend curves with the help of numerical examples of agricultural data. Also a light is thrown on Single exponential smoothing method, double exponential smoothing method, Holt’s winter method, Auto Regressive Moving average Model (ARMA), Auto Regressive Integrated Moving average Model (ARIMA), Seasonal Auto Regressive Integrated Moving average Model (SARIMA) with numerical examples. An accuracy measures viz. Mean Absolute Deviation (MAD), Mean Absolute Percent Error (MAPE) and Mean Squared Deviation and Lunge Box Chi-square statistics are also taken into consideration while analyzing the trends and forecasts which helps in determining the proper model for given time series data using MINITAB 19 Statistical software.