Effect of Urbanization on Agriculture in India Pramanik, Chanchal National Academy of Agricultural Research Management, Information and Communication Management Division Rajendranagar Hyderabad (500407), India E-mail: cpramanik@gmail.com Sarkar, Ananta National Academy of Agricultural Research Management, Information and Communication Management Division Rajendranagar Hyderabad (500407), India E-mail: anantasarkar@naarm.ernet.in Introduction Agriculture is the backbone of Indian economy, which is providing livelihood for about 65 to 70 per cent of total population and employs about 52 per cent of country’s workforce and presently contributing nearly 17.5 per cent to GDP (2009). India is the second most populated country in the world after China. Presently the study on India is concerned with effect of urbanization, population growth and total cropped area on total food grain production based on 60 years of data (1950-51 to 2009-10). The data of the above mentioned variables is collected from www.indiastat.com . Here Multiple Linear Regression, Auto Regressive Integrated Moving Average (ARIMA) and Markov Chain modeling techniques are used. Similar studies were carried out on Andhra Pradesh state of India which showed that the urbanization and population growth have adverse effect on the agriculture of the state. It is the aim of this research investigation to see if the condition of overall India is similar to that of Andhra Pradesh. Methodology The data have been collected online from www.indiastat.com from 1950-51 to 2009-10 on Total Food Grain production (TFG), Total Cropped Area (TCA), Urbanized Area (UA) and Population. Then on the basis of 60 years of data a Multiple Linear Regression (MLR) has been fitted taking TFG as dependent variable and TCA, UA and Population as independent variables. The statistical significance of the model is determined based on Coefficient of Determination (R 2 ). The significance of the estimated independent variables is tested based on the respective p-values, taking 5 percent level of significance as standard. Next after fitting the MLR equation ARIMA models are fitted to forecast all the variables involved in the study. Based on the respective ARIMA models TFG, TCA, UA and Population are forecasted for next 11 years (2010-11 to 2020-21). Now based on the TCA, UA and Population forecasted values TFG values are obtained based on the previous fitted MLR equation for 2010-11 to 2020-21. Here two sets of TFG values have been obtained, one from the time series analysis of ARIMA model and other from fitted MLR equation where independent variables are estimated based on ARIMA model. Differences of two sets of TFGs are calculated and fitted with a suitable trend equation. The forecasted TFGs based on MLR up to 2020-21 consider the trend of TCA, UA and Population which signifies the possible TFGs. The forecasted TFGs based on ARIMA technique up to 2020-21 signify the expected TFGs considering the present trend of TFG. The differences between two sets of TFGs signify the shortage of Total Food Grain production due to the present trend of UA, TCA and Population.