Agricultural productivity and technological changes are important for enhancing growth in agriculture and measurement of these would help to determine the direction of investments in agriculture. The measure that compares output with the levels of use of inputs would be the most ideal one. Keeping this in view, the total factor productivity (TFP) approach was used to decompose productivity. The analysis was performed for the state as a whole as well as for the 10 agro-climatic zones and comparisons were made between high and low productive zones. The constraints for achieving higher productivity were identified so as to suggest suitable policy options that could be adopted to achieve higher productivity. The study made use of both cross-section and time series data from 1997-2007 to 2008-2018 and were obtained from the Directorate of Economics and Statistics, planning department and the department of agriculture. The analysis considered 12 crops and comprised of variables such as area, production, prices, seeds, fertilizers, farm yard manure, maintenance and repair charges of fixed assets, irrigation charges, marketing costs, electricity, pesticides, diesel oil, depreciation, land rent and labour costs. The Tornqvist-Theil divisia chained indices for TFP The total factor productivity (TFP) in Odisha increased at the rate of 0.05 per cent per annum during the entire period of study. This trend was due to higher growth of output (0.38 per cent) in relation to the growth of input use (0.33 per cent). During the first phase, the TFP declined by 0.02 per cent per annum while in the second phase TFP increased by 0.18 per cent per annum. The variation in TFP among the zones around the trend was mainly due to variation in output. The growth in agricultural labour force in the state was positive and higher in the second phase when compared to the first phase. variables in order to identify the major determinants. These determinants of the TFP growth suggest areas for policymaking and the policy discussions should be indicative rather than directive. Government expenditure on Agricultural research, education and extension per ha, average rainfall in mm, percentage of irrigated crop area, and rural literacy percentage and cropping intensity were identified as the determinants of the TFP of all crops in the state. The R2 value of the regression model was 80.7% (significant at 5% level) implying that 81% variation in the TFP growth was explained had its own influence on the TFP.