Relationship between Technological Advancement and Agricultural Production: Evidence from India
Chumki Handique

Chumki Handique, Department of Economics, Dibrugarh University, Assam, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 2088-2094 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7961038620/2020©BEIESP | DOI: 10.35940/ijrte.F7961.038620

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Abstract: Indian economy has more than 60% of the work force engaged in it and with the sectoral contribution of 17-18% to country’s GDP in 2018-19. Despite such heavy dependence and high significance of the agricultural sector, per capita productivity in agriculture over the past few decades is less in comparison to the productivity in other sectors. Available statistics shows that agricultural production has rose marginally during the period of green revolution (starting in 1960s) which was driven by the technology revolution. Technology revolution here means- ‘seed-fertiliser-water technology’ or modern technology. In the present study, a detailed time series analysis for a time period of 36 years (1981-2017) is made to study the impact of technology in production in both the short run and long run. Firstly, the present status of technology use is studied and secondly a crop-output model is considered depicting the role of technology in production of India. Here, the impact of technology is measured using variables such as gross irrigated area, Pesticide use, Synthetic nitrogen fertilizer (NPK) uses, use of improved seed varieties (HYVs) etc. and their impact upon agricultural production (food grains as well as non-food grains) is tested using various econometric tests such as Johansen Co-integration test, regression estimates etc. A composite index has been constructed using PCA method as a proxy to technology. To examine the linkage between technological advancement and agricultural production in India, we employed the Vector auto regression (VAR) model proposed by Sims. To draw inferences on the results of VAR, we also used forecast error variance decomposition (FEVD) which gives both short-run impact and long-run impact of each variable in explaining the forecast error variance of the dependent variable.
Keywords: Role, Technology, Production, Agriculture, Green Revolution.
Scope of the Article: Design and Performance of Green Building.