Pattern of Agricultural Progress in India’s North-East and the Contributing Factors: An Econometric Analysis

Year 2023, Volume: 20 Issue: 3, 509 - 527, 26.09.2023

Utpal Kumar De Ratna Kumari Tamang

https://doi.org/10.33462/jotaf.1114386

Abstract

Despite being blessed with rich agro-climatic conditions, the largest agrarian state in India’s North-East, Assam recorded relatively poor agricultural growth, since independence. The question of agricultural performance in terms of use of factors and growth pattern always arise that seems to vary in different stages of policy shift. Agricultural diversity increased in the initial phase with the expansion of agricultural area but slowed down in the later stages. However, the nature of agricultural diversity and use of resources including land allocations reflects the adaptation of farming community, absorption of labour force and sustainability of earning of farmers. The objectives are to analyze: i) the pattern of agricultural growth, diversity; ii) relative contribution of crop diversification, yield and area towards output growth in the pre-Green Revolution, Green Revolution and Post-Reform period; iii) association of various factors with crop yields in the short run and the adjustment process in the long run. Using secondary data, semi-log linear and spline regression functions we examined the growth and stationarity of growth processes is checked by ADF test. Times series analyses like cointegration and ARDL bound testing approach has been followed to examine the relation of various factors with yield of various crops in the short and long run. The ECM also provides the process of adjustment and CUSUM(Q) test is used for checking fitness of the models. Changes in diversity are analyzed through Herfindahl Index and the additive decomposition technique is employed to examine changing contribution of growth of yield, area and cropping pattern and their interactions. The result reveals varied impacts of main weather variable (rainfall), technological factors and cropping intensity on the yields of crops in different phases since 1950-51. Area effect on output and cropping pattern growth though declined, yield growth contributed increasingly in successive sub-periods in Assam. However, the contribution of modern technology towards the growth has not been uniform in the three major stages of agricultural transformation in Assam.

Keywords

Agricultural Growth, Diversity, Agro-technology, Stationarity, Cointegration, ARDL Bound Test, North-East India

Project Number

PhD Programme

References

• Bhalla, G. S. and Singh, G. (1997) Recent developments in Indian agriculture: A state level analysis. Economic and Political Weekly, 32(13): A2- A18.
• Bhalla, G. S. and Singh, G. (2009). Economic liberalisation and Indian agriculture: A state-wise analysis. Economic and Political Weekly, 44(52): 34-44.
• Bora, K. (2022). Spatial patterns of fertilizer use and imbalances: Evidence from rice cultivation in India. Environmental Challenges. 7: 100452.
• Brown, R. L., Durbin, J. and Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society Series B (Methodological), 37(2): 149–192.
• Chandio, A. A., Jiang, Y., Rehman, A. and Rauf, A. (2020). Short and long-run impacts of climate change on agriculture: An empirical evidence from China. International Journal of Climate Change Strategies and Management, 12(2): 201-221.
• De, U. K. (2000). Cropping Pattern and Agricultural Development in West Bengal during 1970-71 to 1994-95. Indian Economic Journal, 48(4): 68-77.
• De, U. K. (2003). Changing cropping system in theory and practice – An economic insight into the Agrarian West Bengal. Indian Journal of Agricultural Economics, 58(1): 64–83.
• De, U. K. and Bodosa, K. (2015). Crop Diversification in Assam and Use of Modern Inputs under Changing Climatic Condition. Journal of Climatology & Weather Forecasting, 2(2): 1-14.
• De, U. K. and Chattopadhyay, M. (2010). Crop diversification by poor peasants and role of infrastructure: Evidence from west Bengal. Journal of Development and Agricultural Economics, 2(9): 340-350.
• De, U. K. and Pal, M. (2019). Impact of climate change on agricultural productivity in India’s North-Eastern Region: A panel data analysis. International Journal of Statistical Sciences, 17: 1-38.
• Dickey, D. A. and Fuller, W. A. (1979). Distribution of the estimates for autoregressive time series with a unit root. Journal of Statistical American Association, 74(366a): 427-431.
• Dickey, D. A. and Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(40): 1057-1052.
• Government of Assam (2020 and various issues). Statistical Handbook of Assam 2020. (Various issues), Directorate of Economics and Statistics, Assam.
• Government of India (2020). Agricultural Statistics at a Glance 2020, Government of India, Ministry of Agriculture & Farmers Welfare, Department of Agriculture, Co-operation & Farmers Welfare, Directorate of Economics and Statistics.
• Guntukula, R., and Goyari, P. (2020). Climate change effects on the crop yield and its variability in Telangana, India. Studies in Microeconomics, 8(1): 119-148.
• Jena, P. K. (2021). Nexus between climate change and agricultural production in Odisha, India: An ARDL approach. International journal of Environment, Agriculture and Biotechnology, 6(2): 136-144.
• Johnston, J. (1972). Econometrics Methods, New York: McGraw Hill Publishing.
• Kalamkar, S. S., Atkare, V. G. and Shende, N. V. (2002). An analysis of growth trends of principal crops in India. Agricultural Science Digest, 22(3): 153-156.
• Khajuria, A. (2016). Impact of nitrate consumption: Case study of Punjab, India. Journal of Water Resource and Protection, 8(2): 211-216.
• Kumar, S. and Singh, S. (2014). Trends in growth rates in area, production and productivity of sugarcane in Haryana. International Journal of Advanced Research in Management and Social Sciences, 3(4): 117-124.
• Minhas, B. S. and Vaidyanathan, A. (1965). Growth of crop output in India 1951-54 to 1958-61: An analysis by component elements, Journal of the Indian Society of Agricultural Statistics, 17(2): 230-252.
• Paria, B., Pani, A., Mishra, P. and Behera, B. (2021). Irrigation-based agricultural intensification and future groundwater potentiality: Experiences of Indian States. SN Applied Sciences, 3(4): 1-22.
• Pattnaik, I. and Shah, A. (2015). Trends and decomposition of agricultural growth and crop output in Gujarat: Recent evidence. Indian Journal of Agricultural Economics, 70(2): 182-197.
• Pesaran, M. and Shin, Y. (1999). An autoregressive distributed lag modelling approach to cointegration analysis (Vol.9514). Cambridge, UK: Department of Applied Economics, University of Cambridge.
• Pesaran, M. H., Shin, Y. and Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships, Journal of Applied Econometrics, 16(3): 289-326.
• Poirier, D. J. (1973). Poirier Piecewise Regression Using Cubic Splines. Journal of the American Statistical Association, 68(343): 515-524.
• Reddy, T.K. and Dutta, M. (2018). Impact of agricultural inputs on agricultural GDP in Indian economy. Theoretical Economics Letters, 8(10): 1840–1853.
• Subrahmanyam, S. and Satya Sekhar, P. (2003). Agricultural growth: Pattern and Prospects. Economic and Political Weekly, 38(12/13): 1202-1211.
• Zhai, S., Song, G., Qin, Y., Ye, X., and Lee, J. (2017). Modelling the Impacts of Climate Change and Technical Progress on the Wheat Yield in inland China: An Autoregressive Distributed Lag Approach. PLoS One, 12(9): e0184474.