Factorization of agricultural production in India: A quantile regression approach

PHILIP KURIACHEN; BISWAJIT SEN; PRAVEEN K V

doi:10.56093/ijas.v90i1.98602

Authors

PHILIP KURIACHEN ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India
BISWAJIT SEN Ph D Scholar, Agricultural Research Institute, New Delhi 110 012, India
PRAVEEN K V Scientist, Division of Agricultural Economics, ICAR-Indian Agricultural Research Institute, New Delhi.

https://doi.org/10.56093/ijas.v90i1.98602

Keywords:

Agricultural productivity, Land fragmentation, Market concentration, Productivity gap, Quantile regression

Abstract

Realizing the importance of farm mechanization in purview of shortage of farm labour and increasing demand from land for higher productivity, the Government of India implemented a subsidy scheme for promoting purchase and use of power tillers by farmers during 2007–2015. The present study aimed at assessing perception of the beneficiaries about the status of implementation of the scheme with a focus on power tiller purchase, use, and hindrances (if any). The study was conducted in randomly selected 23 districts from 5 purposively selected states of India with a total of 746 beneficiary farmers (n=746). Primary cross sectional data were collected with the help of a structured personal interview schedule. Variation was noticed among the states regarding cost and subsidy received to buy the power tillers. The average cost of power tillers including subsidy, was the highest for Tripura (₹ 171577) followed by Assam (₹ 169317). The average amount of subsidy was `₹ 70701 with the highest reported in Andhra Pradesh (₹ 90626). Overall, a majority (91.96%) of the respondents reported not to face difficulties in availing the subsidy. Overall 89.01% of the beneficiaries expressed satisfaction with the quality of power tillers supplied under the scheme. However, more than half of the beneficiaries were not satisfied with the overall services provided by the dealers including training and maintenance services. The findings of the study will be helpful for policy makers to evaluate the scheme and make improvements based on the lacuna investigated in the study.

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References

Birthal P S, Roy D and Negi D S. 2015. Assessing the impact of crop diversification on farm poverty in India. World Development 72: 70–92. DOI: https://doi.org/10.1016/j.worlddev.2015.02.015

Christiaensen L, Demery L and Kuhl J. 2011. The evolving role of agriculture in poverty reduction- An empirical perspective. Journal of Development Economics 96(2): 239–54. DOI: https://doi.org/10.1016/j.jdeveco.2010.10.006

Gupta I and Mitra A. 2004. Economic growth, health and poverty: An exploratory study for India. Development Policy Review 22(2): 193–206. DOI: https://doi.org/10.1111/j.1467-7679.2004.00245.x

Koenker R. 2004. Quantile regression for longitudinal data. Journal of Multivariate Analysis 91(1): 74–89. DOI: https://doi.org/10.1016/j.jmva.2004.05.006

Koenker R and Hallock K F. 2001. Quantile regression. Journal of Economic Perspectives 15(4): 143–56. DOI: https://doi.org/10.1257/jep.15.4.143

Kumar P and Mittal S. 2006. Agricultural productivity trends in India: Sustainability issues. Agricultural Economics Research Review 19(3): 71–88.

Kumar S and Gupta S. 2015. Crop diversification towards high-value crops in India: A state level empirical analysis. Agricultural Economics Research Review 28(2): 339–50. DOI: https://doi.org/10.5958/0974-0279.2016.00012.4

Rahman S and Rahman M. 2009. Impact of land fragmentation and resource ownership on productivity and efficiency: The case of rice producers in Bangladesh. Land Use Policy 26(1): 95–103. DOI: https://doi.org/10.1016/j.landusepol.2008.01.003

Sen B, Venkatesh P, Jha G K and Singh D R. 2017. Agricultural diversification and its impact on farm income: A case study of Bihar. Agricultural Economics Research Review 30(3): 77–88 DOI: https://doi.org/10.5958/0974-0279.2017.00023.4

Singh A K, Gautam U S, Singh J, Singh A and Shrivastava P. 2015. Impact of nutrient management technologies in transplanted rice under irrigated domains of Central India. African Journal of Agricultural Research 10(5): 345–50. DOI: https://doi.org/10.5897/AJAR2014.8688

Singh N P, Kumar R and Singh R P. 2006. Diversification of Indian agriculture: composition, determinants and trade implications. Agricultural Economics Research Review 19(3): 23–26

Syverson C. 2004. Product substitutability and productivity dispersion. Review of Economics and Statistics 86(2): 534–50. DOI: https://doi.org/10.1162/003465304323031094

Yu K, Lu Z and Stander J. 2003. Quantile regression: applications and current research areas. Journal of the Royal Statistical Society: Series D (The Statistician) 52(3): 331–50. DOI: https://doi.org/10.1111/1467-9884.00363