Estimates of Growth and Acreage Response in Cotton-Producing States of India
Authors
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B. Gandhimathy
Assistant Professor (Corresponding Author), PG and Research Department of Economics, Chikkaiah Government Arts and Science College, (Formerly Chikkaiah Naicker College), Erode - 638 004, Tamil Nadu
https://orcid.org/0009-0007-2338-3664
DOI:
https://doi.org/10.17010/aijer/2025/v14i4/174511Keywords:
growth rate, component analysis, adoptive expectation model, short run and long run equilibrium.JEL Classification Codes : D10, Q10, Q11
Publication Chronology: Paper Submission Date : June 12, 2025 ; Paper sent back for Revision : November 20, 2025 ; Paper Acceptance Date : November 30, 2025
Abstract
Purpose : The present study investigated the growth and supply response of the major cotton-producing states of India. Three cotton zones, namely the North Zone (Punjab, Haryana, and Rajasthan), the Central Zone (Madhya Pradesh and Gujarat), and the South Zone (Andhra Pradesh, Karnataka, and Tamil Nadu), were taken for analysis.
Methodology : Growth analysis, component analysis, and adaptive expectation models were applied to time series data spanning 25 years, from 1998–1999 to 2022–2023, subdivided into two phases from 1998–1999 to 2010–2011 and from 2010–2011 to 2022–2023. Microsoft Excel and EViews’ 12th version software were used in this study.
Findings : The study revealed that Gujarat, Karnataka, and Rajasthan had relatively higher growth rates, while Punjab and Andhra Pradesh had the lowest growth rates. Cultivation and labor costs were much higher in Andhra Pradesh and Punjab; particularly, Tamil Nadu’s cost of cultivation was doubled compared to Haryana. The study revealed that every 100% increase in lagged farm price increased the acreage under cultivation at 21% in the short run and 55% in the long run in Rajasthan, which was the highest among the cotton-producing states. Every 100% increase in farm prices reduced cotton cultivation by 9% and 12% in Punjab and Andhra Pradesh, respectively, in the short run, and by 73% and 53% in the long run. Hence, perverse supply was found in these states. This implied that the price increase was sufficient to cover the cost of production, reflecting changes in farmers’ decisions regarding the allocation of land to cotton.
Practical Implications : Cost variations and wage disparity appeared across states. When BT cotton was introduced in 2002, phenomenal growth was witnessed; now, there is a need to upgrade new varieties. Minimum support prices should be increased, as price has a significant impact on the growth of cotton growers. The minimum support prices will be favorable only to the low-cost cultivating states and vice versa. Increase in price was not sufficient in Punjab, Andhra Pradesh, and Madhya Pradesh, which need to be focused upon.
Originality : The study comprehensively analyzed the growth of the cotton crop across the Indian states. It analyzed the impact of cost and price variations among the states and how these variations reflected on the farming of the cotton crop.
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1) Choi, J.-S., & Helmberger, P. G. (1993). Acreage response, expected price functions, and endogenous price expectations. Journal of Agriculture and Resource Economics, 18(1), 37–46. https://www.jstor.org/stable/40986774
2) Gandhimathy, B. (2013). An economic analysis of production industrial co-operatives in Tamil Nadu: The stochastic production function model. Arthashastra Indian Journal of Economics & Research, 2(6), 40–46. https://doi.org/10.17010/aijer/2013/v2i6/54539
3) Gandhimathy, B. (2024). Coconut cultivation in Indian peninsular states: Analysing production trends, price fluctuations and economic challenges. Agricultural Research Journal, 61(4), 549–557. https://doi.org/10.5958/2395-146X.2024.00068.9
4) Gandhimathy, B. (2025). Estimates of acreage response towards farm prices in the North Western Zone of Tamil Nadu. Artha Vijnana, 67(1), 44–62. https://gipe.ac.in/publication/artha-vijnana/
5) Gyan, P., Sharma, H., & Kalamkar, S. S. (2023). The growth and export competitiveness of cotton in India. Current Agriculture Research Journal, 11(3), 1041–1049. https://doi.org/10.12944/CARJ.11.3.32
6) Han, Z.-H., Zhao, P., Ning, X.-Y., Xie, Y.-Q., Li, Z., & Liu, X.-X. (2024). Nanomaterial-encapsulated dsRNA-targeting chitin pathway─A potential efficient and eco-friendly strategy against cotton aphid, Aphis gossypii (Hemiptera: Aphididae). Journal of Agriculture and Food Chemistry, 72(38), 20905–20917. https://doi.org/10.1021/acs.jafc.4c06390
7) Iqbal, J., Bhutta, S. A., Alqarni, A. S., Owayss, A. A., & Ansari, M. J. (2018). Seasonal population dynamics of dusky cotton bug (Oxycarenus spp.) in transgenic cotton varieties under field conditions. Saudi Journal of Biological Science, 25(6), 1122–1127. https://doi.org/10.1016/j.sjbs.2017.05.004
8) Kamal, N. (2025, February 1). Mission for cotton productivity: A hope or hype for Punjab's cotton farmers amid declining cultivation? The Times of India. https://timesofindia.indiatimes.com/city/chandigarh/mission-for-cotton-productivity-a-
hope-or-hype-for-punjabs-cotton-farmers-amid-declining-cultivation/articleshow/117840441.cms
9) Kulkarni, V. (2025, July 19). India set to overtake China to become largest cotton producer by 2034: OECD-FAO agricultural outlook projects global cotton output to rise to 29.5 million tonnes by 2034. Business Line. https://epaper.thehindubusinessline.com/ccidist-ws/bl/bl_bangalore/issues/140685/OPS/GEVELLAFJ.1+G2KELN3ND.1.html
10) Kumar, P. (2017). Supply response function in Indian agriculture. In S. Bathla, & A. Dubey (eds.), Changing contour of Indian agriculture (pp. 71–91). Springer. https://doi.org/10.1007/978-981-10-6014-4_5
11) Nerlove, M. (1956). Estimates of the elasticities of supply of selected agricultural commodities. American Journal of Agricultural Economics, 38(2), 496–509. https://doi.org/10.2307/1234389
12) Ramesh, Krishamurthy, K. N., Naveenkumar, G. S., Narasamma, & Avinash, G. (2020). Growth in area, production and productivity of cotton crop in selected districts of Karnataka, India. International Journal of Current Microbiology and Applied Sciences, 9(2), 689–694. https://doi.org/10.20546/ijcmas.2020.902.084
13) Saddiq, M., Fayaz, M., Hussain, Z., Shahab, M., & Ullah, I. (2013). Acreage response of sugarcane to price and non-price factors in Khyber Pakhtunkhwa. International Journal of Food and Agricultural Economics (IJFAEC), 2(3), 121–128. https://doi.org/10.22004/ag.econ.186272
14) Sall, I., & Tronstad, R. (2021). Simultaneous analysis of insurance participation and acreage response from subsidized crop insurance for cotton. Journal of Risk Financial Management, 14(11), 562. https://doi.org/10.3390/jrfm14110562
15) Shwetha, M. N., Devi, I. S., Lavanya, T., Suhasini, K., & Meena, A. (2022). Growth rates of cotton crop in India and pesticide consumption: A challenge ahead. International Journal of Environment and Climate Change, 12(11), 51–57. https://doi.org/10.9734/ijecc/2022/v12i1130944
16) Smith, B. (1928). Factors affecting the price of cotton. Technical Bulletin, 50. https://ageconsearch.umn.edu/record/156303/files/tb50.pdf
17) Suresh, A., Ramasundaram, P., Samuel, J., & Wankhade, S. (2014). Cotton cultivation in India since the green revolution: Technology, policy, and performance. Review of Agrarian Studies, 4(2), 25–52.
18) Suresh, A., Palanisamy, R., Samuel, J., & Wankhade, S. (2013). Impact of technology and policy on growth and instability of agricultural production: The case of cotton in India. The Indian Journal of Agricultural Sciences, 83(8), 939–948. https://epubs.icar.org.in/index.php/IJAgS/article/view/32852
19) Vikas, Manocha, V., & Goyal, S. K. (2018). Estimation of acreage response of major crops in Haryana using co-integration approach. International Journal of Current Microbiology and Applied Science, 7(3), 2890–2903. https://doi.org/10.20546/ijcmas.2018.703.334
