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Agricultural growth and crop diversification in India: a state-level analysis

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Abstract

This paper focuses on trend in India’s agricultural growth estimated based on structural breaks in agricultural GDP from 1981–82 to 2019–20, using Bai–Perron multiple breakpoint method. The paper also examines the relationship between agricultural growth and crop diversification. At the national level, five structural breaks in agricultural GDP were identified: 1987–88, 1992–93, 1997–98, 2003–04, and 2011–12. At the state level, structural break points occurred at different time periods indicating the effect of state-specific policy changes or occurrence of extreme climatic events. The southern, western, and central regions have highly diversified cropping pattern, whereas eastern and northern regions follow a specialised cropping pattern. Panel instrumental variable regression results show that crop diversification has a positive and statistically significant effect on agricultural output controlling for effects of other variables such as gross terms of trade, irrigation, cropping intensity, public capital expenditure, fertiliser use and labour. The study results have policy implications for promoting crop diversification that holds the key to sustain agricultural growth in the long run.

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Notes

  1. See Kurosaki (2003) for a long-term analysis of crop diversification and agricultural growth in West Punjab from 1903 to 1992.

  2. According to Kurosaki (2003), CDI has the intuitive meaning of the probability of hitting different crop if two points are randomly chosen from the whole area under cultivation in a state/district. Besides Herfindahl–Hirschman index, there are other alternative measures used for computing the level of crop diversification (for details see, Shiyani and Pandya 1998; Chand 1999). Conceptual definition and approaches on diversification in agriculture can be found in Vyas (1996) and Chand (1999). Although many studies have considered proportion of area under individual crops in total cropped area (i.e. shift in area from one crop to another crop) as a measure of crop diversification, role of price in influencing the decision of farmers for a shift in cropping pattern can be incorporated in the crop diversification index. The modified index can be written as, CDI* \(=1-\sum_{i=1}^{n}({Q}_{i}{P}_{i}/\sum_{i=1}^{n}{Q}_{i}{P}_{i})\) 2, where Q is i th crop output and p is price of ith crop output.

  3. In this study, Eq. (5) is estimated as a production function. We acknowledge the limitation of the current approach as there could be a theoretical linkage between road density and agricultural output.

  4. The entire analysis of performance of agriculture relates to GDP from agriculture (crop) sector. The output from the allied sector is not taken into consideration.

  5. There is a broad consensus among various studies that there were four major national-scale meteorological drought events that took place between 1980 and 2020, namely in 1987, 2002, 2009, and 2012 (Udmale et. al., 2020, Kumar et. al., 2013). India witnessed one of the major national-scale meteorological drought events during 1987.

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Acknowledgements

Authors sincerely thank three anonymous referees for their insightful comments, which helped to revise the paper substantially. Authors are also thankful to the Editors of this journal for their constructive suggestions.

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  1. Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    Nikkita Gupta & Elumalai Kannan

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Gupta, N., Kannan, E. Agricultural growth and crop diversification in India: a state-level analysis. J. Soc. Econ. Dev. (2024). https://doi.org/10.1007/s40847-023-00311-7

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