Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Early Access

Diversification towards horticulture crops: District level evidence from Indian state of Karnataka - A panel regression approach

DOI
https://doi.org/10.14719/pst.8856
Submitted
13 April 2025
Published
01-08-2025
Versions

Abstract

A growing strand of literature has highlighted a positive association between diversification towards high-value crops and poverty reduction in developing countries, largely drawing on household-level data. However, many socio-economic and biophysical factors operate at the meso (district) level, and their role in diversification towards high-value crops is not well understood. Against this backdrop, the present paper examines the case of the Indian state of Karnataka by drawing upon the panel data from 2001 to 2021 to study the determinants of diversification towards horticulture crops and their sub-sector at the district level. The results of a fixed-effects panel regression model show that increasing the share of horticulture does not necessarily promote diversification. The finding further revealed that factors driving the increasing share of horticulture crops vary in importance across different sub-sectors. Government and donor agencies would do well to note that a one-size-fit-all approach does not work form promoting diversification. It is imperative to develop tailored strategies that address the specific needs and conditions of diverse regions to effectively promote sustainable land-use practices. The study’s findings reveals that demand-side factors like gross GDP (gross domestic product) per capita of the district and the degree of urbanization affect horticulture diversification positively. Furthermore, the study provides crucial insights into the impact of increasing population pressure, which is largely associated with rapid expansion of non–agricultural land. This trend negatively affects the availability of land allocated to horticulture and its sub-categories at the district level.

References

  1. 1. Ruben R. What smallholders want: Effective strategies for rural poverty reduction. Sustainability. 2024;16(13):5525. https://doi.org/10.3390/su16135525
  2. 2. Joshi PK, Gulati A, Birthal PS, Tewari L. Agriculture diversification in South Asia: Patterns, determinants and policy implications. Econ Pol Weekly. 2004;12:2457-67.
  3. 3. Reardon T, Timmer CP, Minten B. Supermarket revolution in Asia and emerging development strategies to include small farmers. Proc Nat Acad Sci. 2012;109(31):12332-37. https://doi.org/10.1073/pnas.1003160108
  4. 4. Dolan C, Humphrey J. Governance and trade in fresh vegetables: The impact of UK supermarkets on the African horticulture industry. J Dev Stud. 2000;37(2):147-76.
  5. 5. Narayanan S. Profits from participation in high value agriculture: Evidence of heterogeneous benefits in contract farming schemes in Southern India. Food Policy. 2014;44:142-57. https://doi.org/10.1016/j.foodpol.2013.10.010
  6. 6. Government of India. The Economic Survey -2022-23. Ministry of Finance. 2023. Available from: https://www.indiabudget.gov.in/budget2023-24/economicsurvey/index.php
  7. 7. Bisht IS, Rana JC, Pal Ahlawat S. The future of smallholder farming in India: Some sustainability considerations. Sustainability. 2020;12(9):3751. https://doi.org/10.3390/su12093751
  8. 8. Government of India. Situation assessment survey of agricultural households, 77th Round. National sample survey office, Ministry of Statistics and Programme Implementation. 2019. Available from: https://ruralindiaonline.org/en/library/resource/situation-assessment-of-agricultural households-and-land-and-livestock-holdings-of-households-in-rural-india/
  9. 9. Government of Karnataka. Directorate of Economics and Statistics. 2024. Available from: https://des.karnataka.gov.in/info2/Agricultural+Census++Rainfall+and+Sample+check+on+Developmental+Programmes+(ARC)/Reports/en
  10. 10. Kennedy J, King L. The political economy of farmers’ suicides in India: Indebted cash-crop farmers with marginal landholdings explain state-level variation in suicide rates. Glob Health. 2014;10:1-9.
  11. 11. Government of India. The national crime records bureau (NCRB) data report. 2023. Available from: https://www.data.gov.in/ministrydepartment/National%20Crime%20Records%20Bureau%20(NCRB)
  12. 12. Birthal PS, Roy D, Negi DS. Assessing the impact of crop diversification on farm poverty in India. World Dev. 2015;72:70-92. https://doi.org/10.1016/j.worlddev.2015.02.015
  13. 13. Kumar S, Gupta S. Crop diversification towards high-value crops in India: A state level empirical analysis. Agric Econ Res Rev. 2015;28(2):339-50. http://dx.doi.org/10.5958/0974-0279.2016.00012.4
  14. 14. Anuja AR, Kumar A, Saroj S, Singh KN. The impact of crop diversification towards high-value crops on economic welfare of agricultural households in eastern India. Curr Sci. 2020;118(10):1575-82.
  15. 15. Government of India. Report of the inter-ministerial committee constituted in April, 2016 on “Strategy for doubling of farmers income (DFI). Ministry of Agriculture & Farmers Welfare. 2022. Available from: Press Release: Press Information Bureau (pib.gov.in)
  16. 16. Singh RP. Horticultural (high value agricultural) crops diversification in eastern India: II–Employment opportunities and income generation strategies. Int J Innov Hortic. 2013;2(1):28-43.
  17. 17. Government of India. Agricultural statistics at a glance-2021. Ministry of Agriculture & Farmers Welfare, Department of Agriculture & Farmers Welfare, Directorate of Economics & Statistics. 2021. Available from: https://ruralindiaonline.org/or/library/resource/agricultural-statistics-at-a-glance-2021/
  18. 18. Gupta PR, Joshi PK. Agricultural research for sustainable productivity growth in India. Transforming Indian agriculture-India. 2013.
  19. 19. Tsusaka TW, Otsuka K. The changing effects of agro-climate on cereal crop yields during the green revolution in India, 1972 to 2002. J Sustain Dev. 2013;6(4):11. http://dx.doi.org/10.5539/jsd.v6n4p11
  20. 20. Aggarwal N, Jain S, Narayanan S. The long road to transformation of agricultural markets in India: Lessons from Karnataka. Econ Pol Weekly. 2017;47-55.
  21. 21. Potapov P, Turubanova S, Hansen MC, Tyukavina A, Zalles V, Khan A, et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nature Food. 2022;3(1):19-28.
  22. 22. Su S, Zhou X, Wan C, Li Y, Kong W. Land use changes to cash crop plantations: Crop types, multilevel determinants and policy implications. Land Use Policy. 2016;50:379-89. https://doi.org/10.1016/j.landusepol.2015.10.003
  23. 23. Anuja AR, Shivaswamy GP, Ray M, Singh KN. Pattern of crop diversification and its implications on undernutrition in India. Curr Sci. 2022;122(10):1154.
  24. 24. India Meteorological Department. IMD Pune. 2024. Available from: http://www.imdpune.gov.in
  25. 25. Government of India. Office of the Registrar General & Census Commissioner, India. Census of India 2011. Available from: https://censusindia.gov.in/
  26. 26. Public Works, Ports & Inland Water Transport Department, Government of Karnataka. 2024. Available from: https://kpwd.karnataka.gov.in/english
  27. 27. State Level Bankers’ Committee, Karnataka. SLBC Karnataka. 2024. Available from: https://slbckarnataka.com/
  28. 28. Department of Horticulture, Government of Karnataka. 2024. Available from: https://horticulturedir.karnataka.gov.in/en
  29. 29. Karnataka State Remote Sensing and Applications Cent K GIS portal. 2024. Available from: https://kgis.ksrsac.in/kgis1/portal.aspx
  30. 30. Acharya SP, Basavaraja H, Kunnal LB, Mahajanashetti SB, Bhat AR. Crop diversification in Karnataka: An economic analysis. Agric Econ Res Rev. 2011;24(2):351-57.
  31. 31. Das A, Kimoto K, Ravi Kumar M, Umakanth R, Sengupta, D, Vishwanth HR. Land use sustainability of agricultural zones. In: Himiyama Y, editor. Exploring sustainable land use in monsoon Asia. Springer: Singapore; 2018. p. 103-135. https://doi.org/10.1007/978-981-10-5927-8_6
  32. 32. Singha K, Chakravorty A. Crop diversification in India: A study of maize cultivation in Karnataka. Sci J Rev. 2013;2(1):1-10.
  33. 33. Rao PP, Birthal PS, Joshi PK. Diversification towards high value agriculture: Role of urbanisation and infrastructure. Econ Pol Weekly. 2006;27:47-53.
  34. 34. Mithiya D, Mandal K, Datta L. Trend, pattern and determinants of crop diversification of small holders in West Bengal: A district-wise panel data analysis. J Dev Agric Econ. 2018;10(4):110-19. https://doi.org/10.5897/JDAE2018.0921
  35. 35. Birthal PS, Joshi PK. Smallholder farmers' access to markets for high-value agricultural commodities in India (6-4). In: Pinstrup-Andersen P, Cheng F, editors. Case studies in food policy for developing countries: Domestic policies for markets, production, and environment. Cornell University Press: New York; 2009. p. 51-60. https://doi.org/10.7591/9780801466373-007
  36. 36. Birthal PS, Hazrana J, Negi DS. Diversification in Indian agriculture towards high value crops: Multilevel determinants and policy implications. Land Use Policy. 2020;91:104427. https://doi.org/10.1016/j.landusepol.2019.104427
  37. 37. Pesaran MH. General diagnostic tests for cross-sectional dependence in panels. Empir Econ. 2021;60(1):13-50. https://doi.org/10.1007/s00181-020-01875-7
  38. 38. Felix KT, Ramappa KB. An economic analysis of crop diversification and dynamics of cropping pattern in Karnataka, India. Humanit Soc Sci Commun. 2023;10(1):1-9. https://doi.org/10.1057/s1599-023-02078-y
  39. 39. Schaffer M, Stillman S. XTOVERID: Stata module to calculate tests of overidentifying restrictions after xtreg, xtivreg, xtivreg2, xthtaylor. Statistical Software Components , Department of Economics, Boston College. 2016. Available from: https://EconPapers.repec.org/RePEc:b4oc:bocode:s456779
  40. 40. Greene W. Estimating econometric models with fixed effects. Department of Economics, Stern School of Business, New York University. 2001. Available from: http://repec.org/usug2020/Wooldridge_u20.pdf
  41. 41. Cameron AC, Miller DL. A practitioner’s guide to cluster-robust inference. J Hum Resour. 2015;50(2):317-72. https://doi.org/10.3368/jhr.50.2.317
  42. 42. Ramsey JB. Tests for specification errors in classical linear least-squares regression analysis. J Royal Stat Soc Series B: Stat Methodol. 1969;31(2):350-71. https://doi.org/10.1111/j.2517-6161.1969.tb00796.x
  43. 43. Wooldridge JM. Correlated random effects methods for panel data models with heterogeneous time effects. 2020 UK Stata Conference, Department of Economics Michigan State University. 2020. Available from: http://repec.org/usug2020/Wooldridge_u20.pdf
  44. 44. Arellano M. Computing robust standard errors for within-groups estimators. Oxf Bull Econ Stat. 1987;49(4):4006. https://doi.org/10.1111/j.1468-0084. 1987.mp49004006
  45. 45. Balarajan Y, Villamor E, Subramanian SV. Decomposing malnutrition inequalities between scheduled castes and tribes and the remaining Indian population. Int J Epidemiol. 2011;40(2).
  46. 46. Birthal PS, Joshi PK, Roy D, Thorat A. Diversification in Indian agriculture toward high‐value crops: The role of small farmers. Can J Agric Econ. 2013;61(1):61-91.
  47. 47. Radhika C, Mahesh E, Sutradhar R, Udaya Bhaskar K. Trends and direction of land use change in the perspective of urbanization in Karnataka: A district level study. Indian J Agric Res. 2024:1-9.

Downloads

Download data is not yet available.