Exploring the impact of diversification on agricultural commercialisation in North Karnataka, India: K-means clustering and regression analysis approach


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Authors

  • HARSHITHA H C College of Agriculture, University of Agricultural Sciences, Dharwad, Karnataka 580 005, India image/svg+xml
  • V R KIRESUR University of Agricultural Sciences, Dharwad, Karnataka 580 005, India image/svg+xml
  • J A HOSMATH AICRP-Integrated Farming System on Farm Research, Agricultural Research Station, Mundgod, Karnataka
  • SAROJANI J KARAKANNAVAR University of Agricultural Sciences, Dharwad, Karnataka 580 005, India image/svg+xml
  • M Y TEGGI College of Agriculture, University of Agricultural Sciences, Dharwad, Karnataka 580 005, India image/svg+xml

https://doi.org/10.56093/ijas.v96i6.169526

Keywords:

Crop commercialisation index, Farm households, Gross irrigated area, Herfindahl hirschman index, Regression adjustment

Abstract

This study delves into the pivotal role of agricultural diversity in shaping the commercialisation of agriculture in northern Karnataka, India. While diversification is often viewed as a risk mitigation strategy, its influence on the transition toward market driven agriculture is critical for rural transformation. The research was conducted across two distinct regions, Cluster-I (Dharwad and Gadag) and Cluster-II (Belagavi and Bagalkote) based on the Gross Irrigated Area (GIA). Data were gathered from 240 farm households through meticulously designed personal interviews during the 2022–23 agricultural season. The analysis employed a combination of descriptive statistics, K-means clustering and the Herfindahl-Hirschman Index (HHI) to quantify diversification, as well as the Household Crop Commercialisation Index (CCI) to assess commercialisation. The clustering results revealed stark contrasts between the two regions, with Cluster-I exhibiting higher level of crop diversification and lower-level commercialisation (HHI: 0.45) and Cluster-II demonstrating more market-oriented farming practices coupled with a lower degree of diversification (HHI: 0.66). To determine the causal impact, a Regression Adjustment (RA) model was employed, which confirmed that diversification significantly enhances commercialisation levels. The study highlights that diversification and commercialisation are complementary pathways for sustainable agricultural development. Therefore, region-specific policies focusing on irrigation expansion, promotion of diversified cropping systems, market infrastructure and institutional support are essential for strengthening resilient and market-oriented agriculture in North Karnataka.

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References

Bahinipati C S and Patnaik U. 2022. What motivates farm-level adaptation in India? A systematic review. (In) Climate Change and Community Resilience. Haque A K E, Mukhopadhyay P, Nepal M and Shammin M R (Eds). Springer, Singapore. https://doi.org/10.1007/978-981-16-0680-9_4

Baraker S K, Manjunath K V, Lalitha K C and Madhu Latha C. 2021. Profile characteristics and marketing behaviour of onion growers in Gadag district of Karnataka. Indian Journal of Economics and Development 9: 1–6.

Barrett C B, Reardon T and Webb P. 2001. Nonfarm income diversification and household livelihood strategies in rural Africa: Concepts, dynamics and policy implications. Food Policy 26(4): 315–31.

Beillouin D, Ben-Ari T, Malézieux E, Seufert V and Makowski D. 2021. Positive but variable effects of crop diversification on biodiversity and ecosystem services. Global Change Biology 27: 4697–710. https://doi.org/10.1111/gcb.15747

Braun J V and Kennedy E T. 1994. Agricultural Commercialisation, Economic Development and Nutrition. International Food Policy Research Institute, Washington, DC.

Das V K and Kumar A G. 2019. Commercialisation, diversification and structural determinants of farmer’s income in India. IGIDR Working Paper No. 042/2019. Indira Gandhi Institute of Development Research, Mumbai. http://www.igidr.ac.in/pdf/publication/WP-2019-042.pdf

Freedman D A. 2008. On regression adjustments in experiments with several treatments. The Annals of Appiled Statistics 2(1): 176–96. https://www.jstor.org/stable/30244182

Gudvalli M, Vidyasree P and Viswanadharaju S. 2017. Clustering analysis for appropriate crop prediction using hierarchical fuzzy C-means, K-means and model-based techniques. International Journal of Advanced Engineering and Research Development 4(11): 1233–41. https://www.ijaerd.org/index.php/IJAERD/article/view/4303

Horner D and Wollni M. 2011. The effects of integrated soil fertility management on household welfare in Ethiopia. Global Food Discussion Papers No. 142/2011. University of Goettingen, Germany. https://doi.org/10.22004/ag.econ.302924

Joshi P K, Gulati A, Birthal P S and Tewari L. 2004. Agricultural diversification in South Asia: Patterns, determinants and policy implications. Economic and Political Weekly 39(24): 2457–67. https://www.jstor.org/stable/4415148

Karnataka State at a Glance. 2021–22. Government of Karnataka, Bengaluru, India. https://kgis.ksrsac.in/kag/index.aspx

Kochi G and Rode S. 2026. Spatial dynamics of crop diversification in Dakshina Kannada district of coastal Karnataka, India. Tropical Agriculture 103(1): 61–71. https://journals.sta.uwi.edu/ojs/index.php/ta/article/view/9733

Kumar R, Krishna B, Sundaram P K, Kumawat N, Jeet P and Singh A K. 2022. Crop diversification. (In) Sustainable Agriculture Systems and Technologies. Kumar P, Pandey A K, Singh S K, Singh S S and Singh V K (Eds). https://doi.org/10.1002/9781119808565.ch5

Manda J, Gardebroek C, Kuntashula E and Alene A D. 2018. Impact of improved maize varieties on food security in Eastern Zambia: A doubly robust analysis. Review of Development Economics 22(4): 1709–28. https://doi.org/10.22004/ag.econ.277004

Muriithi B W and Matz J A. 2015. Welfare effects of vegetable commercialisation: evidence from smallholder producers in Kenya. Food Policy 50(C): 80–91. https://doi.org/10.1016/j.foodpol.2014.11.001

Myers J and Thomas L A. 2010. Regression adjustment and stratification by propensity score in treatment effect estimation. Department of Biostatistics Working Paper No. 203/2010. Johns Hopkins University, United States.

Neogi S and Ghosh B K. 2022. Evaluation of crop diversification on Indian farming practices: A panel regression approach. Sustainability 14(24): 16861. https://doi.org/10.3390/su142416861

Owino J O, Olago D, Wandiga S O and Ndambi A. 2020. A cluster analysis of variables essential for climate change adaptation of smallholder dairy farmers of Nandi County, Kenya. African Journal of Agricultural Research 16(7): 1007–14. https://doi.org/10.5897/AJAR2020.14965

Oyelade O J, Oladipupo O O and Obagbuwa I C. 2010. Application of k-means clustering algorithm for prediction of student’s academic performance. International Journal of Computer Science and Information Security 7(1): 292–95. https://doi.org/10.48550/arXiv.1002.2425

Pal S and Kar S. 2012. Implications of the methods of agricultural diversification in reference with Malda District. International Journal of Food, Agriculture and Veterinary Sciences 2(2): 97–105.

Pawar R and Devendrappa S. 2022. Socio-economic profile of sugarcane growers. The Pharma Innovation Journal 11(12): 4386–89.

Pingali P L. 2012. Green revolution: Impacts, limits and the path ahead. Proceedings of the National Academy of Sciences 109(31): 12302–08. https://doi.org/10.1073/pnas.0912953109

Ralte R and Priscilla L. 2023. Crop diversification in India: A review. International Journal of Bioresource Science 10(1):135–42. https://doi.org/10.30954/2347-9655.01.2023.14

Sharma S and Shastri S. 2025. Economic determinants of agricultural diversification and their impact on household food security: Evidence from Haryana, India. Annals of Arid Zone 64(1): 91–101. https://doi.org/10.56093/aaz.v64i1.158142

Singh P, Adhale P, Guleria A and Vaidya M K. 2022. Is crop diversification vulnerable to climate, agricultural and socioeconomic factors in Himachal Pradesh, India? Current Science 123(5): 707–11. https://doi.org/10.18520/cs/v123/i5/707-711

Sivaraman K, Thankamani C K and Srinivasan V. 2024. Crop diversification: Cropping/system approach for enhancing farmers’ income. (In) Handbook of Spices in India: 75 Years of Research and Development. Ravindran P N, Sivaraman K,Devasahayam S and Babu K N (Eds). Springer, Singapore. https://doi.org/10.1007/978-981-19-3728-6_61

Sridhar R, Longkumer L T, Pilla A, Bharteey P K, Jatav H S, Hareesh D, Vilakar K, Singh A P, Srikar K, Aruna K and Reddy M S P. 2026. Diverse fields for stronger yields: Crop diversification strategies for sustainable agriculture and climateresilient ecosystems. Frontiers in Agronomy 7: 1746895. https://doi: 10.3389/fagro.2025.1746895

Swain K P, Nayak S R, Ravi V, Mishra S, Alahmadi T J, Singh P and Diwakar M. 2024. Empowering crop selection with ensemble learning and K-means clustering: A modern agricultural perspective. The Open Agriculture Journal 18(1). https://doi.org/10.2174/0118743315291367240207093403

Thejashree H N and Umesh K B. 2022. Can diversification be a strategy towards commercialisation of agriculture? Evidence from rural urban interface of Bengaluru. Mysore Journal of Agricultural Sciences 56(2): 1–13.

Thejashree H N. 2022. ‘Commercialisation of agriculture and its impact on food, nutrition and health security of farm households in rural-urban interface of North Bengaluru’. PhD Thesis. University of Agricultural Science, Bengaluru, Karnataka, India.

Vernooy R. 2022. Does crop diversification lead to climate-related resilience? Improving the theory through insights on practice. Agroecology and Sustainable Food Systems 46(6): 877–901. https://doi.org/10.1080/21683565.2022.2076184

Wooldridge J and Negi A. 2018. Regression Adjustment in Experiments with Heterogeneous Treatment Effects. Michigan State University, United States.

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Submitted

2025-07-25

Published

2026-07-16

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How to Cite

H C, H. ., KIRESUR, V. R. ., HOSMATH, J. A. ., KARAKANNAVAR, S. J. ., & TEGGI, M. Y. . (2026). Exploring the impact of diversification on agricultural commercialisation in North Karnataka, India: K-means clustering and regression analysis approach. The Indian Journal of Agricultural Sciences, 96(6), 756–763. https://doi.org/10.56093/ijas.v96i6.169526
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