Research Articles
Vol. 13 No. sp1 (2026): Recent Advances in Agriculture
Economic vulnerability and farm-size inequality in the Cauvery Delta: A multidimensional quantitative assessment
Department of Agricultural Extension & Rural Sociology, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai 625 104, Tamil Nadu, India
Department of Agricultural Extension & Rural Sociology, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai 625 104, Tamil Nadu, India
Open Distance Learning Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India
Rice Research Station, Tirur 602 025, Tamil Nadu, India
Department of Agricultural Economics, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India
Department of Agricultural Economics, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai 625 104, Tamil Nadu, India
Abstract
The Cauvery Delta of Tamil Nadu is experiencing accelerating agrarian stress, characterised by rising input costs, groundwater dependence and deepening indebtedness among paddy cultivators. This study assesses economic vulnerability and farm-size inequality using a mixed-methods, explanatory sequential design in 3 delta districts (Thanjavur, Tiruvarur and Nagapattinam). A stratified multi-stage sample of 450 farmers (marginal n=178; small n=197; medium n=63; large n=12) was surveyed using a pre-tested structured questionnaire, corroborated with land passbooks, input bills and focus group discussions. Quantitative analyses comprised descriptive statistics, one-way analysis of variance (ANOVA) with Tukey’s HSD, construction of a composite economic vulnerability index (CEVI) using min–max standardisation and estimation of a farm size economic stress model (FSESM) using multiple regression; qualitative data were thematically analysed to contextualise trends. Results reveal pronounced stratification: marginal farmers show the highest debt–asset ratio (3.47) and input cost intensity (≈29 % higher per ha than large farms), with net returns per hectare (₹17644) markedly below medium (₹33304) and large (₹45830) categories (all group differences p<0.001). The CEVI scores decline with farm size (marginal 0.78; small 0.62; medium 0.41; large 0.22), indicating severe vulnerability among the smallest holders. The FSESM explains 62 % of observed income variability (R² = 0.62); standardised β weights identify farm size (β = 0.61, p<0.001), irrigation reliability (β = 0.42, p<0.01), formal credit access (β = 0.38, p<0.01) and mechanisation (β = 0.29, p<0.05) as key determinants. Qualitative evidence underscores the role of informal credit dependence, tail-end irrigation delays and market access asymmetries in perpetuating risk. The findings indicate that structural factors-not farmer inefficiency-drive economic vulnerability and support a smallholder-first resilience agenda. Recommended measures include targeted low-interest/no-collateral credit, community machinery banks, strengthened tail-end irrigation governance and market and storage interventions (such as village procurement centres and stronger farmer producer organisations (FPOs)) to redress inequality and improve adaptive capacity in the Cauvery Delta.
References
- 1. Adger WN. Vulnerability. Glob Environ Change. 2006;16(3):268–81. https://doi.org/10.1016/j.gloenvcha.2006.02.006
- 2. Ribot JC. Cause and response: Vulnerability and climate in the Anthropocene. J Peasant Stud. 2014;41(5):667–705. https://doi.org/10.1080/03066150.2014.894911
- 3. Folke C. Resilience. Ecol Soc. 2016;21(4):44. https://doi.org/10.5751/ES-09088-210444
- 4. Chand R, Srivastava SK, Singh J. Changing structure of rural economy of India: Implications for employment and growth. NITI Aayog; 2017.
- 5. Lowder SK, Skoet J, Raney T. The number, size and distribution of farms worldwide. World Dev. 2016;87:16–29. https://doi.org/10.1016/j.worlddev.2015.10.041
- 6. Vasavi A. Shadow Space: Suicides and the predicament of rural India. New Delhi; 2012.
- 7. FAO, IFAD, UNICEF, WFP, WHO. The state of food security and nutrition in the world 2023. Rome: FAO; 2023. https://doi.org/10.4060/cc3017en
- 8. Béné C, Frankenberger T, Nelson S. Design, monitoring and evaluation of resilience interventions. Food Secur. 2015;7(3):419–37. https://doi.org/10.1007/s12571-015-0466
- 9. Bangalore M, Hallegatte S, Bonzanigo L, Kane T, Fay M, Narloch U, et al. Shock waves: Managing the impacts of climate change on poverty. World Bank; 2016. https://doi.org/10.1596/978-1-4648-0673-5
- 10. Creswell JW, Plano Clark VL. Designing and conducting mixed methods research. 3rd ed. SAGE Publications; 2018.
- 11. Field A. Discovering statistics using IBM SPSS Statistics. 4th ed. SAGE Publications; 2013.
- 12. Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied linear statistical models. 5th ed. McGraw-Hill; 2005.
- 13. OECD/European Union/EC-JRC. Handbook on constructing composite indicators. OECD Publishing; 2008. https://doi.org/10.1787/533411815016
- 14. Narayanan S. The productivity of agricultural credit in India. Food Policy. 2016;65:1–12. https://doi.org/10.1016/j.foodpol.2016.09.00
- 15. Narayanan S, Vijayabaskar M, Srinivasan S. The youth dividend and agricultural revival in India. In: Becoming a Young Farmer. Springer; 2023. p. 221–51. https://doi.org/10.1007/978-3-031-15233-7_8
- 16. Meinzen DR. Property rights and sustainable irrigation. Agric Water Manag. 2014;145:23–31. https://doi.org/10.1016/j.agwat.2014.03.017
- 17. Shah T. Groundwater governance and irrigation transitions in India. Water Policy. 2016;18(S1):1–15. https://doi.org/10.2166/wp.2016.174
- 18. Tittonell P. Ecological intensification of agriculture. Agric Syst. 2014;126:1–10. https://doi.org/10.1016/j.agsy.2013.08.006
- 19. Araya A, Prasad PVV, Gowda PH, Djanaguiramana M, Gebretsadkan Y. Modeling the effects of crop management on food barley production under a midcentury changing climate in northern Ethiopia. Clim Risk Manag. 2021;32:100308. https://doi.org/10.1016/j.crm.2021.100308
- 20. Morton JF. The impact of climate change on smallholder agriculture. Proc Natl Acad Sci U S A. 2007;104(50):19680–685. https://doi.org/10.1073/pnas.0701855104
- 21. Rogers EM. Diffusion of innovations. 5th ed. Free Press; 2003.
- 22. Anderson JR, Feder G. Agricultural extension: Good intentions and hard realities. World Bank Res Obs. 2004;19(1):41–60. https://doi.org/10.1093/wbro/lkh013
- 23. Swanson BE, Rajalahti R. Strengthening agricultural extension and advisory systems. World Bank; 2010. https://doi.org/10.1596/23993
- 24. Davis K, Nkonya E, Kato E, Mekonnen DA, Odendo M, Miiro R, et al. Impact of farmer field schools on agricultural productivity. World Dev. 2012;40(2):402–13. https://doi.org/10.1016/j.worlddev.2011.05.019
- 25. Asiimwe R, Ainembabazi JH, Egeru A, Isoto R, Aleper DK, Namaalwa J, et al. The role of camel production on household resilience to droughts in pastoral and agro-pastoral households in Uganda. Pastoralism. 2020;10(1):5. https://doi.org/10.1186/s13570-020-0160-x
- 26. Rao N, Raju S. Gendered vulnerabilities in agrarian livelihoods. World Dev. 2020;127:104789. https://doi.org/10.1016/j.worlddev.2019.104789
Downloads
Download data is not yet available.