Cyclone Induced Paddy Yield Losses and Smallholder Adaptation: Structural Equation Evidence from Balasore District in Coastal Odisha
Authors
Research Scholar, Department of Economics, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha, India (India)
Assistant Professor in Economics, Controller of Examinations, Maharaja Sriram Chandra Bhanja Deo University, Mayurbhanj, Odisha, India (India)
Article Information
DOI: 10.51244/IJRSI.2026.1303000132
Subject Category: Climate Adaptation
Volume/Issue: 13/3 | Page No: 1539-1556
Publication Timeline
Submitted: 2026-03-19
Accepted: 2026-03-24
Published: 2026-04-08
Abstract
This paper examines the character and scale of tropical cyclone effects on paddy production and systematically evaluates the socio-economic and institutional factors of climate adaptation behaviour among small holder paddy farmers in Balasore district, Odisha- one of the most at risk cyclone landfall regions in India. The study used a quantitative and cross-sectional survey design, which resulted into 300 paddy farming households stratified multi-stage random sampling that was stratified into 12 administrative blocks. The SPSS 26.0 and AMOS 26.0 were used to analyse the data based on descriptive statistics, hierarchical multiple regression, one-way ANOVA, and covariance-based structural equation modelling (CB-SEM) with maximum-likelihood estimation. The intensity of cyclones decreased average paddy production by 42.38 (SD = 18.76), and blocks with high exposure to coastlines registered a loss of 52.64. Practises of adaptation, namely the adoption of flood-tolerant varieties, adjusted sowing schedules, and diversification of crops, play a significant role with regards to mitigating the relationship between cyclone and yield loss (β = -.38, p <.001; interaction β = -.21, p <.001). The 68 percent of the variation in the production loss was jointly explained by socio-economic factors and institutional support. The structural model was found to fit well (CFI = .963; RMSEA = .041; SRMR = .048) and all the four hypothesised pathways were confirmed. The cross-sectional nature of the design does not allow causal inference. The results apply only to the Balasore district, but the experimented structural model can serve as a template to be reproduced in other cyclone prone coastal agricultural districts. It is the first district-level analysis to use CB-SEM to simulate cyclone intensity, adaptation determinants, recovery capacity, and resilience across the paddy farming system of Odisha that incorporates the Sustainable Livelihoods Framework with the technology adoption theory to produce policy implications.
Keywords
cyclone impact; paddy production loss; climate adaptation; Balasore; Odisha; structural equation modelling; farmer resilience; flood-tolerant varieties; crop insurance; disaster risk reduction
Downloads
References
1. Acharya, R., Nanda, G., & Mohanty, M. (2021). Post-disaster agricultural recovery support: Evidence from Cyclone Fani in Odisha. Disaster Prevention and Management, 30(3), 298–314. https://doi.org/10.1108/DPM-06-2020-0189 [Google Scholar] [Crossref]
2. Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268–281. https://doi.org/10.1016/j.gloenvcha.2006.02.006 [Google Scholar] [Crossref]
3. American Psychological Association. (2017). Ethical principles of psychologists and code of conduct. https://www.apa.org/ethics/code [Google Scholar] [Crossref]
4. Arora-Jonsson, S. (2011). Virtue and vulnerability: Discourses on women, gender and climate change. Global Environmental Change, 21(2), 744–751. https://doi.org/10.1016/j.gloenvcha.2011.01.005 [Google Scholar] [Crossref]
5. Barnett, B. J., & Mahul, O. (2007). Weather index insurance for agriculture and rural areas in lower-income countries. American Journal of Agricultural Economics, 89(5), 1241–1247. https://doi.org/10.1111/j.1467-8276.2007.01091.x [Google Scholar] [Crossref]
6. Barnett, B. J., Barrett, C. B., & Skees, J. R. (2008). Poverty traps and index-based risk transfer products. World Development, 36(10), 1766–1785. https://doi.org/10.1016/j.worlddev.2007.10.016 [Google Scholar] [Crossref]
7. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173 [Google Scholar] [Crossref]
8. Béné, C., Headey, D., Haddad, L., & von Grebmer, K. (2016). Is resilience a useful concept in the context of food security and nutrition programmes? Some conceptual and practical considerations. Food Security, 8(1), 123–138. https://doi.org/10.1007/s12571-015-0526-x [Google Scholar] [Crossref]
9. Berlemann, M., & Wenzel, D. (2018). Cyclones, economic growth and development. Applied Economics, 50(56), 6067–6082. https://doi.org/10.1080/00036846.2018.1489501 [Google Scholar] [Crossref]
10. Bhatt, U. S. (2019). Evaluating disaster relief effectiveness: Post-cyclone Titli agricultural rehabilitation in Odisha. Indian Journal of Disaster Management, 13(2), 44–58. [Google Scholar] [Crossref]
11. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). SAGE. [Google Scholar] [Crossref]
12. Bryman, A. (2016). Social research methods (5th ed.). Oxford University Press. [Google Scholar] [Crossref]
13. Census of India. (2011). District census handbook: Balasore, Odisha. Office of the Registrar General and Census Commissioner, India. https://censusindia.gov.in [Google Scholar] [Crossref]
14. Chambers, R., & Conway, G. (1992). Sustainable rural livelihoods: Practical concepts for the 21st century (IDS Discussion Paper 296). Institute of Development Studies. https://www.ids.ac.uk/publications/sustainable-rural-livelihoods [Google Scholar] [Crossref]
15. Cochran, W. G. (1977). Sampling techniques (3rd ed.). Wiley. [Google Scholar] [Crossref]
16. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum. [Google Scholar] [Crossref]
17. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE. [Google Scholar] [Crossref]
18. Dar, M. H., de Janvry, A., Emerick, K., Sadoulet, E., & Singh, U. S. (2018). Flood-tolerant rice reduces yield variability and promotes crop diversification in flood-prone areas. Scientific Reports, 8(1), Article 1562. https://doi.org/10.1038/s41598-018-19866-9 [Google Scholar] [Crossref]
19. Dash, S. K., Alone, A. T., & Sahoo, P. (2018). Farmer coping strategies post-cyclone in coastal Odisha. Indian Journal of Agricultural Sciences, 88(6), 889–895. [Google Scholar] [Crossref]
20. Dasgupta, S., Kamal, F. A., Khan, Z. H., Choudhury, S., & Nishat, A. (2014). River salinity and climate change: Evidence from coastal Bangladesh (World Bank Policy Research Working Paper No. 6817). World Bank. https://doi.org/10.1596/1813-9450-6817 [Google Scholar] [Crossref]
21. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]
22. DFID. (1999). Sustainable livelihoods guidance sheets. Department for International Development. https://www.livelihoodscentre.org/documents/114097690/114097750/Sustainable+livelihoods+guidance+sheets.pdf [Google Scholar] [Crossref]
23. District Statistical Handbook — Balasore. (2022). Government of Odisha, Directorate of Economics and Statistics. [Google Scholar] [Crossref]
24. Emanuel, K. (2020). Response of global tropical cyclone activity to increasing CO₂: Results from downscaling CMIP6 models. Journal of Climate, 34(1), 57–70. https://doi.org/10.1175/JCLI-D-20-0367.1 [Google Scholar] [Crossref]
25. Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE. [Google Scholar] [Crossref]
26. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312 [Google Scholar] [Crossref]
27. Gaurav, S., & Mishra, S. (2015). Rural household access to insurance in India: Evidence from NSSO data. Journal of Development Studies, 51(7), 854–872. https://doi.org/10.1080/00220388.2014.997221 [Google Scholar] [Crossref]
28. Ghosh, T., Hajra, R., & Rudra, S. (2015). Island changes and adaptation in the cyclone-prone coastal agro-ecosystem of India. Regional Environmental Change, 17(5), 1425–1437. https://doi.org/10.1007/s10113-015-0886-0 [Google Scholar] [Crossref]
29. Government of Odisha. (2021). Annual report: Cyclone disaster management and agricultural relief — Yaas and Fani. Department of Agriculture and Farmers' Empowerment, Government of Odisha. [Google Scholar] [Crossref]
30. Hahn, M. B., Riederer, A. M., & Foster, S. O. (2009). The Livelihood Vulnerability Index: A pragmatic approach to assessing risks from climate variability and change. Global Environmental Change, 19(4), 482–494. https://doi.org/10.1016/j.gloenvcha.2009.07.005 [Google Scholar] [Crossref]
31. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. [Google Scholar] [Crossref]
32. Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis (2nd ed.). Guilford Press. [Google Scholar] [Crossref]
33. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modelling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 [Google Scholar] [Crossref]
34. Hossain, M. S., Ramirez, J., McLean, K. G., van den Bogaerde, B., & Boyle, G. (2016). Community-based cyclone risk management in coastal Bangladesh. World Development, 78, 57–66. https://doi.org/10.1016/j.worlddev.2015.10.023 [Google Scholar] [Crossref]
35. Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118 [Google Scholar] [Crossref]
36. Ismail, A. M., Singh, U. S., Singh, S., Dar, M. H., & Mackill, D. J. (2013). The contribution of submergence-tolerant (Sub1) rice varieties to food security in flood-prone rainfed lowland areas in Asia. Field Crops Research, 152, 83–93. https://doi.org/10.1016/j.fcr.2012.09.016 [Google Scholar] [Crossref]
37. Kelkar, U. (2014). Drivers of agricultural adaptation to climate change in India: A review and synthesis. MDPI Climate, 2(4), 285–300. https://doi.org/10.3390/cli2040285 [Google Scholar] [Crossref]
38. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press. [Google Scholar] [Crossref]
39. Knowler, D., & Bradshaw, B. (2007). Farmers' adoption of conservation agriculture: A review and synthesis of recent research. Food Policy, 32(1), 25–48. https://doi.org/10.1016/j.foodpol.2006.01.003 [Google Scholar] [Crossref]
40. Ministry of Agriculture and Farmers Welfare, Government of India. (2022). Agricultural statistics at a glance 2021–22. Directorate of Economics and Statistics. https://eands.dacnet.nic.in [Google Scholar] [Crossref]
41. Mishra, A., Sehgal, V. K., & Singh, R. (2021). Satellite-based assessment of cyclone Amphan-induced crop damage along the coastal districts of Odisha and West Bengal. Remote Sensing Applications: Society and Environment, 22, Article 100496. https://doi.org/10.1016/j.rsase.2021.100496 [Google Scholar] [Crossref]
42. Mobarak, A. M., & Rosenzweig, M. R. (2013). Informal risk sharing, index insurance, and risk taking in developing countries. American Economic Review, 103(3), 375–380. https://doi.org/10.1257/aer.103.3.375 [Google Scholar] [Crossref]
43. Mohapatra, M., Bandyopadhyay, B. K., & Tyagi, A. (2012). Review of operational procedures of cyclone warning in India. In U. C. Mohanty, M. Mohapatra, O. P. Singh, B. K. Bandyopadhyay, & L. S. Rathore (Eds.), Monitoring and prediction of tropical cyclones in the Indian Ocean and climate change (pp. 44–63). Springer. https://doi.org/10.1007/978-94-007-7720-0_4 [Google Scholar] [Crossref]
44. Nageswara Rao, K., Subraelu, P., Venkateswara Rao, T., Malini, B. H., Ratheesh, R., Bhattacharya, S., Rajawat, A. S., & Ajai. (2008). Sea-level rise and coastal vulnerability: An assessment of Andhra Pradesh coast, India. Journal of Coastal Conservation, 12(4), 195–207. https://doi.org/10.1007/s11852-008-0042-4 [Google Scholar] [Crossref]
45. National Disaster Management Authority (NDMA). (2019). Annual report 2018–19: Towards disaster resilient India. Government of India. https://ndma.gov.in [Google Scholar] [Crossref]
46. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. [Google Scholar] [Crossref]
47. Ostrom, E. (2009). A general framework for analyzing sustainability of social-ecological systems. Science, 325(5939), 419–422. https://doi.org/10.1126/science.1172133 [Google Scholar] [Crossref]
48. Odisha State Disaster Management Authority (OSDMA). (2022). District-wise cyclone risk assessment and mapping for Odisha. OSDMA. https://osdma.odisha.gov.in [Google Scholar] [Crossref]
49. Pallant, J. (2020). SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS (7th ed.). McGraw-Hill. [Google Scholar] [Crossref]
50. Panda, A., Sharma, U., Ninan, K. N., & Patt, A. (2021). Rainfall, yield shocks, and livelihood vulnerability in climate-exposed coastal districts of Odisha. Climate and Development, 13(5), 432–443. https://doi.org/10.1080/17565529.2020.1820032 [Google Scholar] [Crossref]
51. Patnaik, U., Das, P. K., & Bahinipati, C. S. (2016). Coping with climatic shocks: Empirical evidence from rural coastal Odisha, India. Global Business Review, 17(1), 161–175. https://doi.org/10.1177/0972150915610935 [Google Scholar] [Crossref]
52. Pelling, M. (2011). Adaptation to climate change: From resilience to transformation. Routledge. [Google Scholar] [Crossref]
53. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879 [Google Scholar] [Crossref]
54. Punch, K. F. (2014). Introduction to social research: Quantitative and qualitative approaches (3rd ed.). SAGE. [Google Scholar] [Crossref]
55. Quisumbing, A. R., Rubin, D., Manfre, C., Ostergaard, E., Bell, W., Johnson, N., Meinzen-Dick, R., & Mwangi, E. (2014). Gender, assets, and market-oriented farming: The case of Georgia, Bangladesh, and Ethiopia. Oxford Development Studies, 42(1), 36–65. https://doi.org/10.1080/13600818.2013.848461 [Google Scholar] [Crossref]
56. Sahoo, B., & Bhaskaran, P. K. (2015). Assessment on historical cyclone tracks in the Bay of Bengal, east coast of India. International Journal of Climatology, 35(7), 3311–3327. https://doi.org/10.1002/joc.4213 [Google Scholar] [Crossref]
57. Saunders, M. N. K., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson. [Google Scholar] [Crossref]
58. Senapati, S., & Gupta, V. (2017). Livelihood vulnerability index analysis: An approach to study vulnerability in the context of Bihar. Climate Change, 5(2), 133–140. https://doi.org/10.4172/2469-4134.1000165 [Google Scholar] [Crossref]
59. Singh, R., Karthikeyan, T., & Alam, G. (2020). Institutional support as a mediator of climate adaptation determinants: Path analysis evidence from Punjab. Climate and Development, 12(7), 620–632. https://doi.org/10.1080/17565529.2019.1668004 [Google Scholar] [Crossref]
60. Swaminathan, M. S. (2010). From green revolution to evergreen revolution: Pathways and terminologies. Current Science, 99(7), 893–897. https://www.currentscience.ac.in/Volumes/99/07/0893.pdf [Google Scholar] [Crossref]
61. Teklewold, H., Kassie, M., & Shiferaw, B. (2013). Adoption of multiple sustainable agricultural practices in rural Ethiopia. Journal of Agricultural Economics, 64(3), 597–623. https://doi.org/10.1111/1477-9552.12011 [Google Scholar] [Crossref]
62. Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375 [Google Scholar] [Crossref]
63. Turner, B. L., Kasperson, R. E., Matson, P. A., McCarthy, J. J., Corell, R. W., Christensen, L., Eckley, N., Kasperson, J. X., Luers, A., Martello, M. L., Polsky, C., Pulsipher, A., & Schiller, A. (2003). A framework for vulnerability analysis in sustainability science. Proceedings of the National Academy of Sciences, 100(14), 8074–8079. https://doi.org/10.1073/pnas.1231335100 [Google Scholar] [Crossref]
64. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 [Google Scholar] [Crossref]