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Plant Science Today (PST)

Research Articles

Vol. 13 No. sp1 (2026): Recent Advances in Agriculture

Development of a scale to assess flood consequence perception among rice and wheat growers in Odisha and Bihar

DOI
https://doi.org/10.14719/pst.13661
Submitted
13 January 2026
Published
11-03-2026

Abstract

Floods are emerging as a formidable and recurrent natural hazard, causing multidimensional impacts, including mortality, agricultural damage, economic hardship, infrastructure deterioration, environmental degradation and social disruption. Understanding farmers’ perception of flood consequences is therefore essential for designing effective coping and adaptation strategies. Since perception is a psychological construct, it can be more accurately measured using a standardised scale. Accordingly, this study aimed to develop a reliable and valid scale using the scale product method to quantitatively assess farmers' perception. The scale values of Thurstone and the weights of Likert were combined to produce higher reliability of the scale. From identification of the dimensions to final administration, the scale has undergone various steps. Judges showed substantial agreement on dimension selection, as measured by Kendall's coefficient of concordance (W = 0.762, X2 =182.932, p ≤ 0.01). Initially, 93 statements were compiled from various sources, of which 66 were retained and distributed to 100 judges for evaluation. The judges were requested to rate the relevance of each item on a seven-point continuum ranging from most unfavourable to most favourable. Following Thurstone's elimination criteria, responses from 60 judges were retained for analysis. Based on the S (median) value (2.5 to 6.5) and Q (quartile) values (1.04 to 1.92), 18 statements were eliminated, leaving 48 final items for the scale. The accuracy and reproducibility of the scale were determined through a content validity ratio (CVR) greater than 0.78 and a reliability coefficient of 0.874. Each statement was then assigned a 4-point response continuum to measure farmers' perceptions.

References

  1. 1. Gunadal NM, Madhu DM, Harshitha HC, Honyal AS. Assessing the effects of flood on crop and livestock production in Bagalkot District of Karnataka, India. Int J Environ Clim Change. 2024;14(3):682–93. https://doi.org/10.9734/ijecc/2024/v14i34076
  2. 2. Ahmed MR. Climate shocks' impact on agricultural income and household food security in Bangladesh: An implication of the food insecurity experience scale. Heliyon. 2024;10(4):e25687. https://doi.org/10.1016/j.heliyon.2024.e25687
  3. 3. Maharjan M, Ayer S, Newa M, Timilsina S, Thakuri S, Manandhar B, et al. Farmers' flood adaptation strategies in the Mohana–Khutiya and East Rapti River Basins in the Chure–Terai region of Nepal. Int J Disaster Risk Reduct. 2025;117:105182. https://doi.org/10.1016/j.ijdrr.2025.105182
  4. 4. Nwakile TC, Onah FC, Ekenta LU, Onah O, Aneke AO. Farmers’ perception on the use of agrochemicals in crop production in Nsukka, Enugu State. Int J Multidiscip Curr Res. 2020;8:365–70. https://doi.org/10.14741/ijmcr/v.8.3.4
  5. 5. Datta P, Behera B. Climate change and Indian agriculture: A systematic review of farmers’ perception, adaptation and transformation. Environ Challenges. 2022;8:100543. https://doi.org/10.1016/j.envc.2022.100543
  6. 6. Savari M, Jafari A, Sheheytavi A. Determining factors affecting flood risk perception among local communities in Iran. Sci Rep. 2025;15:4076. https://doi.org/10.1038/s41598-025-88673-2
  7. 7. Raghuvanshi R, Ansari MA. A scale to measure farmers’ risk perceptions about climate change and its impact on agriculture. Asian J Agric Ext Econ Sociol. 2019;32(1):1–10. https://doi.org/10.9734/ajaees/2019/v32i130145
  8. 8. Jha CK, Gupta V. Farmer’s perception and factors determining the adaptation decisions to cope with climate change: Evidence from rural India. Environ Sustain Indic. 2021;10:100112. https://doi.org/10.1016/j.indic.2021.100112
  9. 9. Reddy KV, Paramesh V, Arunachalam V, Das B, Ramasundaram P, Pramanik M, et al. Farmers’ perception and efficacy of adaptation decisions to climate change. Agronomy. 2022;12(5):1023. https://doi.org/10.3390/agronomy12051023
  10. 10. Balaji G, Meenambigai J, Vengatesan D. Perceptions and awareness of climate change realities in agriculture among farmers in Tamil Nadu, India. Int J Environ Clim Change. 2025;15(8):457–64. https://doi.org/10.9734/ijecc/2025/v15i84988
  11. 11. Shrestha R, Rakhal B, Adhikari TR, Ghimire GR, Talchabhadel R, Tamang D, et al. Farmers’ perception of climate change and its impacts on agriculture. Hydrology. 2022;9(12):212. https://doi.org/10.3390/hydrology9120212
  12. 12. Fahad M. Understanding climate change perception and knowledge in Bangladesh: A review from a non-global north perspective. Asian J Geogr Res. 2025;8(3):221–34. https://doi.org/10.9734/ajgr/2025/v8i3288
  13. 13. Thurstone LL. The measurement of opinion. J Abnorm Soc Psychol. 1928;22(4):415–30. https://doi.org/10.1037/h0070476
  14. 14. Likert R. A technique for the measurement of attitudes. Arch Psychol. 1932;140:1–55.
  15. 15. Rai R, Satpathy B, Singh AK. Gender-based variations in perception of flood impacts: A micro study. Indian J Ext Educ. 2023;59(2):69–74. https://doi.org/10.48165/IJEE.2023.59215
  16. 16. Kendall MG, Smith BB. The problem of m-rankings. Ann Math Stat. 1939;10(3):275–87. https://doi.org/10.1214/aoms/1177732186
  17. 17. Kendall MG. Rank correlation methods. London: Griffin & Co; 1962.
  18. 18. Legendre P. Species associations: The Kendall coefficient of concordance revisited. J Agric Biol Environ Stat. 2005;10(2):226–45. https://doi.org/10.1198/108571105X46642
  19. 19. Edward AL, Kilpatrick FP. A technique for the construction of attitude scales. J Appl Psychol. 1948;32:374–84. https://doi.org/10.1037/h0057313
  20. 20. Chaudhari R, Patel JB. Development and validation of farmer’s attitude scale towards Anand Agricultural University. Indian J Ext Educ. 2023;59(3):138–40. https://doi.org/10.48165/IJEE.2023.59326
  21. 21. Thurstone LL, Chave EJ. The measurement of attitude. Chicago: University of Chicago Press; 1929. https://doi.org/10.1037/11574-007
  22. 22. Spearman CC. Correlation calculated from faulty data. Br J Psychol. 1910;3(3):271–95. https://doi.org/10.1111/j.2044-8295.1910.tb00206.x
  23. 23. Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16(3):297–334. https://doi.org/10.1007/BF02310555
  24. 24. Taherdoost H. Validity and reliability of the research instrument; how to test the validation of a questionnaire/survey in a research. Int J Acad Res Manag. 2016;5(3):28–36. https://doi.org/10.2139/ssrn.3205040
  25. 25. Lewis BR, Snyder CA, Rainer KR. An empirical assessment of the information resources management construct. J Manag Inf Syst. 1995;12:199–223. https://doi.org/10.1080/07421222.1995.11518075
  26. 26. Lawshe CH. A quantitative approach to content validity. Pers Psychol. 1975;28(4):563–75. ttps://doi.org/10.1111/j.1744-6570.1975.tb01393.x
  27. 27. Lynn MR. Determination and quantification of content validity. Nurs Res. 1986;35(6):382–5. https://doi.org/10.1097/00006199-198611000-00017
  28. 28. Polit DF, Beck CT, Owen SV. Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Res Nurs Health. 2007;30(4):459–67. https://doi.org/10.1002/nur.20199
  29. 29. Tilden VP, Nelson CA, May BA. Use of qualitative methods to enhance content validity. Nurs Res. 1990;39(3):172–5. https://doi.org/10.1097/00006199-199005000-00015
  30. 30. Gilbert GE, Prion S. Making sense of methods and measurement: Lawshe’s content validity index. Clin Simul Nurs. 2016;12(12):530–1. https://doi.org/10.1016/j.ecns.2016.08.002
  31. 31. Mallick B, Lal SP, Basumatary A. Impediments and plausible suggestions to farmers in cyclone affected region of Odisha: Kendall’s coefficient of concordance approach. Curr World Environ. 2023;18(1):235–44. https://doi.org/10.12944/CWE.18.1.20
  32. 32. Borah A, Lal SP, Singh KM, Prakash S. Assessing farmers’ impediments in climate-smart and non-climate smart villages: Kendall’s W approach. Int J Econ Plants. 2025;12(6):1–8. https://doi.org/10.23910/2/2025.6021
  33. 33. Eysenck HJ, Crown S. An experimental study in opinion attitude methodology. Int J Opin Attitude Res. 1949;3:47–86.

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