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

Research Articles

Vol. 13 No. sp5 (2026): Recent Advances in Agriculture

Determinants of bio-inoculant adoption behaviour among farmers in Tamil Nadu: An application of smart partial least squares structural equation modelling

DOI
https://doi.org/10.14719/pst.13944
Submitted
2 February 2026
Published
01-07-2026

Abstract

The transition towards sustainable agriculture necessitates the adoption of eco-friendly alternatives to chemical fertilisers and pesticides. Bio inoculants, comprising beneficial microorganisms, offer significant potential to enhance soil health and crop productivity. However, despite their proven benefits, their adoption among farmers in India remains limited. Existing studies have largely focused on awareness and general adoption patterns, with limited empirical evidence on the combined influence of innovation attributes using advanced modelling approaches such as partial least squares structural equation modelling (PLS-SEM), particularly across diverse agro-climatic regions. This study addresses this gap by examining the determinants of bio-inoculant adoption behaviour among farmers in Tamil Nadu using PLS-SEM based on the innovation diffusion theory (IDT). A total of 240 farmers were selected from eight agro-climatic zones through purposive and snowball sampling techniques. The results indicate that 42.91 % of farmers have adopted bio-inoculants. Relative advantage, compatibility, complexity (low complexity) and observability significantly influence adoption behaviour, whereas trialability has no significant effect. Among these, complexity emerged as the most influential factor, highlighting the importance of ease of use in technology adoption. Despite these positive determinants, key constraints such as lack of awareness, inconsistent product quality and limited availability continue to hinder widespread adoption. The study recommends strengthening extension services, improving quality assurance mechanisms and enhancing farmer awareness through targeted interventions. These findings provide valuable insights for policymakers and extension agencies to promote sustainable agricultural practices through increased adoption of bio-inoculants.

References

  1. 1. Bruinsma J. The resource outlook to 2050: by how much do land, water and crop yields need to increase by 2050? Rome: Food and Agriculture Organization of the United Nations; 2009.
  2. 2. Nosheen S, Ajmal I, Song Y. Microbes as biofertilizers, a potential approach for sustainable crop production. Sustainability. 2021;13(4):1868. https://doi.org/10.3390/su13041868
  3. 3. Mitter EK, Tosi M, Obregón D, Dunfield KE, Germida JJ. Rethinking crop nutrition in times of modern microbiology: innovative biofertilizer technologies. Front Sustain Food Syst. 2021;5:606815. https://doi.org/10.3389/fsufs.2021.606815
  4. 4. Umesha S, Singh PK, Singh RP. Microbial biotechnology and sustainable agriculture. In: Singh RL, Mondal S, editors. Biotechnology for sustainable agriculture. Cambridge: Woodhead Publishing; 2018. p. 185–205. https://doi.org/10.1016/B978-0-12-812160-3.00006-4
  5. 5. Hubbard M, Hynes RK, Erlandson M, Bailey KL. The biochemistry behind biopesticide efficacy. Sustain Chem Process. 2014;2(1):18. https://doi.org/10.1186/s40508-014-0018-x
  6. 6. Habibi S, Djedidi S, Ohkama-Ohtsu N, Sarhadi WA, Kojima K, Rallos RV, et al. Isolation and screening of indigenous plant growth-promoting rhizobacteria from different rice cultivars in Afghanistan soils. Microbes Environ. 2019;34(4):347–55. https://doi.org/10.1264/jsme2.ME18168
  7. 7. O’Meara L, Cohen P, Kafa R, Wate JT, Albert J, Bogard J, et al. Pacific food systems: the role of fish and other aquatic foods for nutrition and health. Rome: FAO; 2023. https://doi.org/10.4060/cc5796en
  8. 8. Pretty J. Intensification for redesigned and sustainable agricultural systems. Science. 2018;362(6417):eaav0294. https://doi.org/10.1126/science.aav0294
  9. 9. Calvo P, Nelson L, Kloepper JW. Agricultural uses of plant biostimulants. Plant Soil. 2014;383(1–2):3–41. https://doi.org/10.1007/s11104-014-2131-8
  10. 10. Vejan P, Abdullah R, Khadiran T, Ismail S, Boyce AN. Role of plant growth promoting rhizobacteria in agricultural sustainability: a review. Molecules. 2016;21(5):573. https://doi.org/10.3390/molecules21050573
  11. 11. Meena RS, Kumar S, Datta R, Lal R, Vijayakumar V, Brtnicky M, et al. Impact of agrochemicals on soil microbiota and management: a review. Land. 2020;9(2):34. https://doi.org/10.3390/land9020034
  12. 12. Rogers EM. Diffusion of innovations. 5th ed. New York: Free Press; 2003.
  13. 13. Beyene AD, Kassie M. Speed of adoption of improved maize varieties in Tanzania: an application of duration analysis. Technol Forecast Soc Change. 2015;96:298–307. https://doi.org/10.1016/j.techfore.2015.04.007
  14. 14. Kassie BT, Asseng S, Rotter RP, Hengsdijk H, Ruane AC, van Ittersum MK. Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models. Clim Change. 2015;129(1–2):145–58. https://doi.org/10.1007/s10584-014-1322-x
  15. 15. Leong LY, Ooi KB, Chong AYL, Lin B. Modeling the stimulators of the behavioral intention to use mobile entertainment: does gender really matter? Comput Hum Behav. 2013;29(5):2109–21. https://doi.org/10.1016/j.chb.2013.04.004
  16. 16. Henseler J, Ringle CM, Sinkovics RR. The use of partial least squares path modeling in international marketing. Adv Int Mark. 2009;20:277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014
  17. 17. Urbach N, Ahlemann F. Structural equation modeling in information systems research using partial least squares. J Inf Technol Theory Appl. 2010;11(2):5–40.
  18. 18. Garson GD. Partial least squares: regression and structural equation models. Asheboro: Statistical Associates Publishing; 2016.
  19. 19. Tama RA, Hoque MM, Liu Y, Alam MJ, Yu M. An application of partial least squares structural equation modeling to examining farmers’ behavioral attitude and intention towards conservation agriculture in Bangladesh. Agriculture. 2023;13(2):503. https://doi.org/10.3390/agriculture13020503
  20. 20. Sarstedt M, Ringle CM, Hair JF. Partial least squares structural equation modeling. In: Homburg C, Klarmann M, Vomberg A, editors. Handbook of market research. Cham: Springer; 2021. p. 587–632. https://doi.org/10.1007/978-3-319-57413-4_15
  21. 21. Dee Dickerson M, Gentry JW. Characteristics of adopters and non-adopters of home computers. J Consum Res. 1983;10(2):225–35. https://doi.org/10.1086/208961
  22. 22. Eriksson K, Kerem K, Nilsson D. The adoption of commercial innovations in the former Central and Eastern European markets: the case of internet banking in Estonia. Int J Bank Mark. 2008;26(3):154–69. https://doi.org/10.1108/02652320810864634
  23. 23. Flight RL, D’Souza G, Allaway AW. Characteristics-based innovation adoption: scale and model validation. J Prod Brand Manag. 2011;20(5):343–55. https://doi.org/10.1108/10610421111157874
  24. 24. Khan MA, Taunk SK, Porte SS. Constraints faced by farmers in adoption of bio-fertilizers and manure in rice crop. J Plant Dev Sci. 2016;8(9):451–55.
  25. 25. Heena M, Pant PO. Constraints faced by farmers practicing organic farming in Haryana. Pollut Res. 2022;41(1):158–63. https://doi.org/10.53550/PR.2022.v41i01.023
  26. 26. Suji DB, Sathish Kumar AM. Adoption of eco-friendly technologies and constraints. Agric Update. 2020;15(1):42–6.

Downloads

Download data is not yet available.