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Plant Science Today (PST)

Research Articles

Vol. 12 No. sp4 (2025): Recent Advances in Agriculture by Young Minds - III

Strategies to promote precision farming in India using the Analytic Hierarchy Process (AHP)

DOI
https://doi.org/10.14719/pst.10342
Submitted
28 June 2025
Published
31-10-2025

Abstract

Precision agriculture (PA), a climate-smart approach, utilizes advanced technologies such as remote sensing, GIS, GPS and variable-rate applications to optimize resource use, reduce environmental impacts and enhance agricultural productivity. Despite its potential, the adoption of PA in India faces significant barriers, particularly for small and marginal farmers who represent 89.4 % of the farming community. This study aimed to identify and prioritize strategies to promote PA adoption in western and north western zones of Tamil Nadu (2023) using the Analytic Hierarchy Process (AHP), a robust decision-making tool suitable for addressing complex multi-criteria problems. Four strategic categories-socio-economic, farm-level, political and technological-were analyzed through expert evaluations. Results revealed that farm-level strategies hold the highest priority (scaling factor = 0.288), followed by technological strategies (0.246), socio-economic strategies (0.234) and political strategies (0.232). The high ranking of farm-level strategies underscores the importance of implementing measures such as laser land levelers, precision nutrient management and water-use efficiency tools. The calculated Consistency Ratio (CR = 0.0081) confirmed the reliability and validity of these results. These findings provide practical and research baked strategies for grassroots agricultural institutions such as Krishi Vigyan Kendras (KVKs), Agricultural Technology Management Agencies (ATMAs) and NGOs, emphasizing the need to redirect resources toward farm-level interventions to scale up PA practices and achieve sustainable agricultural development which could potentially raise the adoption among small and marginal farmers by about 20-25 % within the next five years.

References

  1. 1. Blasch J, van der Kroon B, van Beukering P, Munster R, Fabiani S, Nino P, et al. Farmer preferences for adopting precision farming technologies: a case study from Italy. Eur Rev Agric Econ. 2022;49(1):33-81. https://doi.org/10.1093/erae/jbaa031
  2. 2. Tey YS, Brindal M. A meta-analysis of factors driving the adoption of precision agriculture. Precis Agric. 2022;23(2):353-72.
  3. https://doi.org/10.1007/s11119-021-09840-9
  4. 3. Torero M. Robotics and AI in food security and innovation: why they matter and how to harness their power. In: von Braun J, Archer MS, Reichberg G, Sánchez Sorondo M, editors. Robotics, AI and Humanity. Cham: Springer International Publishing; 2021:99-107. https://doi.org/10.1007/978-3-030-54173-6_8
  5. 4. Khaspuria G, Khandelwal A, Agarwal M, Bafna M, Yadav R, Yadav A. Adoption of precision agriculture technologies among farmers: a comprehensive review. SD Publishers. 2024;30(07). https://doi.org/10.9734/jsrr/2024/v30i72180
  6. 5. Mohapatra T. Precision agriculture in India: a perspective. NAAS News. 2022;22(3):3. https://naas.org.in/News/NN22032022.pdf
  7. 6. Troiano S, Carzedda M, Marangon F. Better richer than environmentally friendly? Describing preferences toward and factors affecting precision agriculture adoption in Italy. Agric Food Econ. 2023;11(1):1-21. https://doi.org/10.1186/s40100-023-00247-w
  8. 7. Edwards DB Jr, Okitsu T, Da Costa R, Kitamura Y. Organizational legitimacy in the global education policy field: Learning from UNESCO and the Global Monitoring Report. Comp Educ Rev. 2018;62(1):31-63. https://doi.org/10.1086/695440
  9. 8. Ministry of Statistics and Programme Implementation (MoSPI). Literacy and education. New Delhi: Government of India; 2016.
  10. 9. Mathanker S. A precision agriculture technology course for regions with lower technology adoption levels. Appl Eng Agric. 2021;37(5):871-7. https://doi.org/10.13031/aea.14669
  11. 10. D'Antoni J, Mishra A, Joo H. Farmers' perception of precision technology: the case of autosteer adoption by cotton farmers. Comput Electron Agric. 2012;87:121-8. https://doi.org/10.1016/j.compag.2012.05.017
  12. 11. Roy S, Sarkar S. Precision agriculture - future of Indian farming. In: Advances in Agricultural Research Methodology. 2024.
  13. 12. Gupta N, Pradhan S, Jain A, Patel N. Centre for Energy Finance: Sustainable Agriculture in India. New Delhi. 2021.
  14. 13. Kumar S, Karaliya SK, Chaudhary S. Precision farming technologies towards enhancing productivity and sustainability of rice-wheat cropping system. Int J Curr Microbiol App Sci. 2017;6(3):142-51. https://doi.org/10.20546/ijcmas.2017.603.016
  15. 14. Department of Agriculture, Cooperation and Farmers Welfare. Directorate of Economics & Statistics. Agricultural Statistics. New Delhi. 2017.
  16. 15. Ministry of Agriculture & Farmers Welfare (MoA&FW), Government of India. National Mission on Sustainable Agriculture (NMSA). New Delhi: MoA&FW; 2023. https://agricoop.nic.in/en/Mission/NMSA
  17. 16. Press Information Bureau (PIB). Namo Drone Didi: revolutionizing agriculture through women empowerment. New Delhi: Government of India; 2024. https://pib.gov.in/PressReleasePage.aspx?PRID=1999876
  18. 17. Department of Agriculture, Cooperation & Farmers Welfare (DAC&FW), Government of India. Sub-Mission on Agricultural Mechanization (SMAM). New Delhi: DAC&FW. 2022. https://agrimachinery.nic.in
  19. 18. Rathod PK, Dixit S. Precision dairy farming: opportunities and challenges for India. Indian J Anim Sci. 2021;90(8):1083-94.
  20. https://doi.org/10.56093/ijans.v90i8.109207
  21. 19. Denny F. An economic analysis of precision farming in Palakkad district of Kerala [dissertation]. New Delhi: Indian Agricultural Research Institute, Division of Agricultural Economics.
  22. 20. Alauddin M, Sarker MAR. Climate change and farm-level adaptation decisions and strategies in drought-prone and groundwater-depleted areas of Bangladesh: an empirical investigation. Ecol Econ. 2014;106:204-13. https://doi.org/10.1016/j.ecolecon.2014.07.025
  23. 21. Osrof H, Tan C, Angappa G, Yeo S, Tan K. Adoption of smart farming technologies in field operations: a systematic review and future research agenda. Technol Soc. 2023;75:102400. https://doi.org/10.1016/j.techsoc.2023.102400
  24. 22. Degieter M, De Steur H, Tran D, Gellynck X, Schouteten JJ. Farmers' acceptance of robotics and unmanned aerial vehicles: a systematic review. Agron J. 2023;115(5):2159-73. https://doi.org/10.1002/agj2.21427
  25. 23. Klerkx L, Jakku E, Labarthe P. A review of social science on digital agriculture, smart farming and agriculture 4.0: new contributions and a future research agenda. NJAS Wageningen J Life Sci. 2019;90-91:100315. https://doi.org/10.1016/j.njas.2019.100315
  26. 24. Rezaei Moghaddam K, Salehi S. Agricultural specialists' intention toward precision agriculture technologies: integrating innovation characteristics to technology acceptance model. Afr J Agric Res. 2010;5(11):1191-9.
  27. 25. Paustian M, Theuvsen L. Adoption of precision agriculture technologies by German crop farmers. Precis Agric. 2017;18(5):701-16.
  28. https://doi.org/10.1007/s11119-016-9482-5
  29. 26. Bai A, Kovách I, Czibere I, Megyesi B, Balogh P. Examining the adoption of drones and categorisation of precision elements among Hungarian precision farmers using a trans-theoretical model. Drones. 2022;6(8):200. https://doi.org/10.3390/drones6080200
  30. 27. Michels M, von Hobe C, Musshoff O. A trans-theoretical model for the adoption of drones by large-scale German farmers. J Rural Stud. 2020;75:80-8. https://doi.org/10.1016/j.jrurstud.2020.01.005
  31. 28. Saaty TL. Decision-making with the analytic hierarchy process. Int J Serv Sci. 2008;1(1):83-98.
  32. https://doi.org/10.1504/IJSSCI.2008.017590
  33. 29. Mazurek J. On preselection of alternatives in the analytic hierarchy process. J Appl Econ Sci. 2012;7:410-17.
  34. 30. Taherdoost H. Decision making using the analytic hierarchy process (AHP); a step by step approach. Int J Econ Manag Syst. 2017. https://hal.science/hal-02557320
  35. 31. Saaty TL. A scaling method for priorities in hierarchical structures. J Math Psychol. 1977;15(3):234-81. https://doi.org/10.1016/0022-2496(77)90033-5
  36. 32. Schimmelpfennig D, Ebel R. Sequential adoption and cost savings from precision agriculture. J Agric Resour Econ. 2016;41:97-115.
  37. 33. Chen W, Bell R, Brennan R, Bowden J, Dobermann A, Rengel Z, et al. Key crop nutrient management issues in the Western Australia grains industry: a review. Soil Res. 2009;47:1-18. https://doi.org/10.1071/SR08097
  38. 34. Hudson D, Hite D. Producer willingness to pay for precision application technology: implications for government and the technology industry. Can J Agric Econ. 2003;51:39-53. https://doi.org/10.1111/j.1744-7976.2003.tb00163.x
  39. 35. Pierpaoli E, Carli G, Pignatti E, Canavari M. Drivers of precision agriculture technologies adoption: a literature review. Procedia Technol. 2013;8:61-9. https://doi.org/10.1016/j.protcy.2013.11.010
  40. 36. Rahm M, Huffman W. The adoption of reduced tillage: the role of human capital and other variables. Am J Agric Econ. 1984;66(4):405-13. https://doi.org/10.2307/1240918
  41. 37. Khanal A, Mishra A, Lambert D, Paudel K. Modeling post adoption decision in precision agriculture: a Bayesian approach. Comput Electron Agric. 2019;162:466-74. https://doi.org/10.1016/j.compag.2019.04.025
  42. 38. Pandeya S, Gyawali BR, Upadhaya S. Factors influencing precision agriculture technology adoption among small-scale farmers in Kentucky and their implications for policy and practice. Agriculture. 2025;15(2):177. https://doi.org/10.3390/agriculture15020177

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