Extension Support and Peer Influence on Climate-Smart Agriculture Adoption among Indonesian Rice Farmers

Dwi Sadono; Sumardjo; Murdianto; Adi  Firmansyah; Rafnel Azhari; Siti Syamsiah

doi:10.48165/IJEE.2026.62415

Authors

Dwi Sadono IPB University
Sumardjo
Murdianto
Adi Firmansyah
Rafnel Azhari
Siti Syamsiah

https://doi.org/10.48165/IJEE.2026.62415

Keywords:

Agricultural extension, Behavioral intention, Climate-smart sgriculture, Peer influence, Technology acceptance model

Abstract

Extension support and peer learning are widely promoted to accelerate climate-smart agriculture (CSA), but their relationships with farmers’ cognitive evaluations and realized adoption remain insufficiently understood. This study examined CSA adoption among Indonesian smallholder rice farmers using a TAM-informed exploratory framework. A cross-sectional survey during 2025 involved 342 farmers in Sukra District, West Java, and Praya District, West Nusa Tenggara. Data were analyzed using partial least squares structural equation modelling. Extension support was positively associated with behavioural intention and perceived ease of use, while peer influence strongly predicted perceived ease of use but not behavioural intention. Farmer characteristics were associated with perceived ease of use but not behavioural intention. Behavioural intention was the strongest direct predictor of CSA adoption, followed by attitude toward CSA, whereas perceived ease of use had no significant direct relationship with adoption. The model explained 31.5% of the variance in adoption and demonstrated positive predictive relevance, although its out-of-sample predictive accuracy was limited. These findings indicate that extension should combine participatory demonstrations with farmer-to-farmer learning to strengthen operational confidence and intention to adopt CSA.

References

1. Acevedo, M., Pixley, K. V., Zinyengere, N., Meng, S., Tufan, H., Cichy, K. A., Bíziková, L., Isaacs, K., Ghezzi-Kopel, K., & Porciello, J. (2020). A scoping review of adoption of climate-resilient crops by small-scale producers in low- and middle-income countries. Nature Plants, 6(10), 1231–1241. https://doi.org/10.1038/s41477-020-00783-z

2. Aggarwal, P., Jarvis, A., Campbell, B., Zougmoré, R. B., Khatri-Chhetri, A., Vermeulen, S., Loboguerrero, A. M., Sebastian, L. S., Kinyangi, J., Bonilla-Findji, O., Radeny, M., Recha, J., Barón, D. M., Ramírez-Villegas, J., Huyer, S., Thornton, P. K., Wollenberg, E., Hansen, J., Alvarez-Toro, P., & Yen, B. T. (2018). The climate-smart village approach: Framework of an integrative strategy for scaling up adaptation options in agriculture. Ecology and Society, 23(1). https://doi.org/10.5751/es-09844-230114

3. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.

4. Ajzen, I., & Schmidt, P. (2020). Changing behavior using the Theory of Planned Behavior. In Cambridge University Press eBooks (pp. 17–31). Cambridge University Press. https://doi.org/10.1017/9781108677318.002

5. Amanah, S., Syamsiah, S., Azhari, R., Snider, A., McNamara, P., & Bhushan, B. (2026). Enhancing extensionists’ capabilities in female farmers’ groups (FFGs) empowerment: An integrative approach. Indian Journal of Extension Education, 62(3), 97–104. https://doi.org/10.48165/IJEE.2026.62316

6. Atta-Aidoo, J., Antwi-Agyei, P., Dougill, A. J., Ogbanje, C. E., Akoto-Danso, E. K., & Eze, S. (2022). Adoption of climate-smart agricultural practices by smallholder farmers in rural Ghana: An application of the theory of planned behavior. PLOS Climate, 1(10). https://doi.org/10.1371/journal.pclm.0000082

7. Azhari, R., Amanah, S., Fatchiya, A., & Kinseng, R. A. (2025b). The influence of agricultural extension services and livelihood capitals on farmers’ climate resilience in West Java, Indonesia: A structural equation modelling approach. Journal of Natural Resources and Environmental Management, 15(5), 904–922. https://doi.org/10.29244/jpsl.15.5.904

8. Azhari, R., Amanah, S., Fatchiya, A., & Kinseng, R. A. (2025a). Can agricultural extension enhance the climate resilience of smallholder farmers? Evidence from West Java, Indonesia. BIO Web of Conferences, 171, 04001. https://doi.org/10.1051/bioconf/202517104001

9. Bandura, A. (1977). Social learning theory. Prentice-Hall. https://doi.org/10.1177/105960117700200317

10. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

11. 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

12. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982

13. Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434–449. https://doi.org/10.1007/s11747-011-0300-3

14. Ding, Y., Zhang, M., & Li, X. (2022). Social learning, information acquisition, and farmers’ adoption of e-commerce technologies. Information Technology for Development, 28(4), 873–894. https://doi.org/10.1080/02681102.2021.2018046

15. Ejigu, A. K., & Yeshitela, K. (2024). Exploring the factors influencing urban farmers’ perception and attitude toward the use of excreta-based organic fertilizers in Arba Minch City, Ethiopia. Frontiers in Sustainable Food Systems, 7. https://doi.org/10.3389/fsufs.2023.1271811

16. Gemtou, M., Kakkavou, K., Anastasiou, E., Fountas, S., Pedersen, S. M., Isakhanyan, G., Tarekegn, K., & Pazos-Vidal, S. (2024). Farmers’ transition to climate-smart agriculture: A systematic review of the decision-making factors affecting adoption. Sustainability, 16(7), 2828. https://doi.org/10.3390/su16072828

17. Genius, M., Koundouri, P., Nauges, C., & Tzouvelekas, V. (2014). Information transmission in irrigation technology adoption and diffusion: Social learning, extension services, and spatial effects. American Journal of Agricultural Economics, 96(1), 328–344. https://doi.org/10.1093/ajae/aat054

18. Gerba, M., Sartorius, K., & Klerkx, L. (2015). A social network analysis of innovation platforms: Implications for agricultural innovation diffusion. Agricultural Systems, 132, 1–13. https://doi.org/10.1016/j.agsy.2014.08.003

19. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications.

20. Hansen, J., Hellin, J., Rosenstock, T. S., Fisher, E., Cairns, J. E., Stirling, C., Lamanna, C., van Etten, J., Rose, A., & Campbell, B. (2018). Climate risk management and rural poverty reduction. Agricultural Systems, 172, 28–46. https://doi.org/10.1016/j.agsy.2018.01.019

21. Helmi, Azhari, R., Henmaidi, Silfia, & Riyadhie, I. (2019). Identifying key factors affecting integrated and sustainable development of red onion horticulture cluster area. International Journal on Advanced Science, Engineering and Information Technology, 9(2), 448–454.

22. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

23. Islam, Z., Sabiha, N. E., & Salim, R. (2022). Integrated environment-smart agricultural practices: A strategy towards climate-resilient agriculture. Economic Analysis and Policy, 76, 59–72. https://doi.org/10.1016/j.eap.2022.07.011

24. Lipper, L., McCarthy, N., Zilberman, D., Asfaw, S., & Branca, G. (2017). Climate smart agriculture. Springer International Publishing. https://doi.org/10.1007/978-3-319-61194-5

25. Ma, W., & Rahut, D. B. (2024). Climate-smart agriculture: Adoption, impacts, and implications for sustainable development. Mitigation and Adaptation Strategies for Global Change, 29(5). https://doi.org/10.1007/s11027-024-10139-z

26. Mohr, S., & Kühl, R. (2021). Acceptance of artificial intelligence in agriculture: The influence of attitudes and expectations. Agricultural Systems, 190, 103078. https://doi.org/10.1016/j.agsy.2021.103078

27. Mpala, T. A., & Simatele, M. D. (2024). Climate-smart agricultural practices among rural farmers in Masvingo district of Zimbabwe: Perspectives on the mitigation strategies to drought and water scarcity for improved crop production. Frontiers in Sustainable Food Systems, 7. https://doi.org/10.3389/fsufs.2023.1298908

28. Rezaei-Moghaddam, K., & Salehi, S. (2010). Agricultural specialists’ intention toward precision agriculture technologies: Integrating innovation characteristics to technology acceptance model. African Journal of Agricultural Research, 5(11), 1191–1199.

29. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022). Partial least squares structural equation modeling. In Handbook of market research (pp. 587–632). Springer International Publishing. https://doi.org/10.1007/978-3-319-57413-4_15

30. Shitu A. G., Nain M. S., & Singh R. (2018). Developing extension model for smallholder farmers’ uptake of precision conservation agricultural practices in developing nations: Learning from rce-wheat system of Africa and India. Current Science, 114(4), 814-825.

31. Shitu A.G. & Nain M.S. (2024). Benefits of precision conservation agriculture practices as perceived by Indo-Gangetic Plain (IGP) community for climate-smart agriculture, SKUAST Journal of Research 26(2), 219-226, https://doi.org/10.5958/2349-297X.2024.00029.2

32. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189

33. Shilomboleni, H., Epstein, G., & Mansingh, A. (2024). Building resilience in Africa’s smallholder farming systems: Contributions from agricultural development interventions — a scoping review. Ecology and Society, 29(3). https://doi.org/10.5751/es-15373-290322

34. Slovin, E. (1960). Slovin’s formula for sampling technique. https://prudencexd.weebly.com/

35. Utami, A. S., Azhari, R., & Syarfi, I. W. (2020). Smallholders’ diversification in Pauh Sub District Padang City West Sumatera Indonesia. IOP Conference Series: Earth and Environmental Science, 583, 012019. https://doi.org/10.1088/1755-1315/583/1/012019

36. Vatsa, P., Ma, W., Zheng, H., & Li, J. (2023). Climate-smart agricultural practices for promoting sustainable agrifood production: Yield impacts and implications for food security. Food Policy, 121. https://doi.org/10.1016/j.foodpol.2023.102551

37. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x

38. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

39. 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

40. Weisenfeld, P., & Wetterberg, A. (2015). Technological advances to improve food security: Addressing challenges to adoption. RTI Press. https://doi.org/10.3768/rtipress.2015.rb.0011.1510

41. Zarei, L., Bagheri, A., & Karimi, H. (2022). Factors affecting farmers’ adoption of e-commerce platforms for selling agricultural products. Technology in Society, 68, 101854. https://doi.org/10.1016/j.techsoc.2021.101854

42. Zheng, H., Ma, W., & He, Q. (2024). Climate-smart agricultural practices for enhanced farm productivity, income, resilience, and greenhouse gas mitigation: A comprehensive review. Mitigation and Adaptation Strategies for Global Change, 29(4). https://doi.org/10.1007/s11027-024-10124-6