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Research Article

Extension, Resources and Institutional Support Associated with Oil Palm Farmers’ Adaptive Capacity in Riau, Indonesia

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Dyah Retna Puspita — Study Program of Communication and Community Development, Faculty of Social Sciences and Human Ecology, IPB University, Bogor, Indonesia
Adi Firmansyah — Study Program of Communication and Community Development, Faculty of Social Sciences and Human Ecology, IPB University, Bogor, Indonesia
Anna Fatchiya — Study Program of Communication and Community Development, Faculty of Social Sciences and Human Ecology, IPB University, Bogor, Indonesia
Hana Indriana — Study Program of Sociology, Faculty of Social Sciences and Human Ecology, IPB University, Bogor, Indonesia
Meyzi Heriyanto — Faculty of Social and Political Sciences, Riau University, Pekanbaru, Indonesia
Retno Widihastuti — The Research Center for Society and Culture, National Research and Innovation Agency, Jakarta, Indonesia
Masrul Ikhsan — Faculty of Social and Political Sciences, Riau University, Pekanbaru, Indonesia
Elsa Destriapani — Study Program of Communication and Community Development, Faculty of Social Sciences and Human Ecology, IPB University, Bogor, Indonesia
Published 29 Sep 2026 Vol. 62 No. 4 (2026), pp. 220-225 ● Open access
❝ How to cite 🔗 https://doi.org/10.48165/IJEE.2026.62432
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Abstract

Agricultural extension may strengthen farmers’ responses to production, market and information change, but its contribution is rarely separated from supporting institutions. This cross-sectional study examined how extension methods, household resources and institutional support predicted adaptive capacity among oil palm farmers in three villages of Tapung Subdistrict, Indonesia, from December 2023 to April 2024. Of 214 farmers interviewed, 11 incomplete questionnaires were excluded and 203 complete cases were analysed using partial least squares structural equation modelling with 5,000 bootstrap samples, PLSpredict and the cross-validated predictive ability test. Extension methods (β=0.106; p=0.016) and household resources (β=0.492; p<0.001) positively predicted adaptive capacity. Institutional support was positive (β=0.151; p=0.032), but its bias corrected interval included zero; therefore, H3 was inconclusive. Separating the resource construct showed that household income carried the association (β=0.483; p<0.001), whereas formal education did not (β=0.022; p=0.764). The model explained 37.3% of adaptive-capacity variance. All indicator-level Q²predict values were positive, and PLS SEM outperformed the linear benchmark for four of six indicators. The findings suggest combining accessible, dialogical extension with financially feasible recommendations, while coordinated public–private support requires further testing. 

 

KeywordsAdaptive capacityAdvisory servicesHousehold incomeIndonesiaInstitutional supportOil palmSmallholders

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  1. Antwi-Agyei, P., & Stringer, L. C. (2021). Improving the effectiveness of agricultural extension services in supporting farmers to adapt to climate change: Insights from northeastern Ghana. Climate Risk Management, 32, 100304. https://doi.org/10.1016/j.crm.2021.100304
  2. Atube, F., Malinga, G. M., Nyeko, M., Okello, D. M., Alarakol, S. P., & Okello-Uma, I. (2021). Determinants of smallholder farmers’ adaptation strategies to the effects of climate change: Evidence from northern Uganda. Agriculture & Food Security, 10, 6. https://doi.org/10.1186/s40066-020-00279-1
  3. Bartelet, H. A., Barnes, M. L., Bakti, L. A. A., & Cumming, G. S. (2023). Testing the reliability of adaptive capacity as a proxy for adaptive and transformative responses to climate change. Global Environmental Change, 81, 102700. https://doi.org/10.1016/j.gloenvcha.2023.102700
  4. BPS-Statistics Indonesia. (2023). Indonesian oil palm statistics 2022. BPS-Statistics Indonesia. https://www.bps.go.id/en/publication/2023/11/30/160f211bfc4f91e1b77974e1/indonesian-oil-palm-statistics-2022.html
  5. Dosso, F., Gouroubera, M. W., Idrissou, L., & Moumouni-Mousa, I. (2023). The combination of extension approaches strengthens farmers’ innovativeness in sustainable land management. Environment, Development and Sustainability, 26, 10043–10062. https://doi.org/10.1007/s10668-023-03134-y
  6. Godara, S., Bana, R. S., Godara, S., Bishnoi, S., Nain, M. S., Parsad, R., & Marwaha, S. (2024a). Data-driven insights for agricultural extension services in Rajasthan: A study of Kisan Call Center queries. Indian Journal of Extension Education, 60(1), 53–58. https://doi.org/10.48165/IJEE.2024.60110
  7. Godara, S., Bana, R. S., Marwaha, S., Parsad, R., Nain, M. S., Sahu, S., Mehta, A., Singh, D., & Kumar, R. (2024b). Uncovering farmers’ information need through Kisan Call Centre data analytics of Haryana state. Indian Journal of Extension Education, 60(4), 59–66. https://doi.org/10.48165/IJEE.2024.60411
  8. 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.
  9. Kamruzzaman, M., Daniell, K. A., Chowdhury, A., & Crimp, S. (2021). The role of extension and advisory services in strengthening farmers’ innovation networks to adapt to climate extremes. Sustainability, 13(4), 1941. https://doi.org/10.3390/su13041941
  10. Kangogo, D., Dentoni, D., & Bijman, J. (2020). Determinants of farm resilience to climate change: The role of farmer entrepreneurship and value chain collaborations. Sustainability, 12(3), 868. https://doi.org/10.3390/su12030868
  11. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/IJEC.2015100101
  12. Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling. Decision Sciences, 52(2), 362–392. https://doi.org/10.1111/deci.12445
  13. Ma, W., & Rahut, D. B. (2024). Climate-smart agriculture: Adoption, impacts, and implications for sustainable development. Mitigation and Adaptation Strategies for Global Change, 29, 44. https://doi.org/10.1007/s11027-024-10139-z
  14. Maguire-Rajpaul, V. A., Khatun, K., & Hirons, M. A. (2020). Agricultural information’s impact on the adaptive capacity of Ghana’s smallholder cocoa farmers. Frontiers in Sustainable Food Systems, 4, 28. https://doi.org/10.3389/fsufs.2020.00028
  15. Manevska-Tasevska, G., Petitt, A., Larsson, S., Bimbilovski, I., Meuwissen, M. P. M., Feindt, P. H., & Urquhart, J. (2021). Adaptive governance and resilience capacity of farms: The fit between farmers’ decisions and agricultural policies. Frontiers in Environmental Science, 9, 668836. https://doi.org/10.3389/fenvs.2021.668836
  16. Maurya, A. S., Yadav, B., Patel, S., Kumar, R., & Kumar, A. (2026). Assessing the role of digital platforms in strengthening agricultural extension services: Advisory to empowerment. Indian Journal of Extension Education, 62(2), 47–53. https://doi.org/10.48165/IJEE.2026.62208
  17. Nain, M. S., Singh, R., Mishra, J. R., & Singh, A. K. (2024). Developing model for diffusion of farmers’ innovations for maximizing farm income: Indian Agricultural Research Institute experiences. Indian Journal of Extension Education, 60(1), 105–110. https://doi.org/10.48165/IJEE.2024.60120
  18. Niranjan, S., Singh, D. R., Kumar, N. R., Jha, G. K., Venkatesh, P., Nain, M. S., & Krishnakumare, B. (2023). Do information networks enhance adoption of sustainable agricultural practices? Evidence from northern dry zone of Karnataka, India. Indian Journal of Extension Education, 59(1), 86–91. https://doi.org/10.48165/IJEE.2023.59118
  19. Novira, N. (2023). The vulnerability of Indonesian smallholder oil palm farmers in the times of global crisis. IOP Conference Series: Earth and Environmental Science, 1190, 012044. https://doi.org/10.1088/1755-1315/1190/1/012044
  20. Ogundeji, A. A., Danso-Abbeam, G., & Jooste, A. (2022). Climate information pathways and farmers’ adaptive capacity: Insights from South Africa. Environmental Development, 44, 100743. https://doi.org/10.1016/j.envdev.2022.100743
  21. Osumba, J. J. L., Recha, J. W., & Oroma, G. W. (2021). Transforming agricultural extension service delivery through innovative bottom–up climate-resilient agribusiness farmer field schools. Sustainability, 13(7), 3938. https://doi.org/10.3390/su13073938
  22. Paterson, R. R. M. (2023). Future climate effects on yield and mortality of conventional versus modified oil palm in Southeast Asia. Plants, 12(12), 2236. https://doi.org/10.3390/plants12122236
  23. Reich, C.-E., Ölkers, T., Bonke, V., & Musshoff, O. (2025). Beyond the ban: Shedding light on smallholders’ price vulnerability in Indonesia’s palm oil industry. Applied Economic Perspectives and Policy, 48(3), 653–678. https://doi.org/10.1002/aepp.70040
  24. Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4. SmartPLS GmbH. https://www.smartpls.com
  25. Shitu, A. G., Ojo, D. T., Shitu, M. V., & Nain, M. S. (2026). Small scale farmers’ willingness to pay for extension services: Evidence from Nigeria. Indian Journal of Extension Education, 62(1), 86–91. https://doi.org/10.48165/IJEE.2026.62114
  26. 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
  27. Sibhatu, K. T., Tabe-Ojong, M. P., Jr., & Siregar, H. (2025). Nature-based land management practices and yield dynamics in oil palm production: Insights from Indonesian smallholder growers. Agricultural Economics, 56(5), 713–727. https://doi.org/10.1111/agec.70018
  28. Sthitaprajna, S., & Shasani, S. (2026). Factors influencing farmers’ adaptive capacity to climate change. Indian Journal of Extension Education, 62(1), 195–199. https://doi.org/10.48165/IJEE.2026.621RN02
  29. van den Berg, H., Ketelaar, J. W., Dicke, M., & Fredrix, M. (2020). Is the farmer field school still relevant? Case studies from Malawi and Indonesia. NJAS: Wageningen Journal of Life Sciences, 92(1), 100329. https://doi.org/10.1016/j.njas.2020.100329
  30. Vijayabhinandana, B., Asha, R., & Gowtham Kumar, B. S. N. S. (2022). Adaptation methods practiced by farmers in response to perceived climate change in Andhra Pradesh. Indian Journal of Extension Education, 58(2), 81–85. https://doi.org/10.48165/IJEE.2022.58216
  31. Yadav, P. K., & Ghosh, S. (2023). Effectiveness of adaptation mechanisms of farmers and its determinants in flood-prone areas of Bihar. Indian Journal of Extension Education, 59(3), 32–37. https://doi.org/10.48165/IJEE.2023.59306

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