Digital Readiness and Agricultural Productivity in ASEAN: A Fertilizer-Intensity Threshold Analysis
DOI:
https://doi.org/10.36877/mjae.a0000682Abstract
Digital readiness is widely expected to improve agricultural productivity, yet its benefits may differ across agricultural systems with varying input-use conditions. This study examines whether the productivity relevance of national-level digital readiness in ASEAN changes across fertilizer-intensity regimes. Evidence from ten ASEAN economies over 2011–2023, based on a fixed-effects threshold design, indicates a non-linear pattern. Below the estimated fertilizer-intensity threshold of 44.17 kg/ha, digital readiness is not significantly associated with agricultural productivity; above the threshold, the association becomes positive and statistically significant (coefficient = 0.210). The findings suggest that digital connectivity alone does not automatically translate into productivity gains; its value appears stronger where agricultural systems provide greater scope to adjust inputs and act on information. Fertilizer intensity is therefore interpreted as an observable indicator of input-use conditions rather than a direct measure of commercialisation, management quality, or precision agriculture adoption. The study contributes to the digital-agriculture literature by showing that the productivity relevance of digital readiness is conditional rather than uniform across ASEAN. The results support policies that combine digital infrastructure with extension services, input access, rural finance, and farmers’ capacity to use digital information. The estimated threshold should nevertheless be understood as a sample-specific statistical boundary rather than an agronomic recommendation or commercialisation benchmark.
References
Aker, J. C. (2011). Dial “A” for agriculture: A review of information and communication technologies for agricultural extension in developing countries. Agricultural Economics, 42(6), 631–647. doi:https://doi.org/10.1111/j.1574-0862.2011.00545.x
Anderson, J. R. & Feder, G. (2007). Agricultural extension. In R. Evenson & P. Pingali (Eds.), Handbook of Agricultural Economics (Vol. 3, pp. 2343–2378). Amsterdam: Elsevier. doi:https://doi.org/10.1016/S1574-0072(06)03044-1
Bongiovanni, R. & Lowenberg-DeBoer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5(4), 359–387. doi:https://doi.org/10.1023/B:PRAG.0000040806.39604.aa
Cameron, A. C. & Miller, D. L. (2015). A practitioner’s guide to cluster-robust inference. Journal of Human Resources, 50(2), 317–372. doi:https://doi.org/10.3368/jhr.50.2.317
Chandio, A. A., Gokmenoglu, K. K., Sethi, N., et al. (2023). Examining the impacts of technological advancement on cereal production in ASEAN countries: Does information and communication technology matter? European Journal of Agronomy, 144, 126747. doi:https://doi.org/10.1016/j.eja.2023.126747
Fabregas, R., Kremer, M. & Schilbach, F. (2019). Realizing the potential of digital development: The case of agricultural advice. Science, 366(6471), eaay3038. doi:https://doi.org/10.1126/science.aay3038
Feder, G., Just, R. E. & Zilberman, D. (1985). Adoption of agricultural innovations in developing countries: A survey. Economic Development and Cultural Change, 33(2), 255–298. doi:https://doi.org/10.1086/451461
Food and Agriculture Organization of the United Nations. (2024). FAOSTAT: Fertilizers by nutrient. Rome: FAO.
Foster, A. D. & Rosenzweig, M. R. (2010). Microeconomics of technology adoption. Annual Review of Economics, 2(1), 395–424. doi:https://doi.org/10.1146/annurev.economics.102308.124433
Gebbers, R. & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828–831. doi:https://doi.org/10.1126/science.1183899
Hansen, B. E. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 93(2), 345–368. doi:https://doi.org/10.1016/S0304-4076(99)00025-1
Hwang, B. N., Jitanugoon, S. & Puntha, P. (2024). The impact of smart farming technology on agricultural productivity: Evidence from a large-scale database in Thailand. KnE Social Sciences, 9(32), 25–55. doi:https://doi.org/10.18502/kss.v9i32.17425
Klerkx, L., Jakku, E. & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS: Wageningen Journal of Life Sciences, 90–91, 100315. doi:https://doi.org/10.1016/j.njas.2019.100315
Kozono, M., Diyanah, S. M. & Hazmi, A. (2024). Accelerating the digitalisation of the agriculture and food system in the ASEAN region: Lessons learned, findings, and recommendations. Jakarta: Economic Research Institute for ASEAN and East Asia.
MacKinnon, J. G. & White, H. (1985). Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. Journal of Econometrics, 29(3), 305–325. doi:https://doi.org/10.1016/0304-4076(85)90158-7
Montesclaros, J. M. L. & Teng, P. S. (2023). Digital technology adoption and potential in Southeast Asian agriculture. Asian Journal of Agriculture and Development, 20(2), 7–30. doi:https://doi.org/10.37801/ajad2023.20.2.2
OECD & European Commission. (2008). Handbook on constructing composite indicators: Methodology and user guide. Paris: OECD Publishing. doi:https://doi.org/10.1787/9789264043466-en
Pingali, P. L. (2012). Green Revolution: Impacts, limits, and the path ahead. Proceedings of the National Academy of Sciences, 109(31), 12302–12308. doi:https://doi.org/10.1073/pnas.0912953109
Ren, C., Zhang, X., Reis, S., et al. (2023). Climate change unequally affects nitrogen use and losses in global croplands. Nature Food, 4, 294–304. doi:https://doi.org/10.1038/s43016-023-00730-z
Rose, D. C., Sutherland, W. J., Parker, C., et al. (2016). Decision support tools for agriculture: Towards effective design and delivery. Agricultural Systems, 149, 165–174. doi:https://doi.org/10.1016/j.agsy.2016.09.009
Ruzzante, S., Labarta, R. & Bilton, A. (2021). Adoption of agricultural technology in the developing world: A meta-analysis of the empirical literature. World Development, 146, 105599. doi:https://doi.org/10.1016/j.worlddev.2021.105599
Sen, L. T. H., Chou, P., Dacuyan, F. B., et al. (2024). Barriers and enablers of digital extension services’ adoption among smallholder farmers: The case of Cambodia, the Philippines and Vietnam. International Journal of Agricultural Sustainability, 22(1), 2368351. doi:https://doi.org/10.1080/14735903.2024.2368351
Takahashi, K., Muraoka, R. & Otsuka, K. (2020). Technology adoption, impact, and extension in developing countries’ agriculture: A review of the recent literature. Agricultural Economics, 51(1), 31–45. doi:https://doi.org/10.1111/agec.12539
Thann, O. H., Yuhuan, Z., Uddin, M., et al. (2025). Technological innovation and agricultural performance in the ASEAN region: The role of digitalization. Food Policy, 135, 102939. doi:https://doi.org/10.1016/j.foodpol.2025.102939
Timmer, C. P. (1988). The agricultural transformation. In H. Chenery & T. N. Srinivasan (Eds.), Handbook of Development Economics (Vol. 1, pp. 275–331). Amsterdam: Elsevier. doi:https://doi.org/10.1016/S1573-4471(88)01011-3
Wolfert, S., Ge, L., Verdouw, C., et al. (2017). Big data in smart farming: A review. Agricultural Systems, 153, 69–80. doi:https://doi.org/10.1016/j.agsy.2017.01.023
World Bank. (2024). World Development Indicators. Washington, DC: World Bank.
Zhang, X., Davidson, E. A., Mauzerall, D. L., et al. (2015). Managing nitrogen for sustainable development. Nature, 528(7580), 51–59. doi:https://doi.org/10.1038/nature15743
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