Analysis of Government Interventions to Address the Impacts of Climate Change on the Agricultural Sector in West Nusa Tenggara
Abstract
Research Originality — Existing studies on climate change and agriculture primarily focus on the physical impacts and food security outcomes. However, how local governments prioritize budget allocations between mitigation and adaptation strategies in the agricultural sector remains underexplored.
Research Objective — This study aims at evaluating whether the government's budget policy is appropriate in addressing impacts of climate change by focusing the climate change budget only on mitigation budget without allocating a budget for climate change adaptation.
Research Methods — This study adopts a mixed methods approach that integrates both quantitative and qualitative techniques. Many studies have analyzed the impact of climate change on the agricultural sector, but studies using forecasting models such as the autoregressive integrated moving average (ARIMA) to predict this impact combined with policy evaluation are still rare.
Empirical Results — The forecasting results using the ARIMA (12,1,1) model reveal a continued decline in paddy production in West Nusa Tenggara from 2025 to 2028. Furthermore, it also shows a negative monthly production trend between 2021 to 2024. The evaluation results using the ARIMA model and the large number of cases of drought data and pests indicate that in dealing with climate change in West Nusa Tenggara, the government should not only allocate budgets for climate change mitigation, but also for adaptation to the impacts of climate change.
Implications — The optimal strategy for addressing the impacts of climate change on the agricultural sector require prioritizing a combination of adaptation measures across key areas such as crop management, farming systems, water resources, soil health, and pest control.
Full text article
References
Agricultural Plant Protection Center. (2024a). Land Affected by Drought.
Agricultural Plant Protection Center. (2024b). Land Affected by Plant-Disturbing Organisms.
Ansari, A., Lin, Y.-P., & Lur, H.-S. (2021). Evaluating and adapting climate change impacts on rice production in Indonesia: A case study of the Keduang Subwatershed, Central Java. Environments, 8(11), 117. https://doi.org/10.3390/environments8110117
Box, G. E. P., Gwilym M. Jenkins, Gregory C. Reinsel, & Greta M. Ljung. (2015). Time series analysis: forecasting and control (5th ed.). John Wiley & Sons, Inc.
Campbell, B. M., Vermeulen, S. J., Aggarwal, P. K., Corner-Dolloff, C., Girvetz, E., Loboguerrero, A. M., Ramirez-Villegas, J., Rosenstock, T., Sebastian, L., Thornton, P. K., & Wollenberg, E. (2016). Reducing risks to food security from climate change. Global Food Security, 11, 34–43. https://doi.org/10.1016/j.gfs.2016.06.002
Connor, M., de Guia, A. H., Pustika, A. B., Sudarmaji, Kobarsih, M., & Hellin, J. (2021). Rice farming in Central Java, Indonesia: Adoption of sustainable farming practices, impacts and implications. Agronomy, 11(5), 881. https://doi.org/10.3390/agronomy11050881
Dresselhaus, T., & Hückelhoven, R. (2018). Biotic and abiotic stress responses in crop plants. Agronomy, 8(11), 267. https://doi.org/10.3390/agronomy8110267
Duangchaemkarn, K., Boonchieng, W., Wiwatanadate, P., & Chouvatut, V. (2022). SARIMA model forecasting performance of the covid-19 daily statistics in Thailand during the omicron variant epidemic. Healthcare, 10(7), 1310. https://doi.org/10.3390/healthcare10071310
El-Jardali, F., Fadlallah, R., Bou Karroum, L., & Akl, E. A. (2023). Evidence synthesis to policy: development and implementation of an impact-oriented approach from the Eastern Mediterranean Region. Health Research Policy and Systems, 21(1), 40. https://doi.org/10.1186/s12961-023-00989-5
Erdoğan, S., Nohekhan, A., Shokputov, A., & Sadabadi, K. F. (2024). Multi-criteria decision-making framework for determining public micro transit service zones. Transportation Research Record: Journal of the Transportation Research Board, 2678(6), 504–522. https://doi.org/10.1177/03611981231198464
Ferretti, V., Pluchinotta, I., & Tsoukiàs, A. (2019). Studying the generation of alternatives in public policy making processes. European Journal of Operational Research, 273(1), 353–363. https://doi.org/10.1016/j.ejor.2018.07.054
Fleischer, J., & Krieger, J. (2018). Insect pheromone receptors – key elements in sensing intraspecific chemical signals. Frontiers in Cellular Neuroscience, 12. https://doi.org/10.3389/fncel.2018.00425
González Guzmán, M., Cellini, F., Fotopoulos, V., Balestrini, R., & Arbona, V. (2022). New approaches to improve crop tolerance to biotic and abiotic stresses. Physiologia Plantarum, 174(1). https://doi.org/10.1111/ppl.13547
Gryboś, A., Staniszewska, P., Bryś, M. S., & Strachecka, A. (2025). The pheromone landscape of apis mellifera: Caste-determined chemical signals and their influence on social dynamics. Molecules, 30(11), 2369. https://doi.org/10.3390/molecules30112369
Guodaar, L., Bardsley, D. K., & Suh, J. (2021). Indigenous adaptation to climate change risks in northern Ghana. Climatic Change, 166(1–2), 24. https://doi.org/10.1007/s10584-021-03128-7
Habib-ur-Rahman, M., Ahmad, A., Raza, A., Hasnain, M. U., Alharby, H. F., Alzahrani, Y. M., Bamagoos, A. A., Hakeem, K. R., Ahmad, S., Nasim, W., Ali, S., Mansour, F., & EL Sabagh, A. (2022). Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.925548
Hämäläinen, R. P., Lahtinen, T. J., & Virtanen, K. (2024). Generating policy alternatives for decision making: A process model, behavioural issues, and an experiment. EURO Journal on Decision Processes, 12, 100050. https://doi.org/10.1016/j.ejdp.2024.100050
Hao, W., Yang, J., Hu, X., Zhang, Z., Shi, Z., & Zhou, H. (2024). Can fiscal expenditure for agriculture mitigate the impact of climate change on agricultural production? Frontiers in Sustainable Food Systems, 8. https://doi.org/10.3389/fsufs.2024.1349840
Hasegawa, T., Fujimori, S., Havlík, P., Valin, H., Bodirsky, B. L., Doelman, J. C., Fellmann, T., Kyle, P., Koopman, J. F. L., Lotze-Campen, H., Mason-D’Croz, D., Ochi, Y., Pérez Domínguez, I., Stehfest, E., Sulser, T. B., Tabeau, A., Takahashi, K., Takakura, J., van Meijl, H., … Witzke, P. (2018). Risk of increased food insecurity under stringent global climate change mitigation policy. Nature Climate Change, 8(8), 699–703. https://doi.org/10.1038/s41558-018-0230-x
Ilyés-Vincze, C., Leelőssy, Á., & Mészáros, R. (2025). ARIMAX modeling of hive weight dynamics using meteorological factors during robinia pseudoacacia blooming. Atmosphere, 16(8), 918. https://doi.org/10.3390/atmos16080918
Indonesian Meteorology, Climatology, and Geophysical Agency. (2024). Waspada! Pertanian jadi sektor paling terdampak perubahan iklim. 2003. Retrieved August 5, 2024, from https://www.bmkg.go.id/berita/?p=bmkg-waspada-pertanian-jadi-sektor-paling-terdampak-perubahan-iklim&lang=ID&tag=press-release
Johnson, R. E., Grove, A. L., & Clarke, A. (2019). Pillar integration process: A joint display technique to integrate data in mixed methods research. Journal of Mixed Methods Research, 13(3), 301–320. https://doi.org/10.1177/1558689817743108
Lozano, S., & Contreras, I. (2022). Centralised resource allocation using lexicographic goal programming: Application to the Spanish public university system. Socio-Economic Planning Sciences, 84, 101419. https://doi.org/10.1016/j.seps.2022.101419
Malhi, G. S., Kaur, M., & Kaushik, P. (2021). Impact of climate change on agriculture and its mitigation strategies: A review. Sustainability, 13(3), 1318. https://doi.org/10.3390/su13031318
Mandler, M. (2021). The lexicographic method in preference theory. Economic Theory, 71(2), 553–577. https://doi.org/10.1007/s00199-020-01256-2
Mertens, D. M., Bazeley, P., Bowleg, L., Fielding, N., Maxwell, J., Molina-Azorin, J. F., & Niglas, K. (2016). Expanding thinking through a kaleidoscopic look into the future. Journal of Mixed Methods Research, 10(3), 221–227. https://doi.org/10.1177/1558689816649719
Misiurek, K., Olkuski, T., & Zyśk, J. (2025). Review of methods and models for forecasting electricity consumption. Energies, 18(15), 4032. https://doi.org/10.3390/en18154032
Molina-Azorin, J. F. (2016). Mixed methods research: An opportunity to improve our studies and our research skills. European Journal of Management and Business Economics, 25(2), 37–38. https://doi.org/10.1016/j.redeen.2016.05.001
National Research and Innovation Agency. (2024). Produksi Tanaman Pangan Spasial dan Temporal pada Perubahan Iklim di Indonesia. https://brin.go.id/reviews/118564/produksi-tanaman-pangan-spasial-dan-temporal-pada-perubahan-iklim-di-indonesia
Perone, G. (2022). Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy. The European Journal of Health Economics, 23(6), 917–940. https://doi.org/10.1007/s10198-021-01347-4
Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., Browell, J., Carnevale, C., Castle, J. L., Cirillo, P., Clements, M. P., Cordeiro, C., Cyrino Oliveira, F. L., De Baets, S., Dokumentov, A., … Ziel, F. (2022). Forecasting: Theory and practice. International Journal of Forecasting, 38(3), 705–871. https://doi.org/10.1016/j.ijforecast.2021.11.001
Petropoulos, F., & Spiliotis, E. (2021). The wisdom of the data: Getting the most out of univariate time series forecasting. Forecasting, 3(3), 478–497. https://doi.org/10.3390/forecast3030029
Pluchinotta, I., Kazakçi, A. O., Giordano, R., & Tsoukiàs, A. (2019). Design theory for generating alternatives in public decision-making processes. Group Decision and Negotiation, 28(2), 341–375. https://doi.org/10.1007/s10726-018-09610-5
Ray, D. K., West, P. C., Clark, M., Gerber, J. S., Prishchepov, A. V., & Chatterjee, S. (2019). Climate change has likely already affected global food production. PLOS ONE, 14(5), e0217148. https://doi.org/10.1371/journal.pone.0217148
Rubio, L., Gutiérrez-Rodríguez, A. J., & Forero, M. G. (2021). EBITDA index prediction using exponential smoothing and ARIMA model. Mathematics, 9(20), 2538. https://doi.org/10.3390/math9202538
Safarzadeh, S., & Rasti-Barzoki, M. (2018). A modified lexicographic semi-order model using the best-worst method. Journal of Decision Systems, 27(2), 78–91. https://doi.org/10.1080/12460125.2018.1498046
Skendžić, S., Zovko, M., Živković, I. P., Lešić, V., & Lemić, D. (2021). The Impact of climate change on agricultural insect pests. Insects, 12(5), 440. https://doi.org/10.3390/insects12050440
Smyl, S. (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36(1), 75–85. https://doi.org/10.1016/j.ijforecast.2019.03.017
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Subarsono, A. G. (2022). Analisis kebijakan publik: Konsep, teori dan aplikasi. Pustaka Pelajar.
Statistics Indonesia. (2024). Analisis Pasar Tenaga Kerja Provinsi Nusa Tenggara Barat 2023.
Tan, B. T., Fam, P. S., Firdaus, R. B. R., Tan, M. L., & Gunaratne, M. S. (2021). Impact of climate change on rice yield in Malaysia: A panel data analysis. Agriculture, 11(6), 569. https://doi.org/10.3390/agriculture11060569
Usta, A. T., & Gök, M. Ş. (2024). Adaptation to climate change: state of art technologies. Kybernetes. https://doi.org/10.1108/K-11-2023-2517
Vaghefi, N., Shamsudin, M. N., Radam, A., & Rahim, K. A. (2016). Impact of climate change on food security in Malaysia: Economic and policy adjustments for rice industry. Journal of Integrative Environmental Sciences, 13(1), 19–35. https://doi.org/10.1080/1943815X.2015.1112292
West Nusa Tenggara Provincial Government. (2024). West Nusa Tenggara Provincial budget.
World Meteorological Organization. (2024). Agriculture and food security. https://wmo.int/site/frontline-of-climate-action/priorities/agriculture-and-food-security
Xie, S., Liu, H., Liu, D., Hu, H., Dong, Z., Wang, T., & Ming, G. (2023). Projection of rainfed rice yield using CMIP6 in the lower Lancang–Mekong River Basin. Agronomy, 13(6), 1504. https://doi.org/10.3390/agronomy13061504
Zhang, Z., Chen, Y.-H., & Tian, Y. (2023). Effect of agricultural fiscal expenditures on agricultural carbon intensity in China. Environmental Science and Pollution Research, 31(7), 10133–10147. https://doi.org/10.1007/s11356-023-25763-6
Zhao, M., & Boll, J. (2022). Adaptation of water resources management under climate change. Frontiers in Water, 4. https://doi.org/10.3389/frwa.2022.983228
Authors
Copyright (c) 2025 Indonesian Treasury Review: Jurnal Perbendaharaan, Keuangan Negara dan Kebijakan Publik

This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright notice can be accessed here