ECONOMIC VALUATION OF RICE FIELD LAND AFFECTED BY FLOODS
Keywords:
Agricultural Flood Mitigation, Geographic Information System (GIS)Abstract
Indonesia's agricultural sector faces significant challenges, with a declining production trend and a shift from "Indonesia as a rice granary" to "national food production capacity under pressure." Annual losses to the agricultural sector due to flooding in Indonesia are estimated at IDR 12.3 trillion, with 60% of these occurring in rice paddies. The 2023 National Disaster Management Agency (BNPB) report further underscores the scale of this problem, with losses reaching IDR 412 billion and more than 48,000 hectares of land experiencing crop failure due to flooding at the start of the planting season. This study aims to analyze and map flood-prone areas in Sebakung Taka Village, Long Kali District, Paser Regency using Geographic Information System (GIS) spatial analysis and calculate the valuation of economic losses in flood-affected rice paddies using a dual approach: Loss Production and Willingness to Pay (WTP). This study uses primary data from questionnaires, interviews, and focus group discussions with farmers and stakeholders, as well as secondary data from various agencies, and quantitative data from spatial, rainfall, and demographic data, to analyze agricultural production statistics and WTP responses. Econometric analysis results show that 69.8% of the variation in willingness to pay for mitigation is influenced by variables such as occupation, family size, land price, land area, and distance from rice fields to rivers. Farmers with additional employment, larger land areas, higher economic value, and land closer to rivers tend to have a higher willingness to pay for flood mitigation.
References
Abbas, A., Amjath-Babu, T.S., Kächele, H., & Müller, K. (2015). Non-Structural Flood Risk Mitigation Under Developing Country Conditions: An Analysis On The Determinants Of Willingness To Pay For Flood Insurance In Rural Pakistan. Natural Hazards , 75 (3), 2119–2135. https://doi.org/10.1007/s11069-014-1415-x
Aprillya, MR, & Chasanah, U. (2021). Analysis of Flood-Prone Agricultural Land Using the Multi-Attribute Utility Theory Method Based on Geographic Information Systems. Mulawarman Informatics: Scientific Journal of Computer Science , 16 (2), 148. https://doi.org/10.30872/jim.v16i2.6554
Barman, S., & Neog, P. K. (2024). Farmers’ Willingness to Pay for Climate Smart Agriculture in Flood Vulnerable Areas of Assam. Indian Journal of Extension Education, 60(4), 13–18. https://doi.org/10.48165/IJEE.2024.60403
Gabriels, K., Willems, P., & Van Orshoven, J. (2022). A Comparative Flood Damage And Risk Impact Assessment Of Land Use Changes. Natural Hazards and Earth System Sciences, 22(2), 395–410. https://doi.org/10.5194/nhess-22-395-2022
Ibrahim, S., & Balzter, H. (2024). Evaluating Flood Damage to Paddy Rice Fields Using PlanetScope and Sentinel-1 Data in North-Western Nigeria: Towards Potential Climate Adaptation Strategies. Remote Sensing, 16(19). https://doi.org/10.3390/rs16193657
Isa, M., & Mardalis, A. (2022). Flood vulnerability and economic valuation of small and medium-sized enterprise owners to enhance sustainability. Jamba: Journal of Disaster Risk Studies, 14(1), 1–7. https://doi.org/10.4102/JAMBA.V14I1.1306
Kaharuddin et al., 2020. (2020). Unveiling And Modelling The Flood Risk And Multidimensional Poverty Determinants Using Geospatial Multi-Criteria Approach: Evidence From Jigawa, Nigeria. Preprint, 11(Cicc), 76–84.
Komolafe, A. A., Awe, B. S., Olorunfemi, I. E., & Oguntunde, P. G. (2020). Modelling Flood-Prone Area And Vulnerability Using Integration Of Multi-Criteria Analysis And HAND Model In The Ogun River Basin, Nigeria. Hydrological Sciences Journal, 65(10), 1766–1783. https://doi.org/10.1080/02626667.2020.1764960
Kong, F., Xiong, K., & Zhang, N. (2014). Determinants Of Farmers' Willingness To Pay And Its Level For Ecological Compensation Of Poyang Lake Wetland, China: A Household-Level Survey. Sustainability (Switzerland) , 6 (10), 6714–6728. https://doi.org/10.3390/su6106714
Kusumo, P., & Nursari, E. (2016). Flood Vulnerability Zoning Using Geographic Information Systems in the Cidurian Watershed, Serang Regency, Banten. STRING (Research and Technology Innovation Writing Unit) , 1 (1), 29–38. https://doi.org/10.30998/string.v1i1.966
Kwak, J., Kim, J., Lee, H., Kim, S., Kim, S., & Kang, M. S. (2024). Evaluation of future flood probability in agricultural reservoir watersheds using an integrated flood simulation system. Journal of Hydrology, 628(November 2023), 130463. https://doi.org/10.1016/j.jhydrol.2023.130463
MUHAMAD, D. S. S. (2015). SIG untuk memetakan daerah banjir dengan metode skoring dan pembobotan (studi kasus kabupaten Jepara). Skripsi, Fakultas Ilmu Komputer.
Mulu, A., Kassa, S. B., Wossene, M. L., Adefris, S., & Meshesha, T. M. (2025). Identification Of Flood Vulnerability Areas Using Analytical Hierarchy Process Techniques In The Wuseta Watershed, Upper Blue Nile Basin, Ethiopia. Scientific Reports, 15(1), 28680.
Nahin, K. T. K., Islam, S. B., Mahmud, S., & Hossain, I. (2023). Flood Vulnerability Assessment In The Jamuna River Floodplain Using Multi-Criteria Decision Analysis: A Case Study In Jamalpur District, Bangladesh. Heliyon, 9(3), e14520. https://doi.org/10.1016/j.heliyon.2023.e14520
Nugroho, S. F., Bestari, A. H., Nurkhalifah, A., Restuaji, Y., & Sekaranom, A. B. (2023). Flood Vulnerability Analysis on Paddy Fields Using the Spatial Multi-criteria Evaluation Method: A Case Study of Bantul Regency-Indonesia. In Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022) (Vol. 2015, pp. 113–123). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-122-7_11
Osawa, T., Nishida, T., & Oka, T. (2021). Potential Of Mitigating Floodwater Damage To Residential Areas Using Paddy Fields In Water Storage Zones. International Journal of Disaster Risk Reduction, 62(June). https://doi.org/10.1016/j.ijdrr.2021.102410
Osawa, T., Nishida, T., & Oka, T. (2025). Evaluating the impact of agricultural abandonment on flood mitigation functions. Scientific Reports, 15(1), 2–5. https://doi.org/10.1038/s41598-025-04419-0
Pratiwi, E. P. A., Ramadhani, E. L., Nurrochmad, F., & Legono, D. (2020). The Impacts of Flood and Drought on Food Security in Central Java. Journal of the Civil Engineering Forum, 6(1), 69. https://doi.org/10.22146/jcef.51872
Purwanto, A., Andrasmoro, D., & Eviliyanto. (2024). Flood Vulnerability Analysis Based on Gis and Remote Sensing at Silat Hulu. Indonesian Journal of Geography, 56(2), 264–273. https://doi.org/10.22146/ijg.91114
Rahmi, R., Ahmad, A., Yulianur, A., Ramli, I., & Izzaty, A. (2024). Spatial Analysis of Flood Vulnerability Base on Biophysics Factor the Krueng Baro Watershed in Flood Mitigation Efforts at Aceh, Indonesia. BIO Web of Conferences, 96. https://doi.org/10.1051/bioconf/20249604002
Rakuasa, H. (2023). Spatial Modeling of Flood Prone Areas in Huamual Sub-district of Seram, Western Regency of Indonesia. Journal of Geographical Sciences and Education , 1 (2), 47–57. https://doi.org/10.69606/geography.v1i2.70
Rifani, H., & Soeryamassoeka, SBK (2023). Mapping of Flood Vulnerability Levels Based on Geographic Information Systems in Jeruju Besar Village, Sungai Kakap District. JeLAST: Journal of Marine Engineering, PWK, Civil Engineering, and Mining , 10 (4), 1–9.
Saptutyningsih, E., Akhtar, R., Setyawati Dewanti, D., & Anggoro, T. (2024). Climate Change and Agriculture: An Economic Valuation of Flood Risk Mitigation. E3S Web of Conferences , 595 , 1–11. https://doi.org/10.1051/e3sconf/202459501041
Sebastian, L. (2008). Flood prevention and mitigation approaches. Journal of Civil Engineering Dynamics , 8 (2), 162–169.
Sigit, A., & Harada, M. (2024). Land Cover and Socioeconomic Analysis for Recommended Flood Risk Reduction Strategies in Java Island, Indonesia. Sustainability (Switzerland) , 16 (15). https://doi.org/10.3390/su16156475
Singha, C., Chakraborty, N., Sahoo, S., Pham, Q. B., & Xuan, Y. (2025). A Novel Framework For Flood Susceptibility Assessment Using Hybrid Analytic Hierarchy Process-Based Machine Learning Methods. In Natural Hazards (Vol. 121, Issue 11). Springer Netherlands. https://doi.org/10.1007/s11069-025-07335-8
Sugiyono, D. (2013). Metode penelitian pendidikan pendekatan kuantitatif, kualitatif dan R&D.
the World Bank Group. (2021). Climate Risk Country Profile. In Climate Risk Country Profile. https://doi.org/10.1596/36382
van Rutten, P., Benito Lazaro, I., Muis, S., Teklesadik, A., & van den Homberg, M. (2025). Flood and Rice Damage Mapping for Tropical Storm Talas in Vietnam Using Sentinel-1 SAR Data. Remote Sensing, 17(13), 1–12. https://doi.org/10.3390/rs17132171
Vavadaki, E. (2020). Exploring Farmers ’ Willingness To Pay For Index-Based Insurance In Nepal. YSSP Report Young Scientists Summer Program Exploring.
Wiraatmaja, M. F., Kusumaningrum, L., Herdiansyah, G., Mujiyo, M., Anggita, A., Romadhon, M. R., & Irmawati, V. (2024). Flood Vulnerability Assessment Trough Overlay-Scoring Data Method Based On Geographical Information System (GIS) In Giriwoyo, Wonogiri, Indonesia. IOP Conference Series: Earth and Environmental Science, 1314(1). https://doi.org/10.1088/1755-1315/1314/1/012109
Yosua, H., Kusuma, M. S. B., & Nugroho, J. (2023). An Assessment Of Pluvial Hazard In South Jakarta Based On Land-Use/Cover Change From 2016 To 2022. Frontiers in Built Environment, 9(January), 1–8. https://doi.org/10.3389/fbuil.2023.1345894
Zhang, X., Luo, Z., Wang, Y., Yi, S., Zhang, W., & Wu, Z. (2020). Factors Influencing Farmers’ Willingness to Pay for Weather Index Insurance through Fuzzy-Set Qualitative Comparative Analysis: Insights from a Pilot in Jiangxi Province, China. Frontiers in Artificial Intelligence and Applications, 329, 83–97. https://doi.org/10.3233/FAIA200643
