Mathematical Modeling for Climate-Based Optimization of Rice Planting Schedules
DOI:
https://doi.org/10.23960/jtepl.v14i6.2285-2296
Abstract View: 33
Keywords:
ARIMA, Climate change, Linear regression, Optimal planting schedule, Rice productionAbstract
The stability of rice production is greatly influenced by the dynamics of climate variability that changes rapidly and is unpredictable. This study developed a climate-based planting scheduling model that utilizes daily climate data and annual production data for the period 2016–2024. The predictive model was built through multiple linear regression to examine the effects of temperature, rainfall, humidity, and wind speed on crop yields and ARIMA to project climate and rice production until 2029. Data were obtained from BMKG, BPS, and related regional agencies, then processed to produce an adaptive planting schedule. The regression results showed high accuracy with R² = 0.99, Adjusted R² = 0.961, MAE = 5.980, and RMSE = 6.770. Rainfall showed a negative effect (p = 0.025) on rice production. The optimization model produced the two most profitable planting months each year and provided more stable yields than conventional planting patterns. Five-year production projections show fluctuations influenced by climate conditions, including a sharp decline in 2027 and a rebound in 2029. The development of an adaptive schedule model allows for alternative decision-making in areas vulnerable to climate change.
Downloads
References
Chen, Q., Zhang, J., & Zhang, L. (2015). Risk assessment, partition and economic loss estimation of rice production in China. Sustainability, 7(1), 563–583. https://doi.org/10.3390/su7010563
Dhaene, J., Kukush, A., Linders, D., & Tang, Q. (2012). Remarks on quantiles and distortion risk measures. European Actuarial Journal, 2(2), 319–328. https://doi.org/10.1007/s13385-012-0058-0
Ensafi, Y., Amin, S.H., Zhang, G., & Shah, B. (2022). Time-series forecasting of seasonal items sales using machine learning – A comparative analysis. International Journal of Information Management Data Insights, 2(1), 100058. https://doi.org/10.1016/j.jjimei.2022.100058
Ghoshal, P., & Goswami, B. (2017). Cobb-douglas production function for measuring efficiency in Indian Agriculture: A region-wise analysis. Economic Affairs, 62(4), 573. https://doi.org/10.5958/0976-4666.2017.00069.9
Herliana, S., Ratnaningtyas, S., Aina, Q., Zuraida, U., Sutardi, A., & Qorina, S. (2025). Comparison between food security in Indonesia and Thailand: Rice export and import. Journal of the Community Development in Asia, 8(1), 97–115. https://doi.org/10.32535/jcda.v8i1.3666
IRRI. (2014). Standard Evaluation System for Rice. (5th ed.). International Rice Research Institute, Los Baños.
Islam, T., Mazumder, T., Roni, M.N.S., & Nur, M.S. (2024). A comparative study of machine learning models for predicting Aman rice yields in Bangladesh. Heliyon, 10(23), e40764. https://doi.org/10.1016/j.heliyon.2024.e40764
Joseph, M., Moonsammy, S., Davis, H., Warner, D., Adams, A., & Oyedotun, T.D.T. (2023). Modelling climate variabilities and global rice production: A panel regression and time series analysis. Heliyon, 9(4), e15480. https://doi.org/10.1016/j.heliyon.2023.e15480
Joshi, R., Sahoo, K.K., Singh, A.K., Anwar, K., Pundir, P., Gautam, R.K., Krishnamurthy, S.L., Sopory, S.K., Pareek, A., & Singla-Pareek, S.L. (2020). Enhancing trehalose biosynthesis improves yield potential in marker-free transgenic rice under drought, saline, and sodic conditions. Journal of Experimental Botany, 71(2), 653–668. https://doi.org/10.1093/jxb/erz462
Joshi, R., Singh, B., & Shukla, A. (2018). Evaluation of elite rice genotypes for physiological and yield attributes under aerobic and irrigated conditions in tarai areas of western Himalayan region. Current Plant Biology, 13, 45–52. https://doi.org/10.1016/j.cpb.2018.05.001
Kaur, J., Parmar, K.S., & Singh, S. (2023). Autoregressive models in environmental forecasting time series: a theoretical and application review. Environmental Science and Pollution Research, 30(8), 19617–19641. https://doi.org/10.1007/s11356-023-25148-9
Langsdorf, S., Löschke, S., Möller, V., Okem, A., Officer, S., Rama, B., Belling, D., Dieck, W., Götze, S., Kersher, T., Mangele, P., Maus, B., Mühle, A., Nabiyeva, K., Nicolai, M., Niebuhr, A., Petzold, J., Prentzler, E., Savolainen, J., … Weyer, N. (2022). Technical Summary Frequently Asked Questions Part of the Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. www.environmentalgraphiti.org
Marvi, M.T., & Linders, D. (2021). Decomposition of natural catastrophe risks: Insurability using parametric cat bonds. Risks, 9(12), 215. https://doi.org/10.3390/risks9120215
Mol, J.M., Botzen, W.J.W., & Blasch, J.E. (2020). Risk reduction in compulsory disaster insurance: Experimental evidence on moral hazard and financial incentives. Journal of Behavioral and Experimental Economics, 84, 101500. https://doi.org/10.1016/j.socec.2019.101500
Norddin, N.I., Mohd Ali, M.R., Fadhilah, N.H., Atikah, N., Shahida, A., & Nohd Noh, N.H. (2019). Multiple linear regression model of rice production using conjugate gradient methods. MATEMATIKA, 35(2), 229–236. https://doi.org/10.11113/matematika.v35.n2.1180
Qin, H., Xiao, M., Li, Y., & Huang, R. (2024). Ethylene modulates rice root plasticity under abiotic stresses. Plants, 13(3), 432. https://doi.org/10.3390/plants13030432
Ran, Y., Chen, H., Ruan, D., Liu, H., Wang, S., Tang, X., & Wu, W. (2018). Identification of factors affecting rice yield gap in southwest China: An experimental study. PLoS ONE, 13(11). https://doi.org/10.1371/journal.pone.0206479
Riaman, Sukono, Supian, S., & Ismail, N. (2022). Mathematical modeling for estimating the risk of rice farmers’ losses due to weather changes. Computation, 10(8), 140. https://doi.org/10.3390/computation10080140
Rozi, F., Santoso, A.B., Mahendri, I G.A.P., Hutapea, R.T.P., Wamaer, D., Siagian, V., Elisabeth, D.A.A., Sugiono, S., Handoko, H., Subagio, H., & Syam, A. (2023). Indonesian market demand patterns for food commodity sources of carbohydrates in facing the global food crisis. Heliyon, 9(6), e16809. https://doi.org/10.1016/j.heliyon.2023.e16809
Salam, M., Rukka, R.M., Samma, M.A.N.K., Tenriawaru, A.N., Rahmadanih, Muslim, A.I., Ali, H.N.B., & Ridwan, M. (2024). The causal-effect model of input factor allocation on maize production: Using binary logistic regression in search for ways to be more productive. Journal of Agriculture and Food Research, 16, 101094. https://doi.org/10.1016/j.jafr.2024.101094
Sheehy, J.E., Mitchell, P.L., & Ferrer, A.B. (2006). Decline in rice grain yields with temperature: Models and correlations can give different estimates. Field Crops Research, 98(2–3), 151–156. https://doi.org/10.1016/j.fcr.2006.01.001
Tran, P.T., Vu, B.T., Ngo, S.T., Tran, V.D., & Ho, T.D.N. (2022). Climate change and livelihood vulnerability of the rice farmers in the North Central Region of Vietnam: A case study in Nghe An province, Vietnam. Environmental Challenges, 7, 100460. https://doi.org/10.1016/j.envc.2022.100460
Wang, X., Kang, Y., Hyndman, R.J., & Li, F. (2023). Distributed ARIMA models for ultra-long time series. International Journal of Forecasting, 39(3), 1163–1184. https://doi.org/10.1016/j.ijforecast.2022.05.001
Wang, Y., Wang, L., Zhou, J., Hu, S., Chen, H., Xiang, J., Zhang, Y., Zeng, Y., Shi, Q., Zhu, D., & Zhang, Y. (2019). Research progress on heat stress of rice at flowering stage. Rice Science, 26(1), 1–10. https://doi.org/10.1016/j.rsci.2018.06.009
Yulianis, N., Sarastuti, Risfaheri, & Rachman, B. (2021). The implementation and synergy of Indonesian national food reserves. IOP Conference Series: Earth and Environmental Science, 892, 012054. https://doi.org/10.1088/1755-1315/892/1/012054
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Masahid Masahid, Moh Yusuf Dawud, Eko Wahyu Abryandoko
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International Lice that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Jurnal Teknik Pertanian Lampung
JTEPL is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
