NUMERICAL SIMULATION OF THE EFFECT OF RAINFALL ON INCREASING CORN YIELDS TO SUPPORT FOOD SECURITY
Published:
2026-10-14Downloads
Abstract
Rainfall variability and climate change can increase uncertainty in corn productivity, particularly in rain-fed farming systems. This study aims to analyze the relationship between rainfall and corn productivity, develop a numerical simulation model, evaluate productivity responses to changes in rainfall, and examine how these findings can be used to support local food security. The study employed a quantitative approach based on rainfall data, corn productivity, and agronomic parameters across 10 harvest seasons at the Buah Page Dua Farmers’ Group in Rumah Sumbul Village. Rainfall data from the 90 days prior to harvest was used as the input variable, while corn productivity served as the output variable. The analysis was conducted using correlation, quadratic function modeling, calibration, Leave-One-Out Cross-Validation (LOOCV), and scenario simulations of rainfall changes ranging from −30% to +30%. The results show a relatively weak linear relationship between rainfall and productivity, with a Pearson correlation coefficient of 0.0696. The simulation model produced the equation Y(R) = 7.547011 − 0.0116913R + 0.0000199827R², with an MAE of 0.0950 metric tons/ha, an RMSE of 0.1108 metric tons/ha, and a MAPE of 1.60% during the calibration phase. LOOCV validation yielded an MAE of 0.1438 metric tons/ha, an RMSE of 0.1717 metric tons/ha, a MAPE of 2.43%, and an R² of −0.9743. The simulation showed a nonlinear response of productivity to changes in rainfall. The model can be used as a scenario simulation tool to support adaptation analysis and corn production planning based on local conditions, but it cannot yet be used as a universal prediction model. Future research needs to integrate more comprehensive agronomic and water balance parameters to improve the model’s ability to represent field conditions.
Keywords:
food security numerical simulation rainfall corn productivity mathematical modelingReferences
Chidzalo, P., Ngare, P. O., & Mung’Atu, J. K. (2022). Trivariate Stochastic Weather Model for Predicting Maize Yield. Journal of Applied Mathematics, 2022. https://doi.org/10.1155/2022/3633658
Disasa, K. N., Yan, H., Bao, R., Zhang, J., Zhang, C., Wang, B., & Wang, G. (2025). Modelling the impacts of future climate variable changes on rainfed maize production, evapotranspiration, and adaptation measures in Northeast China. Journal of Hydrology, 662, 134051. https://doi.org/https://doi.org/10.1016/j.jhydrol.2025.134051
Ferdiansyah, E. (2020). Model Simulasi PertumbuhanTanaman Jagung Manis Hibrida pada Jarak Tanam yang Berbeda ( Growth Simulation Model of Hybrid Sweet Corn Plant in the Different Plant Densities ). 25(3), 396–404. https://doi.org/10.18343/jipi.25.3.396
Fu, J., Wang, C., Qin, Y., Lesk, C., Müller, C., Zscheischler, J., Liu, X., Liang, H., Jian, Y., Wang, X., & Zhou, F. (2025). Regionally variable responses of maize and soybean yield to rainfall events in China. Agricultural and Forest Meteorology, 364, 110458. https://doi.org/https://doi.org/10.1016/j.agrformet.2025.110458
Haris Bahrun, A., & Saputra Ishak, M. (2023). Validasi dan Analisis Produksi Jagung (Zea mays L.) di Bawah Naungan Kelapa dengan Menggunakan Aplikasi DSSAT Validation and Analysis of Maize (Zea mays L.) Production under Coconut Shade Using DSSAT Application. In J. Agrivigor (Vol. 14, Number 2).
Irmak, S., Sandhu, R., & Kukal, M. S. (2022). Multi-model projections of trade-offs between irrigated and rainfed maize yields under changing climate and future emission scenarios. Agricultural Water Management, 261, 107344. https://doi.org/https://doi.org/10.1016/j.agwat.2021.107344
Ma, R., Cao, N., Li, Y., Hou, Y., Wang, Y., Zhang, Q., Wang, T., Cui, J., Li, B., Shi, W., & Zhang, Y. (2024). Rational reduction of planting density and enhancement of NUE were effective methods to mitigate maize yield loss due to excessive rainfall. European Journal of Agronomy, 160, 127326. https://doi.org/https://doi.org/10.1016/j.eja.2024.127326
Maitah, M., Malec, K., & Maitah, K. (2021). Influence of precipitation and temperature on maize production in the Czech Republic from 2002 to 2019. Scientific Reports, 1–11. https://doi.org/10.1038/s41598-021-89962-2
Ocwa, A., Harsanyi, E., Széles, A., Holb, I. J., Szabó, S., Rátonyi, T., & Mohammed, S. (2023). A bibliographic review of climate change and fertilization as the main drivers of maize yield: implications for food security. In Agriculture and Food Security (Vol. 12, Number 1). BioMed Central Ltd. https://doi.org/10.1186/s40066-023-00419-3
Qudriyah, R. A., Prasetyo, Y., Yusuf, M. A., Teknik, D., Universitas, G. T., Indonesia, J. T., Winong, K., Padi, F. T., & Padi, P. (2022). ANALISIS PENGARUH CURAH HUJAN TERHADAP ESTIMASI PRODUKTIVITAS PADI BERBASIS PEMROSESAN CITRA SENTINEL 2A PADA SUBROUND I DAN II TAHUN 2018-2021 ( STUDI KASUS : KECAMATAN WINONG , KABUPATEN PATI ). 05(01).
Rezaei, E. E., Webber, H., Asseng, S., Boote, K., Durand, J. L., Ewert, F., Martre, P., & MacCarthy, D. S. (2023). Climate change impacts on crop yields. Nature Reviews Earth & Environment, 4(12), 831–846. https://doi.org/10.1038/s43017-023-00491-0
Roem, R., Fiky Yulianto, W., & Grace Ananda, N. (2024). Rainfall and Temperature Change Analysis and their Correlation on Maize Productionin Karawang, West Java. Agromet, 38(2), 88–96. https://doi.org/10.29244/j.agromet.38.2.88-96
Sinaga, B., Yuhandri, & Nurcahyo, G. W. (2026). Development Of Cost-Sensitive Artificial Neural Network Optimization In Solving Imbalanced Multiclass Classification Problems. Eastern-European Journal of Enterprise Technologies, 2(9), 40–63. https://doi.org/10.15587/1729-4061.2026.356119
Sura, S. G., Ludong. D, P., & Wullur, h. (2024). Estimasi Kebutuhan Air Pada Tanaman Jagung (Zea mays L.)Untuk Perencanaan Penjadwalan Irigasi Berbasis CROPWAT 8.0 di Desa Paniki Atas Kecamatan Talawaan. Jurnal MIPA, 13(2), 88–93. https://doi.org/https://doi.org/10.35799/jm.v13i2.56445
Taremwa, D., Ahishakiye, E., Obbo, A., Kisozi, P. K., & Kaggwa, F. (2026). Climate-driven maize yield prediction across Uganda’s ZARDI zones using pooled learning and uncertainty-aware modeling. Discover Artificial Intelligence. https://doi.org/10.1007/s44163-026-01350-9
Wang, T., Li, N., Li, Y., Lin, H., Yao, N., Chen, X., Li Liu, D., Yu, Q., & Feng, H. (2022). Impact of climate variability on grain yields of spring and summer maize. Computers and Electronics in Agriculture, 199, 107101. https://doi.org/https://doi.org/10.1016/j.compag.2022.107101
White, K. E., Fleisher, D. H., Cavigelli, M. A., Timlin, D. J., & Schomberg, H. H. (2025). Assessing long-term weather variability impacts on annual grain yields using a maize simulation model. Agricultural and Forest Meteorology, 370, 110593. https://doi.org/https://doi.org/10.1016/j.agrformet.2025.110593
Xiang, Y., Cheng, R., Wang, M., & Ding, Y. (2024). Response of Matching Degree between Precipitation and Maize Water Requirement to Climate Change in China. Agronomy, 14(1). https://doi.org/10.3390/agronomy14010181
Zhang, L., Bai, Z., Xi, W., Zhang, D., Olesen, J. E., Harrison, M. T., Bernacchi, C., Makowski, D., McDonald, A. J., Shi, S., Liu, K., Rezaei, E. E., Zheng, Q., Shan, Y., Chen, F., & Yin, X. (2026). Cultivar evolution underpins maize yield sensitivity to adverse climate conditions. Nature Communications , 17(1). https://doi.org/10.1038/s41467-026-71045-3
Zhao, Z.-W., Leng, P., Han, X.-J., & Shang, G.-F. (2025). Global maize yield responses to essential climate variables: Assessment using atmospheric reanalysis and future climate scenarios. Computers and Electronics in Agriculture, 232, 110140. https://doi.org/https://doi.org/10.1016/j.compag.2025.110140
License
Copyright (c) 2026 Puteri Fajar Addini, Eviyanti Br Barus, Amran Sitohang, Angel Nurfadilah, Sri Atika Sitinjak
This work is licensed under a Creative Commons Attribution 4.0 International License.
