ANALYZING CLIMATE IMPACTS ON RICE PRODUCTION IN SUMATRA THROUGH SPATIOTEMPORAL MACHINE LEARNING MODELS
DOI:
https://doi.org/10.33480/jitk.v11i2.7344Keywords:
climate change , machine learning , spatiotemporal analysis , rice productionAbstract
Climate variability poses a major challenge to rice production in Sumatra, a key contributor to Indonesia’s food security. This study aims to analyze spatiotemporal climate impacts on rice yields by integrating climatic, geographical, and agricultural datasets. Historical records from 1993–2024, including rainfall, temperature, humidity, and rice production statistics, were collected from BMKG, BPS, and the Ministry of Agriculture. After preprocessing and feature selection, six machine learning algorithms—Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, Decision Tree, and K-Nearest Neighbors—were evaluated for predictive performance. Results show significant spatial heterogeneity: rainfall strongly affects yields in Aceh and North Sumatra, while temperature stress is critical in southern provinces. Among the tested models, Random Forest achieved the best accuracy (R² = 0.985), outperforming other algorithms. These findings highlight the importance of localized adaptation strategies and demonstrate the potential of ensemble machine learning to support climate-resilient rice production.
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H.-I. Lin, Y.-Y. Yu, F.-I. Wen, and P.-T. Liu, “Status of Food Security in East and Southeast Asia and Challenges of Climate Change,” Climate, vol. 10, no. 3, p. 40, Mar. 2022, doi: 10.3390/cli10030040.
S. E. Abebaw, “A Global Review of the Impacts of Climate Change and Variability on Agricultural Productivity and Farmers’ Adaptation Strategies,” Food Sci Nutr, vol. 13, no. 5, May 2025, doi: 10.1002/fsn3.70260.
M. Yamin, M. A. Rejo, and E. Mulyana, “Rice Farmers Facing Climate Change By Optimizing Technology Adoption Base On Different Land Typologies,” Agrisocionomics: Jurnal Sosial Ekonomi Pertanian, vol. 9, no. 1, pp. 160–178, Apr. 2025, doi: 10.14710/agrisocionomics.v9i1.22235.
I. Chatterjee et al., “Novel avenues of mitigation of rice paddy methane: a review,” J Crop Sci Biotechnol, vol. 28, no. 3, pp. 321–334, Jun. 2025, doi: 10.1007/s12892-025-00290-7.
A. Ansari et al., “Evaluating the effect of climate change on rice production in Indonesia using multimodelling approach,” Heliyon, vol. 9, no. 9, p. e19639, Sep. 2023, doi: 10.1016/j.heliyon.2023.e19639.
N. Uyar and A. Uyar, “Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning,” Atmosphere (Basel), vol. 16, no. 4, p. 418, Apr. 2025, doi: 10.3390/atmos16040418.
A. Nurcahyo, H. Soeparno, L. A. Wulandari, and W. Budiharto, “Rice Yield Prediction in Sumatra Indonesia Using Machine Learning and Climate Data,” in 2023 3rd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), IEEE, Dec. 2023, pp. 207–212. doi: 10.1109/ICICyTA60173.2023.10428960.
F. Ramadhani, Misnawati, and H. Syahbuddin, “An early investigation of spatial correlation between Sentinel-2 based rice growth stages maps with satellite-based precipitation data to support digital agriculture development in Indonesia,” IOP Conf Ser Earth Environ Sci, vol. 648, no. 1, p. 012002, Feb. 2021, doi: 10.1088/1755-1315/648/1/012002.
E. Palagi, M. Coronese, F. Lamperti, and A. Roventini, “Climate change and the nonlinear impact of precipitation anomalies on income inequality,” Proceedings of the National Academy of Sciences, vol. 119, no. 43, Oct. 2022, doi: 10.1073/pnas.2203595119.
L. Gandharum et al., “Advancing Land Use Modeling with Rice Cropping Intensity: A Geospatial Study on the Shrinking Paddy Fields in Indonesia,” Geographies, vol. 5, no. 3, p. 31, Jul. 2025, doi: 10.3390/geographies5030031.
C. Huang et al., “Synergistic Application of Multiple Machine Learning Algorithms and Hyperparameter Optimization Strategies for Net Ecosystem Productivity Prediction in Southeast Asia,” Remote Sens (Basel), vol. 16, no. 1, p. 17, Dec. 2023, doi: 10.3390/rs16010017.
A. W. Wijayanto et al., “Deep Learning and Remote Sensing for Agricultural Land Use Monitoring: A Spatio-Multitemporal Analysis of Rice Field Conversion using Optical Satellite Images,” International Journal of Advances in Data and Information Systems, vol. 6, no. 2, Jun. 2025, doi: 10.59395/ijadis.v6i2.1385.
L. Xu et al., “Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model,” Remote Sens (Basel), vol. 13, no. 19, p. 3994, Oct. 2021, doi: 10.3390/rs13193994.
U. H. Wannasingha, M. Waqas, S. Ahmad, A. Wangwongchai, and P. Dechpichai, “Quantification and prediction of the impact of ENSO on rainfed rice yields in Thailand,” Environmental Challenges, vol. 19, p. 101123, Jun. 2025, doi: 10.1016/j.envc.2025.101123.
H. B. Amat et al., “Value addition to forecasting: towards Kharif rice crop predictability through local climate variations associated with Indo-Pacific climate drivers,” Theor Appl Climatol, vol. 144, no. 3–4, pp. 917–929, May 2021, doi: 10.1007/s00704-021-03572-6.
A. Ansari et al., “Evaluating the effect of climate change on rice production in Indonesia using multimodelling approach,” Heliyon, vol. 9, no. 9, p. e19639, Sep. 2023, doi: 10.1016/j.heliyon.2023.e19639.
Md. A. Al Mamun, S. A. I. Nihad, M. R. Sarker, M. A. R. Sarkar, Md. I. Hossain, and Md. S. Kabir, “Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh,” PLoS One, vol. 19, no. 3, p. e0300648, Mar. 2024, doi: 10.1371/journal.pone.0300648.
A. Feng et al., “Developing an image processing pipeline to improve the position accuracy of single UAV images,” Comput Electron Agric, vol. 206, p. 107650, Mar. 2023, doi: 10.1016/j.compag.2023.107650.
K. Khechba, M. Belgiu, A. Laamrani, A. Stein, A. Amazirh, and A. Chehbouni, “The impact of spatiotemporal variability of environmental conditions on wheat yield forecasting using remote sensing data and machine learning,” International Journal of Applied Earth Observation and Geoinformation, vol. 136, p. 104367, Feb. 2025, doi: 10.1016/j.jag.2025.104367.
L. Li, H. Tang, J. Lei, and X. Song, “Spatial autocorrelation in land use type and ecosystem service value in Hainan Tropical Rain Forest National Park,” Ecol Indic, vol. 137, p. 108727, Apr. 2022, doi: 10.1016/j.ecolind.2022.108727.
L. Li, H. Tang, J. Lei, and X. Song, “Spatial autocorrelation in land use type and ecosystem service value in Hainan Tropical Rain Forest National Park,” Ecol Indic, vol. 137, p. 108727, Apr. 2022, doi: 10.1016/j.ecolind.2022.108727.
H. Kamangir, B. S. Sams, N. Dokoozlian, L. Sanchez, and J. M. Earles, “Large-scale spatio-temporal yield estimation via deep learning using satellite and management data fusion in vineyards,” Comput Electron Agric, vol. 216, p. 108439, Jan. 2024, doi: 10.1016/j.compag.2023.108439.
Y. Apriyana et al., “The Integrated Cropping Calendar Information System: A Coping Mechanism to Climate Variability for Sustainable Agriculture in Indonesia,” Sustainability, vol. 13, no. 11, p. 6495, Jun. 2021, doi: 10.3390/su13116495.
M. Waqas, A. Naseem, U. W. Humphries, P. T. Hlaing, M. Shoaib, and S. Hashim, “A comprehensive review of the impacts of climate change on agriculture in Thailand,” Farming System, vol. 3, no. 1, p. 100114, Jan. 2025, doi: 10.1016/j.farsys.2024.100114.
Md. A. Jabed and M. A. Azmi Murad, “Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability,” Heliyon, vol. 10, no. 24, p. e40836, Dec. 2024, doi: 10.1016/j.heliyon.2024.e40836.
J. Botero-Valencia et al., “Machine Learning in Sustainable Agriculture: Systematic Review and Research Perspectives,” Agriculture, vol. 15, no. 4, p. 377, Feb. 2025, doi: 10.3390/agriculture15040377.
A. Ansari et al., “Evaluating the effect of climate change on rice production in Indonesia using multimodelling approach,” Heliyon, vol. 9, no. 9, p. e19639, Sep. 2023, doi: 10.1016/j.heliyon.2023.e19639.
C. Ferman Carral, M. Bockarjova, M. van den Homberg, F. Osei, and N. Kerle, “Spatial econometric modeling of socioeconomic vulnerability and flood impact: Towards a risk-layering approach in southern Malawi,” International Journal of Disaster Risk Reduction, vol. 121, p. 105433, Apr. 2025, doi: 10.1016/j.ijdrr.2025.105433.
F. Noroozi, G. Ghanbarian, R. Safaeian, and H. R. Pourghasemi, “Forest fire mapping: a comparison between GIS-based random forest and Bayesian models,” Natural Hazards, vol. 120, no. 7, pp. 6569–6592, May 2024, doi: 10.1007/s11069-024-06457-9.
A. Alazeb et al., “A robust deep learning model for fall action detection using healthcare wearable sensors,” PeerJ Comput Sci, vol. 11, p. e3300, Nov. 2025, doi: 10.7717/peerj-cs.3300.
J. Suen, R. L. Fernando, M. C. Inacio, M. Crotty, X. Lin, and G. E. Caughey, “Identification of Quality Indicators Used to Monitor, Evaluate and Improve Rural and Remote Care for Older People: A Scoping Review,” Australian Journal of Rural Health, vol. 33, no. 6, Dec. 2025, doi: 10.1111/ajr.70105.
I. P. Malashin, D. Martysyuk, V. Nelyub, A. Borodulin, A. Gantimurov, and V. Tynchenko, “A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites,” Advanced Manufacturing: Polymer & Composites Science, vol. 11, no. 1, Dec. 2025, doi: 10.1080/20550340.2025.2547335.
N. Štefelová, A. Alfons, J. Palarea-Albaladejo, P. Filzmoser, and K. Hron, “Robust regression with compositional covariates including cellwise outliers,” Adv Data Anal Classif, vol. 15, no. 4, pp. 869–909, Dec. 2021, doi: 10.1007/s11634-021-00436-9.
A. Y. Muaad et al., “Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques,” Math Probl Eng, vol. 2022, pp. 1–16, Apr. 2022, doi: 10.1155/2022/3720358.
M. M. Nishat, A. Ahsan, and N. O. E. Olsson, “Applying Machine Learning for Predictive Analysis in Project-Based Data: Insights into Variation Orders,” Journal of Information Technology in Construction, vol. 30, pp. 807–825, May 2025, doi: 10.36680/j.itcon.2025.033.
N. Pudjihartono, T. Fadason, A. W. Kempa-Liehr, and J. M. O’Sullivan, “A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction,” Frontiers in Bioinformatics, vol. 2, Jun. 2022, doi: 10.3389/fbinf.2022.927312.
M. A. K. Raiaan et al., “A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks,” Decision Analytics Journal, vol. 11, p. 100470, Jun. 2024, doi: 10.1016/j.dajour.2024.100470.
S. V. Panwar and S. Singh, “A Review on Crop Yield Prediction using Deep Learning,” in 2024 8th International Conference on Inventive Systems and Control (ICISC), IEEE, Jul. 2024, pp. 106–111. doi: 10.1109/ICISC62624.2024.00025.
L. Benos, A. C. Tagarakis, G. Dolias, R. Berruto, D. Kateris, and D. Bochtis, “Machine Learning in Agriculture: A Comprehensive Updated Review,” Sensors, vol. 21, no. 11, p. 3758, May 2021, doi: 10.3390/s21113758.
K. Khosravi et al., “Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models,” Ecol Inform, vol. 85, p. 102933, Mar. 2025, doi: 10.1016/j.ecoinf.2024.102933.
P. D. Hart, “Using Statistical Machine Learning to Find Complex Interactions and Important CVD Risk Factors When Predicting General Health in Adults,” Am J Public Health Res, vol. 13, no. 3, pp. 90–102, May 2025, doi: 10.12691/ajphr-13-3-1.
G. Airlangga and A. Liu, “A Hybrid Gradient Boosting and Neural Network Model for Predicting Urban Happiness: Integrating Ensemble Learning with Deep Representation for Enhanced Accuracy,” Mach Learn Knowl Extr, vol. 7, no. 1, p. 4, Jan. 2025, doi: 10.3390/make7010004.
Aviva Pradasyah and A. Baita, “Comparative Study of Support Vector Regression and Long Short-Term Memory for Stock Price Prediction,” Journal of Applied Informatics and Computing, vol. 9, no. 4, pp. 1301–1311, Aug. 2025, doi: 10.30871/jaic.v9i4.9425.
G. Luo, M. A. Arshad, and G. Luo, “Decision Trees for Strategic Choice of Augmenting Management Intuition with Machine Learning,” Symmetry (Basel), vol. 17, no. 7, p. 976, Jun. 2025, doi: 10.3390/sym17070976.
J. Wang, Z. Zhou, Z. Li, and S. Du, “A Novel Fault Detection Scheme Based on Mutual k-Nearest Neighbor Method: Application on the Industrial Processes with Outliers,” Processes, vol. 10, no. 3, p. 497, Mar. 2022, doi: 10.3390/pr10030497.
E. Ozturk Kiyak, B. Ghasemkhani, and D. Birant, “High-Level K-Nearest Neighbors (HLKNN): A Supervised Machine Learning Model for Classification Analysis,” Electronics (Basel), vol. 12, no. 18, p. 3828, Sep. 2023, doi: 10.3390/electronics12183828.
S. Roy and S. Bagchi, “Decoupling Between Functional Diversity and Stability of Decomposer Functions in Natural and Agroecosystems Can Favor Resistance to Land‐Use Change,” Ecol Evol, vol. 15, no. 9, Sep. 2025, doi: 10.1002/ece3.72190.
M. Yoo and H. Koo, “Exploring the impact of spatial autocorrelation on optimistic bias in cross-validation and assessing the effectiveness of spatial cross-validation,” Cartogr Geogr Inf Sci, vol. 52, no. 5, pp. 596–609, Sep. 2025, doi: 10.1080/15230406.2024.2422593.
A. W. Putra, J. Supriatna, R. H. Koestoer, and T. E. B. Soesilo, “Differences in Local Rice Price Volatility, Climate, and Macroeconomic Determinants in the Indonesian Market,” Sustainability, vol. 13, no. 8, p. 4465, Apr. 2021, doi: 10.3390/su13084465.
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Copyright (c) 2025 Zaqi Kurniawan, Rizka Tiaharyadini, Windhy widhyanty , Puguh Jayadi, Windhy widhyanty
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