Data-Driven Assessment of Rice Yield Gaps in Rainfed Agriculture Using Predictive Modeling and Cluster Analysis
DOI:
https://doi.org/10.21831/elinvo.v11i1.95776Keywords:
Yield Gap, Rainfed Rice, Machine Learning, Efficiency Clustering, Agricultural PolicyAbstract
Optimizing agricultural productivity in marginal areas of Merauke Regency, South Papua Province, Indonesia, faces significant challenges due to a high yield gap and low input efficiency. This study proposes an innovative machine learning-based approach to evaluate and map the performance of upland rice farmer groups by using PFPL (Prospective Farmer Prospective Location) data, which only has previously been used administratively. By integrating a predictive model (Random Forest Regressor), success classification, and K-Means Clustering, this study builds an adaptive and replicable analytical framework to support Data-Driven agricultural decision-making. The analyzed dataset includes 30 farmer groups which containing technical information such as land area, seed use, pesticide use, and herbicide use, as well as actual and targeted yields. The feature engineering process yielded the input efficiency ratio as the primary variable. The Random Forest regression model achieved a near-perfect fit on the available dataset (R² = 0.95; RMSE = 0.41). However, given the limited sample size (30 farmer groups), the result should be interpreted cautiously and regarded as exploratory rather than conclusive. Cluster analysis revealed two segments: a high-input but inefficient group and an efficient group with very high yields. These results highlight that input quantity does not guarantee productivity without efficient use. This study not only expands the literature on agricultural intelligence but also offers a practical approach for policymakers to design efficiency-based interventions, incentives, and training. This approach is also relevant for accelerating digital transformation and food security in underdeveloped regions.
References
[1] U. Faoziyah, M. F. Rosyaridho, and R. Panggabean, “Unearthing Agricultural Land Use Dynamics in Indonesia: Between Food Security and Policy Interventions,” Land (Basel), vol. 13, no. 12, p. 2030, Nov. 2024, doi: 10.3390/land13122030.
[2] S. Wang et al., “Rice yield stability and its determinants across different rice-cropping systems in China,” Agric For Meteorol, vol. 364, p. 110452, Apr. 2025, doi: 10.1016/j.agrformet.2025.110452.
[3] A. El Mane, Y. Chihab, K. Tatane, and R. Korchiyne, “Agriculture Supply Chain Management Based on Blockchain Architecture and Smart Contracts,” Applied Computational Intelligence and Soft Computing, vol. 2022, pp. 1–23, Oct. 2022, doi: 10.1155/2022/8011525.
[4] S. Raodah, M. Nur, and M. S. Lestari, “Jurus Jitu Tingkatkan Produksi Padi Sawah Tadah Hujan,” KEMENTERIAN PERTANIAN RI, 2023.
[5] R. Raza, R. Director, I. Cossio, and C. Country, “Technical questions: International Fund for Agricultural Development-Executive Board Republic of Indonesia Country strategic opportunities programme,” Dec. 2023. [Online]. Available: www.ifad.org
[6] R. Tirtalistyani, M. Murtiningrum, and R. S. Kanwar, “Indonesia Rice Irrigation System: Time for Innovation,” Sustainability, vol. 14, no. 19, p. 12477, Sep. 2022, doi: 10.3390/su141912477.
[7] I. P. N. Damanik, M. E. Tahitu, and E. Kembauw, “Analysis of Farmer’s Group Functions in the Adoption Process of Superior Seed Innovation in Waimital Village, Kairatu District,” International Journal of Multidisciplinary Sciences and Arts, vol. 1, no. 2, pp. 140–146, Jan. 2023, doi: 10.47709/ijmdsa.v1i2.2017.
[8] S. M. Shawon, F. B. Ema, A. K. Mahi, F. L. Niha, and H. T. Zubair, “Crop yield prediction using machine learning: An extensive and systematic literature review,” Smart Agricultural Technology, vol. 10, p. 100718, Mar. 2025, doi: 10.1016/j.atech.2024.100718.
[9] R. Gupta, T. Shrikant Padmawar, D. Kumar, D. Ray, and P. Kadam, “Significance of Machine Learning in Crop Yield Prediction,” in 2024 2nd World Conference on Communication & Computing (WCONF), IEEE, Jul. 2024, pp. 1–7. doi: 10.1109/WCONF61366.2024.10692141.
[10] 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.
[11] K. Ajaykumar and S. Madhavi, “Review on Crop Yield Prediction with Deep Learning and Machine Learning Algorithms,” in 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, Sep. 2022, pp. 903–909. doi: 10.1109/ICIRCA54612.2022.9985016.
[12] P. Schatte and M. Meyer, “Assessing holistic agroecological resilience of agroecosystems from a landscape perspective: a systematic review,” Ecology and Society, vol. 30, no. 2, p. art24, 2025, doi: 10.5751/ES-16137-300224.
[13] 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.
[14] L. Fan, S. Fang, J. Fan, Y. Wang, L. Zhan, and Y. He, “Rice Yield Estimation Using Machine Learning and Feature Selection in Hilly and Mountainous Chongqing, China,” Agriculture, vol. 14, no. 9, p. 1615, Sep. 2024, doi: 10.3390/agriculture14091615.
[15] E. A. Jiya, U. Illiyasu, and M. Akinyemi, “Rice Yield Forecasting: A Comparative Analysis of Multiple Machine Learning Algorithms,” Journal of Information Systems and Informatics, vol. 5, no. 2, pp. 785–799, Jun. 2023, doi: 10.51519/journalisi.v5i2.506.
[16] T. Islam, T. Mazumder, Md. N. Shahriair Roni, and Md. S. Nur, “A comparative study of machine learning models for predicting Aman rice yields in Bangladesh,” Heliyon, vol. 10, no. 23, p. e40764, Dec. 2024, doi: 10.1016/j.heliyon.2024.e40764.
[17] P. Sharma, P. Dadheech, N. Aneja, and S. Aneja, “Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning,” IEEE Access, vol. 11, pp. 111255–111264, 2023, doi: 10.1109/ACCESS.2023.3321861.
[18] S. Iniyan, V. Akhil Varma, and C. Teja Naidu, “Crop yield prediction using machine learning techniques,” Advances in Engineering Software, vol. 175, p. 103326, Jan. 2023, doi: 10.1016/j.advengsoft.2022.103326.
[19] S. Sow et al., “Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies,” Phyton (B Aires), vol. 93, no. 7, pp. 1569–1598, 2024, doi: 10.32604/phyton.2024.052241.
[20] S. O. Araújo, R. S. Peres, J. C. Ramalho, F. Lidon, and J. Barata, “Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives,” Agronomy, vol. 13, no. 12, p. 2976, Dec. 2023, doi: 10.3390/agronomy13122976.
[21] B. Dey, J. Ferdous, and R. Ahmed, “Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables,” Heliyon, vol. 10, no. 3, p. e25112, Feb. 2024, doi: 10.1016/j.heliyon.2024.e25112.
[22] K. Singh, M. Yadav, D. Barak, S. Bansal, and F. Moreira, “Machine-Learning-Based Frameworks for Reliable and Sustainable Crop Forecasting,” Sustainability, vol. 17, no. 10, p. 4711, May 2025, doi: 10.3390/su17104711.
[23] G. Mohyuddin, M. A. Khan, A. Haseeb, S. Mahpara, M. Waseem, and A. M. Saleh, “Evaluation of Machine Learning Approaches for Precision Farming in Smart Agriculture System: A Comprehensive Review,” IEEE Access, vol. 12, pp. 60155–60184, 2024, doi: 10.1109/ACCESS.2024.3390581.
[24] K. P. Swain et al., “Empowering Crop Selection with Ensemble Learning and K-means Clustering: A Modern Agricultural Perspective,” Open Agric J, vol. 18, no. 1, Feb. 2024, doi: 10.2174/0118743315291367240207093403.
[25] A. J. Rivera, M. D. Pérez-Godoy, D. Elizondo, L. Deka, and M. J. del Jesus, “Analysis of clustering methods for crop type mapping using satellite imagery,” Neurocomputing, vol. 492, pp. 91–106, Jul. 2022, doi: 10.1016/j.neucom.2022.04.002.
[26] C. P. Osorio, F. Leucci, and D. Porrini, “Analyzing the Relationship between Agricultural AI Adoption and Government-Subsidized Insurance,” Agriculture, vol. 14, no. 10, p. 1804, Oct. 2024, doi: 10.3390/agriculture14101804.
[27] L. Hu, C. Zhang, M. Zhang, Y. Shi, J. Lu, and Z. Fang, “Enhancing FAIR Data Services in Agricultural Disaster: A Review,” Remote Sens (Basel), vol. 15, no. 8, p. 2024, Apr. 2023, doi: 10.3390/rs15082024.
[28] M. Waqas, A. Naseem, U. W. Humphries, P. T. Hlaing, P. Dechpichai, and A. Wangwongchai, “Applications of machine learning and deep learning in agriculture: A comprehensive review,” Green Technologies and Sustainability, vol. 3, no. 3, p. 100199, Jul. 2025, doi: 10.1016/j.grets.2025.100199.
[29] D. Tamayo-Vera, X. Wang, and M. Mesbah, “A Review of Machine Learning Techniques in Agroclimatic Studies,” Agriculture, vol. 14, no. 3, p. 481, Mar. 2024, doi: 10.3390/agriculture14030481.
[30] Y. Xing and X. Wang, “Precision Agriculture and Water Conservation Strategies for Sustainable Crop Production in Arid Regions,” Plants, vol. 13, no. 22, p. 3184, Nov. 2024, doi: 10.3390/plants13223184.
[31] L. Xiong and W. Wu, “Can additional agricultural resource inputs improve maize yield, resource use efficiencies and emergy based system efficiency under ridge-furrow with plastic film mulching?,” J Clean Prod, vol. 379, p. 134711, Dec. 2022, doi: 10.1016/j.jclepro.2022.134711.
[32] L. Sweet et al., “Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning,” One Earth, vol. 8, no. 4, p. 101233, Apr. 2025, doi: 10.1016/j.oneear.2025.101233.
[33] E. Grigorieva, A. Livenets, and E. Stelmakh, “Adaptation of Agriculture to Climate Change: A Scoping Review,” Climate, vol. 11, no. 10, p. 202, Oct. 2023, doi: 10.3390/cli11100202.
[34] A. M. Hiywotu, “Advancing sustainable agriculture for goal 2: zero hunger - a comprehensive overview of practices, policies, and technologies,” Agroecology and Sustainable Food Systems, pp. 1–29, Jan. 2025, doi: 10.1080/21683565.2025.2451344.
[35] A. J. Atapattu, C. S. Ranasinghe, T. D. Nuwarapaksha, S. S. Udumann, and N. S. Dissanayaka, “Sustainable Agriculture and Sustainable Development Goals (SDGs),” 2024, pp. 1–27. doi: 10.4018/979-8-3693-4864-2.ch001.
Downloads
Published
How to Cite
Issue
Section
Citation Check
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The article published in ELINVO became ELINVO's right in publication.
This work by ELINVO is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
