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Sharifi, S., Asakereh, A., Kiani Deh Kiani, M., Janatrostami, S. (2025). Analysis and Comparison of Energy and Yield Modeling of Local and High-Yielding Rice Cultivars Using the Multi-Gene Genetic Programming Model. , (), -. doi: 10.22067/jam.2025.92838.1364
S. Sharifi; A. Asakereh; M. Kiani Deh Kiani; S. Janatrostami. "Analysis and Comparison of Energy and Yield Modeling of Local and High-Yielding Rice Cultivars Using the Multi-Gene Genetic Programming Model". , , , 2025, -. doi: 10.22067/jam.2025.92838.1364
Sharifi, S., Asakereh, A., Kiani Deh Kiani, M., Janatrostami, S. (2025). 'Analysis and Comparison of Energy and Yield Modeling of Local and High-Yielding Rice Cultivars Using the Multi-Gene Genetic Programming Model', , (), pp. -. doi: 10.22067/jam.2025.92838.1364
Sharifi, S., Asakereh, A., Kiani Deh Kiani, M., Janatrostami, S. Analysis and Comparison of Energy and Yield Modeling of Local and High-Yielding Rice Cultivars Using the Multi-Gene Genetic Programming Model. , 2025; (): -. doi: 10.22067/jam.2025.92838.1364

Analysis and Comparison of Energy and Yield Modeling of Local and High-Yielding Rice Cultivars Using the Multi-Gene Genetic Programming Model

ماشین های کشاورزی
Articles in Press, Accepted Manuscript, Available Online from 20 August 2025    PDF (1.03 M)
Document Type: Research Article
DOI: 10.22067/jam.2025.92838.1364
Authors
S. Sharifi1; A. Asakereh* 1; M. Kiani Deh Kiani1; S. Janatrostami2
1Department of Biosystems Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
Abstract
Introduction
Global challenges threaten food security. In Iran, rice is a staple, with 770,000 ha under cultivation and an annual production of 3.75 million tonnes. The northen provinces of Guilan and Mazandaran dominant rice production, with Guilan only supplying about 50% of the nation’s demand. Advanced computational techniques such as metaheuristic algorithms and artificial intelligence offer powerful tools for problem-solving and modeling inspired by the adaptability of living organisms', and are increasingly applied to support agricultural management and sustainability. However, multi-gene genetic programming (MGP) has not yet been used for rice yield modeling in Iran. This study addresses that gap by evaluating the effectiveness of MGP and exploring its potential to enhance agricultural decision-making.
Materials and Methods
This research examines the yield performance of local rice cultivars, namely Hashemi and Ali Kazemi alongside high-yielding cultivars, including Fajr and Shiroudi in Rasht County, Iran. It utilizes library documents, face-to-face interviews, and MGP analysis. Data were collected from 385 randomly selected farmers and landowners in the region, during the 2020-2024 rice production years. Inputs from the energy sector, including fuel and electricity, water pumping equipment, agricultural machinery, fertilizers, pesticides, organic materials, and the energy output of paddy production, were examined. Energy equivalents were used to convert various types of energy into a common unit. To predict the yield of the two types of paddy cultivars using MGP, the structure of MGP trees was first designed with two objectives: maintaining the model accuracy and avoiding structural complexity. Parameter setting include population, generation and tournament sizes, gene limits, tree depth and size, and probabilities for elitism, crossover, and mutation. Additionally, various mathematical functions were utilized.
Results and Discussion
In examining total energy consumption and production in local and high-yielding cultivar farms, the results indicated a significant difference between the two varieties. In farms producing local varieties, the average energy consumption was 41,081.4 MJ, while the average energy production in these farms reached 23,771.9 MJ. In contrast, the average energy consumption in high-yielding cultivar farms was estimated at 41,118.8 MJ, whereas the average energy production in these farms was 42,220.04 MJ. The energy ratio, energy productivity, and specific energy indices for high-yielding varieties were 76.47%, 76.92%, and 77.70% higher, respectively, compared to local varieties, with the net energy gain index showing an improvement of more than 15 times. The higher energy ratio and energy productivity, along with lower specific energy and net energy gain, indicate that the high-yielding cultivar is more energy-efficient in terms of energy consumption. The MGP model converged after 100 iterations, providing the optimal solution. Changes in the best and mean fitness values indicated that as the iterations increased, the error gradually decreased and eventually stabilized, reflecting continuous improvement of the model during the training process and parameter tuning. Through cross-validation with varying training data set sizes, the findings revealed that the MGP model, when utilizing 65% of the total dataset, generated results that were remarkably similar to those achieved with 80% of the data. Hence, 65% is established as the optimal proportion for the training dataset. The coefficient of determination (R2) for the regression line in the training data set was higher than in the test phase for both varieties. In evaluating the MGP equations to assess the accuracy of the proposed model, the tree depth was increased from 4 to 12. For the local cultivar, the highest coefficient of determination (R2) at a tree depth of 4 was 0.95, while for the high-yielding cultivar, it was 0.94. The simpler structure at depth 4 resulted in a simpler mathematical equation. Finally, the effects of independent variables on the dependent variable, paddy yield, were examined. It was found that organic materials, such as compost, seed, rice straw and husks, were the most significant factors influencing the estimation of paddy yield in both varieties.
Conclusion
Since modeling focuses on predicting crop yield and making data-driven scientific and practical decisions, the results of this study represent an important step toward advancing sustainable agriculture. It is recommended that farmers seek up to date insights from consultants and participate in workshops to increase their sustainable yields.
Keywords
Energy indices; Guilan; Rice; Simulation
References
  1. Aghkhani, M. H., Ahmadipour, S., Soltanali, H., & Rohani, A. (2018). Greenhouse gas emissions, energy use, and cost analysis of citrus production: A case study of Mazandaran Province. Quarterly Journal of Energy Policy and Planning Research, 4(3), 181-229. (in Persian with English abstract).
  2. Ali, M., & Deo, R. C. (2020). Modeling wheat yield with data-intelligent algorithms: Artificial neural network versus genetic programming and minimax probability machine regression. In Handbook of Probabilistic Models. https://doi.org/10.1016/B978-0-12-816514-0.00002-3
  3. Ali, M., Deo, R. C., Downs, N. J., & Maraseni, T. (2018). Cotton yield prediction with Markov Chain Monte Carlo-based simulation model integrated with genetic programming algorithm: A new hybrid copula-driven approach. Agricultural and Forest Meteorology, 263, 428-448. https://doi.org/10.1016/j.agrformet.2018.09.002
  4. Almassi, M., Kiani, S., & Loveimi, N. (2014). Principles of Agricultural Mechanization (5th ed.). Gofteman Andisheh Moaser.
  5. Ayoubikia, R., Janatrostami, S., Ashrafzadeh., A., Shafiei-Sabet, (2019). Optimization of regional water resources allocation in Sefidroud river basin by social equity approach. Iran-Water Resources Research, 14(5), 205-218. (in Persian with English abstract).
  6. Babaee, M., Maroufpoor, S., Jalali, M., Zarei, M., & Elbeltagi, A. (2021). Artificial intelligence approach to estimating rice yield. Irrigation and Drainage, 70(4), 732-742. https://doi.org/10.1002/ird.2566
  7. Bagheri, S. M., Gheysari, S., Ayoubi, N., & Lavaee. (2013). Silage maize yield prediction using artificial neural networks. Journal of Plant Production Research, 19(14), 77-96. (in Persian with English abstract).
  8. Barikloo, A., Alamdari, P., Moravej, K., & Servati, M. (2017). Prediction of irrigated wheat yield by using hybrid algorithm methods of artificial neural networks and genetic algorithm. Journal of Water and Soil, 31(3), 715-726. (in Persian with English abstract). https://doi.org/10.22067/jsw.v31i3.56158
  9. Chauhan, N. S., Mohapatra, P. K. J., & Pandey, K. P. (2006). Improving energy productivity in paddy production through benchmarking: An application of data envelopment analysis. Energy Conversion and Management, 47, 1063-1085. https://doi.org/10.1016/j.enconman.2005.07.004
  10. Cochran, W. G. (1977). Sampling Techniques. New York: John Wiley and Sons Publishing.
  11. Gu, J., & Yang, J. (2022). Nitrogen (N) transformation in paddy rice field: Its effect on N uptake and relation to improved N management. Crop and Environment, 1(1), 7-14. https://doi.org/10.1016/j.crope.2022.03.003
  12. Gummert, M., Cabardo, C., Quilloy, R., Aung, Y. L., Thant, A. M., Kyaw, M. A., Labios, R., Htwe, N. M., & Singleton, G. R. (2020). Assessment of post-harvest losses and carbon footprint in intensive lowland rice production in Myanmar. Scientific Reports, 10(1), 1-13. https://doi.org/10.1038/s41598-020-76639-5
  13. Guo, Y. (2024). Integrating genetic algorithm with ARIMA and reinforced random forest models to improve agriculture economy and yield forecasting. Soft Computing, 28, 1685-1706. https://doi.org/10.1007/s00500-023-09516-8
  14. Hafezi, N., Bahrami, H., Sheikh Davoodi, M. J., & Alavi, S. E. (2020). Hybrid artificial neural network with metaheuristic algorithms for predicting sugarcane yield. Iranian Journal of Biosystems Engineering, 51 (3), 515-526. (in Persian with English abstract). https://doi.org/10.22059/ijbse.2020.290905.665234
  15. Haroni, S., Sheikhdavoodi, M. J., Kiani Deh Kiani, M. (2018). Application of artificial neural networks for predicting the yield and GHG emissions of sugarcane production. Journal of Agricultural Machinery, 8(2(, 389-401. (in Persian with English abstract). https://doi.org/10.22067/jam.v8i2.52870
  16. Hushyar, N., & Ashraf Talesh, S. S. (2016). Optimum prediction of the T-shape mixing chamber behavior based on multi-objective genetic programming. Modares Mechanical Engineering, 16(12), 612-616. (in Persian with English abstract).
  17. Janatrostami S, & Mahmoudpour H. (2020). Environmental assessment of groundwater pumping by using water and energy nexus. Journal of Water and Soil Science, 23 (4), 227-240. (in Persian with English abstract). https://doi.org/10.47176/jwss.23.4.40841
  18. Janatrostami, S., Kholghi, M., & Bozorg Haddad, O. (2010). Management of reservoir operation system using improved harmony search algorithm. Water and Soil Science, 20(3), 61-71. (in Persian with English abstract).
  19. Kavoosi Kalashami, M., Zanipoor, M., Yavari, G., & Adibi, S. (2017). Evaluation of the effect of national plan implemention of increasing rice production on technical efficiency of paddy farms (A case study: Pirbazar region of Rasht city). Cereal Research, 7(2), 235-246. (in Persian with English abstract). https://doi.org/10.22124/c.2017.2549
  20. Kima, A. S., Traore, S., Wang, Y. M., & Chung, W. G. (2014). Multi-genes programing and local scale regression for analyzing rice yield response to climate factors using observed and downscaled data in Sahel. Agricultural Water Management, 146, 149-162. https://doi.org/10.1016/j.agwat.2014.08.007
  21. Kitani, O., Jungbluth, T., Peart, R. M., & Ramdani, A. (1999). CIGR handbook of agricultural engineering. Energy and Biomass Engineering, 5, 330.
  22. Lu, M., Bi, Y., Xue, B., Hu, Q., Zhang, M., Wei, Y., Yang, P., & Wu, W. (2022). Genetic programming for high level feature learning in crop classification. Remote Sensing, 14, 3982. https://doi.org/10.3390/rs14163982
  23. Mahmud, T., Datta, N., Chakma, R., Das, U. K., Aziz, M. T., Islam, M., Salimullah, A. H. M., Hossain, M. S., & Andersson. K. (2024). An approach for crop prediction in agriculture: integrating genetic algorithms and machine learning. IEEE Access, 12, 173583-173598. https://doi.org/10.1109/ACCESS.2024.3478739
  24. Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V., Borodulin, A., & Tynchenko, Y. (2024). Predicting sustainable crop yields: Deep learning and explainable AI tools. Sustainability, 16, 9437. https://doi.org/10.3390/su16219437
  25. Mirshekari, F. )2011(. Optimization of energy use efficiency (energy ratio) and revenue in smallholder agriculture of Naragh City. Faculty of Agriculture University of Tehran, Tehran.
  26. Nassiri, S. M., & Singh, S. (2009). Study on energy use efficiency for paddy crop using data envelopment analysis (DEA) technique. Applied Energy, 86(7), 1320-1325. https://doi.org/10.1016/j.apenergy.2008.10.007
  27. Ozkan, B., Akcaoz, H., & Fert, C. (2004). Energy input–output analysis in Turkish agriculture. Renewable Energy, 29(1), 39-51. https://doi.org/10.1016/S0960-1481(03)00135-6
  28. Panahi, S., Samadianfard, S., & Nazemi, A. H. (2021). Modeling the yield of rain-fed wheat, barley and alfalfa products using support vector regression and genetic programming. Journal of Water and Soil Science, 32(2), 97-111. (in Persian with English abstract). https://doi.org/10.22034/ws.2021.35741.2287
  29. Peng, X., Guan, X., Zeng, Y., & Zhang, J. (2024). Artificial intelligence-driven multi-energy optimization: promoting green transition of rural energy planning and sustainable energy economy. Sustainability, 16, 4111. https://doi.org/10.3390/su16104111
  30. Sadeghi, S. M. (2023). Investigating the effect of water stress and different levels of organic and chemical fertilizers on rice yield and its components. Iranian Journal of Irrigation and Drainage, 17(5), 843-855. (in Persian with English abstract).
  31. Shahdi Kumleh, A., Seyedi S. R., Haghighi Hasanalideh A. R., & Karamniya. S. )2021(. Effect of source and application rate of organic fertilizers on grain yield and quality of local and improved rice (Oryza sativa) cultivars. Iranian Journal of Crop Sciences, 23(3), 278-289. (in Persian with English abstract).
  32. Sharifi, S., Hafezi, N., & Aghkhani, M. H. (2025). Investigation and optimization of energy consumption and yield modeling of two rice cultivars using the genetic-artificial bee colony algorithm. Journal of Agricultural Machinery, 15(2), 145-164. https://doi.org/10.22067/jam.2022.77064.1108
  33. Taghizadeh Merjerdi, R., Seyed Jalali, S. A., & Sarmadian, F. (2016). Spatial prediction of wheat crop yield using digital soil mapping in Gotvand, Khuzestan Province. Iranian Journal of Agricultural Sciences, 47(1), 175-184. (in Persian with English abstract). https://doi.org/10.22059/ijswr.2016.57989
  34. Taheri-Rad, A., Khojastehpour, M., Rohani, A., & Khoramdel, S. (2017). Assessing the energy consumption efficiency of different long grain rice varieties in Golestan province. Cereal Research, 7(1), 51-66. (in Persian with English abstract). https://doi.org/10.22124/c.2017.2428
  35. Taheri-Rad, A., Khojastehpour, M., Rohani, A., Khoramdel, S., & Nikkhah, A. (2017). Energy flow modeling and predicting the yield of Iranian paddy cultivars using artificial neural networks. Energy, 135, 192-198. https://doi.org/10.1016/j.energy.2017.06.089
  36. Tchonkouang, R. D., Onyeaka, H., & Nkoutchou, H. (2024). Assessing the vulnerability of food supply chains to climate change-induced disruptions. Science of The Total Environment, 920, 171047. https://doi.org/10.1016/j.scitotenv.2024.171047
  37. Tung, C. P., Lee, T. Y., Yang, Y. C., & Chen, Y. J. (2009). Application of genetic programming to project climate change impacts on the population of Formosan Landlocked Salmon. Environmental Modelling & Software 24, 1062-1072. https://doi.org/10.1016/j.envsoft.2009.02.012
  38. Van-Hung, N., Sander, B. O., Quilty, J., Balingbing, C., Castalone, A. G., Romasanta, R., Alberto, M. C. R., Sandro, J. M., Jamieson, C., & Gummert, M. (2019). An assessment of irrigated rice production energy efficiency and environmental footprint with in-field and off-field rice straw management practices. Scientific Reports, 9(1), 1-12. https://doi.org/10.1038/s41598-019-53072-x
  39. Zeinalie, M., Golabi, M. R., Azari, A., & Farzi, S. (2022). The study of the performance of the Modflow conceptual model and the genetic programming simulator meta model in the modeling of the hydrograph of the aquifer (Case Study: Lour-Andimeshk Plain). Journal of Aqiufer and Qanat, 1(4), 1-15. (in Persian with English abstract). https://doi.org/10.22077/jaaq.2018.1236.1000
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