Simultaneously Management of Surface and Groundwater Resources and Increasing Farmers' Resilience to Water Scarcity by Predicting the Price of Agricultural Products and using GA (Case Study of Irrigation and Drainage Network of Qazvin Plain)

Document Type : Research Paper

Authors

1 Department of Water Science and Engineering, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran.

2 associated professor, water eng. group, Imam Khomeini International University، Qazvin

Abstract

Taleghan Dam is the main supplier of required water to the agricultural sector of Qazvin plain. The amount of water allocated from Taleghan Dam to this plain has decreased for various reasons, including increasing the allocation of drinking water to Tehran. The reduction of allocated water and the fluctuation prices of agricultural products due to the time lag between the farmer's decision to cultivate and offer it to the market, make farmers to be uncertain to their future earnings. In order to deal with the uncertainty of the prices of agricultural products and their livelihood, despite the reduction of allocated water, farmers have started to discharge the groundwater by stabilizing the cultivated area and combining the cultivation pattern. In this study, in order to increase farmers' resilience and preserve groundwater resources, water distribution pattern with price prediction and simultaneous water cultivation and distribution pattern with price prediction has been optimized using genetic algorithm. For predicting the price of agricultural products with guaranteed purchase such as wheat, barley, sugar beet and rapeseed the ANN model was ued. For predicting the price of maize, tomato, alfalfa, peas, beans, potatoes, corn and lentils, the reverse demand function method was used. The price elasticity of demand for maize, tomato, alfalfa, peas, beans, potatoes, corn and lentils were estimated -0.508,-1.111,-0.954,-0.914,-0.374,-0.529,-0.363 and -0.332, respectively. MAE and RSME indeces indicated the ability of reverse demand function and ANN in price forecasting. The results also showed that the use of water distribution optimization models with price forecasting will increase revenue by 25% and the simultaneous optimization model of water cultivation and distribution model with price forecasting will increase network revenue by 160% compared to the current situation.

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Main Subjects

References

Abkhan Consulting Engineers (2013) Water Budget of Qazvin Plain, Ministry of Energy, 71pp (in Farsi)
Asaadi, M., Khalilian, S., and Mousavi, S. (2019). Management of Irrigation Water Allocation and Cropping Pattern with emphasis on Deficit Irrigation Strategy (Case study: Qazvin Irrigation Network). Iran-Water Resources Research, 14(5), 1-14.
Borimnejad, V., and Bakeshloo, M. (2018). Forecasting the price of Tomatoes: comparison of syncretistic methods of Neural Network Auto-Regressive and ARIMA. Agricultural Economics and Development, 21(83), 89-103. (In Farsi)
Divisekara, R. W., Jayasinghe, G., and Kumari, K. (2020). Forecasting the red Lentils commodity market price using SARIMA models. SN Business & Economics, 1(1), 1-13.
Hao, L., Su, X., and Singh, V. P. (2018). Cropping pattern optimization considering uncertainty of water availability and water saving potential. International Journal of Agricultural and Biological Engineering, 11(1), 178-186.
Heydari, H. (2013). Estimation of detergent powder demand function in Iran. M.Sc. dissertation, University of Allameh Tabataba'I, Tehran. (In Farsi)
Hosseini Yekani, S., and Kashiri Kolaei F. (2017). Investigating the effect of crop price fluctuations on the optimal cropping pattern in Sari. Agricultural Economics, 11(2), 75-94. (In Farsi)
Minhaj, M. B. (1998) Fundamentals of Neural Networks(1th ed.). Tehran: Professor Hesabi Publishing. (In Farsi)
Najafi, B., Zibaei, M., Sheikhi, M. H., and Tarazkar, M. H. (2007). Forecasting price of some crop products in fars province: application of Artificial Neural Network. Journal of Science and Technology of Agriculture and Natural Resources, 11(1), 501-511. (In Farsi)
Piadeh Koohsar, J., Mazandarani Zadeh, H., and sadr, S. (2019). Evaluation of GA and PSO optimization algorithms in operation of multi-reservoir systems Case study: Gorgan-Rood basin dams. Journal of Water and Soil Conservation, 26(2), 239-250. (In Farsi)
Rao, N.H., Sarma, P.B.S., and Chander, S. (1988). A simple dated water-production function for use in irrigated agriculture. Agricultural Water Management, 13(1), 25-32.
Reddy, A. A. (2019). Price Forecasting of Tomatoes. International Journal of Vegetable Science, 25(2), 176-184.
Sa'adat Mehr, M. (2017). Estimated demand for chicken meat in Iran. Economic Journal, 16(11),101-117. (In Farsi)
Salami, H., and Rezaei, S. (2010). Forecasting meat prices: An inverse demand approach. Journal of Economics and Agriculture Development, 24(3), 298-303. (In Farsi)
shekari, h., Najarchi, M., jafarinia, r., Mokhtari, S., and Alizadeh, H. (2019). Optimization of Cropping Pattern and Water Resources at Different Levels of Irrigation for Hot and Dry Areas (Case study: Dehloran Plains, Ilam Province). Iranian Journal of Soil and Water Research, 50(6), 1351-1361. (In Farsi)
Simiari, F., Mazandaranizadeh, H., and Gomrokchi, A. (2017). Optimal redistribution of agricultural water using genetic algorithm. In: Proceedings of 2nd National Congress on Hydrology of Iran,11-12 July., Shahrekord University, Shahrekord, Iran. (In Farsi)
Soltani, H. A., and Khajehpour, E. (2020). Optimal Cropping Pattern in Afghanistan Considering Environmental Sustainability. International Journal of Agricultural Management and Development, 10(4), 333-346.
Wu, S. I. (1995). Artificial neural networks in forecasting. Neural Network World, 2, 199-220.
Iranian Journal of Soil and Water Research
Volume 52, Issue 2
May 2021
Pages 563-576

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