Optimal allocation of water and land in Moghan irrigation network using crop model and genetic algorithm

Document Type : Research Paper

Authors

1 Water Engineering Department, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

2 Department Water Engineering, College Agriculture, Ferdowsi University, Mashhad, Iran

3 Water engineering, Agriculture faculty, Ferdowsi university of Mashhad, Mashhad, Iran

4 Water Engineering Department, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili

5 Department of Applied Mathematics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.

Abstract

Water is one of the most important physical factors to provide the food security of the world’s growing population. The limitation of Iran’s water resources, as well as the increasing competition of different sectors for the use of water make the optimal management of water resources necessary. Different strategies are used in irrigation networks for water resources management. One of these strategies is optimal allocation of water and land. In this research, an optimization model for water and land allocation with the aim of maximizing economic benefit is presented based on genetic algorithm and using the AquaCrop plug-in model. For this purpose, #C coding was done in Visual Studio. In order to measure the model performance, the lands covered by one of the Moghan irrigation network channels were investigated. In this model, the agricultural year was divided into 36 periods of ten days. The irrigation water depth in each period and the cultivated area were considered as decision variables. The results show that the highest increase in percentage of economic benefit is related to the first-cultivation maize, alfalfa and wheat by 9, 7.3 and 7 percent respectively. Although the lowest increase in economic benefit is related to the second-cultivation seed maize and soybeans. The optimal allocated water volume was decreased by 14.7 percent, meanwhile the economic benefit was increased by 5.7 percent. Therefore, the optimal water allocation in this region encourages saving water consumption more than increasing economic benefit.

Keywords

Main Subjects

References

Adabi, V., Azizian, A., Ramezani Etedali, H., Kaviani, A., &  Ababaei, B. (2020). Local sensitivity analysis of AquaCrop model for wheat and maize in qazvin plain and Moghan Pars-Abad in Iran. Iranian Journal of Irrigation and Drainage, 6(13), 1565-1579. (In Persian)
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration, guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper, No 56, Food and Agriculture Organization of the United Nations, Rome.
Bastiaanssen, W. G. M., Allen, R. G., Droogers, P., D’Urso, G., & Steduto, P. (2007). Twenty five years modeling irrigated and drained soils: State of the art. Agricultural Water Management, 92(3), 111–125.
Chu, L., & Grafton, R. Q. (2020). Water pricing and the value-add of irrigation water in Vietnam: Insights from a crop choice model fitted to a national household survey. Agricultural Water Management, 183, 453- 459.
Cortignani, R., & Severini, S. (2009). Modeling farmlevel adoption deficit irrigation using positive mathematical programming. Agricultural water management, 96, 1785-1791.
Doorenbos, J., Kassam, A. H., Bentvelsen, C. L. M., & Branscheid, V. (1979). Yieid response to water. FAO Irrigation and Drainage Paper. FAO Library AN: 206617.
García-Vilaa, M., & Fereresa, E. (2012). Combining the simulation crop model AquaCrop with an economic model for the optimization of irrigation management at farm level. European Journal of Agronomy, 36, 21– 31.
Ghochanian, M., & Fashaee, M. (2021). Water resources management indicators focusing on governance. Journal of Water and Sustainable Development, 9(1), 1-10.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley-Longman, Reading, Massachusetts, USA.
Guo, D., Eivind Olesen, J., Manevski, K., & Ma, X. ( 2021). Optimizing irrigation schedule in a large agricultural region under different hydrologic scenarios. Agricultural Water Management, https://doi.org/10.1016/j.agwat.2020.106575.
Hanks, R. J. (1983). Yield and water-use relationships. in: Lange, O. L., Kappen, L. and Schulze, E. D. (Eds.) ecological studies. nalysis and synthesis. Vol. 19. Water and Plant Life. Springer-Verlag. Berlin.
Huang, Q., Rozelle, S., & Howitt, R. (2006). Irrigation water pricing policy in china. American agricultural economics association annual meeting. Long Beach, CA.
Izadfard, A., Sarmadian, F., Jahansooz, M. R., Peykani, GH. R., & Chaichi, M. R. (2017). Comparison of AquaCrop model and heat-radiation production potential model in estimating yield potential in a part of Mughan plain lands in Ardabil province. Iran water and soil research, 48(4), 853-864. (In Persian)
Izadfard, A., Sarmadian, F., Jahansooz, M. R., & Asadi Oskouei, E. (2021). Optimum cropping pattern based on irrigation water productivity using AquaCrop simulation model. Journal of Agricultural Science and Technology, 23(5), 1163-1178.
Kanooni, A.  (2013). Development of an integrated optimal water allocation and distribution model at different levels of irrigation networks. Ph.D. thesisin the field water structures engineering under the guidance Mohammad Javad Monem. Tehran: Tarbiat Modares University, Department of water structures engineering. 177 p.. (In Persian)
Kanooni, A., & Monem, M. J. (2014). Integrated stepwise approach for optimal water allocation in irrigation canal. Irrigation and Drainage, 63, 12–21.
Kashefi Nezhad, P., & Hooshmand, A. R. (2020). Optimal allocation of water resources of Maroon irrigation network to maximize net benefit. Iranian Water Research Journal, 14 (1), 41-49. (In Persian)
Katerji, N., Pasquale, C., & Marcello. M. (2013). Productivity, evapotranspiration and water use efficiency of corn and tomato crops simulated by Aquacrop under contrasting water stress conditions in the Mediterranean region. Agricultural Water Management, 130, 14-26.
Kazem Attar, H., Noory, H., & Ebrahimian, H. (2020). Effect of water pricing on persuading farmers to use modern irrigation systems and increasing the economic productivity of irrigation water (Case Study: Qazvin plain irrigation network). Iranian Journal of Irrigation and Drainage, 2(4), 616-625. (In Persian)
Kumar, V., and Yadav, S. M. (2019). Optimization of cropping patterns using elitist-Jaya and elitist- TLBO algorithms. Water Resources Management, 33(5), 1817-1833.
Kuo, S.F, Merkley, G.P., & Liu, C.W. (2000). Decision support for irrigation project planning using a genetic algorithm. Agricultural Water Management, 45, 243-266.
Li, J., Song, J., Li, M., Shang, S., Mao, X., Yang, J., & Adeloye, A. J. (2018). Optimization of irrigation scheduling for spring wheat based on simulation-optimization model under uncertainty. Agricultural Water Management, 208, 245–260..
Linker, R., Ioslovich, I., Sylaios, G., Plauborg, F., & Battilani, A. (2016). Optimal model-based deficit irrigation scheduling using AquaCrop: a simulation study with cotton, potato and tomato. Agricultural Water Management,  163, 236–243.
Mohammadi, M., Davary, K., & Ghahraman, B. (2019). Optimizing irrigation water depth and leaching based on different water management and salinity treatments using AquaCrop model. Journal of Water Research in Agriculture, 33(2), 299-315. (In Persian)
Rabie Z. Honar T. & Bateni M. (2015). Determination of optimal and water allocation under limited water resources using soil water balance in Ordibehesht canal of Doroodzan water district. Iran Agricultural Research, 34(2), 21-28.
Raes, D., Steduto, P., Hsiao, T. C., & Fereres, E. (2012). Reference Manual: AquaCrop Plugin Program (Version 4.0). FAO Irrigation and Drainage Paper, FAO, Rome.
Rath A. Prakash S. & Swain C. (2017). Optimal irrigation management at Mundoghat Command area of Hirakud canal system using genetic algorithm. International journal of Recent Advances in Multidisciplinary Research, 4(8), 2175- 2719.
Rath A. Samantary S. Biswal S. & Swain P. C. (2018). Application of genetic algorithm to derive an optimal cropping pattern in part of Hirakud Command. Advances in intelligence and computing. Singapore. 845 p..
Statistical center of Iran. Presidential program and budget organization. (2021). Seling prices of products and cost of agricultural services in rural areas of the country. (In Persian)
Steduto, P., Hsiao, T. C., Fereres, E., & Raes, D. (2012). Crop yield response to water. FAO Irrigation and Drainage Paper, vol. 66. FAO, Rome.
Steduto, P., Hsiao, T. C., Raes, D. & Fereres, E. (2009). AquaCrop-The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal, 101, 426-437.
Tomohari, H., Okamoto, K., yoshihiro, M., & Nohara, D. (2015). An optimization scheme of cropping pattern under the variation of water and climate condition. Proceeding of the 36th IAHR World Congress, The Hague, the Netherlands.
Valizadegan. E., & Dindar Sooha, A. (2020). Model of optimal allocation of water and land to agricultural crops in deterministic and stochastic conditions. Journal of Water and Soil Resources Conservation, 10(3), 31-46. (In Persian)
Wang, H. R., Dong, Y. Y., Wang, Y., & Liu, Q. (2008). Water right institution and strategies of the Yellow River valley. Water Resource. Management, 22, 1499–1519.
Zargan J. & Waez-Mousavi S. M. (2016). Water crisis in Iran: its intensity, causes and confronting strategies. Indian journal of science and technology, 9(44), 2-6.
Iranian Journal of Soil and Water Research
Volume 53, Issue 12
February 2023
Pages 2921-2935

Files

Share

How to cite

Statistics