Water and Irrigation Management

Water and Irrigation Management

Optimization of cropping pattern, taking into account limited water resources, cultivated area and biodiversity in the Mehran Plain

Document Type : Research Paper

Authors
Javad Sarvarian 1
Parisa Soleimani 2
1 Associate Professor, Department of Agriculture, Ilam University, Ilam, Iran.
2 M.Sc. Student, Department of Water Engineering, Faculty of agriculture, Ilam University, Ilam, Iran.
10.22059/jwim.2022.326446.899
Abstract
Due to limited water resources in Iran, the optimal use of water resources and improvement of water use efficiency is necessary, especially in agriculture. In current work, cropping pattern optimization was carried out in Mehran Plain of Ilam Province based on water resources, cultivated area and biodiversity constraints using genetic algorithm. The optimization model was applied to three different scenarios based on a combination of different constraints. The results showed that the cropping pattern in 2016-17 was not optimal and the biodiversity index was low. The resulting profit and biodiversity in all scenarios are higher than the current situation in Mehran plain. The amount of profit increase in combinations one, two and three is 70, 101 and 132% higher than the profit of the existing cropping pattern, respectively, and in terms of biodiversity, the Shannon-Wiener criterion is more than twice as high as the Shannon-Wiener criterion in the existing simple cropping pattern in all scenarios. Wheat, canola, sesame and okra are strongly represented in most optimal cropping patterns. Wheat has the largest acreage and tomato and alfalfa and corn have the least acreage due to the low profit and high water consumption of these products. Crops such as corn, sesame, okra and cucumber are strongly represented in the optimal cropping patterns and can be used as alternatives to the current crops to increase agricultural profits.
Keywords
Algorithm Genetic
Biodiversity index
Cultivated area
Optimal crop pattern
Water consumption
Subjects
  1. Abbasi, F., Sohrab, F., & Abbasi, N. (2017). Evaluation of irrigation efficiencies in Iran. Irrigation and Drainage Structures Engineering Research, 17(67), 113-120.‏
  2. Alizadeh, A. (2007). Surface irrigation system design. Mashhad: Imam Reza University.
  3. Bahadori, M., Joolaie, R., Eshraghi, F., & Rezaee, A. (2019). Optimization of cropping pattern regarding risk in Rey county of Iran. Agricultural Economics and Development, 27(107), 131-161. (In Persian).
  4. (1992). A computer program for irrigation planning and management (No. 46). Food and Agriculture Organization.
  5. Di Falco, S., Bezabih, M., & Yesuf, M. (2010). Seeds for livelihood: crop biodiversity and food production in Ethiopia. Ecological Economics, 69(8), 1695-1702.
  6. (1999). Agrobiodiversity. Multilingual Glossary Forest Genetic Resources. Available in: http://www.iufro-archive.boku.ac.at/silvavoc/glossary/af2_1en.html
  7. Ghasemi, M.M., Karamouz, M., & Shui, L.T. (2016). Farm-based cropping pattern optimization and conjunctive use planning using piece-wise genetic algorithm (PWGA): a case study. Modeling Earth Systems and Environment, 2(1), 25.‏
  8. Goldberg, D. E., & Holland, J.H. (1988). Genetic algorithms and machine learning. Machine Learning, 3, 95–99.
  9. Haq, F., Parveen, A., Hussain, S., & Hussain, A. (2020). Optimization of the Cropping Pattern in District Hunza, Gilgit-Baltistan. Sarhad Journal of Agriculture, 36(2).‏
  10. Heidarizad, Z., Mohamadi, A., & Yaghobi, S. (2019). Evaluating the Groundwater Status of Mehran Plain and Factors Affecting the Quantity of these Resources. Journal of Hydrogeology, 3(2), 59-68.‏
  11. Ifo, S. A., Moutsambote, J. M., Koubouana, F., Yoka, J., Ndzai, S. F., Bouetou-Kadilamio, L. N. O., ... & Joel, L. J. (2016). Tree species diversity, richness, and similarity in intact and degraded forest in the tropical rainforest of the Congo Basin: case of the forest of Likouala in the Republic of Congo. International Journal of Forestry Research.‏
  12. Jafarzadeh, A., Khaseii, A., & Shahidi, A. (2016). Designing a multi objective decision-making model to determine optimal crop pattern influenced by climate change phenomenon (case study: Birjand plain). Iranian Journal of Soil and Water Research, 47(4), 849-859. (In Persian).
  13. Kumar, V., & Yadav, S.M. (2019). Optimization of cropping patterns using elitist-Jaya and elitist-TLBO algorithms. Water Resources Management, 33(5), 1817-1833.‏
  14. Magurran, A.E. (2013). Measuring biological diversity. John Wiley and Sons.‏
  15. Mohammadi, H., Boustani, F., & Kafilzadeh, F. (2012). Optimal cropping pattern using a multi-objectives fuzzy non-linear optimization algorithm: a case study. Journal of water and wastewater, 23(84), 43-55. (In Persian).
  16. Mohammadrezapour, O., Yoosefdoost, I., & Ebrahimi, M. (2017). Cuckoo optimization algorithm in optimal water allocation and crop planning under various weather conditions (case study: Qazvin plain, Iran). Neural Computing and Applications, 31(6), 1879-1892.‏
  17. Najafabadi, M.M., Ziaee, S., Nikouei, A., & Borazjani, M.A. (2019). Mathematical programming model (MMP) for optimization of regional cropping patterns decisions: A case study. Agricultural Systems, 173, 218-232.‏
  18. Osama, S., Elkholy, M., and Kansoh, R.M. (2017). Optimization of the cropping pattern in Egypt. Alexandria Engineering Journal, 56(4), 557-566.‏
  19. Rafiee, V., Shourian, M., and Attari, J. (2017). Optimum crop patterning by integrating SWAT and the harmony search optimization algorithm. Iran-Water Resources Research, 13(3), 73-88. (In Persian).
  20. Roy, R.K. (2001). Design of experiments using the Taguchi approach: 16 steps to product and process improvement. John Wiley & Sons.
  21. Sabzzadeh, I., & Shourian, M. (2020). Maximizing crops yield net benefit in a groundwater-irrigated plain constrained to aquifer stable depletion using a coupled PSO-SWAT-MODFLOW hydro-agronomic model. Journal of Cleaner Production, 262, 121349.‏
  22. Singh, A. & Panda, S.N. (2012). Development and application of an optimization model for the maximization of net agricultural return. Agricultural water management, 115, 267-275.
  23. Soares, F. M., Saraiva, A. M., & Drucker, D. P. (2020). Linking agrobiodiversity data through metadata standards. In Embrapa Informática Agropecuária-Resumo em anais de congresso (ALICE). Biodiversity Information Science and Standards, 4, 1-3.
  24. Srivastava, P., & Singh, R.M. (2015). Optimization of cropping pattern in a canal command area using fuzzy programming approach. Water Resources Management, 29(12), 4481-4500.‏
Water and Irrigation Management
Volume 11, Issue 4
Winter 2022
Pages 725-737