Skip to main content

Advertisement

SpringerLink
Log in

Field-Scale Improvement of Water Allocation for Maize Cultivation Using Grey Wolf Optimization Algorithm

  • Research Paper
  • Published:
Iranian Journal of Science and Technology, Transactions of Civil Engineering Aims and scope Submit manuscript

Abstract

It is necessary to develop methods to improve soil moisture capacity and agricultural productions in arid and semiarid areas. This study was conducted to evaluate the impact of optimal water allocation to improve the yield production in Gotvand Plain, Khuzestan Province, south-western Iran. A field-scale experiment with three scenarios of sugarcane bagasse compost application (0, 15 and 30 ton/ha) and four levels of water supply (50%, 75%, 100% and 125% of total allowable water) was performed in three replications (March–July 2019). Porous media texture, infiltration rate and irrigation demand were simulated at daily time steps to provide the root zone moisture content using real-time analysis of soil water balance. Furthermore, the maximization of readily available water based on the agricultural demand were considered using grey wolf optimization algorithm under deep percolation and runoff constraints. The results showed that the water allocation strategies and compost scenarios can improve water use efficiency and soil moisture.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Achieng KO (2020) Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models. Comput Geosci 133:104320. https://doi.org/10.1016/j.cageo.2019.104320

    Article  Google Scholar 

  • Ahmadi SH, Mosallaeepour E, Kamgar-Haghighi AK, Sepaskhah AR (2015) Modeling maize yield and soil water content with AquaCrop under full and deficit irrigation managements. Water Resour Manag 29:2837–2853

    Google Scholar 

  • Alaya AB, Souissi A, Tarhouni J, Ncib K (2003) Optimization of Nebhana reservoir water allocation by stochastic dynamic programming. Water Res Manag 17(4):259–272

    Google Scholar 

  • Andarziana B, Bannayanb M, Stedutoc P, Mazraeha H, Barati ME, Barati MA, Rahnama A (2011) Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agri Water Manag 100:1–8

    Google Scholar 

  • Arora S, Singh H, Sharma M, Sharma S, Anand P (2019) A new hybrid algorithm based on grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection. IEEE Access 7:26343–26361

    Google Scholar 

  • Bender DA, Peart RM, Doster DH, Barrett JR, Bagby MO (1984) Energy crop evaluation by linear programming. Energy Agric 3:199–210

    Google Scholar 

  • Brumbelow K, Georgakakos A (2007) Determining crop-water production functions using yield–irrigation gradient algorithms. Agri Water Manag 87:151–161

    Google Scholar 

  • Chen S, Shao D, Tan X, Gu W, Lei C (2019) Nonstationary stochastic simulation-based water allocation method for regional water management. J Water Resour Plan Manag 145(3):0014523. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001042

    Article  Google Scholar 

  • Cho J, Mostaghimi S, Kang MS (2010) Development and application of a modeling approach for surface water and groundwater interaction. Agri Water Manag 97:123–130

    Google Scholar 

  • Dash CJ, Sarangi A, Adhikary PP, Singh DK (2016) Simulation of nitrate leaching under maize–wheat cropping system in a semiarid irrigated area of the Indo-Gangetic Plain, India. J Irrig Drain Eng. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000965

    Article  Google Scholar 

  • Dehghani M, Riahi-Madvar H, Hooshyaripor F, Mosavi A, Shamshirband S, Zavadskas EK, Chau KW (2019) Prediction of hydropower generation using grey wolf optimization adaptive neuro-fuzzy inference system. Energies 12(289):1–20

    Google Scholar 

  • Dhargupta S, Ghosh M, Mirjalili S, Sarkar R (2020) Selective opposition based grey wolf optimization. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113389

    Article  Google Scholar 

  • FAO (2013) FAO statistical yearbook 2013, World food and agriculture. Food and agriculture organization of the United Nations, Rome, pp. 289, Fao.org/docrep/018/i3107e/i3107e00.htm

  • FAO (2012) Crop yield response to water. In: Steduto P, Hsiao TC, Fereres E, Raes D FAO Irrigation and Drainage Paper No. 66. Rome

  • Gholamin R, Khayatnezhad M (2020a) The effect of dry season stretch on chlorophyll content and RWC of wheat genotypes (Triticum Durum L.). Biosc Biotech Res Comm 13(4). https://bit.ly/36zMJX4

  • Gholamin R, Khayatnezhad M (2020b) The study of path analysis for durum wheat (Triticum durum Desf.) yield components. Biosc Biotech Res Comm 13(4). https://bit.ly/2GVB7oD

  • Gholamin R, Khayatnezhad M (2020c) Assessment of the correlation between chlorophyll content and drought resistance in corn cultivars (Zea Mays). Helix 10(05):93–97

    Google Scholar 

  • Gholamin R, Khayatnezhad M (2020d) Study of bread wheat genotype physiological and biochemical responses to drought stress. Helix 10(05):87–92

    Google Scholar 

  • Heng LK, Hsiao TC, Evett S, Howell T, Steduto P (2009) Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agron J 101(3):488–498

    Google Scholar 

  • Hsiao TC, Heng L, Steduto P, Rojas-Lara B, Raes D, Fereres E (2009) AquaCrop—the FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agron J 101(3):448–459

    Google Scholar 

  • Irmak A, Kamble B (2009) Evapotranspiration data assimilation with genetic algorithms and SWAP model for on-demand irrigation. Irrig Sci 28:101–112

    Google Scholar 

  • Jia Y, Khayatnezhad M, Mehri S (2020) Population differentiation and gene flow in Erodium cicutarium: a potential medicinal plant. Genetika 52(3):1127–1144

    Google Scholar 

  • Khayatnezhad M, Gholamin R (2020a) A modern equation for determining the dry-spell resistance of crops to identify suitable seeds for the breeding program using modified stress tolerance index (MSTI). Biosc Biotech Res Comm 13(4). https://bit.ly/38C6bFk

  • Khayatnezhad M, Gholamin R (2020b) Study of durum wheat genotypes’ response to drought stress conditions. Helix 10(05):98–103

    Google Scholar 

  • Kuo SF (2013) Evaluation of irrigation water requirements and crop yields with different irrigation schedules for paddy fields in ChiaNan irrigated area. Paddy Water Environ, Taiwan. https://doi.org/10.1007/s10333-013-0360-8

    Book  Google Scholar 

  • Lalehzari R, Kerachian R (2020a) An integrated framework for optimal irrigation planning under uncertainty: application of soil, water, atmosphere and plant modeling. Iran J Sci Technol Trans Civil Eng. https://doi.org/10.1007/s40996-020-00442-5

    Article  Google Scholar 

  • Lalehzari R, Kerachian R (2020b) Developing a framework for daily common pool groundwater allocation to demands in agricultural regions. Agric Water Manag 241:106278

    Google Scholar 

  • Lalehzari R, Nasab SB, Moazed H, Haghighi A, Yaghoobzadeh M (2020) Simulation-optimization modeling for water resources management using NSGAII-OIP and Modflow. Irrig Drain. https://doi.org/10.1002/ird.2424

    Article  Google Scholar 

  • Lalehzari R, Boroomand-Nasab S, Moazed H, Haghighi A (2016) Multi-objective management of water allocation to sustainable water allocation and optimal cropping pattern. J Irri Drain Eng 142(1):05015008

    Google Scholar 

  • Li M, Guo P, Singh VP (2017) Biobjective optimization for efficient irrigation under fuzzy uncertainty. J Irrig Drain Eng 142(8):05016003

    Google Scholar 

  • Loh YR, Sujana D, Rahmana ME, Das CA (2013) Sugarcane bagasse-the future composite material: a literature review. Resour Conserv Recycl 75:14–22

    Google Scholar 

  • Maroufpoor S, Maroufpoor E, Bozorg-Haddad O, Shiri J, Mundher Yaseen Z (2019) Soil moisture simulation using hybrid artificial intelligent model: hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. J Hydrol. https://doi.org/10.1016/j.jhydrol.2019.05.045

    Article  Google Scholar 

  • Melati RB, Schmatz AA, Pagnocca F, Contiero J, Brienzo M (2017) Sugarcane bagasse: production, composition, properties, and feedstock potential. Sugarcane: production systems, uses and economic importance, pp. 1–38. <http://hdl.handle.net/11449/174692>

  • Mirjalili SA, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  • Mousavizadeh SF, Honar T, Ahmadi SH (2016) Assessment of the AquaCrop model for simulating canola under different irrigation management in a semiarid area. Int J Plant Prod 10(4):1735–6814

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. Part 1. A discussion of principles. J Hydrol 10(3):282–292

    Google Scholar 

  • Nasiri A, Samdaliri M, Rad AS, Shahsavari N, Kale AM, Jabbari H (2017) Effect of plant density on yield and physiological characteristics of six canola cultivars. J Sci Agri 1:249–253

    Google Scholar 

  • Osama S, Elkholy M, Kansoh RM (2017) Optimization of the cropping pattern in Egypt. Alexandria Eng J 56(4):557–566

    Google Scholar 

  • Parameswaran B (2009) Sugarcane bagasse. In: Singh nee’ Nigam P, Pandey A (eds) Biotechnology for agro-industrial residues utilization. Springer, Dordrecht, pp 239–252

    Google Scholar 

  • Parsinejad M, Bemani-Yazdi A, Araghinejad S, Nejadhashemi AP, Sarai Tabrizi M (2013) Optimal water allocation in irrigation networks based on real time climatic data. Agri Water Manag 117:1–8

    Google Scholar 

  • Paydar Z, Qureshi ME (2012) Irrigation water management in uncertain conditions—application of modern portfolio theory. Agri Water Manag 115:47–54

    Google Scholar 

  • Pennell KD, Hornsby AG, Jessup RE, Rao PSC (1990) Evaluation of five simulation models for predicting aldicarb and bromide behavior under field conditions. Water Resour Res 26(11):2679–2693

    Google Scholar 

  • Porse E, Mika KB, Williams R, Gold M, Blomquist W, Pincetl S (2018) Groundwater exchange pools and urban water supply sustainability: modeling directed and undirected networks. J Water Res Plan Manag 144(8):04018040

    Google Scholar 

  • Rabelo SC, Costa AC, Rossel CEV (2015) Sugarcane, Chapter 17–Industrial waste recovery. Agricultural production, bioenergy and ethanol, pp: 365-381

  • Raes D, Steduto P, Hsiao TC, Fereres E (2009) AquaCrop—the FAO crop model to simulate yield response to water II. Main algorithms and software description. Agron J 101:438–447

    Google Scholar 

  • Raes D, Steduto P, Hsiao TC, Freres E (2012) Reference manual AquaCrop, FAO, land and water division. Rome, Italy

  • Roosjen PPJ, Brede B, Suomalainen JM, Bartholomeus HM, Kooistra L, Clevers JGPW (2018) Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data—potential of unmanned aerial vehicle imagery. Int J Appl Earth Obs Geoinf 66:14–26

    Google Scholar 

  • Safavi HR, Darzi F, Marino MA (2010) Simulation-optimization modelling of conjunctive use of surface water and groundwater. Water Res Manag 24(10):1965–1988

    Google Scholar 

  • Sahoo B, Lohani AK, Sahu RK (2006) Fuzzy multiobjective and linear programming based management models for optimal land-water-crop system planning. Water Resour Manag 20:931–948

    Google Scholar 

  • Sedghamiz A, Nikoo MR, Heidarpour M, Sadegh M (2018) Developing a non-cooperative optimization model for water and crop area allocation based on leader-follower game. J Hydrol 567:51–59

    Google Scholar 

  • Sedki A, Ouazar D (2011) Simulation-optimization modeling for sustainable groundwater development, a Moroccan coastal aquifer case study. Water Res Manag 2511:2855–2875

    Google Scholar 

  • Shahhosseini M, Martinez-Feria RA, Hu G, Archontoulis SV (2019) Maize yield and nitrate loss prediction with machine learning algorithms. Environ Res Lett 14:124026

    Google Scholar 

  • Sharma V, Irmak S, Djaman K, Sharma V (2016) Large-scale spatial and temporal variability in evapotranspiration, crop water-use efficiency and evapotranspiration water-use efficiency of irrigated and rainfed maize and soybean. J Irrig Drain Eng. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000985

    Article  Google Scholar 

  • Si X, Gao L, Song Y, Khayatnezhad M, Minaeifar AA (2020) Understanding population differentiation using geographical, morphological and genetic characterization in Erodium cicunium. Indian J Genet 80(4):459–467

    Google Scholar 

  • Singh A (2015) Land and water management planning for increasing farm income in irrigated dry areas. Land Use Pol 42:244–250

    Google Scholar 

  • Steduto P, Hsiao TC, Raes D, Fereres E (2009) AquaCrop—the FAO crop model to simulate yield response to water I. Concepts and underlying principles. Agron J 101:426–437

    Google Scholar 

  • Stricevic R, Cosic M, Djurovic N, Pejic B, Maksimovic L (2011) Assessment of the FAOAquaCrop model in the simulation of rainfed andsupplementary irrigated maize sugar beet andsunflower. Agri Water Manag 98:1615–1621

    Google Scholar 

  • Sun Q, Xu G, Ma C, Chen L (2017) Optimal crop-planting area considering the agricultural drought degree. J Irrig Drain Eng 143(12):04017050

    Google Scholar 

  • Teixeira SR, Arenales A, de Souza AAE, Da Silva R, Peña AFV, Aquino D, Freire R (2015) Sugarcane bagasse: applications for energy production and ceramic materials. J Solid Waste Tech Manag 41(3):229–238

    Google Scholar 

  • Tikhamarine Y, Souag-Gamane D, Najah Ahmed A, Kisi O, El-Shafie A (2020) Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey wolf optimization (GWO) algorithm. J Hydrol. https://doi.org/10.1016/j.jhydrol.2019.124435

    Article  Google Scholar 

  • Trout TJ, DeJonge KC (2018) Crop water use and crop coefficients of maize in the great plains. J Irrig Drain Eng 144(6):04018009

    Google Scholar 

  • United Nations (2012) World Population prospects: 2012 revision population database. United Nations http://www.un.org/esa/population/unpop.htm. Accessed 19 July 2013

  • Varade S, Patel JN (2018) Determination of optimum cropping pattern using advanced optimization algorithms. J Hydrol Eng 23(6):05018010

    Google Scholar 

  • Varzi M, Trout TJ, DeJonge KC, Oad R (2019) Optimal water allocation under deficit irrigation in the context of colorado water law. J Irri Drain Eng 145(5):0015634. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001374

    Article  Google Scholar 

  • Xu X, Huang G, Qu Z, Pereira LS (2010) Assessing the groundwater dynamics and impacts of water saving in the Hetao Irrigation District Yellow River basin. Agri Water Manag 98:301–313

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Irrigation and Drainage, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran

    Hossein Behdarvandi & Saeb Khoshnavaz

  2. Department of Civil Engineering-Water Resources Engineering and Management, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran

    Hossein Ghorbanizadeh Kharazi

  3. Department of Irrigation and Drainage, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Saeed Boroomand Nasab

Authors
  1. Hossein Behdarvandi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saeb Khoshnavaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hossein Ghorbanizadeh Kharazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Saeed Boroomand Nasab
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saeb Khoshnavaz.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Behdarvandi, H., Khoshnavaz, S., Ghorbanizadeh Kharazi, H. et al. Field-Scale Improvement of Water Allocation for Maize Cultivation Using Grey Wolf Optimization Algorithm. Iran J Sci Technol Trans Civ Eng 46, 631–641 (2022). https://doi.org/10.1007/s40996-020-00571-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40996-020-00571-x

Keywords

Search

Navigation