Agricultural Research

, Volume 7, Issue 2, pp 158–166 | Cite as

Simulating the Response of Sugarcane Production to Water Deficit Irrigation Using the AquaCrop Model

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Abstract

A water shortage has always been the main limiting factor of agricultural development in arid and semiarid areas. In this research, the yield response to water stress for sugarcane in Khuzestan, Iran, was studied using an AquaCrop model under deficit irrigation scenarios (2004–2010). Treatments were contained 100% (I1), 125% (I2), 85% (I3) and 70% (I4) of full irrigation. The results indicated that the simulating sugarcane yield was acceptable with normalized root-mean square error (NRMSE) = 1.89 and root-mean-square error (RMSE) = 1.7. The statistical analysis of yield showed that there was no significant difference between I1, I2 and I3 treatments. Simulating the water productivity (WP) was acceptable (NRMSE = 1.44 and RMSE = 0.06). All the treatments were significantly different than the treatment I1 in simulated WP. Agreement index for yield and WP (0.91 and 0.99) showed compatibility of these predicted values with the actual value. The results showed that the AquaCrop model is an appropriate tool to simulate sugarcane yield and water productivity under deficit irrigation conditions in Iran. According to the results, the I3 scenario can be recommended.

Keywords

AquaCrop Sugarcane Water productivity Water stress 

References

  1. 1.
    Abedinpour M, Sarangi A, Rajput TBS, Singh M, Pathak H, Ahmad T (2012) Performance evaluation of AquaCrop model for maize crop in a semi-arid environment. Agric Water Manag 110:55–66CrossRefGoogle Scholar
  2. 2.
    Abi Saab MT, Albrizio R, Nangia V, Karam F, Rouphae Y (2014) Developing scenarios to assess sunflower and soybean yield under different sowing dates and water regimes in the Bekaa valley (Lebanon): simulations with Aquacrop. Int J Plant Prod 8(4):457–482Google Scholar
  3. 3.
    Alizadeh HA, Nazari B, Parsinejad M, Ramezani-Eetedali H, Janbaz HR (2010) Evaluation of AquaCrop model on wheat deficit irrigation in Karaj area. Iran J Irrig Drain 2(4):273–283 (in Persian) Google Scholar
  4. 4.
    Amer KH, Sally AM, Jerry LH (2009) Effect of deficit irrigation and fertilization on cucumber. Agron J 101:1556–1564CrossRefGoogle Scholar
  5. 5.
    Andarzian B, Bannayan M, Steduto P, Mazraeh H, Barati ME, Barati MA, Rahnama A (2011) Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agric Water Manag 100:1–8CrossRefGoogle Scholar
  6. 6.
    Araya A, Habtub S, Hadguc KM, Kebedea A, Dejened T (2010) Test of AquaCrop model in simulating biomass and yield of water deficient and irrigated barley (Hordeum vulgare). Agric Water Manag 97:1838–1846CrossRefGoogle Scholar
  7. 7.
    Araya A, Keesstra SD, Stroosnijder L (2010) Simulating yield response to water of Teff (Eragrostis tef) with FAO’s AquaCrop model. Field Crop Res 116:196–204CrossRefGoogle Scholar
  8. 8.
    Bahmani O (2011) The management of water stress to efficient use of water and nitrogen fertilizer in sugarcane fields. Iran J Water Res 8:153–160 (in Persian) Google Scholar
  9. 9.
    Baumhardt RL, Staggenborg SA, Gowda PH, Colaizzi PD, Howell TA (2009) Modelling irrigation management strategies to maximize cotton lint yield and water use efficiency. Agron J 101:460–468CrossRefGoogle Scholar
  10. 10.
    Conesa MR, Falagan N, De la Rosa JM, Aguayo E, Domingo R, Perez Pastor A (2016) Post-version deficit irrigation regimes enhance berry coloration and health-promoting bioactive compounds in ‘Crimson Seedless’ table grapes. Agric Water Manag 163:9–18CrossRefGoogle Scholar
  11. 11.
    CPF (2012) Country Programming Framework (2012–2016) for Iran’s Agriculture Sector Prepared by Government of Islamic Republic of Iran (GOI), Ministry of Jihad-e-Agriculture (MOJA) and Food and Agriculture Organization (FAO) of the United NationsGoogle Scholar
  12. 12.
    Deng XP, Shan L, Zhang H, Turner NC (2006) Improving agricultural water use efficiency in arid and semi-arid areas of China. Agric Water Manag 80:23–40CrossRefGoogle Scholar
  13. 13.
    Doorenbos J, Kassam AH (1979) Yield Response to Water. FAO Irrigation and Drainage Paper No. 33. Rome, FAOGoogle Scholar
  14. 14.
    FAO (2015) Crop production. Food and Agriculture Organization of the United Nations. Retrieved 2015Google Scholar
  15. 15.
    Geerts S, Raes D, Garcia M, Miranda R, Cusicanqui JA, Taboada C, Mendoza J, Huanca R, Mamani A, Condori O, Mamani J, Morales B, Osco V, Steduto P (2009) Simulating yield response of quinoa to water availability with AquaCrop. Agron J 101:499–508CrossRefGoogle Scholar
  16. 16.
    Heng LK, Hsiao T, Evett S, Howell T, Steduto P (2009) Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agron J 101:488–498CrossRefGoogle Scholar
  17. 17.
    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 101:448–459CrossRefGoogle Scholar
  18. 18.
    Hussein F, Janat M, Yakoub A (2011) Simulating cotton yield response to deficit irrigation with the FAO AquaCrop model. J Agric 9:1319–1330Google Scholar
  19. 19.
    Iqbal MA, Shen Y, Stricevic R, Pei H, Sun H, Amiri E, Penas A, Del Rio S (2014) Evaluation of the FAO AquaCrop model for winter wheat on the North China Plain under deficit irrigation from field experiment to regional yield simulation. Agric Water Manag 135:61–72CrossRefGoogle Scholar
  20. 20.
    Krause P, Boyle DP, Bäse F (2005) Advances in geosciences comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97CrossRefGoogle Scholar
  21. 21.
    Liu HF, Genard M, Guichard S, Bertin N (2007) Model-assisted analysis of tomato fruit growth in relation to carbon and water fluxes. J Exp Bot 58(13):3567–3580CrossRefPubMedGoogle Scholar
  22. 22.
    Mabhaudhi T, Modi AT, Beletse YG (2014) Parameterization and evaluation of the FAO-AquaCrop model for a South African taro (Colocasia esculenta L. Schott) landrace. Agric For Meteorol 192–193:132–139CrossRefGoogle Scholar
  23. 23.
    Masanganise J, Basira K, Chipindu B, Mashonjowa E, Mhizha T (2013) Testing the utility of a crop growth simulation model in predicting maize yield in a changing climate in Zimbabwe. J Agric Food Sci 3(4):157–163Google Scholar
  24. 24.
    Mkhabela MS, Bullock PR (2012) Performance of the FAO AquaCrop model for wheat grain yield and soil moisture simulation in Western Canada. Agric Water Manag 110:16–24CrossRefGoogle Scholar
  25. 25.
    Mulubrehan K, Gebretsadikan TG (2016) Yield and water use efficiency of furrow irrigated potato under regulated deficit irrigation, Atsibi-Wemberta, North Ethiopia. Agric Water Manag 170:133–139CrossRefGoogle Scholar
  26. 26.
    Salemi HR, Soom MAM, Lee TS, Mousavi SF, Ganji A, Yusoff MK (2011) Application of AquaCrop model in deficit irrigation management of Winter wheat in arid region. Afr J Agric Res 610:2204–2215Google Scholar
  27. 27.
    Sincik M, Candogan BM, Demirtas C, BüyükCangaz H, Yazgan S, Göksoy AT (2008) Deficit irrigation of soya bean (Glycine max (L.) Merr.) in a sub-humid climate. J Agron Crop Sci 194:200–205CrossRefGoogle Scholar
  28. 28.
    Steduto P, Hsiao TC, Fereres E (2007) On the conservative behavior of biomass water productivity. Irrig Sci 25:189–207CrossRefGoogle Scholar
  29. 29.
    Sugarcane Research Center (2010) Sugarcane Development Company and Side Industries. Annual Report. Ahvaz, IranGoogle Scholar
  30. 30.
    Todorovic M, Albrizio R, Zivotic L, Saab MTA, Stockle C, Steduto P (2009) Assessment of Aqua Crop, CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes. Agron J 101(3):509–521CrossRefGoogle Scholar
  31. 31.
    Van Gaelen H, Delbecque N, Abrha B, Tsegay A, Raes D (2016) Simulation of crop production in weed-infested fields for data-scarce regions. J Agric Sci 154(6):1026–1039CrossRefGoogle Scholar
  32. 32.
    Vanuytrecht E, Raes D, Willems P (2011) Considering sink strength to model crop production under elevated atmospheric CO2. Agric For Meteo 151:1753–1762CrossRefGoogle Scholar
  33. 33.
    Wise R, Cacho O (2005) Tree-crop interactions and their environmental and economic implications in the presence of carbon-sequestration payments. Environ Modell Softw 20:1139–1148CrossRefGoogle Scholar
  34. 34.
    Zeleke KT, Luckett D, Cowley R (2011) Calibration and testing of the FAO AquaCrop model for canola. Agron J 103:1610–1618CrossRefGoogle Scholar
  35. 35.
    Zinyengere N, Mhizha T, Mashonjowa E, Chipindu B, Geerts S, Raes D (2011) Using seasonal climate forecasts to improve maize production decision support in Zimbabwe. Agric For Meteorol 151:1792–1799CrossRefGoogle Scholar

Copyright information

© NAAS (National Academy of Agricultural Sciences) 2018

Authors and Affiliations

  1. 1.Department of Water Engineering, Faculty of AgricultureBu-Ali Sina UniversityHamedanIran
  2. 2.Faculty of AgricultureBu-Ali Sina UniversityHamedanIran

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