Skip to main content
Log in

Investigation of deficit irrigation strategies combining SVAT-modeling, optimization and experiments

  • Thematic Issue
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Irrigation farming is the greatest consumer of the Earth’s freshwater resources. In the light of an increasing water demand caused by growing population, effective methods are required that use the available water resources efficiently and increase the overall productivity of irrigation systems. In this contribution, a combined approach of simulation–optimization and experiments was applied to investigate and evaluate two different irrigation strategies and their parameters for maize with the objective to achieve high water productivity (WP) with high reliability. Thereby, a soil–vegetation–atmosphere transfer (SVAT) model was used to simulate crop growth and soil water transport, together with task-specific optimization algorithms to determine optimal parameters for irrigation schedules and sensor-based full and deficit irrigation controls. An intensively monitored 3-year irrigation experiment was conducted for testing different irrigation designs and verifying the simulation–optimization approach. A new sensor for measuring soil water potentials from pF 0 to pF 7 allowed for applying optimized irrigation thresholds greater than 1,000 hPa. Attained \(\mathrm{WP}_{\mathrm{ET}}\) from the irrigation experiments were generally high and ranged from 1.8 to \(2.3\hbox { kg m}^{-3}\). The impact of irrigation system parameters on WP, such as irrigation interval, sensor depth, number of irrigation thresholds, and the soil’s initial water content were evaluated and discussed. Results indicate that thresholds beyond the measurement range of commonly used tensiometers are feasible. Furthermore, the combination of SVAT-modeling and optimization has the potential to systematically investigate and improve irrigation systems as well as to reduce the number of required field experiments.

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

Access this article

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

  • Abrahamsen P, Hansen S (2000) Daisy: an open soil-crop-atmosphere system model. Environ Model Softw 15(3):313–330. doi:10.1016/S1364-8152(00)0000-7

    Article  Google Scholar 

  • Asseng S, Ritchie J, Smucker A, Robertson M (1998) Root growth and water uptake during water deficit and recovering in wheat. Plant Soil 201(2):265–273. doi:10.1023/A:1004317523264

    Article  Google Scholar 

  • Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn PJ, Rotter RP, Cammarano D, Brisson N, Basso B, Martre P, Aggarwal PK, Angulo C, Bertuzzi P, Biernath C, Challinor AJ, Doltra J, Gayler S, Goldberg R, Grant R, Heng L, Hooker J, Hunt LA, Ingwersen J, Izaurralde RC, Kersebaum KC, Muller C, Nendel C (2013) Uncertainty in simulating wheat yields under climate change. Nat Clim Chang 3(9):827–832. doi:10.1038/nclimate1916

    Article  Google Scholar 

  • Brown PD, Cochrane TA, Krom TD (2010) Optimal on-farm irrigation scheduling with a seasonal water limit using simulated annealing. Agric Water Manag 97(6):892–900. doi:10.1016/j.agwat.2010.01.020

    Article  Google Scholar 

  • Brumbelow K, Georgakakos A (2007) Consideration of climate variability and change in agricultural water resources planning. J Water Resour Plan Manag ASCE 133(3):275–285. doi:10.1061/(ASCE)0733-9496(2007)133:3(275)

    Article  Google Scholar 

  • Dabach S, Lazarovitch N, Simunek J, Shani U (2013) Numerical investigation of irrigation scheduling based on soil water status. Irrig Sci 31(1):27–36. doi:10.1007/s00271-011-0289-x

    Article  Google Scholar 

  • English M (1990) Deficit irrigation: 1. Analytical framework. J Irrig Drain Eng ASCE 116(3):399–412. doi:10.1061/(ASCE)0733-9437(1990)116:3(399)

    Article  Google Scholar 

  • FAO (2010) Fao’s information system on water and agriculture—water use. http://www.fao.org/nr/water/aquastat/water_use/index.stm, (Sept. 25, 2013)

  • Feng S, Huo Z, Kang S, Tang Z, Wang F (2011) Groundwater simulation using a numerical model under different water resources management scenarios in an arid region of China. Environ Earth Sci 62(5):961–971. doi:10.1007/s12665-010-0581-8

    Article  Google Scholar 

  • Garcia y, Garcia A, Guerra LC, Hoogenboom G (2008) Impact of generated solar radiation on simulated crop growth and yield. Ecol Model 210(3):312–326. doi:10.1016/j.ecolmodel.2007.08.003

    Article  Google Scholar 

  • Grundmann J, Schütze N, Schmitz GH, Al-Shaqsi S (2012) Towards an integrated arid zone water management using simulation-based optimisation. Environ Earth Sci 65(5, SI):1381–1394. doi:10.1007/s12665-011-1253-z

    Article  Google Scholar 

  • Hansen N (2006) Towards a new evolutionary computation. In: Lozano J, Larranaga P, Inza I, Bengoetxea E (eds) The CMA evolution strategy: a comparing review, Studies in Fuzziness and Soft Computing. Springer, pp 75–102. doi:10.1007/3-540-32494-1_4

  • Jones HG (2004) Irrigation scheduling: advantages and pitfalls of plant-based methods. J Exp Bot 55(407):2427–2436. doi:10.1093/jxb/erh213

    Article  Google Scholar 

  • Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes JP, Silburn M, Wang E, Brown S, Bristow KL, Asseng S, Chapman S, McCown RL, Freebairn DM, Smith CJ (2003) An overview of APSIM, a model designed for farming systems simulation. Eur J Agron 18(3–4):267–288. doi:10.1016/S1161-0301(02)00108-9

    Article  Google Scholar 

  • Kloss S, Pushpalatha R, Kamoyo KJ, Schütze N (2012) Evaluation of crop models for simulating and optimizing deficit irrigation systems in arid and semi-arid countries under climate variability. Water Resour Manag 26(4):997–1014. doi:10.1007/s11269-011-9906-y

    Article  Google Scholar 

  • Kloss S, Schütze N, Schmidhalter U (2014) Evaluation of very high soil water tension threshold values in sensor-based deficit irrigation. J Irrig Drain Eng. doi:10.1061/(ASCE)IR.1943-4774.0000722

  • Liu S, Wang T (2012) Climate change and local adaptation strategies in the middle Inner Mongolia, northern China. Environ Earth Sci 66(5):1449–1458. doi:10.1007/s12665-011-1357-5

    Article  Google Scholar 

  • Mailhol JC, Olufayo AA, Ruelle P (1997) Sorghum and sunflower evapotranspiration and yield from simulated leaf area index. Agric Water Manag 35(1–2):167–182. doi:10.1016/S0378-3774(97)00029-2

    Article  Google Scholar 

  • Mailhol JC, Ruelle P, Walser S, Schütze N, Dejean C (2011) Analysis of AET and yield predictions under surface and buried drip irrigation systems using the crop model PILOTE and Hydrus-2D. Agric Water Manag 98(6):1033–1044. doi:10.1016/j.agwat.2011.01.014

    Article  Google Scholar 

  • Masoud A, Atwia M (2011) Spatio-temporal characterization of the Pliocene aquifer conditions in Wadi El-Natrun area, Egypt. Environ Earth Sci 62(7):1361–1374. doi:10.1007/s12665-010-0623-2

    Article  Google Scholar 

  • McCarthy AC, Hancock NH, Raine SR (2013) Advanced process control of irrigation: the current state and an analysis to aid future development. Irrig Sci 31(3):183–192. doi:10.1007/s00271-011-0313-1

    Article  Google Scholar 

  • Molden D, Oweis T, Steduto P, Bindraban P, Hanjra MA, Kijne J (2010) Improving agricultural water productivity: between optimism and caution. Agric Water Manag 97(4, SI):528–535. doi:10.1016/j.agwat.2009.03.023

    Article  Google Scholar 

  • Palosuo T, Kersebaum KC, Angulo C, Hlavinka P, Moriondo M, Olesen JE, Patil RH, Ruget F, Rumbaur C, Takáč J, Trnka M, Bindi M, Çaldağ B, Ewert F, Ferrise R, Mirschel W, Şaylan L, Šiška B, Rötter R (2011) Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. Eur J Agron 35(3):103–114. doi:10.1016/j.eja.2011.05.001

    Article  Google Scholar 

  • Panda RK, Behera SK, Kashyap PS (2004) Effective management of irrigation water for maize under stressed conditions. Agric Water Manag 66(3):181–203. doi:10.1016/j.agwat.2003.12.001

    Article  Google Scholar 

  • Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci 11(5):1633–1644. doi:10.5194/hess-11-1633-2007

    Article  Google Scholar 

  • Rivera-Hernandez B, Carrillo-Avila E, Obrador-Olan JJ, Juarez-Lopez JF, Aceves-Navarro L (2010) Morphological quality of sweet corn (\(Zea\, mays\) L.) ears as response to soil moisture tension and phosphate fertilization in Campeche, Mexico. Agric Water Manag 97(9):1365–1374. doi:10.1016/j.agwat.2010.04.001

    Article  Google Scholar 

  • Schütze N, Schmitz GH (2010) OCCASION: new planning tool for optimal climate change adaption strategies in irrigation. J Irrig Drain Eng ASCE 136(12):836–846. doi:10.1061/(ASCE)IR.1943-4774.0000266

    Article  Google Scholar 

  • Schütze N, Kloss S, Lennartz F, Schmitz GH (2012a) Optimal Planning and Operation of irrigation systems under water resource constraints in Oman considering climatic uncertainty. Environ Earth Sci 65(5,SI):1511–1521. doi:10.1007/s12665-011-1135-4

    Article  Google Scholar 

  • Schütze N, de Paly M, Shamir U (2012b) Novel simulation-based algorithms for optimal open-loop and closed-loop scheduling of deficit irrigation systems. J Hydroinf 14(1):136–151. doi:10.2166/hydro.2011.073

    Article  Google Scholar 

  • Seidel S (2012) Optimal simulation based design of deficit irrigation experiments, Dresdner Schriften zur Hydrologie, vol 12. Techn. Univ., Institut für Hydrologie und Meteorologie, Dresden

    Google Scholar 

  • Semenov MA (2007) Development of high-resolution UKCIP02-based climate change scenarios in the UK. Agric For Meteorol 144(1–2):127–138. doi:10.1016/j.agrformet.2007.02.003

    Article  Google Scholar 

  • Semenov MA, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10(2):95–107. doi:10.3354/cr010095

    Article  Google Scholar 

  • Shock CC, Wang F (2011) Soil water tension, a powerful measurement for productivity and stewardship. Hortscience 46(2):178–185

    Google Scholar 

  • Soltani A, Hoogenboom G (2007) Assessing crop management options with crop simulation models based on generated weather data. Field Crops Res 103(3):198–207. doi:10.1016/j.fcr.2007.06.003

    Article  Google Scholar 

  • Soundharajan B, Sudheer KP (2009) Deficit irrigation management for rice using crop growth simulation model in an optimization framework. Paddy Water Environ 7(2):135–149. doi:10.1007/s10333-009-0156-z

    Article  Google Scholar 

  • Steele DD, Stegman EC, Gregor BL (1994) Field comparison of irrigation scheduling methods for corn. Trans ASAE 37(4):1197–1203

    Article  Google Scholar 

  • Vazifedoust M, van Dam JC, Feddes RA, Feizi M (2008) Increasing water productivity of irrigated crops under limited water supply at field scale. Agric Water Manag 95(2):89–102. doi:10.1016/j.agwat.2007.09.007

    Article  Google Scholar 

  • Wang D, Kang Y, Wan S (2007) Effect of soil matric potential on tomato yield and water use under drip irrigation condition. Agricu Water Manag 87(2):180–186. doi:10.1016/j.agwat.2006.06.021

    Article  Google Scholar 

  • Wang F, Kang Y, Liu S, Hou X (2007) Effects of soil matric potential on potato growth under drip irrigation in the North China plain. Agric Water Manag 88(1–3):34–42. doi:10.1016/j.agwat.2006.08.006

    Article  Google Scholar 

  • Weaver J (1926) Root development of field crops, 1st edn. McGraw-Hill Book Co., New York

    Google Scholar 

  • Werisch S, Grundmann J, Al-Dhuhli H, Algharibi E, Lennartz F (2014) Multiobjective parameter estimation of hydraulic properties for a sand soil in Oman. Environ Earth Sci. doi:10.1007/s12665-014-3537-6

  • Zwart SJ, Bastiaanssen WGM (2004) Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric Water Manag 69(2):115–133. doi:10.1016/j.agwat.2004.04.007

    Article  Google Scholar 

Download references

Acknowledgments

The manuscript was prepared within the research project IWAS funded by the German Federal Ministry of Education and Research (BMBF) under Grant No. 02WM1166.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sebastian Kloss.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kloss, S., Grundmann, J., Seidel, S.J. et al. Investigation of deficit irrigation strategies combining SVAT-modeling, optimization and experiments. Environ Earth Sci 72, 4901–4915 (2014). https://doi.org/10.1007/s12665-014-3463-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-014-3463-7

Keywords

Navigation