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Optimization of Irrigation Scheduling Using Soil Water Balance and Genetic Algorithms

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Abstract

In arid and semi-arid countries, the use of irrigation is essential to ensure agricultural production. Irrigation water use is expected to increase in the near future due to several factors such as the growing demand of food and biofuel under a probable climate change scenario. For this reason, the improvement of irrigation water use efficiency has been one of the main drivers of the upgrading process of irrigation systems in countries like Spain, where irrigation water use is around 70 % of its total water use. Pressurized networks have replaced the obsolete open-channel distribution systems and on farm irrigation systems have been also upgraded incorporating more efficient water emitters like drippers or sprinklers. Although pressurized networks have significant energy requirements, increasing operational costs. In these circumstances farmers may be unable to afford such expense if their production is devoted to low-value crops. Thus, in this work, a new approach of sustainable management of pressurized irrigation networks has been developed using multiobjective genetic algorithms. The model establishes the optimal sectoring operation during the irrigation season that maximize farmer’s profit and minimize energy cost at the pumping station whilst satisfying water demand of crops at hydrant level taking into account the soil water balance at farm scale. This methodology has been applied to a real irrigation network in Southern Spain. The results show that it is possible to reduce energy cost and improve water use efficiency simultaneously by a comprehensive irrigation management leading, in the studied case, to energy cost savings close to 15 % without significant reduction of crop yield.

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Abbreviations

Ah :

Irrigated area supplied by hydrant h (ha)

AT :

Total irrigated area by all hydrants (ha)

ADI:

Accumulated Deep infiltration losses (mm)

DIh :

Deep infiltration losses corresponding to the hydrant h (mm)

ERd :

Effective rainfall of the day d (mm)

ET,h :

Seasonal energy cost in the pumping station corresponding to the hydrant h (€)

ET0,d :

Reference crop evapotranspiration of the day d (mm day−1)

ETh :

Actual evapotranspiration for the crop of the hydrant h (mm day−1)

ETmax d,h :

Evapotranspiration in no water stress conditions for the crop of the hydrant h in the days d of crop development (mm day−1)

ETmax,h :

Evapotranspiration in no water stress conditions for the crop of the hydrant h (mm day−1)

ETw :

Evapotranspiration in so-called wilting point (mm day−1)

Fd,h :

Demanded flow in the pumping station corresponding to the hydrant h on the day d (m3 s−1)

Hd,h :

Pressure head at the pumping station corresponding to irrigation sector of the hydrant h on the day d (mm)

hT :

Total number of hydrants of the irrigation network

Id,h :

Applied irrigation depth to the crop of the hydrant h on the day d (mm)

Kc d,h :

Crop coefficient of the hydrant h taking into account days d of crop development

Ksat :

Saturated hydraulic conductivity of the soil in the study area (mm day−1)

ky :

Yield response factor

n:

Soil porosity

Prh :

Average market price of the crop of the hydrant h during irrigation season (€ kg−1)

s* :

Relative soil moisture from which the crops start to reduce transpiration

sd,h :

Relative soil moisture corresponding to the field of the hydrant h on the day d

sd-1,h :

Relative soil moisture of the hydrant h on the day d-1

sfc :

Relative soil moisture in field capacity

shg :

Relative soil moisture in so-called hygroscopic point

sirrigation :

Relative soil moisture that determines the beginning of the irrigation

sw :

Relative soil moisture in the wilting point

td,h :

Irrigation time of the hydrant h on the day d (hours)

UCE :

Unit energy cost (€ kWh−1)

Yh :

Yield under actual conditions for the crop of the hydrant h (kg ha−1)

Ymax,h :

Potential yield for the crop of the hydrant h when there are not limitations of water (kg ha−1)

Yr,h :

Relation between yield under actual conditions for the crop of the hydrant h and Y max, h

Zr d,h :

Active soil depth (where most of crop roots associated to hydrant h on the day d are located) (mm)

β:

Coefficient which is used to fit the above expression to the power law

γ:

water specific weight (kN m−3)

η:

Pumping system efficiency

θd-1,h :

Volumetric soil moisture corresponding to the field of the hydrant h on the day d (cm3 cm−3)

θhg :

Volumetric soil moisture at hygroscopic point (cm3 cm−3)

θw :

Volumetric soil moisture at wilting point (cm3 cm−3)

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Acknowledgments

This research is part of the TEMAER project (AGL2014-59747-C2-2-R), funded by the Spanish Ministry of Economy and Competitiveness.

This research was supported by an FPU grant (Formación de Profesorado Universitario) from the Spanish Ministry of Education, Culture and Sports to Rafael González Perea.

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Authors and Affiliations

  1. Department of Agronomy, University of Córdoba, Campus Rabanales, Edif. da Vinci, 14071, Córdoba, Spain

    R. González Perea, E. Camacho Poyato, P. Montesinos & J. A. Rodríguez Díaz

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  1. R. González Perea
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  2. E. Camacho Poyato
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  3. P. Montesinos
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  4. J. A. Rodríguez Díaz
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Correspondence to R. González Perea.

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González Perea, R., Camacho Poyato, E., Montesinos, P. et al. Optimization of Irrigation Scheduling Using Soil Water Balance and Genetic Algorithms. Water Resour Manage 30, 2815–2830 (2016). https://doi.org/10.1007/s11269-016-1325-7

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  • DOI: https://doi.org/10.1007/s11269-016-1325-7

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