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Assessment of optimal irrigation water allocation for pressurized irrigation system using water balance approach, learning machines, and remotely sensed data

Author

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  • Hassan-Esfahani, Leila
  • Torres-Rua, Alfonso
  • McKee, Mac

Abstract

Efficient irrigation can help avoid crop water stress, undesirable levels of nutrient leaching, and yield reduction due to water shortage, runoff or over irrigation. Gains in water use efficiency can be achieved when water application is precisely matched to the spatially distributed crop water demand. Thus, greater irrigation efficiency will facilitate quality crops and help to minimize additional agricultural and financial inputs. Irrigation efficiency is defined based on indicators such as irrigation uniformity, crop production, economic return, and water resources sustainability. This paper introduces a modeling approach for optimal water allocation relative to maximizing irrigation uniformity and minimizing yield reduction. Landsat images, local weather data, and field measurements were used to develop a model that describes field conditions using a soil water balance approach. The model includes two main modules: optimization of water allocation and forecasting the components of soil water balance model. Each module includes two sub-modules that consider two objectives. The optimization sub-module use genetic algorithms (GA) to identify optimal crop water application rates based on the crop type, growing stage, and sensitivity to water stress. Results from the optimization module are passed to the forecasting sub-module, which allocate water through time across the area covered by the center pivot based on the results from the previous period of irrigation (previous day) and the operational capacity of the center pivot irrigation system. The model was tested for a farm installed with alfalfa and oats and equipped with a center pivot in Scipio, Utah. The model products were assessed based on ground data (soil moisture measurements) under optimized and simulated (irrigator decisions) center pivot operations. Based on the simulation and optimization results obtained from the model, study area irrigator could use up to 20 percent less water (saved quantity over total quantity of water) over the growing season, compared to traditional operating procedures, without reducing the benefits.

Suggested Citation

  • Hassan-Esfahani, Leila & Torres-Rua, Alfonso & McKee, Mac, 2015. "Assessment of optimal irrigation water allocation for pressurized irrigation system using water balance approach, learning machines, and remotely sensed data," Agricultural Water Management, Elsevier, vol. 153(C), pages 42-50.
    • Handle: RePEc:eee:agiwat:v:153:y:2015:i:c:p:42-50
    DOI: 10.1016/j.agwat.2015.02.005
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    References listed on IDEAS

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    1. Torres, Alfonso F. & Walker, Wynn R. & McKee, Mac, 2011. "Forecasting daily potential evapotranspiration using machine learning and limited climatic data," Agricultural Water Management, Elsevier, vol. 98(4), pages 553-562, February.
    2. Cruz-Blanco, M. & Lorite, I.J. & Santos, C., 2014. "An innovative remote sensing based reference evapotranspiration method to support irrigation water management under semi-arid conditions," Agricultural Water Management, Elsevier, vol. 131(C), pages 135-145.
    3. repec:iwt:rerpts:h040313 is not listed on IDEAS
    4. Ines, Amor V.M. & Honda, Kiyoshi & Das Gupta, Ashim & Droogers, Peter & Clemente, Roberto S., 2006. "Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture," Agricultural Water Management, Elsevier, vol. 83(3), pages 221-232, June.
    5. Kuo, Sheng-Feng & Merkley, Gary P. & Liu, Chen-Wuing, 2000. "Decision support for irrigation project planning using a genetic algorithm," Agricultural Water Management, Elsevier, vol. 45(3), pages 243-266, August.
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