Research Article Linear Optimization Model for Efficient Use of Irrigation Water Wafa Difallah, 1 Khelifa Benahmed, 2 Belkacem Draoui, 1 and Fateh Bounaama 1 1 Laboratory of Energetic in Arid Zones (ENERGARID), Department of Electrical Engineering, Faculty of Technology, Tahri Mohammed University of Bechar, Bechar, Algeria 2 Department of Mathematics and Computer Science, Faculty of Exact Sciences, Tahri Mohammed University of Bechar, Bechar, Algeria Correspondence should be addressed to Wafa Difallah; wafadif@gmail.com Received 8 May 2017; Accepted 27 June 2017; Published 26 July 2017 Academic Editor: David Clay Copyright © 2017 Wafa Difallah et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Te implementation of innovative and efcient irrigation techniques is among the greatest challenges facing agriculture. In this regard, a linear programming model is presented in order to optimize water use. Te idea behind this model is to assess the efectiveness or inefectiveness of precipitation to determine the amount of irrigation water required to optimize water use. To achieve this idea, the “knapsack” problem decisional form was used, and the combination of the linear programming and the above-mentioned form proved satisfactory. Field experiments were conducted in Algeria. Based on calculated budgets a model using linear programming was developed. A comparison between the model results and the feld fndings suggests that the model could reduce water consumption by 28.5%. 1. Introduction Irrigation is the most used means of agricultural intensif- cation and will stay a cornerstone in the domain of food security policies towards climatic variability [1]. It can be defned as artifcial implementation of water in agriculture and is regarded as a very signifcant constituent of agrarian activity [2]. In most countries, water is used for irrigating land more than for any other purpose, and any decrease in water supply can cause production and yield to decrease. In order to reduce water consumption without compro- mising agricultural yield as an economic mainstay, various studies have been carried out by researchers, who had diferent visions on the subject. Some researchers attempted to estimate evapotranspira- tion to determine water needs and hence to optimize the use of irrigation water. A neural network approach was proposed by Laaboudi et al. [3] to estimate the reference evapotranspiration and resolve the problem of the high num- ber of climatic parameters needed to use Penman Montheith equation. Takakura et al. [4] used the energy balance equation to measure evapotranspiration. Takakura et al. proved in their study the simplicity and suitability of their method for irrigation. For the same purpose, support vector machine method was used by Yao et al. [5] and artifcial neural networks technique was used by King et al. [6]. Extraterrestrial radi- ation, air temperature, and humidity were used in [7] to estimate potential evapotranspiration and to assess future climate change efects on the vegetation. Other researchers insist on the necessity of considering the economic factors [8, 9], to decide about irrigation depths and select crop zones for economic beneft; Kuo & Liu [8] developed a customized genetic algorithm. For the same purpose, the exact optimization methods were also used, to mention but a few, linear programming [10–12], nonlinear programming [13], quadratic programming [14], and dynamic programming [9]. Many authors used real time monitoring applications [15] by means of wireless sensor networks (WSNs). Tese networks make possible a timely and minute irrigation application according to the existing environmental condi- tions, resource availability, and weather forecasts [16, 17]. Te potential applications of WSN in irrigation cover a large set of scenarios and applications [18]. For instance, a real time intelligent irrigation controller has been conceived in order to detect the optimal irrigation time [19]. For the same purpose, a wireless data acquisition network was Hindawi International Journal of Agronomy Volume 2017, Article ID 5353648, 8 pages https://doi.org/10.1155/2017/5353648