Optimization of Irrigation Scheduling Using Soil Water Balance and Genetic Algorithms

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.