New model for sustainable management of pressurized irrigation networks. Application to Bembézar MD irrigation district (Spain)

Abstract

Pressurized irrigation networks require large amounts of energy for their operation which are linked to significant greenhouse gas (GHG) emissions. In recent years, several management strategies have been developed to reduce energy consumption in the agricultural sector. One strategy is the reduction of the water supplied for irrigation but implies a reduction in crop yields and farmer's profits. In this work, a new methodology is developed for sustainable management of irrigation networks considering environmental and economic criteria. The multiobjective non-dominated Sorting Genetic Algorithm (NSGA II) has been selected to obtain the optimum irrigation pattern that would reduce GHG emissions and increase profits. This methodology has been applied to Bembézar Margen Derecha (BMD) irrigation district (Spain). Irrigation patterns that reduce GHG emissions or increase actual profits are obtained. The best irritation pattern reduces the current GHG emissions in 8.56% with increases the actual profits in 14.56%. Thus, these results confirm that simultaneous improvements in environmental and economic factors are possible.

Keywords: A(c); Actual evapotranspiration (mmday(−1)); Annual Relative Irrigation Supply per crop; Application efficiency; Area of each crop (c) (ha); Average price per crop (c) (€kg(−1)); C; Carbon footprint; Conveyance efficiency; Crop index; Daily irrigation need per crop (c) and per month (m) (Lha(−1)day(−1)); Daily irrigation need per hydrant (i) and per month (m) (mm); E(T); ET(a); ET(m); Effective rainfall (mmday(−1)); Energy efficiency; Energy price (€kWh(−1)); Evapotranspiration in no water stress conditions (mmday(−1)); Evolutionary algorithms; H; IN(cm); IN(im); Irrigation; Irrigation area associated with each hydrant (i) (ha); Month index; N(c); N(m); Number of crops in the study area; Number of months of the irrigation season; P(e); Potential yield without limitations of water (kg); Power requirements at the pumping station in month (m) (kW); Power(m); Pr(E); Pr(c); Pressure head at the pumping station (m); Pumping system efficiency; RIS(c); S(i); Sustainability; Total energy consumed (kWh); V(m); Volume of water pumped per month (m) (m(3)); Water management; Water specific weight (Nm(−3)); Y(a); Y(m); Yield response factor; Yield under actual conditions (kg); d(m); day of each month (day); e(a); e(c); k(y); m; γ; η.