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Real-time irrigation scheduling improves irrigation water management and achieves higher irrigation system performance. This scheduling requires the prediction of daily reference evapotranspiration (ETo), which has been performed in some areas by using the Hargreaves-Samani (HS) equation or the Penman-Monteith (PM) equation with all of the required parameters obtained from forecasting services. Artificial neural networks (ANNs) and HS, which use forecasted daily maximum and minimum temperatures (T-MAX and T-MIN) as input data, were used to forecast ETo from 2011 to 2012 using PM as the reference methodology. A tool named FORETo (ETo forecasting) was implemented to transfer the proposed methodology to final users. This methodology and FORETo software were applied in the Hydro-geologic System 08.29 (Spain), where there is a high concentration of crops with high water consumption in semi-arid conditions. Two seasons of weekly field observations were collected to analyse the ability of FORETo to predict maize and onion crop water requirements. The results obtained from the comparison of daily ETo forecasts by FORETo and HS versus PM showed a better performance of the developed model. However, HS had good agreement, with root mean squared errors (RMSEs) lower than 0.98 mm day(-1) and an index of agreement (d) higher than 0.95. (C) 2015 Elsevier Ltd. All rights reserved.