Introduction: Designing cropping systems that are well-adapted to water-limited conditions is one challenge of adapting agriculture to climate change. It requires estimating their impacts on crop water use and water resource availability in agricultural areas. Quantifying the evapotranspiration (ET) (plant transpiration + soil evaporation) according to their management is crucial because it has a direct impact on water availability in areas where water is scarce. Crop models such as AqYield are useful tools for evaluating effects of climate, soil and agricultural practices that directly impact water balance. While extending evaluation of model performance to the rotation scale is a priority since it is the temporal scale that makes sense for environmental assessment of cropping systems such an evaluation is rarely performed; probably due to difficulty to access to data on ET. Our main objective was then to evaluate the ability of AqYield to predict water flux dynamic like ET at the field scale over crop rotations. Materials and Methods: AqYield is a simple model with few input data. It only requires 4 soil properties, 3 daily climate features and dates for crop managements. The model simulates water balance components (soil available water, ET and drainage) at a daily time step, phenological stages and yield. It has already been satisfactory evaluated for spring crops in southwestern France. Predictions from continuous run of AqYield were evaluated with data (7 years with continuous measurements of daily turbulent ET fluxes) coming from two crop rotations (FR-Aur: wheat/sunflower/wheat/ rapeseed; FR-Lam: maize/wheat). Results and Discussion: The simple equations and empirical processes in AqYield appears relevant enough to predict daily and monthly ET fluxes at the crop rotation scale on both sites. AqYield adequately reproduced intra- and inter-annual variability of observed ET fluxes, with accurate prediction of low and high ET for a variety of crops and contrasting climatic years. ET at the end of growing seasons, however, was slightly underpredicted. Moreover, daily ET predictions were as accurate or more accurate than those predicted with the spatially distributed and more complex TNT2 model, which was evaluated using the same dataset. Conclusion: Whereas AqYield is simple and requires only a few input data, it allows accurate prediction of ET along cropping systems. It therefore could be useful as a module in more complex modeling approaches. For example, it can be used in the modeling platform MAELIA (http://maelia-platform.inra.fr/) for simulating cropping system scenarios at the watershed level.