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Monitoring soil salinity over time is a crucial issue in Saharan oases to anticipate salinisation related to insufficient irrigation management. This project tested the ability of electromagnetic conductivity surveys to describe, by means of regression-tree inference models, spatiotemporal changes in soil salinity at different depths within a complex 10-ha pattern of irrigated plots in an Algerian oasis. Soils were sandy Aridic Salic Solonchaks with a fluctuating saline watertable at less than 2 m. Apparent electrical conductivity (ECa) was measured by an EM38 device at fixed 10- or 20-m intervals (2889 points) at four sampling dates between March 2009 and November 2010. For calibration and validation purposes, soil salinity was measured from a 1 : 5 diluted extract (EC1:5) in three layers (0-10, 10-25, 25-50 cm) at 30 of these points randomly chosen at each date. ECa measurements were used to predict EC1: 5 using calibration regression trees created with the software Cubist, including either parameters specific to the study site (specific model) or more general parameters (general model), allowing extrapolation to other sites. Performance of regression tree predictions was compared with predictions derived from a multiple linear regression (MLR) model adjusted for each date using the software ESAP. Salinity was better predicted by Cubist regression tree models than MLR models. For the deep layer (25-50 cm), Cubist models were more accurate with the specific model (r(2) = 0.8, RMSE = 1.6 dS/m) than the general model (r(2) = 0.4, RMSE = 2.5 dS/m). Prediction accuracy of both models decreased from the bottom to the top of the soil profile. Salinity maps showed high inter-plot variability, which was captured better by the more flexible regression-tree inference models than the classic MLR models, but they need to build site-specific prediction models. Overall, the monitoring surveys, combined with the Cubist prediction tool, revealed both the seasonal dynamics and spatial variability of salinity at different depths.