Rapid and reliable observations of soil electrical conductivity are essential in order to maintain sustainable irrigated
agriculture. Direct measurement of the electrical conductivity of saturated soil paste (ECe), however, is tedious and
time consuming. Therefore, there are needs to find efficient indirect methods to predict the soil salinity from other readily
available observations. In this paper we explore the application of multiple linear regression (MLR) and artificial neural
networks (ANN) to predict ECe variation from easily measured soil and groundwater properties under highly complex and
heterogeneous field conditions in semiarid Tunisia. We compare two methods for dividing the data set into training and
validation sub-sets; a statistical (SD) and a random data set division (RD), and their effect on model performance. The input
variables were chosen from the plot coordinates, groundwater table properties (depth, electrical conductivity, piezometric
level), and soil particle size at 5 depths. The results obtained with ANN and MLR indicate that the statistical
properties of data in the training and validation sets need to be taken into account to ensure that optimal model performance
is achieved. The SD can be considered as a solution to resolve the problem of over-fitting a model when using ANN.
For the SD, the determination coefficient (R2) when using an ANN model varied from 0.85 to 0.88 and the root mean
square error from 1.23 to 1.80 dS m-1. Because of the complexity of the field soil salinity process and the spatial variability
of the data, this clearly indicates the potential to use ANN models to predict ECe.