LAUSR

In variable interval irrigation, simply including soil salinity data in the soil salinity model is not valid for making predictions, because changes in irrigation frequency must also be taken into account. This study on variable interval irrigation used capacitance soil sensors simultaneously to obtain hourly measurements of bulk electrical conductivity (σb), soil temperature (t) and soil water content (θ). Observations of σb were converted so that the electrical conductivity of the pore water (σp) could be estimated as an indicator of soil salinity. Values of θ, t and σp were used to test a mathematical model for studying how σp cross-correlates with t and θ to predict soil salinity at a given depth. These predictions were based on measurements of σp, t, and θ at a shallow depth. As a result, prediction at shallow depth was successful after integrating intervention analysis and outlier detection into the seasonal autoregressive integrated moving average (ARIMA) model. We then used the (multiple-input/ one-output) transfer function models to logically predict soil salinity at the depths of interest. The model could also correctly determine the effect of the irrigation event on soil salinity. Copyright © 2017 John Wiley & Sons, Ltd. key words: capacitance device; pore water electrical conductivity; autoregressive integrated moving average (ARIMA) model; outlier detection; transfer function model Received 9 July 2016; Revised 27 September 2017; Accepted 27 September 2017