Transfer Function and Time Series Outlier Analysis: Modelling Soil Salinity in Loamy Sand Soil by Including the Influences of Irrigation Management and Soil Temperature

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.

[1]  F. E. Grubbs.  Procedures for Detecting Outlying Observations in Samples , 1969 .

[2]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[3]  Richard A. Berk,et al.  Applied Time Series Analysis for the Social Sciences , 1980 .

[4]  K. Beven,et al.  Macropores and water flow in soils , 1982 .

[5]  Walter Vandaele,et al.  Applied Time Series and Box-Jenkins Models , 1983 .

[6]  John C. Hoff,et al.  A practical guide to Box-Jenkins forecasting , 1983 .

[7]  Alan Pankratz,et al.  Forecasting with univariate Box-Jenkins models : concepts and cases , 1983 .

[8]  R. E. White,et al.  The Influence of Macropores on the Transport of Dissolved and Suspended Matter Through Soil , 1985 .

[9]  T. Quinn.  Catch-Per-Unit-Effort: A Statistical Model for Pacific Halibut (Hippoglossus stenolepis) , 1985 .

[10]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[11]  R. Mckenzie,et al.  CONVERSION OF ELECTROMAGNETIC INDUCTANCE READINGS TO SATURATED PASTE EXTRACT VALUES IN SOILS FOR DIFFERENT TEMPERATURE, TEXTURE, AND MOISTURE CONDITIONS , 1989 .

[12]  William W. S. Wei,et al.  Time series analysis - univariate and multivariate methods , 1989 .

[13]  Irma J. Terpenning,et al.  STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .

[14]  Peter J. Shouse,et al.  Determining soil salinity from soil electrical conductivity using different models and estimates , 1990 .

[15]  G. Kienitz.  Hydrological interactions between atmosphere, soil and vegetation : proceedings of an international symposium held during the XXth General Assembly of the International Union of Geodesy and Geophysics at Vienna, 11-24 August 1991 , 1991 .

[16]  Shmulik P. Friedman,et al.  Theoretical Prediction of Electrical Conductivity in Saturated and Unsaturated Soil , 1991 .

[17]  Lon-Mu Liu,et al.  Joint Estimation of Model Parameters and Outlier Effects in Time Series , 1993 .

[18]  Peter G Slavich,et al.  Estimating the electrical conductivity of saturated paste extracts from 1:5 soil, water suspensions and texture , 1993 .

[19]  A. C. Chang,et al.  Time Series Analysis of Field-Measured Water Content of a Sandy Soil , 1997 .

[20]  M. A. Hilhorst.  A Pore Water Conductivity Sensor , 2000 .

[21]  M. Chaloupka.  Historical trends, seasonality and spatial synchrony in green sea turtle egg production , 2001 .

[22]  R. Munns.  Comparative physiology of salt and water stress. , 2002, Plant, cell & environment.

[23]  A. Mishra,et al.  Drought forecasting using stochastic models , 2005 .

[24]  P. Vlek,et al.  Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions , 2005 .

[25]  Akshay K. Singh,et al.  Subsurface drainage performance study using SALTMOD and ANN models , 2006 .

[26]  竹安 数博,et al.  Time series analysis and its applications , 2007 .

[27]  M. Th. van Genuchten,et al.  Recent Progress in Modelling Water Flow and Chemical Transport in the Unsaturated Zone , 2007 .

[28]  Jose D. Salas,et al.  Relating crop yield to topographic attributes using Spatial Analysis Neural Networks and regression , 2007 .

[29]  Javad Khazaei,et al.  Yield Estimation and Clustering of Chickpea Genotypes Using Soft Computing Techniques , 2008 .

[30]  Minghua Zhang,et al.  Prediction of Soybean Growth and Development Using Artificial Neural Network and Statistical Models , 2009 .

[31]  Ryan Hafen,et al.  Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts , 2009, BMC Medical Informatics Decis. Mak..

[32]  F. Anctil,et al.  A neural network experiment on the site-specific simulation of potato tuber growth in Eastern Canada , 2010 .

[33]  Yubin Lan,et al.  Review: Development of soft computing and applications in agricultural and biological engineering , 2010 .

[34]  Y. Huanga,et al.  Development of soft computing and applications in agricultural and biological engineering , 2010 .

[35]  Guangming Liu,et al.  Artificial neural network and time series models for predicting soil salt and water content. , 2010 .

[36]  Richard K. Kiang,et al.  Modeling and Predicting Seasonal Influenza Transmission in Warm Regions Using Climatological Parameters , 2010, PloS one.

[37]  Joaquim Monserrat,et al.  Time series outlier and intervention analysis: Irrigation management influences on soil water content in silty loam soil , 2012 .

[38]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[39]  Basem Aljoumani,et al.  An advanced process for evaluating a linear dielectric constant–bulk electrical conductivity model using a capacitance sensor in field conditions , 2015 .