High-resolution prediction of soil available water content within the crop root zone

Summary A detailed understanding of soil hydraulic properties, particularly soil available water content (AWC) within the effective root zone, is needed to optimally schedule irrigation in fields with substantial spatial heterogeneity. However, it is difficult and time consuming to directly measure soil hydraulic properties. Therefore, easily collected and measured soil properties, such as soil texture and/or bulk density, that are well correlated with hydraulic properties are used as proxies to develop pedotransfer functions (PTF). In this study, multiple modeling scenarios were developed and evaluated to indirectly predict high resolution AWC maps within the effective root zone. The modeling techniques included kriging, co-kriging, regression kriging, artificial neural networks (NN) and geographically weighted regression (GWR). The efficiency of soil apparent electrical conductivity (ECa) as proximal data in the modeling process was assessed. There was a good agreement (root mean square error (RMSE) = 0.052 cm3 cm−3 and r = 0.88) between observed and point prediction of water contents using pseudo continuous PTFs. We found that both GWR (mean RMSE = 0.062 cm3 cm−3) and regression kriging (mean RMSE = 0.063 cm3 cm−3) produced the best water content maps with these accuracies improved up to 19% when ECa was used as an ancillary soil attribute in the interpolation process. The maps indicated fourfold differences in AWC between coarse- and fine-textured soils across the study site. This provided a template for future investigations for evaluating the efficiency of variable rate irrigation management scenarios in accounting for the spatial heterogeneity of soil hydraulic attributes.

[1]  Susan A. Murphy,et al.  Monographs on statistics and applied probability , 1990 .

[2]  Mark S. Seyfried,et al.  Geophysical imaging of watershed subsurface patterns and prediction of soil texture and water holding capacity , 2008 .

[3]  L. Cockx,et al.  A pedotransfer function to evaluate the soil profile textural heterogeneity using proximally sensed apparent electrical conductivity , 2009 .

[4]  E. Rivers,et al.  AVAILABLE WATER CAPACITY OF SANDY AND GRAVELLY NORTH DAKOTA SOILS , 1972 .

[5]  D. Corwin,et al.  Apparent soil electrical conductivity measurements in agriculture , 2005 .

[6]  Ian J. Yule,et al.  Soil water status and water table depth modelling using electromagnetic surveys for precision irrigation scheduling , 2013 .

[7]  Raghavendra B. Jana,et al.  Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation , 2011 .

[8]  M. Julià,et al.  Constructing a saturated hydraulic conductivity map of Spain using pedotransfer functions and spatial prediction , 2004 .

[9]  J. Šimůnek,et al.  An alternative deterministic method for the spatial interpolation of water retention parameters , 2009 .

[10]  Marcel G. Schaap,et al.  Development of Pedotransfer Functions for Estimation of Soil Hydraulic Parameters using Support Vector Machines , 2009 .

[11]  Z. L. Frogbrook,et al.  Sampling in Precision Agriculture , 2010 .

[12]  F. J. Pierce,et al.  Relating apparent electrical conductivity to soil properties across the north-central USA , 2005 .

[13]  I. Yule,et al.  A method for spatial prediction of daily soil water status for precise irrigation scheduling , 2009 .

[14]  F. D. Whisler,et al.  Spatial Variability Analysis of Soil Physical Properties of Alluvial Soils , 2005 .

[15]  Marcel G. Schaap,et al.  Description of the unsaturated soil hydraulic database UNSODA version 2.0 , 2001 .

[16]  Raghavan Srinivasan,et al.  GIS‐Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches 1 , 2009 .

[17]  Mathieu Javaux,et al.  Using Pedotransfer Functions to Estimate the van Genuchten–Mualem Soil Hydraulic Properties: A Review , 2010 .

[18]  Heath Adam Duncan,et al.  Locating the Variability of Soil Water Holding Capacity and Understanding Its Effects on Deficit Irrigation and Cotton Lint Yield , 2012 .

[19]  Edzer Pebesma,et al.  Spatial aggregation and soil process modelling , 1999 .

[20]  Wim Cornelis,et al.  A Simple Nearest-Neighbor Technique to Predict the Soil Water Retention Curve , 2015 .

[21]  Ian J. Yule,et al.  Soil water status mapping and two variable-rate irrigation scenarios , 2009, Precision Agriculture.

[22]  W. Cornelis,et al.  Pedotransfer functions related to spatial variability of water retention attributes for lowland soils , 2010 .

[23]  Suat Irmak,et al.  Application of GIS and Geographically Weighted Regression to Evaluate the Spatial Non-Stationarity Relationships Between Precipitation vs. Irrigated and Rainfed Maize and Soybean Yields , 2011 .

[24]  A. Klute,et al.  Methods of soil analysis , 2015, American Potato Journal.

[25]  J. Niedźwiecki,et al.  Effect of the number of calibration samples on the prediction of several soil properties at the farm-scale , 2014 .

[26]  Luis A. Garcia,et al.  Comparison of Ordinary Kriging, Regression Kriging, and Cokriging Techniques to Estimate Soil Salinity Using LANDSAT Images , 2010 .

[27]  M. Schaap,et al.  ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions , 2001 .

[28]  P. Goovaerts,et al.  Using Ancillary Data to Improve Prediction of Soil and Crop Attributes in Precision Agriculture , 2010 .

[29]  Walter J. Rawls,et al.  Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics , 2001 .

[30]  Johan Bouma,et al.  Using Soil Survey Data for Quantitative Land Evaluation , 1989 .

[31]  M. A. Oliver,et al.  An Overview of Geostatistics and Precision Agriculture , 2010 .

[32]  Van Genuchten,et al.  A closed-form equation for predicting the hydraulic conductivity of unsaturated soils , 1980 .

[33]  Karl Auerswald,et al.  Regionalization of soil water retention curves in a highly variable soilscape, II. Comparison of regionalization procedures using a pedotransfer function , 1997 .

[34]  Gerard B. M. Heuvelink,et al.  About regression-kriging: From equations to case studies , 2007, Comput. Geosci..

[35]  B. Diekkrüger,et al.  Geostatistical co-regionalization of soil hydraulic properties in a micro-scale catchment using terrain attributes , 2006 .

[36]  Wim Cornelis,et al.  A pseudo-continuous neural network approach for developing water retention pedotransfer functions with limited data , 2012 .

[37]  Wim Cornelis,et al.  Revisiting the pseudo continuous pedotransfer function concept: impact of data quality and data mining method , 2014 .

[38]  Christopher L. Main,et al.  AGRONOMY AND SOILS Lint Yield and Crop Maturity Responses to Irrigation in a Short-Season Environment , 2011 .

[39]  Luca Bechini,et al.  Spatial interpolation of soil physical properties for irrigation planning. A simulation study in northern Italy , 2003 .

[40]  Martin Charlton,et al.  The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets , 2010 .

[41]  G. E. Archie.  The electrical resistivity log as an aid in determining some reservoir characteristics , 1942 .

[42]  Paul D. Ayers,et al.  Perspectives on delineating management zones for variable rate irrigation , 2015, Comput. Electron. Agric..

[43]  Jin Li,et al.  Application of machine learning methods to spatial interpolation of environmental variables , 2011, Environ. Model. Softw..