Spatial predictions of PAWC, DUL and CLL for grain-growing regions of NSW and Queensland, Australia, from Padarian Campusano pedotransfer functions and SLGA datasets

Brief description

Spatial predictions of plant available water capacity (PAWC), drained upper limit (DUL) and crop lower limit (CLL) for grain-growing regions of NSW and Queensland, Australia, from Padarian Campusano pedotransfer functions and Soil and Landscape Grid of Australia datasets.

PAWC is the amount of water a soil can hold against gravity (i.e. water which does not freely drain) that is available to plants through their roots. This soil property is very important in dryland cropping areas which rely on rainfall. The maximum amount of water which can be held by a soil against gravity is called the DUL. The water that remains in a soil after plants have extracted all that is available to them is called the CLL. PAWC is calculated as DUL minus CLL.

Digital soil mapping (DSM) allows the spatial prediction of soil properties across large areas using modelling techniques which combine point data measured in the field and continuous datasets related to soil forming processes such as climate, topography, land cover, existing soil mapping and lithology. Pedotransfer functions (PTFs) are equations which use the easier to measure soil attributes, e.g. sand, clay, bulk density, to model the harder to measure attributes like DUL and CLL. DSM techniques such as Latin Hypercube (LHC) sampling can be used to incorporate the uncertainties associated with the input datasets in the modelling, and to produce estimates of model output precision and reliability.

This data collection consists of spatially predicted PAWC, DUL and CLL for the grain-growing regions of New South Wales and Queensland, Australia, as defined by the boundary of the Grains Research and Development Corporation's Northern Region. PAWC was modelled using PTFs for DUL and CLL from Padarian Campusano, with LHC sampling to incorporate the uncertainties associated with the input datasets.
The PAWC, DUL and CLL were modelled at the six Global Soil Map depths of 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. The top five depths have been aggregated to create a PAWC prediction for 0-100 cm.

Lineage: INPUT DATASETS
1.\tSoil attribute layers from the Soil and Landscape Grid of Australia (SLGA): clay (%), sand (%), bulk density (BD; g cm-3), and effective cation exchange capacity (CEC; meq/100 g). The estimated value (mean) and the confidence interval limits (5th and 95th percentiles) were used for all six Global Soil Map depths (0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). https://www.clw.csiro.au/aclep/soilandlandscapegrid/ProductDetails-SoilAttributes.html
2.\tThe Northern Region boundary from the Grains Research and Development Corporation (GRDC)

PEDOTRANSFER FUNCTIONS
DUL and CLL equations from Padarian Campusano (2014), which used a subset of 806 soil profiles from the APSoil database that included field measurements of DUL and CLL:
1.\tDUL = 0.2739 + 0.005033*clay + 3.158 x 10^-5*sand*CEC – 1.96 x 10^-5*sand^2 – 0.00256*clay*BD
2.\tCLL = 0.6151*DUL – 0.02192
3.\tPAWC = DUL – CLL

METHODS
These methods are available from Austin et al. (2019), see Related Links section.

The SLGA input datasets were clipped to the study area boundary and divided into tiles of 200 x 200 grid cells prior to parallel processing in a supercomputer environment. Except for the LHC sampling and correlation matrices, all code was written in Python. Layer thickness for each of the six soil depths was calculated in mm from the depth layer upper and lower bounds (e.g. 5 to 15 cm).

A correlation matrix was generated in the R package for the SLGA clay, sand, BD and CEC input datasets for each of the six depths, with correlation values derived using data for the whole study area for each of the inputs.

Each of the six soil depth layers was modelled separately. For every grid cell in each depth layer, the following steps were used to calculate DUL, CLL and PAWC:
1.\tStandard deviation (SD) was calculated from the 5th and 95th percentiles for the clay, sand, CEC and BD input variables using the following equation from Malone et al. (2011):
SDi = (UPLi – LPLi) / 2 x z
where SDi is the variance associated with prediction i, UPL and LPL are the upper and lower prediction limits, and z is the z-value used for a confidence interval (CI) which in this case is 90% and z = 1.64. A normal distribution is assumed

2.\tLHC sampling with a correlation matrix (from the R pse library; Chalom and Prado, 2014), using means, SDs and a correlation matrix as inputs, produced fifty realisations of each input variable. Fifty realisations were chosen following the work of Malone et al. (2015) who found that there was little difference in outcome when using more than 50 samples

3.\t50 DUL and CLL values were calculated from the 50 input variable realisations using the DUL and CLL equations from Padarian Campusano (2014)

4.\t50 PAWC values were calculated from the DUL and CLL values, constrained by the depth layer thickness, with units of mm

5.\tFrom the 50 DUL, CLL and PAWC values for each grid cell, the mean, median, 5th and 95th percentiles, and SD were calculated and written to file as geotiffs

The tiled outputs were merged to form single rasters of the study area for DUL, CLL and PAWC at each of the six depths. Additionally, the 0-5, 5-15, 15-30, 30-60 and 60-100 cm soil depth layers were used to calculate 0-1 m versions of DUL, CLL and PAWC. The mean, median, 5th and 95th percentile values were summed to produce the 0-1 m DUL, CLL or PAWC prediction for each grid cell. This aggregation of depths assumes high correlation between layers – for example, the 95th percentile for the 0 – 1 m layer is the sum of the 95th percentiles for each contributing layer. If the layers were uncorrelated, the 95th percentile would end up closer to the mean. The SD for each of the 0-1 m DUL, CLL and PAWC layers was calculated from the summed 5th and 95th percentiles, as per the equation from Malone et al. (2011).