Digital mapping of a soil drainage index for irrigated enterprise suitability in Tasmania, Australia
D. B. Kidd A B C , B. P. Malone B , A. B. McBratney B , B. Minasny B and M. A. Webb A
+ Author Affiliations
- Author Affiliations
A Department of Primary Industries Parks Water and Environment Tasmania, 171 Westbury Road, Prospect, Tas. 7250, Australia.
B Faculty of Agriculture and Environment, University of Sydney, 1 Central Avenue, Australian Technology Park, Eveleigh, NSW 2015, Australia.
C Corresponding author. Email: darren.kidd@dpipwe.tas.gov.au
Soil Research 52(2) 107-119 https://doi.org/10.1071/SR13100
Submitted: 25 March 2013 Accepted: 24 September 2013 Published: 6 March 2014
Abstract
An operational Digital Soil Assessment was developed to inform land suitability modelling in newly commissioned irrigation schemes in Tasmania, Australia. The Land Suitability model uses various soil parameters, along with other climate and terrain surfaces, to identify suitable areas for various agricultural enterprises for a combined 70 000-ha pilot project area in the Meander and Midlands Regions of Tasmania. An integral consideration for irrigable suitability is soil drainage. Quantitative measurement and mapping can be resource-intensive in time and associated costs, whereas more ‘traditional’ mapping approaches can be generalised, lacking the detail required for statistically validated products. The project was not sufficiently resourced to undertake replicated field-drainage measurements and relied on expert field drainage estimates at ~930 sites (260 of these for independent validation) to spatially predict soil drainage for both areas using various terrain-based and remotely sensed covariates, using three approaches: (a) decision tree spatial modelling of discrete drainage classes; (b) regression-tree spatial modelling of a continuous drainage index; (c) regression kriging (random-forests with residual-kriging) spatial modelling of a continuous drainage index. Method b was chosen as the best approach in terms of interpretation, and model training and validation, with a concordance coefficient of 0.86 and 0.57, respectively. A classified soil drainage map produced from the ‘index’ showed good agreement, with a linearly weighted kappa coefficient of 0.72 for training, and 0.37 for validation. The index mapping was incorporated into the overall land suitability model and proved an important consideration for the suitability of most enterprises.
Additional keywords: decision trees, digital soil mapping, land suitability, regression trees, random forests, soil drainage, spatial modelling.
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