Digital mapping of a soil drainage index for irrigated enterprise suitability in Tasmania, Australia

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.

Digital soil assessment for regional agricultural land evaluation

“…This could introduce inconsistency between datasets and potential modelling errors. Kidd et al (2014a) drew attention to the problem of data inconsistency in relation to soil drainage, and proposed the use of a soil drainage index that combines field-allocated site-drainage classes with quantitative DSM methods to produce enhanced soildrainage surfaces. Another problem associated with the wetness limitation evident in this project is that based on field descriptions, soil sites tend to be clustered in a limited number of site-drainage classes that do not adequately differentiate between the different agronomic requirements of specific crops.…”

Desertification vulnerability index—an effective approach to assess desertification processes: A case study in Anantapur District, Andhra Pradesh, India

“…RFM model helps prediction of land degradation such as alkalinity from the predicted soil maps (Vågen et al, ), and knowledge of the desertification process at a particular location is effectively captured, we can able to extrapolate to surrounding areas based on spatial correlation of available predictors. The training site relies on surveyor experience and local landscape knowledge (Kidd et al, ). The information needed to train classification algorithms can be obtained for training areas through ground truth verification for quickly updating DSM maps at frequent time intervals by using this methodology.…”

“…The information needed to train classification algorithms can be obtained for training areas through ground truth verification for quickly updating DSM maps at frequent time intervals by using this methodology. A another advantage of the RFM model that this model can be applied to any legacy data having desertification/land degradation processes to derive either new maps or to improve existing mapping (Kidd et al, ).…”

Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models

“…347). Campling et al (2002) reported a κ of 0.705, Kidd et al (2014) found κ values of 0.27 and 0.31 for the two study regions, Lemercier et al (2012) reported a κ of 0.27 and Peng et al (2003) found a κ of 0.59 for predictions of three drainage levels. The κ values computed for the models of this study ranged between 0.37 and 0.5 for modelling the presence of waterlogged horizons and was 0.48 for predicting the three levels of drainage class.…”

“…Three studies with up to seven drainage levels achieved PC of 52 to 78 %, and Zhao et al (2013) had 36 % of correctly classified sites. Kidd et al (2014) found PC of 53 and 55 % for two study regions, and Lemercier et al (2012) reported PC of 52 % for a four-level drainage response. The presented models (Table 4 and 5) are almost as good with PC of 78 to 82 % for predicting the presence of waterlogged horizons and PC of 78 % for predicting the three drainage class levels.…”