Darren Kidd

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

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.

Eighty-metre resolution 3D soil-attribute maps for Tasmania, Australia

Until recently, Tasmanian environmental modelling and assessments requiring important soil inputs relied on conventionally derived soil polygons that were mapped up to 75 years ago. In the ‘Wealth from Water’ project, digital soil mapping (DSM) was used in a pilot project to map the suitability of 20 different agricultural enterprises over 70 000 ha. Following on from this, the Tasmanian Department of Primary Industries Parks Water and Environment has applied DSM to existing soil datasets to develop enterprise suitability predictions across the whole state in response to further expansion of irrigation schemes. The soil surfaces generated have conformed and contributed to the Terrestrial Ecosystem Research Network Soil and Landscape Grid of Australia, a superset of GlobalSoilMap.net specifications. The surfaces were generated at 80-m resolution for six standard depths and 13 soil properties (e.g. pH, EC, organic carbon, sand and silt percentages and coarse fragments), in addition to several Tasmanian enterprise-suitability soil-attribute parameters. The modelling used soil site data with available explanatory state-wide spatial variables, including the Shuttle Radar Topography Mission digital elevation model and derivatives, gamma-radiometrics, surface geology, and multi-spectral satellite imagery. The DSM has delivered realistic mapping for most attributes, with acceptable validation diagnostics and relatively low uncertainty ranges in data-rich areas, but performed marginally in terms of uncertainty ranges in areas such as the World Heritage-listed Southwest of the state, with a low existing soil site density. Version 1.0 soil-attribute maps form the foundations of a dynamic and evolving new infrastructure that will be improved and re-run with the future collection of new soil data. The Tasmanian mapping has provided a localised integration with the National Soil and Landscape Grid of Australia, and it will guide future investment in soil information capture by quantitatively targeting areas with both high uncertainties and important ecological or agricultural value.

Taking account of uncertainties in digital land suitability assessment.

Simulations are used to generate plausible realisations of soil and climatic variables for input into an enterprise land suitability assessment (LSA). Subsequently we present a case study demonstrating a LSA (for hazelnuts) which takes into account the quantified uncertainties of the biophysical model input variables. This study is carried out in the Meander Valley Irrigation District, Tasmania, Australia. It is found that when comparing to a LSA that assumes inputs to be error free, there is a significant difference in the assessment of suitability. Using an approach that assumes inputs to be error free, 56% of the study area was predicted to be suitable for hazelnuts. Using the simulation approach it is revealed that there is considerable uncertainty about the ‘error free’ assessment, where a prediction of ‘unsuitable’ was made 66% of the time (on average) at each grid cell of the study area. The cause of this difference is that digital soil mapping of both soil pH and conductivity have a high quantified uncertainty in this study area. Despite differences between the comparative methods, taking account of the prediction uncertainties provide a realistic appraisal of enterprise suitability. It is advantageous also because suitability assessments are provided as continuous variables as opposed to discrete classifications. We would recommend for other studies that consider similar FAO (Food and Agriculture Organisation of the United Nations) land evaluation framework type suitability assessments, that parameter membership functions (as opposed to discrete threshold cutoffs) together with the simulation approach are used in concert.

Spatial analysis of frost risk to determine viticulture suitability in Tasmania, Australia

Background and Aims New sites for viticulture should be located in areas where damaging spring frosts are less frequent and/or severe in order to remain economically viable. The aim of this study was to spatially determine frost risk at a high resolution (80 m) and develop new rules catered to viticultural suitability with respect to frost sensitivity for vines after budburst in spring across the state of Tasmania, Australia. Methods and Results Frost risk was mapped for minimum temperature thresholds at −3, −2, −1, 0, 1 and 2°C using temperature values from 636 short-term recording sites and linked to 57 Australian Bureau of Meteorology climate stations. Frost risk was spatially determined using regression tree interpolation and assessed against historical winegrape frost damage records garnered from survey information. Analysis indicated that the frost risk surfaces were accurate in portraying risk at the mesoclimate level and aligned well with grower expectations for frost risk modelled at ≤−1°C. Conclusions Classifications of suitable, moderately suitable and unsuitable corresponding to 1/10 to 1 frost every 2 years (10–50%) and 1 > frost every 2 years (>50%) for temperature values ≤−1°C was found to correlate well with viticulture suitability with regard to frost risk (after budburst) in Tasmania. Significance of the Study This study presents a methodology for producing high-resolution frost risk maps across large land areas that can accurately identify sites prone to damaging spring frosts and inform on new potential viticulture sites with suitability in mind.