Once functional DSM grids have been produced to standards appropriate for operational use, they can be applied as environmental or agricultural modelling parameters, or queried to inform a range of biophysical, environmental, or agricultural assessments. Throughout this chapter, DSA (Digital Soil Assessment) has been alluded to; it has basically been considered as the application of various biophysical interpretations to DSM (Carré et al., 2007a). DSA is suggested by Carré et al. (2007a) for assessing threats to the soil asset which can result in various forms of degradation, and/ or processes that assess functions of soil, such as biophysical interactions, production and usage, where DSM is an input to the overall DSA. DSA can be used to identify environmental risks and land evaluation, such as agricultural land suitability for various crops, and integrated with other environmental or socio- economic data for tailored end-user requirements (D’haeze et al., 2005; Carré et al., 2007a). The land suitability cases reviewed previously (van Zijl et al., 2014; Harms et al., 2015; Thomas et al., 2015) are all examples of an operational rudimentary DSA framework. For land evaluation, including suitability assessment, the DSA can involve direct digital interpretations to the DSM grids, pixel-by-pixel, to inform conditions that support agricultural uses, and integrated with other parameters such as climate, terrain, distance to markets, market demand or commodity prices
45 (D’haeze et al., 2005) to refine the overall assessment. Identification of potential environmental damage, specifically related to soil can also be a form of DSA, as per the specified factors described by van Zijl et al. (2014) (for example, compaction), or applying existing soil erodibility assessment systems to the DSM layers, such as the ‘Revised Universal Soil Loss Equation’ (RUSLE) (Renard et al., 1991; Renard et al., 1997; Millward and Mersey, 1999; Lu and Yu, 2002).
Rossiter (1996) described and summarised the various qualitative and quantitative approaches of physical land evaluation into a theoretical framework of different categories. These encompassed a range of considerations, including; spatial and non-spatial analysis; static and dynamic concepts of the soil, land resource and suitability; land ‘qualities’ (a set of parametric conditions); physical constraint evaluation to land use and productivity; land use complexity; operations of scale; and multi-faceted or single-use considerations of land suitability. Within these different considerations of land evaluation, Rossiter included the computational considerations (using a range of qualitative and quantitative evaluation methods), complexity of outputs (mechanistic or empirical type modelling), and the spatial hierarchical scale of the evaluation models. Each of these factors is more or less evident within all published land evaluation examples around the world, ranging from simple, static, prescriptive approaches to complex biophysical modelling of spatial yield projections (Burrough, 1996). However, in describing the overall framework and different land evaluation approaches, he stressed there was no single approach applicable to all areas, conditions, requirements or land uses; all must be considered when formulating the most applicable approach for a given scope.
There are many relevant examples of land suitability assessments using a range of complexity, from discrete rulesets based on the FAO system, to more continuous applications of digital technology. An early Australian example of the latter was undertaken in the lower Namoi Valley (Edgeroi, New South Wales) by Triantafilis et al. (2001), who noted that the discrete partitioning of target landscapes by traditional land suitability approaches did not effectively capture the continuous
46 nature of the land. The assessment applied a fuzzy approach (Bezdek et al., 1984; Burrough, 1989; Burrough et al., 1992) for a range of broad-acre crops as a continuous index, where 1 = ‘suited’, and 0 = ‘unsuited’, based on an additive limitation score determined from a range of different soil and terrain parameters. The ratings were applied to individual sites (210 in total) for individual crops, and used to produce an averaged product of overall land versatility. Triantafilis et al. then generated continuous maps from the site ‘suitability scores’, firstly by transforming the site data to a near-normal distribution, then applying ‘punctual kriging’, that is, point-kriging rather than block-kriging (Burgess and Webster, 1980), based on semivariance analysis to determine the appropriate model. The authors showed that the method could provide realistic and effective maps of continuous suitability for a range of different crops across the study area, as well as agricultural versatility; however, relatively large confidence intervals (at 95 %) showed some reliability issues in the spatial estimates interpolated between each site, mainly due to low sampling density. They acknowledged that reliability would be improved by increased sampling, including additional sites placed in close proximity to improve estimates of short-range variability, and the overall potential for this approach to be used as a tool for improved land evaluation, when used in conjunction with both expert field interpretations and additional ground-truthing.
Conventional and traditional land evaluation assessments, such as the FAO 5 class suitability system (FAO, 1976), or the most-limiting factor approach (Klingebiel and Montgomery, 1961), many using discrete parametric rulesets and expert systems, still have a valid use when integrated with DSM into a DSA (Manna et al., 2009); however, with digital and higher quality soil attribute inputs, there is a greater capacity to use and develop more complex and intuitive DSA frameworks, such as weighted multi-parametric systems described by Rabia and Terribile (2013), and integration with Bayesian Theory (Taalab et al., 2015).
Rossiter (2003) discussed the feasibility of integrating DSM with biophysical modelling into DSA, such as APSIM (Keating et al., 2003), which can facilitate biophysical interactions between land management, nutrient inputs, climate, and
47 specific cropping requirements to make temporal predictions of yield. Such models are routinely single-location simulations; however, with digital soils data, the modelling can be executed through each pixel to produce spatial estimates of temporal yields. Using biophysical models within a DSA, temporal effects of land management and present and forecast climate conditions can be integrated to identify areas of higher yields; these can be considered more suited to an enterprise, with variations in management and yield outputs used to spatially identify the soil or climate limitations requiring consideration in specific areas. This could potentially provide a superior product to traditional land evaluation frame-works as a range of simultaneously occurring biophysical inputs and managements can be spatially modelled to show areas expected to produce better production outcomes per unit area. However, Manna et al. (2009) warn that that complex land evaluation systems do not necessarily improve the quality of the outputs.
Although the FAO (1976) and Klingebiel and Montgomery (1961) type of approach is largely qualitative, it can be enhanced and improved through application of more quantitative methods (Triantafilis et al., 2001); this can either be within the suitability framework itself, or the parametric input variables, or both. In the work presented in this thesis, the initial land evaluation enhancements are through the DSM and derived climate grids. This suitability framework adapted and used in Tasmania is presented in more detail in Chapters 4 and 6 (Kidd et al. (2015b)), with the pros and cons of the approach evaluated and alternative methods discussed.