How variable is the soil compared to other soil properties?

Our results from Chapter 4 suggest that soil is not much more variable than other environmental properties, except at the finer scales. It is very possible that results at the finer scales are likely to be affected by the different supports of the different environmental variables.

We found that the relationship of scale to variability (that is the relative importance of short range and long range variability with changing distance) was very similar for the following properties Enhanced vegetation index, soil texture % from the NSSC dataset, fine range slope, fine range elevation. Only rainfall and elevation above sea level showed much lower levels of stochasticity (or a stronger importance of long range variability).

We noted the strong similarity between the roughness index of the soil texture and the roughness index for micro-topography, at all but the very finest scales. Having the strong linkage in variability

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across so many scales might mean that this provides a useful avenue as a proxy for soil. Modelling micro-topography is important for understanding sedimentary processes (Eltner et al., 2018). Microtopography can also exert an influence on plant species composition (Álvarez-Rogel et al., 2007).

7.2 Future work

As described above, the conclusions we have developed from this thesis have opened up a number of further questions and directions for inquiry. We suggest below several directions for further research. These are not comprehensive, but indicate in our opinion the logical next stages of development of this research.

7.2.1 Develop understanding of drivers of spatial variability of soil texture

Our characterization of soil spatial variability in Chapter 2 is highly general. That is, it does not differentiate between regions, climatic zones, land use type or any other factor. All pairs of observations with the same lag were included in the calculation of the semivariance for that lag. This is one of the strengths of the analysis, as it ensures the results are generalisable, but there is a sacrifice in specificity, and potentially in the applicability of the results. Disaggregating the data and testing whether similar degrees of variability occur within the disaggregated data is a logical step in developing this data to a stage that can be used for prediction.

As a starting point we propose bioregion, geology and topography as potentially useful covariates for the disaggregation of the data. Categorical maps of bioregion (Australian Government, 2012) and geology (Geoscience Australia, 2012) will make disaggregation based on these environmental factors relatively straightforward. Disaggregation by local topography would be more complex. Unlike climatic region and underlying geology, local topography cannot be easily spatially disaggregated. It may be more appropriate to consider the covariation of local topography than to disaggregate the texture data based on this metric.

Ideally, we would be able to disaggregate the data based on agricultural management. However, because the NSSC dataset has been sampled primarily from agricultural regions, if we wish to compare managed versus natural soil within Australia, we will need to use a different source of information. It might be possible to conduct this analysis with Gamma Radiometric data (as per Chapter 3), although as discussed in Chapter 5, the wide support and diffuse boundaries make it difficult to observe and predict fine scale variability. Stockmann, et al. (2015) illustrate the potential to combine proximal soil sensing with remote sensing where increased resolution is required.

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Disaggregation of the soil texture data will improve our ability to characterise soil variability. It will also allow us to test the applicability of the roughness index model in more specific settings.

7.2.2. Extend the analysis beyond Australia

With the exception of Chapter 5 and 6, the analysis presented in this thesis focuses on the continent of Australia. Access to the WoSIS dataset, which includes very heavy sampling on the North American continent introduces the possibility of conducting an analogous analysis using North American data. A comparison of the average variability across scales is likely to be illuminating. Even more intriguing, this will allow us to test whether the roughness index model we’ve developed is specific to Australia or whether it applies from field to continental scales across other continents. As we’ve indicated, the usefulness of this analysis will likely be increased if we consider other environmental factors when we characterise variability. It would be logical to include a similar extension to the one we’ve described in Section 7.4.1. to the North American dataset. This analysis would be illuminating in its own right. Also, having a comparison to a continent of a similar size, but different geology, climate, and dominant soil types could provide very illuminating information about the scaling properties of variability under different conditions.

7.2.3. Improve understanding of very fine scale variability

Several components of this thesis point to a need for a more focused strategy if we are to improve our understanding of fine scale variability. The roughness index model we fit in Chapter 2 suggested that there was unresolved spatial variability at scales finer than we were able to model. The wide range of field scale variograms in Chapter 6 highlight the importance of site specificity for field scale variability. Finally, as we describe in Chapter 2, the sampling designs of the variograms we collect in Chapter 6 vary significantly. While this may be in part, due to differing aims between studies, we suspect that this is, at least in part informed by different opinions about the importance of fine scale variability. Studies that aliquot data (average several observations taken within a small area) are inherently implying that very fine scale variability is noise that should be removed to allow more accurate predictions. Studies that make predictions from single very small support (i.e. 10cm, the typical radius of a soil core) are inherently implying that the information in that small support is representative enough of the surrounding area to make predictions from. A better understanding of fine scale variability, and its relationship to other scales of variability will allow us to make better decisions about support and sampling. It will also allow us to better understand the impact of support when we are dealing with very different types of data (as per Chapter 5).

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Formalising the relationship between variability across scales and information will be critical to ensuring that enhanced understanding of fine scale variability leads to better outcomes. Bishop, et al. (2001) propose a modification to Shannon’s information criterion explicitly for evaluating mapping accuracy and informing decisions around grid size in map production. An extension of this framework to take into account support size would also be useful. Combining the development of a formal framework for linking variability to information content at fine scales with a better characterisation of variability at fine scales would be ideal. Before undertaking significant for purpose field studies (with the associated expense) it would be desirable to extend efforts to gather existing information. It might be possible to extend the analysis in Chapter 6 by seeking out raw spatial observations in place of calculated variograms. A focused meta-analysis of studies that explicitly consider very fine scale scaling, as opposed to precision agriculture studies may also provide useful results.

7.2.4. Develop links between variability, scale and process

One of the eventual long term aims of improving our ability to accurately describe soil variability across multiple scales is improving our ability to link variability, scale and process. Our results from Chapter 2 suggest one possible avenue for this. When soil variability is described as multi-fractal in nature, an appealing explanation is that soil variability across particular scales is controlled by specific environmental factors. Abrupt changes in soil variability are associated with a change in the dominant control. Our theory of more gradual change in soil variability with scale suggests that different interpretations might be necessary. Our results from Chapter 4 suggest that the variability of environmental factors themselves might change in a more gradual way than is implicit in Burrough’s (1983) theory. The view of the soil as an inert substance controlled by environmental factors is shifting to a more dynamic role, where the soil exerts control on other environmental factors, in particular vegetation and climate, examples include Govindasamy et al. (1999) and Osborne et al. (2004). Specific mechanistic studies designed to better understand the specific effects and feedbacks of particular variables provide valuable insights into some deterministic components of soil variation and variability. Better understanding variability of soil and other properties across scales and with relation to each other27 _{will complement these mechanistic studies.}

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