ASRIS Technical Specifications V1.6

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• Continuous variables that are not normally distributed are transformed to an approximately normaldistribution and uncertainties are then estimated. Hydraulic conductivity and electrical conductivityare assumed to be distributed log-normally, unless there is evidence to the contrary. The mean isrecorded in untransformed units to improve the ease of interpretation but the standard deviation isrecorded as a transformed value. For example, the hydraulic conductivity may be estimated to be100 mm/hr with a standard deviation of 0.5 log(mm/hr). The range of ± 1 standard deviation (i.e.68% of the population) around the mean of 100 mm/hr would therefore be 32–320 mm/hr. Theadvantage of recording the transformed standard deviation is that that only one value is needed torepresent dispersion of the asymmetric distribution.• Variables with fixed ranges (e.g. percentage coarse fragments) or coarse stepped scales aremodeled with triangular probability distributions unless there is evidence to the contrary. Thetriangular probability distribution is assumed to be symmetric. The mean is estimated anddispersion is defined as (95% quantile – 5% quantile)/2. The distribution between the minimumvalue and the 5% quantile, and between the 95% quantile and the maximum, is assumed to be flat.The distribution is shown in Figure 3.• Uncertainties for nominal variables are represented by the probability that a class is correct (e.g.the uncertainty that a landform element type is a beach ridge is 0.8). Combined uncertainties arecalculated by multiplying component probabilities.Every soil attribute has an estimated uncertainty with two components.• The first component (u1) is associated with the measurement error for the given attribute at theprofile or site – it will be significantly reduced if replicated sampling or bulking has beenundertaken. If the attribute (e.g. water retention at –10 kPa) is being estimated using apedotransfer function, then the uncertainty includes both the measurement error of theexplanatory variables (e.g. texture, structure, and bulk density) and error due to model underlyingthe pedotransfer function.• The second component (u2) of uncertainty is due to spatial variability within the land-unit tract atthe lowest level in the hierarchy for which data are available.In most parts of Australia, there is limited information on both of these sources of uncertainty and it willrequire good judgment to provide estimates. However, the alternative of providing estimates of meanvalues without information on variability is potentially misleading.In the absence of better information, we will use default values of uncertainty drawn from the publishedliterature on spatial variation and our general knowledge (Table 4). The default values are conservative (i.e.most likely on the high side) and intended to encourage more attention to the estimation of uncertainty.The component of uncertainty due to measurement (u 1 ) can be determined using the estimation methodfor each variable as a guide (e.g. Table 25 for bulk density). The component of uncertainty due to spatialvariability (u 2 ) can be determined using several lines of evidence including:• cartographic scale of the survey and intensity of sampling (this is expressed via the Order of Survey(Table 9, Soil Survey Staff 1993))• qualitative assessment of landscape complexity.16
1.0MeanProbability5 %quantile95 %quantile0.00% 100%Abundance of coarse fragmentsFigure 3: Examples of triangular probability distribution functions for coarse fragments. Themean, 5% and 95% quantiles are shown for the material with less variation (solid line).17
Page 1: SUSTAINABLE AGRICULTURE FLAGSHIPThe
Page 6 and 7: Table 1: Complementary benefits of
Page 8 and 9: Table 62: The num_est_method_type t
Page 11 and 12: 1 SummaryThis document specifies th
Page 13 and 14: 2 User needs for soil and land reso
Page 15 and 16: 2.3 Mapping, monitoring, modelling,
Page 17 and 18: estimates of soil erosion by water.
Page 19 and 20: 3 Development of ASRISASRIS was ini
Page 21 and 22: Handbook (NCST 2009) will be famili
Page 23 and 24: 4.2 Upper levels of the hierarchyTh
Page 25: 5 Accuracy, precision and a basis f
Page 29 and 30: 6 Level-1 descriptors (land provinc
Page 31 and 32: Figure 5: Relative reliefFigure 6:
Page 33 and 34: Figure 7: Average annual excess wat
Page 35 and 36: 10 Level-5 descriptors (land system
Page 37 and 38: 10.3 LandformRelief/modal slopeReli
Page 39 and 40: 11 Level-6 descriptors (land facets
Page 41 and 42: Table 15: Landform elements (after
Page 43 and 44: Table 17: Described gilgai componen
Page 45 and 46: (a) (b) (c)A11A11A1 1A21A222B23B22B
Page 47 and 48: Layer-1 Texture and clay contentThe
Page 49 and 50: ZL Silty loam ZLA Sapric silty loam
Page 51 and 52: Coarse fragmentsCoarse fragments ar
Page 53 and 54: pH profileThe pH in a 1:5 CaCl 2 so
Page 55 and 56: Total thickness of A horizonThe tot
Page 57 and 58: Table 31: Method for the estimation
Page 59 and 60: Profile availablewater capacityAdju
Page 61 and 62: Table 32: Permeability classesClass
Page 63 and 64: Table 34: Estimation method for ele
Page 65 and 66: Exchangeable bases, CEC, and ESPEst
Page 67 and 68: Australian Soil ClassificationThe A
Page 69 and 70: FH Palic GZ Bleached-Orthic IS Ferr
Page 71 and 72: Table 47: Reference Soil Group code
Page 73 and 74: Local taxonomic classIf available,
Page 75 and 76: 12 Soil Profile DatabaseAs noted ea
Page 77 and 78:
Figure 12: Database design for ASRI
Page 79 and 80:
Table 57: The ref_sites tableColumn
Page 81 and 82:
Table 61: The param_num_method tabl
Page 83 and 84:
Table 68: The codes table.Column Na
Page 85 and 86:
15 ReferencesACLEP (in prep.) SITES
Page 87 and 88:
McKenzie NJ, Ryan PJ (1999) Spatial
Page 89 and 90:
Appendix 1: Conversion for pH in wa
Page 92:
CONTACT USt 1300 363 400+61 3 9545
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ASRIS Technical Specifications V1.6

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