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Information on the geographic variation in soil has traditionally been presented in polygon (choropleth) maps at coarse scales. Now scientists, planners, managers and politicians want quantitative information on the variation and functioning of soil at finer resolutions; they want it to plan better land use for agriculture, water supply and the mitigation of climate change land degradation and desertification. The GlobalSoilMap project aims to produce a grid of soil attributes at a fine spatial resolution (approximately 100 m), and at six depths, for the purpose. This paper describes the three-dimensional spatial modelling used to produce the Australian soil grid, which consists of Australia-wide soil attribute maps. The modelling combines historical soil data plus estimates derived from visible and infrared soil spectra. Together they provide a good coverage of data across Australia. The soil attributes so far include sand, silt and clay contents, bulk density, available water capacity, organic carbon, pH, effective cation exchange capacity, total phosphorus and total nitrogen. The data on these attributes were harmonised to six depth layers, namely 0–0.05 m, 0.05–0.15 m, 0.15–0.30 m, 0.30–0.60 m, 0.60–1.00 m and 1.00–2.00 m, and the resulting values were incorporated simultaneously in the models. The modelling itself combined the bootstrap, a decision tree with piecewise regression on environmental variables and geostatistical modelling of residuals. At each layer, values of the soil attributes were predicted at the nodes of a 3 arcsecond (approximately 90 m) grid and mapped together with their uncertainties. The assessment statistics for each attribute mapped show that the models explained between 30% and 70% of their total variation. The outcomes are illustrated with maps of sand, silt and clay contents and their uncertainties. The Australian three-dimensional soil maps fill a significant gap in the availability of quantitative soil information in Australia.

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Abstract. The recent development of biomass markets and carbon trading has led to increasing interest in obtaining accurate estimates of woody biomass production. Aboveground woody biomass (B) is often estimated indirectly using allometric models, where representative individuals are harvested and weighed, and regression analyses used to generalise the relationship between individual mass and more readily measured non-destructive attributes such as plant height and stem diameter (D). To satisfy regulatory requirements and/or to provide market confidence, allometric models must be based on sufficient data to ensure predictions are accurate, whilst at the same time being practically and financially achievable.Using computer resampling experiments and allometric models of the form B ¼ aD b the trade-off between increasing the sample size of individuals to construct an allometric model and the accuracy of the resulting biomass predictions was assessed. A range of algorithms for selecting individuals across the stem diameter size-class range were also explored. The results showed marked variability across allometric models in the required number of individuals to satisfy a given level of precision. A range of 17-95 individuals were required to achieve biomass predictions with a standard deviation within 5% of the mean for the best performing stem diameter selection algorithm, while 25-166 individuals were required for the poorest. This variability arises from (a) inherent uncertainty in the relationship between diameter and biomass across allometric models, and (b) differences between the diameter size-class distribution of individuals used to construct a model, and the diameter size-class distribution of the population to which the model is applied. Allometric models are a key component of quantifying land-based sequestration activities, but despite their importance little attention has been given to ensuring the methods used in their development will yield sufficiently accurate biomass predictions. The results from this study address this gap and will be of use in guiding the development of new allometric models; in assessing the suitability of existing allometric models; and in facilitating the estimation of uncertainty in biomass predictions.