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The importance of soil organic carbon (SOC) in maintaining soil health is well understood. However, there is growing interest in studying SOC with an emphasis on quantifying its changes in space and time. This is because of the potential for soil to be used to sequester atmospheric C. There are many issues which make this difficult, for example shortcomings in sampling designs, and differences in vertical and lateral sampling supports between surveys, particularly if legacy data are used as the baseline survey. In this study, we systematically work through these issues and show how a protocol can be developed using design-based and model-based statistical approaches to estimate changes in SOC in space and time at different spatial supports. We demonstrate this protocol in a small subcatchment in the upper Namoi valley for estimating the change in SOC over time, whereby the baseline dataset was collected during 1999-2001 and is compared with a dataset from November 2010. The results from both design-based and model-based approaches revealed a drop in SOC across the catchment between the two survey periods. A 0.26% drop in SOC was reported globally across the catchment. Nevertheless, the change in SOC reported for both approaches was not statistically significant. Additional keywords: monitoring, sampling, soil carbon, space-time, design-based, model-based., Introduction Carbon exists in soil as soil organic carbon (SOC) and as soil inorganic C. Of these two forms, SOC plays an important role in a range of biological, chemical [...]

Accurate estimates of soil carbon stocks at the field scale are required to run market-based instruments for soil carbon, but the soil measurements required to make these estimates are expensive. Therefore, efficient sample designs are required. We explored the costs associated with estimating the mean soil carbon stocks within a 68-ha field on the old alluvial soils of the Macquarie River in central-west New South Wales (Red Chromosols or Red Luvisols). The sampling required to achieve a particular degree of accuracy depends upon the variability of soil carbon within the field. We conducted a 100-site geostatistical survey to estimate the variogram of soil carbon. We then used this variogram to consider the efficiency with which simple random and stratified sample designs can achieve a standard error Additional keywords: costs, measurement, paddock scale, soil carbon, uncertainty., Introduction The implementation of carbon-trading schemes or market-based instruments (MBI) for soil carbon requires accurate estimates of the status and change of soil carbon stocks. Such schemes often perform soil [...]