Estimating change in soil organic carbon using legacy data as the baseline: issues, approaches and lessons to learn
S. B. Karunaratne A D , T. F. A. Bishop A , I. O. A. Odeh A , J. A. Baldock B and B. P. Marchant C
+ Author Affiliations
- Author Affiliations
A Department of Environmental Sciences, Faculty of Agriculture and Environment, The University of Sydney, Sydney, NSW 2006, Australia.
B CSIRO Land and Water/Sustainable Agriculture Flagship, PMB 2, Glen Osmond, SA 5064, Australia.
C Rothamsted Research, Harpenden, AL5 2JQ, UK.
D Corresponding author. Email: senani.karunaratne@sydney.edu.au
Soil Research 52(4) 349-365 https://doi.org/10.1071/SR13081
Submitted: 9 March 2013 Accepted: 7 January 2014 Published: 22 April 2014
Abstract
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.
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