Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change

Abstract

We can effectively monitor soil condition—and develop sound policies to offset the emissions of greenhouse gases—only with accurate data from which to define baselines. Currently, estimates of soil organic C for countries or continents are either unavailable or largely uncertain because they are derived from sparse data, with large gaps over many areas of the Earth. Here, we derive spatially explicit estimates, and their uncertainty, of the distribution and stock of organic C in the soil of Australia. We assembled and harmonized data from several sources to produce the most comprehensive set of data on the current stock of organic C in soil of the continent. Using them, we have produced a fine spatial resolution baseline map of organic C at the continental scale. We describe how we made it by combining the bootstrap, a decision tree with piecewise regression on environmental variables and geostatistical modelling of residuals. Values of stock were predicted at the nodes of a 3-arc-sec (approximately 90 m) grid and mapped together with their uncertainties. We then calculated baselines of soil organic C storage over the whole of Australia, its states and territories, and regions that define bioclimatic zones, vegetation classes and land use. The average amount of organic C in Australian topsoil is estimated to be 29.7 t ha−1 with 95% confidence limits of 22.6 and 37.9 t ha−1. The total stock of organic C in the 0–30 cm layer of soil for the continent is 24.97 Gt with 95% confidence limits of 19.04 and 31.83 Gt. This represents approximately 3.5% of the total stock in the upper 30 cm of soil worldwide. Australia occupies 5.2% of the global land area, so the total organic C stock of Australian soil makes an important contribution to the global carbon cycle, and it provides a significant potential for sequestration. As the most reliable approximation of the stock of organic C in Australian soil in 2010, our estimates have important applications. They could support Australia's National Carbon Accounting System, help guide the formulation of policy around carbon offset schemes, improve Australia's carbon balances, serve to direct future sampling for inventory, guide the design of monitoring networks and provide a benchmark against which to assess the impact of changes in land cover, land management and climate on the stock of C in Australia. In this way, these estimates would help us to develop strategies to adapt and mitigate the effects of climate change.

Diversity patterns, ecological associations and future of research on Costa Rican myxomycetes

“…This is important to mention since the current portrayal of myxomycete study in Costa Rica during the last years may represent an interesting study case under the scope of global ecological perspectives such as climate change, habitat disturbance and microbial distribution (e.g. Viscarra Rossel et al 2014). In this context, for instance, previous regional datasets, such as those generated by Schnittler and Stephenson (2000) for Guanacaste, Monteverde and Cahuita or by Walker et al (2015b) for La Selva Biological Station, are essential to determine diversity patterns over time.…”

No Silver Bullet for Digital Soil Mapping: Country-specific Soil Organic Carbon Estimates across Latin America

“…Data mining techniques have been successfully used to model and predict the spatial variability of soil properties (Rossel and Behrens, 2010;Hengl et al, 2017;Shangguan et al, 2017) and generate country-specific SOC maps (Viscarra Rossel et al, 2014;Adhikari et al, 2014). The combination of regression modeling approaches with geostatistics of model residuals (i.e., regression Kriging) is a combined strategy that 30 has been widely used to map SOC (Hengl et al, 2004;Mishra et al, 2009;Marchetti et al, 2012;Kumar et al, 2012;Peng et al, 2013;Adhikari et al, 2014;Yigini and Panagos, 2016;Nussbaum et al, 2014;Mondal et al, 2017).…”

“…carbon models (Martin et al, 2011;Hashimoto et al, 2017;Hengl et al, 2017) including applications for SOC mapping (Grimm et al, 2008;Sreenivas et al, 2016;Yang et al, 2016;Hengl et al, 2017;Delgado-Baquerizo et al, 2017;Ließ et al, 2016;Viscarra Rossel et al, 2014).Machine learning methods do not necessarily allow to extract information about the main effects of prediction factors in the response variable (e.g., SOC); consequently, a selection strategy is always useful to increase the interpretability of machine learning algorithms. With this diversity of approaches one constant question is if there is a 5 method that systematically improve the prediction capacity of the others aiming to predict SOC across large geographic areas (e.g., Latin America).…”

“…The PLS algorithm integrates the compression and regression steps and it selects successive orthogonal factors that maximize the covariance between predictor and response variables (Wold, 1983;Viscarra Rossel et al, 2014). Most of its development and application is in the fields of chemometrics, but is used in several research areas to effectively solve regression and classification problems.…”

Proximal sensing for soil carbon accounting

“…Sensing on the other hand can be used to cost-efficiently measure soil organic C stocks, to estimate baselines for national inventory reporting and monitoring (e.g. Viscarra Rossel, R.A. et al, 2014).…”