Search

We modelled and mapped the distribution of three principal soil organic carbon (SOC) fractions across New South Wales and gained insights into the factors controlling their distribution. Carbon fractions are important for modelling soil carbon dynamics for carbon accounting, and as a potential indicator of soil quality. We considered particulate organic carbon (POC), humic organic carbon (HOC) and resistant organic carbon (ROC), which represent fractions of increasing bio-chemical stability. A dataset of 427 NSW profiles with mid-infrared (MIR) derived carbon fractions and a set of 16 predictor variables were applied. Random Forest and multiple linear regression techniques were applied, for depth intervals to 30 cm. The derived models and maps were of moderate strength, with validation results revealing Lin's concordance values for the 0–30 cm depth interval of 0.79 for total SOC stocks and 0.60 to 0.74 for the fraction stocks. Maps of mean stocks, and upper and lower 95% prediction intervals are presented. Absolute stocks (in Mg ha−1) of each fraction have a strong linear relationship with total SOC and are controlled by similar environmental and land management factors, normally increasing in a systematic way with increasingly moist climate (considering rainfall and temperatures), increasing mafic lithology (associated with more fertile, clay rich soils) and less disturbed land uses with higher vegetation cover. The environmental factors influencing the relative proportions of each fraction (% of total SOC) are more complex, but climate and lithology appear to be dominant, in addition to depth in profile. An SOC vulnerability index is mapped over the State, identifying areas where the stored carbon is most vulnerable to land management or environmental change. Our findings add to the understanding of factors driving the distribution of soil carbon and its fractions, which may ultimately contribute to more effective climate change mitigation programs. Unlabelled Image • Digital soil maps of three SOC fractions (at 100 m resolution) prepared for NSW • Absolute stocks of SOC fractions have strong linear relationship with total SOC. • All stocks increase with moister climate, more mafic soil and higher vegetation cover. • Relative proportions (% of total SOC) influenced by climate, lithology and depth • SOC vulnerability index mapped over NSW [ABSTRACT FROM AUTHOR]

• MLR and RF were used to estimate current SOC stocks in south-eastern Australia. • Rainfall and minimum temperature were major drivers in determining SOC stocks. • The multi-GCM ensemble means suggested an overall decrease in SOC stocks. • The largest mean decrease of SOC stocks occurs in the high-rainfall alpine regions. Soil organic carbon (SOC) plays a key role in the sequestration of carbon that could otherwise be warming the atmosphere. Climate change including increased temperature and changed rainfall will greatly impact the global SOC cycle. There are still significant gaps in our knowledge of the size of the global SOC pool and how future climate will affect SOC stocks and flows in many parts of the world, including Australia. In this study, we used SOC data in a Digital Soil Mapping framework to predict current and future SOC stocks across the state of New South Wales (NSW) in south-eastern Australia. In the first phase of the study we estimated the current SOC stock using multiple linear regression (MLR) and random forest (RF) modelling, and in the second phase we projected the change of SOC stocks in the near future (2050s) and far future (2090s) under two Shared Socio-economic Pathways (SSPs) scenarios based on 25 global climate models (GCMs) from the Coupled Model Inter-comparison Project Phase 6 (CMIP6). Our spatial modelling showed that estimated current SOC stocks in NSW decreased from east to west. Multi-GCM ensemble means suggested SOC stocks would decrease by 7.6–12.9% under SSP2-4.5 and 9.1–20.9% under SSP5-8.5 across NSW under future climate. The extent of change in SOC stocks varied spatially with the largest mean decrease of SOC stocks occurring in the North Coast and South East (alpine) regions of NSW. Our findings can support decision-making in land management and climate change mitigation strategies in NSW at the regional level. Furthermore, the modelling methods can be applied to other areas where edaphic and landscape properties, land use, and climate data are available. [ABSTRACT FROM AUTHOR]