Search

Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuous set of compounds with different chemical compositions, origins, and susceptibilities to decomposition that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contribution of three SOC fractions to the total SOC content of Australia's soils. The three SOC fractions, mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral models calibrated with measured SOC fractions. We transformed the data using an isometric-log-ratio (ilr) transformation to account for the closed compositional nature of SOC fractions. The resulting back-transformed ilr components were mapped across Australia. SOC fraction stocks for 0–30 cm were derived with maps of total organic carbon concentration, bulk density, coarse fragments, and soil thickness. Mapping was done by a quantile regression forest fitted with the ilr-transformed data and a large set of environmental variables as predictors. The resulting maps along with the quantified uncertainty show the unique spatial pattern of SOC fractions in Australia. MAOC dominated the total SOC with an average of 59 % ± 17 %, whereas 28 % ± 17 % was PyOC and 13 % ± 11 % was POC. The allocation of total organic carbon (TOC) to the MAOC fractions increased with depth. SOC vulnerability (i.e. POC/[MAOC+PyOC]) was greater in areas with Mediterranean and temperate climates. TOC and the distribution among fractions were the most influential variables in SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that different mechanisms will control SOC stabilisation and dynamics across the continent, as shown by the model covariates' importance metric. We estimated the total SOC stocks (0–30 cm) to be 13 Pg MAOC, 2 Pg POC, and 5 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and their stocks can be used for modelling SOC dynamics and forecasting changes in SOC stocks as a response to land use change, management, and climate change. [ABSTRACT FROM AUTHOR]

Soil is a complex system in which biological, chemical and physical interactions take place. The behaviour of these interactions changes in spatial scale from the atomic to the global, and in time. To understand how this system works, soil scientists usually rely on incremental improvements in the knowledge by refinement of theories through hypothesis testing and development using carefully designed experiments. In the last two decades, the primacy of this knowledge construction process has been challenged by the development of large soil databases and algorithms such as machine learning. The data‐driven research approach to soil science, the inference of soil knowledge directly from data by using computational tools and modelling techniques, is becoming more popular. Despite the wide adoption of a data‐driven research approach to soil science, there has been little discussion on how a research driven by data instead of hypotheses affects scientific progress. In this paper, we provide an introductory perspective on data‐driven soil research by discussing some of the issues and opportunities of knowledge discovery from soil data. We show that while data‐driven soil research may seem revolutionary for some, soil science has a long history of exploratory efforts to generate knowledge from data. Empirical and factual soil classifications, for example, were data driven. We further discuss, with examples, (i) data, databases and the logic of data storage for data‐driven soil research, (ii) the issues of extreme empiricist claims that arise corollary to the increase in the use of computational tools, and (iii) the challenge of formulating a scientific explanation based on patterns observed in the data and data analysis tools. By considering the epistemic challenges of the data‐driven scientific research in the light of the historical literature, we found that there is a continuity of practices, some being certainly amplified by recent technological changes, but that the core methods of scientific enquiry from data remain essentially unchanged. Highlights: Historical account of data‐driven soil science research.Describe data to be used for data‐driven soil science.Discuss conceptual issues and opportunities for data‐driven soil science.Investigate the challenge of formulating an explanation from soil data. [ABSTRACT FROM AUTHOR]

Forest species control the quantity and chemistry of organic matter input, which in interaction with the soil physicochemical properties, environmental conditions and microbial community associated with a given ecosystem may result in specific patterns of soil organic carbon (SOC) stabilization and chemistry. The objectives of this study were: (a) to characterize the chemistry of soil organic matter and SOC fractions across the gradient from pure aspen (Populus tremuloides Michx.) to pure conifer (Abies lasiocarpa (Hook.) Nutt. and Pseudotsuga menziesii (Mirbel) Franco) stands in semi‐arid montane forests, and (b) to determine whether the effect of overstory composition on SOC chemistry was patent beyond the influence of site conditions and microbial decomposer community. We used Fourier transform infrared spectroscopy to analyse the chemistry of bulk soil (BS), light fraction (LF) and mineral‐associated SOC (MoM) from mineral soils (0–15 cm) sampled across the natural gradient of aspen and mixed conifer stands from northern and southern Utah. Vegetation overstory had a subtle effect on the MoM fraction, indicating higher proportion of aliphatic C with aspen dominance, whereas there were no differences in LF chemistry between vegetation types. Independently of the vegetation cover type, the MoM fraction was enriched in aliphatic C compared to the LF, although the proportion of polysaccharides and C‐O groups increased in the MoM fraction for plot samples. Differentiation between spectra from soils developed on sedimentary rock and soils developed on basalt, quartzite and limestone, highlighted the influence of parent material and mineralogy on MoM chemistry. The patterns in SOC fractions' chemistry do not allow an affirmation that greater SOC storage under aspen is due to the accumulation of recalcitrant compounds (i.e., aliphatic C) and controlled by litter chemistry. Rather, they suggest that the ensemble of litter chemistry, microbial community and soil properties in aspen stands enhances SOC storage. Highlights: Vegetation overstory and site characteristics (e.g., parent material) influence SOC chemistry and stabilization patterns.Light fraction SOC spectra did not differ between forest species in the aspen‐conifer ecotone.The proportion of aliphatic C in mineral‐associated organic carbon (MoM) increased with aspen dominance.The effect of overstory composition on MoM chemistry was patent beyond the influence of site conditions and microbial decomposer community. [ABSTRACT FROM AUTHOR]