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The accurate measurement of the soil organic carbon (SOC) stock in Australian grazing lands is important due to the major role that SOC plays in soil productivity and the potential influence of soil C cycling on Australia's greenhouse gas emissions. However, the current sampling methodologies for SOC stock are varied and potentially conflicting. It was the objective of this paper to review the nature of, and reasons for, SOC variability; the sampling methodologies commonly used; and to identify knowledge gaps for SOC measurement in grazing lands. Soil C consists of a range of biological materials, in various SOC pools such as dissolved organic C, micro-and meso-fauna (microbial biomass), fungal hyphae and fresh plant residues in or on the soil (particulate organic C, light-fraction C), the products of decomposition (humus, slow pool C) and complexed organic C, and char and phytoliths (inert, passive or resistant C); and soil inorganic C (carbonates and bicarbonates). Microbial biomass and particulate or light-fraction organic C are most sensitive to management or land-use change; resistant organic C and soil carbonates are least sensitive. The SOC present at any location is influenced by a series of complex interactions between plant growth, climate, soil type or parent material, topography and site management. Because of this, SOC stock and SOC pools are highly variable on both spatial and temporal scales. This creates a challenge for efficient sampling. Sampling methods are predominantly based on design-based (classical) statistical techniques, crucial to which is a randomised sampling pattern that negates bias. Alternatively a model-based (geostatistical) analysis can be used, which does not require randomisation. Each approach is equally valid to characterise SOC in the rangelands. However, given that SOC reporting in the rangelands will almost certainly rely on average values for some aggregated scale (such as a paddock or property), we contend that the design-based approach might be preferred. We also challenge soil surveyors and their sponsors to realise that: (i) paired sites are the most efficient way of detecting a temporal change in SOC stock, but destructive sampling and cumulative measurement errors decrease our ability to detect change; (ii) due to (i), an efficient sampling scheme to estimate baseline status is not likely to be an efficient sampling scheme to estimate temporal change; (iii) samples should be collected as widely as possible within the area of interest; (iv) replicate of laboratory analyses is a critical step in being able to characterise temporal change. Sampling requirements for SOC stock in Australian grazing lands are yet to be explicitly quantified and an examination of a range of these ecosystems is required in order to assess the sampling densities and techniques necessary to detect specified changes in SOC stock and SOC pools. An examination of techniques that can help reduce sampling requirements (such as measurement of the SOC fractions that are most sensitive to management...

On-going, high-profile public debate about climate change has focussed attention on how to monitor the soil organic carbon stock (C s ) of rangelands (savannas). Unfortunately, optimal sampling of the rangelands for baseline C s -the critical first step towards efficient monitoring -has received relatively little attention to date. Moreover, in the rangelands of tropical Australia relatively little is known about how C s is influenced by the practice of cattle grazing. To address these issues we used linear mixed models to: (i) unravel how grazing pressure (over a 12-year period) and soil type have affected C s and the stable carbon isotope ratio of soil organic carbon (δ 13 C) (a measure of the relative contributions of C 3 and C 4 vegetation to C s ); (ii) examine the spatial covariation of C s and δ 13 C; and, (iii) explore the amount of soil sampling required to adequately determine baseline C s . Modelling was done in the context of the material coordinate system for the soil profile, therefore the depths reported, while conventional, are only nominal. Linear mixed models revealed that soil type and grazing pressure interacted to influence C s to a depth of 0.3 m in the profile. At a depth of 0.5 m there was no effect of grazing on C s , but the soil type effect on C s was significant. Soil type influenced δ 13C to a soil depth of 0.5 m but there was no effect of grazing at any depth examined. The linear mixed model also revealed the strong negative correlation of C s with δ 13 C, particularly to a depth of 0.1 m in the soil profile. This suggested that increased C s at the study site was associated with increased input of C from C 3 trees and shrubs relative to the C 4 perennial grasses; as the latter form the bulk of the cattle diet, we contend that C sequestration may be negatively correlated with forage production. Our baseline C s sampling recommendation for cattle-grazing properties of the tropical rangelands of Australia is to: (i) divide the property into units of apparently uniform soil type and grazing management; (ii) use stratified simple random sampling to spread at least 25 soil sampling locations about each unit, with at least two samples collected per stratum. This will be adequate to accurately estimate baseline mean C s to within 20% of the true mean, to a nominal depth of 0.3 m in the profile.Crown

This study aimed to unravel the effects of climate, topography, soil, and grazing management on soil organic carbon (SOC) stocks in the grazing lands of north-eastern Australia. We sampled for SOC stocks at 98 sites from 18 grazing properties across Queensland, Australia. These samples covered four nominal grazing management classes (Continuous, Rotational, Cell, and Exclosure), eight broad soil types, and a strong tropical to subtropical climatic gradient. Temperature and vapour-pressure deficit explained >80% of the variability of SOC stocks at cumulative equivalent mineral masses nominally representing 0–0.1 and 0–0.3 m depths. Once detrended of climatic effects, SOC stocks were strongly influenced by total standing dry matter, soil type, and the dominant grass species. At 0–0.3 m depth only, there was a weak negative association between stocking rate and climate-detrended SOC stocks, and Cell grazing was associated with smaller SOC stocks than Continuous grazing and Exclosure. In future, collection of quantitative information on stocking intensity, frequency, and duration may help to improve understanding of the effect of grazing management on SOC stocks. Further exploration of the links between grazing management and above- and below-ground biomass, perhaps inferred through remote sensing and/or simulation modelling, may assist large-area mapping of SOC stocks in northern Australia.

ABSTRACT:Crop mapping and time series analysis of agronomic cycles are critical for monitoring land use and land management practices, and analysing the issues of agro-environmental impacts and climate change. Multi-temporal Landsat data can be used to analyse decadal changes in cropping patterns at field level, owing to its medium spatial resolution and historical availability. This study attempts to develop robust remote sensing techniques, applicable across a large geographic extent, for state-wide mapping of cropping history in Queensland, Australia. In this context, traditional pixel-based classification was analysed in comparison with image object-based classification using advanced supervised machine-learning algorithms such as Support Vector Machine (SVM). For the Darling Downs region of southern Queensland we gathered a set of Landsat TM images from the 2010-2011 cropping season. Landsat data, along with the vegetation index images, were subjected to multiresolution segmentation to obtain polygon objects. Object-based methods enabled the analysis of aggregated sets of pixels, and exploited shape-related and textural variation, as well as spectral characteristics. SVM models were chosen after examining three shape-based parameters, twenty-three textural parameters and ten spectral parameters of the objects. We found that the object-based methods were superior to the pixel-based methods for classifying 4 major landuse/land cover classes, considering the complexities of within field spectral heterogeneity and spectral mixing. Comparative analysis clearly revealed that higher overall classification accuracy (95%) was observed in the object-based SVM compared with that of traditional pixel-based classification (89%) using maximum likelihood classifier (MLC). Object-based classification also resulted speckle-free images. Further, object-based SVM models were used to classify different broadacre crop types for summer and winter seasons. The influence of different shape, textural and spectral variables, and their weights on crop-mapping accuracy, was also examined. Temporal change in the spectral characteristics, specifically through vegetation indices derived from multi-temporal Landsat data, was found to be the most critical information that affects the accuracy of classification. However, use of these variables was constrained by the data availability and cloud cover.