M. J. Pringle

A review of sampling designs for the measurement of soil organic carbon in Australian grazing lands

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 changes and/or measurement at specific times of the year – preferably before rapid plant growth – to decrease temporal variability), and new technologies for in situ SOC measurement is also required.

What determines soil organic carbon stocks in the grazing lands of north-eastern Australia?

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 0-0.3m depths. Once detrended of climatic effects, SOC stocks were strongly influenced by total standing dry matter, soil type, and the dominant grass species. At 0-0.3m 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.

Organic carbon stocks in cropping soils of Queensland, Australia, as affected by tillage management, climate, and soil characteristics

Research both nationally and internationally has indicated that no-till (NT) management used in combination with stubble retention has the potential to increase soil organic carbon (SOC) stocks in cropping soils relative to conventional tillage (CT). However, rates of SOC increase can vary depending on cropping system, climate, and soil type, making the quantification of carbon change difficult on a regional level. Various long-term trials and commercial sites throughout Queensland were used to compare rates of SOC change under CT and NT management in cropping soils, and to determine how climate and soil type interact to influence rates of change. It was observed that NT management was not capable of increasing SOC stocks under the crop–fallow rotation systems practised throughout Queensland, and was unlikely even to hold SOC stocks steady under current management practices. However, SOC losses under NT systems did appear to be slower than under CT, indicating that NT may slow SOC loss following a period of organic carbon input, for example, from a pasture ley. On a regional scale, biomass production (estimated through remote sensing), climate (specifically the vapour pressure deficit), and soil sand content could be used to adequately predict SOC stocks on commercial sites, indicating the importance of considering these factors when assessing SOC stocks following management change across the region.

Pragmatic soil survey design using flexible Latin hypercube sampling

We review and give a practical example of Latin hypercube sampling in soil science using an approach we call flexible Latin hypercube sampling. Recent studies of soil properties in large and remote regions have highlighted problems with the conventional Latin hypercube sampling approach. It is often impractical to travel far from tracks and roads to collect samples, and survey planning should recognise this fact. Another problem is how to handle target sites that, for whatever reason, are impractical to sample - should one just move on to the next target or choose something in the locality that is accessible? Working within a Latin hypercube that spans the covariate space, selecting an alternative site is hard to do optimally. We propose flexible Latin hypercube sampling as a means of avoiding these problems. Flexible Latin hypercube sampling involves simulated annealing for optimally selecting accessible sites from a region. The sampling protocol also produces an ordered list of alternative sites close to the primary target site, should the primary target site prove inaccessible. We highlight the use of this design through a broad-scale sampling exercise in the Burdekin catchment of north Queensland, Australia. We highlight the robustness of our design through a simulation study where up to 50% of target sites may be inaccessible. Sampling design for large spatial regions that takes prior information into account.Pragmatic implementation that works when access to sites cannot be guaranteed.A robust method for objectively and easily selecting alternative sites in the field.

The Analysis of Spatial Experiments

Anyone with an interest in precision agriculture has already formed a hypothesis that the field is a sub-optimum management unit for cropping. The role of experimentation is to test this hypothesis. Geostatistics can play an important role in analysing experiments for site-specific crop management: put simply, spatial autocorrelation must be accounted for if one is to draw valid inferences. We provide here some background to the basic concepts of agronomic experimentation. We then consider two broad classes of experimental design for precision agriculture (management-class experiments and local-response experiments), and show, with the aid of case studies, how each may be analysed geostatistically. Ultimately though, if farmers are compelled to use relatively simple designs and less formal analyses, then researchers must follow and adapt their geostatistical analyses accordingly.

Effects of land-use change and management on soil carbon and nitrogen in the Brigalow Belt, Australia: II. Statistical models to unravel the climate-soil-management interaction

The impact of grazing on soil carbon (C) and nitrogen (N) cycles is complex, and across a large area it can be difficult to uncover the magnitude of the effects. Here, we have linked two common approaches to statistical modelling – regression trees and linear mixed models – in a novel way to explore various aspects of soil C and N dynamics for a large, semiarid bioregion where land use is dominated by grazing. The resulting models, which we term RT-LMM, have the pleasing visual appeal of regression trees, and they account for spatial autocorrelation as per a linear mixed model. Our RT-LMM were developed from explanatory variables that related information on climate, soil and past land management. Response variables of interest were: stocks of soil total organic carbon (TOC), soil total nitrogen (TN), and particulate organic C (POC); the ratio of TOC stock to TN stock; and the relative abundance of stable isotopes δ13C and δ15N in the soil. Each variable was sampled at the depth interval 0–0.3 m. The interactions of land use with, in particular, air temperature and soil phosphorus were strong, but three principal management-related effects emerged: (i) the use of fire to clear native vegetation reduced stocks of TOC and TN, and the TOC : TN ratio, by 25%, 19% and 9%, respectively, suggesting that TOC is more sensitive to fire than TN; (ii) conversion of native vegetation to pasture enriched soil with δ13C by 1.7 ‰; subsequent regrowth of the native vegetation among the pasture restored δ13C to its original level but there was no corresponding change in TOC stock; and, (iii) the time elapsed since clearing reduced POC stocks and the TOC : TN ratio.

An empirical model for prediction of wheat yield, using time-integrated Landsat NDVI

Abstract Information on long-term yield variability is important for tailoring farming practices to the needs of crops. We present a linear mixed-effects model to predict wheat yield at a within-field scale in the northern grain-growing region of Australia. The model predicts yield as a function of Landsat time-integrated Normalized Difference Vegetation Index (iNDVI), and is trained on sparsely sampled yield-monitored data from 17 farms from 2001 to 2016. The model is flexibly parameterized, to use information from specific fields, farms or years, to capture local departures from the global yield-iNDVI relation. We estimated iNDVI numerically by integrating the area under a fitted asymmetric bell-shaped growth function, across the growing season (May to October), using NDVI time-series from April to November. We performed leave-one-out cross-validation, where data from individual farms, fields and years were omitted in a series of tests. Results showed moderate predictive accuracy at a within-field scale, with an average RMSE of 0.79 Mg/ha. The spatial pattern of within-field yield variation was adequately represented. The benefit of the mixed-effects model is that, as well as describing the general relation between wheat yield and iNDVI, it explicitly considers the spatial and temporal differences among farms, fields and years, which are related to variations in soil conditions, farm-management practices and climate. The long-term archive of Landsat imagery – combined with a growing archive of yield maps – provides potential for the model to predict yield variation across large regions and many seasons. Such information could help farmers make decisions on site-specific soil and nutrient management, and simultaneously guide policy-makers towards regional development objectives.