David Clifford

Geographical origin of Sauvignon Blanc wines predicted by mass spectrometry and metal oxide based electronic nose.

Analysis of 34 Sauvignon Blanc wine samples from three different countries and six regions was performed by gas chromatography-mass spectrometry (GC-MS). Linear discriminant analysis (LDA) showed that there were three distinct clusters or classes of wines with different aroma profiles. Wines from the Loire region in France and Australian wines from Tasmania and Western Australia were found to have similar aroma patterns. New Zealand wines from the Marlborough region as well as the Australian ones from Victoria were grouped together based on the volatile composition. Wines from South Australia region formed one discrete class. Seven analytes, most of them esters, were found to be the relevant chemical compounds that characterized the classes. The grouping information obtained by GC-MS, was used to train metal oxide based electronic (MOS-Enose) and mass spectrometry based electronic (MS-Enose) noses. The combined use of solid phase microextraction (SPME) and ethanol removal prior to MOS-Enose analysis, allowed an average error of prediction of the regional origins of Sauvignon Blanc wines of 6.5% compared to 24% when static headspace (SHS) was employed. For MS-Enose, the misclassification rate was higher probably due to the requirement to delimit the m/z range considered.

The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project

Information on the geographic variation in soil has traditionally been presented in polygon (choropleth) maps at coarse scales. Now scientists, planners, managers and politicians want quantitative information on the variation and functioning of soil at finer resolutions; they want it to plan better land use for agriculture, water supply and the mitigation of climate change land degradation and desertification. The GlobalSoilMap project aims to produce a grid of soil attributes at a fine spatial resolution (approximately 100 m), and at six depths, for the purpose. This paper describes the three-dimensional spatial modelling used to produce the Australian soil grid, which consists of Australia-wide soil attribute maps. The modelling combines historical soil data plus estimates derived from visible and infrared soil spectra. Together they provide a good coverage of data across Australia. The soil attributes so far include sand, silt and clay contents, bulk density, available water capacity, organic carbon, pH, effective cation exchange capacity, total phosphorus and total nitrogen. The data on these attributes were harmonised to six depth layers, namely 0–0.05 m, 0.05–0.15 m, 0.15–0.30 m, 0.30–0.60 m, 0.60–1.00 m and 1.00–2.00 m, and the resulting values were incorporated simultaneously in the models. The modelling itself combined the bootstrap, a decision tree with piecewise regression on environmental variables and geostatistical modelling of residuals. At each layer, values of the soil attributes were predicted at the nodes of a 3 arcsecond (approximately 90 m) grid and mapped together with their uncertainties. The assessment statistics for each attribute mapped show that the models explained between 30% and 70% of their total variation. The outcomes are illustrated with maps of sand, silt and clay contents and their uncertainties. The Australian three-dimensional soil maps fill a significant gap in the availability of quantitative soil information in Australia.

Guidelines for constructing allometric models for the prediction of woody biomass: How many individuals to harvest?

The recent development of biomass markets and carbon trading has led to increasing interest in obtaining accurate estimates of woody biomass production. Aboveground woody biomass (B) is often estimated indirectly using allometric models, where representative individuals are harvested and weighed, and regression analyses used to generalise the relationship between individual mass and more readily measured non-destructive attributes such as plant height and stem diameter (D). To satisfy regulatory requirements and/or to provide market confidence, allometric models must be based on sufficient data to ensure predictions are accurate, whilst at the same time being practically and financially achievable. Using computer resampling experiments and allometric models of the form B = aDb the trade-off between increasing the sample size of individuals to construct an allometric model and the accuracy of the resulting biomass predictions was assessed. A range of algorithms for selecting individuals across the stem diameter size-class range were also explored. The results showed marked variability across allometric models in the required number of individuals to satisfy a given level of precision. A range of 17–95 individuals were required to achieve biomass predictions with a standard deviation within 5% of the mean for the best performing stem diameter selection algorithm, while 25–166 individuals were required for the poorest. This variability arises from (a) inherent uncertainty in the relationship between diameter and biomass across allometric models, and (b) differences between the diameter size-class distribution of individuals used to construct a model, and the diameter size-class distribution of the population to which the model is applied. Allometric models are a key component of quantifying land-based sequestration activities, but despite their importance little attention has been given to ensuring the methods used in their development will yield sufficiently accurate biomass predictions. The results from this study address this gap and will be of use in guiding the development of new allometric models; in assessing the suitability of existing allometric models; and in facilitating the estimation of uncertainty in biomass predictions.

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 effect of microbial colonization on the host proteome varies by gastrointestinal location

Endogenous intestinal microbiota have wide-ranging and largely uncharacterized effects on host physiology. Here, we used reverse-phase liquid chromatography-coupled tandem mass spectrometry to define the mouse intestinal proteome in the stomach, jejunum, ileum, cecum and proximal colon under three colonization states: germ-free (GF), monocolonized with Bacteroides thetaiotaomicron and conventionally raised (CR). Our analysis revealed distinct proteomic abundance profiles along the gastrointestinal (GI) tract. Unsupervised clustering showed that host protein abundance primarily depended on GI location rather than colonization state and specific proteins and functions that defined these locations were identified by random forest classifications. K-means clustering of protein abundance across locations revealed substantial differences in host protein production between CR mice relative to GF and monocolonized mice. Finally, comparison with fecal proteomic data sets suggested that the identities of stool proteins are not biased to any region of the GI tract, but are substantially impacted by the microbiota in the distal colon.

Predictive mapping of soil organic carbon stocks in South Australia’s agricultural zone

Better understanding the spatial distribution of soil organic carbon (SOC) stocks is important for the management and enhancement of soils for production and environmental outcomes. We have applied digital soil mapping (DSM) techniques to combine soil-site datasets from legacy and recent sources, environmental covariates and expert pedological knowledge to predict and map SOC stocks in the top 0.3 m, and their uncertainty, across South Australia’s agricultural zone. In achieving this, we aimed to maximise the use of locally sourced datasets not previously considered in national soil C assessments. Practical considerations for operationalising DSM are also discussed in the context of working with problematic legacy datasets, handling large numbers of potentially correlated covariates, and meeting end-user needs for readily interpretable results and accurate maps. Spatial modelling was undertaken using open-source R statistical software over a study area of ~160 000 km2. Legacy-site SOC stock estimates were derived with inputs from an expert-derived bulk-density pedotransfer function to overcome critical gaps in the data. Site estimates of SOC were evaluated over a consistent depth range and then used in spatial predictions through an environmental-correlation regression-kriging DSM approach. This used the contemporary Least Absolute Shrinkage and Selection Operator penalised-regression method, which catered for a large number (63 numeric, four categorical, four legacy-soil mapping themes) of potentially correlated covariates. For efficient use of the available data, this was performed within a k-fold cross-validation (k = 10) modelling framework. Through this, we generated multiple predictions and variance information at every node of our prediction grid, which was used to evaluate and map the expected value (mean) of SOC stocks and their uncertainty. For the South Australian agricultural zone, expected value SOC stocks in the top 0.3 m summed to 0.589 Gt with a 90% prediction interval of 0.266–1.086 Gt.

Selecting subsets of genotyped experimental populations for phenotyping to maximize genetic diversity

Selective phenotyping is a way of capturing the benefits of large population sizes without the need to carry out large-scale phenotyping and hence is a cost-effective means of capturing information about gene–trait relationships within a population. The diversity within the sample gives an indication of the efficiency of this information capture; less diversity implies greater redundancy of the genetic information. Here, we propose a method to maximize genetic diversity within the selected samples. Our method is applicable to general experimental designs and robust to common problems such as missing data and dominant markers. In particular, we discuss its application to multi-parent advanced generation intercross (MAGIC) populations, where, although thousands of lines may be genotyped as a large population resource, only hundreds may need to be phenotyped for individual studies. Through simulation, we compare our method to simple random sampling and the minimum moment aberration method. While the gain in power over simple random sampling for all tested methods is not large, our method results in a much more diverse sample of genotypes. This diversity can be applied to improve fine mapping resolution once a QTL region has been detected. Further, when applied to two wheat datasets from doubled haploid and MAGIC progeny, our method detects known QTL for small sample sizes where other methods fail.

Computation of Spatial Covariance Matrices

In an earlier article, Ghosh derived the density for the distance between two points uniformly and independently distributed in a rectangle. This article extends that work to include the case where the two points lie in two different rectangles in a lattice. This density allows one to find the expected value of certain functions of this distance between rectangles analytically or by one-dimensional numerical integration. In the case of isotropic spatial models or spatial models with geometric anisotropy terms for agricultural experiments one can use these theoretical results to compute the covariance between the yields in different rectangular plots. As the numerical integration is one-dimensional these results are computed quickly and accurately. The types of covariance functions used come from the Matérn and power families of processes. Analytic results are derived for the de Wijs process, a member of both families and for the power models also. Software in R is available. Examples of the code are given for fitting spatial models to the Fairfield Smith data. Other methods for the estimation of the covariance matrices are discussed and their pros and cons are outlined.

Generalized analysis of spatial variation in yield monitor data

Australian lupin and cotton yield monitor data were analysed using spatial models from the Matern class of spatial covariance functions. Despite difficulties with the spatial disposition of the data, the analysis supports the statistical model in which the variation is a linear combination of white noise and the de Wijs process. The de Wijs process, also called the logarithmic covariance function, is a generalized covariance function that is conformally invariant and suggests that there is variation at all spatial scales. The present work also indicates that anisotropy and convolution are properties of yield monitor data and that it is hard to distinguish the two. The degree and causes of anisotropy require further investigation. Fitting this model is relatively easy for small, precision-agriculture datasets and open source software is available to this end. Comparing the de Wijs model with more general models in the Matern class is computationally intensive for precision-agriculture datasets.

Visualisation in imaging mass spectrometry using the minimum noise fraction transform

BackgroundImaging Mass Spectrometry (IMS) provides a means to measure the spatial distribution of biochemical features on the surface of a sectioned tissue sample. IMS datasets are typically huge and visualisation and subsequent analysis can be challenging. Principal component analysis (PCA) is one popular data reduction technique that has been used and we propose another; the minimum noise fraction (MNF) transform which is popular in remote sensing.FindingsThe MNF transform is able to extract spatially coherent information from IMS data. The MNF transform is implemented through an R-package which is available together with example data from http://sta.scm.uws.edu.au/glenn/∖#Software.ConclusionsIn our example, the MNF transform was able to find additional images of interest. The extracted information forms a useful basis for subsequent analyses.