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Calcium (Ca) and magnesium (Mg) are essential for growth of sugarcane leaves and roots, as well as respiration and nitrogen metabolism, respectively. To assist farmers decide suitable application rates of lime and Mg fertiliser, respectively, the Australian sugarcane industry established the Six-Easy-Steps nutrient management guidelines based on topsoil (0-0.3 m) Ca (cmol(+) kg -1 ) and Mg (cmol(+) kg -1 ). Given the heterogeneous nature of soil, digital soil mapping (DSM) methods can be employed to allow for the precise application rate to be determined. In this study, we examine statistical models (i.e., ordinary kriging [OK], linear mixed model [LMM], quantile regression forests [QRF], support vector machine [SVM], and Cubist regression kriging [CubistRK]) to predict topsoil and subsoil (0.6-0.9) Ca and Mg, employing digital data in combination (i.e., proximal sensing electromagnetic induction (EMI) [e.g., 1mPcon, 1mHcon, etc.], gamma-ray [γ-ray] spectrometry [i.e., TC, K, U and Th] and digital elevation model [DEM] derivatives). We also investigate various sampling designs (i.e., spatial coverage [SCS], feature space coverage [FSCS], conditioned Latin hypercube [cLHS] and simple random sampling [SRS]) and calibration sample size (i.e., n = 180, 150, 120, 90, 60 and 30). The predictions are assessed using Lin's concordance correlation coefficient (LCCC) and ratio of performance to interquartile distance (RPIQ) with an independent validation dataset (i.e., n = 40). The best results were for prediction of subsoil Mg, utilising CubistRK (LCCC = 0.82) with the largest calibration sample size (n = 180), followed by LMM (0.79), SVM (0.76), QRF (0.70) and OK (0.65). This was generally the case for topsoil and subsoil Ca. We also conclude that no single sampling design was universally better, and 180 samples were necessary for predicting topsoil Ca and Mg with moderate agreement (0.65 < LCCC < 0.80). However, with FSCS, a minimum of 120 samples were enough to calibrate a CubistRK model and achieve substantial (LCCC > 0.80) agreement for predicting subsoil Ca and Mg. With respect to soil use and management according to the Six-Easy-Steps, the sandy soil in the north and south require large application rate of lime (3.5 t/ha) and Mg (125 kg/ha), respectively. Conversely, varying amounts of fertiliser rates of lime (2.0, 1.5 and 1 t/ha) and Mg (50 kg/ha) were recommended where Vertosols were previously mapped., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The levels of trace elements in whole blood and plasma have been widely used for assessing nutritional status and monitoring exposure and can vary widely in populations from different geographical regions. In this study, whole blood samples ( n = 120) and plasma samples ( n = 120) were obtained from healthy donors attending the Red Cross Blood Bank (Queensland Red Cross Blood Service), which provided information for age and sex. There were 71 males (age range: 19-73 years) and 49 females (age range: 18-72 years) for whole blood samples, and 59 males (age range: 19-81 years) and 61 females (age range: 19-73 years) for plasma samples. The main aim of the study was to provide information on blood reference levels of 21 trace elements (Ag, Al, As, Bi, Br, Cd, Co, Cr, Cu, Hg, I, Mn, Mo, Ni, Pb, Sb, Se, Tl, U, V, Zn) in Queensland. The study also aimed to assess differences in trace element blood levels between males and females and the effect of age. The trace element levels in blood samples were analysed using inductively coupled plasma mass spectrometry (ICP-MS) and the standard reference materials of Seronorm (Trace Elements Whole Blood) and UTAK (Trace Elements Serum) were used for quality control and assurance. The study found wide variations of trace element levels in whole blood and plasma, and generally the levels were comparable to other countries. No detectable levels were found for Bi, Cr, U and V in whole blood, but V levels were found in plasma samples. There were significant differences between males and females for whole blood Cu ( p < 0.001), I ( p = 0.009), Tl ( p = 0.016) and Zn ( p = 0.016). Significant differences were also found for plasma Cu ( p < 0.001) and Se ( p = 0.003) between males and females. There were trends of increased levels of blood Pb, Se and Zn with age. The study has provided further information on a wide range of trace elements in blood as reference levels for Queensland and Australia which are currently lacking.

The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass ( Lolium perenne ) canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) ≈490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors.