Utilizing a DUALEM‐421 and inversion modelling to map baseline soil salinity along toposequences in the Hunter Valley Wine district

In the oldest commercial wine district of Australia, the Hunter Valley, there is the threat of soil salinization because marine sediments underlie the area. To understand the risk requires information about the spatial distribution of soil properties. Electromagnetic (EM) induction instruments have been used to identify and map the spatial variation of average soil salinity to a certain depth. However, soils vary with depth dependent on soil forming factors. We collected data from a single‐frequency and multiple‐coil DUALEM‐421 along a toposequence. We inverted this data using EM4Soil software and evaluated the resultant 2‐dimensional model of true electrical conductivity (σ – mS/m) with depth against electrical conductivity of saturated soil pastes (EC_p – dS/m). Using a fitted linear regression (LR) model calibration approach and by varying the forward model (cumulative function‐CF and full solution‐FS), inversion algorithm (S1 and S2), damping factor (λ) and number of arrays, we determined a suitable electromagnetic conductivity image (EMCI), which was optimal (R² = 0.82) when using the full solution, S2, λ = 3.6 and all six coil arrays. We conducted an uncertainty analysis of the LR model used to estimate the electrical conductivity of the saturated soil‐paste extract (EC_e – dS/m). Our interpretation based on estimates of EC_e suggests the approach can identify differences in salinity, how these vary with parent material and how topography influences salt distribution. The results provide information leading to insights into how soil forming factors and agricultural practices influence salinity down a toposequence and how this can guide soil management practices.     

Modelling long-term in situ soil profile evolution: application to the genesis of soil profiles containing stone layers

Models of soil genesis are potentially of great importance in assessing the effects of global change on ecosystems, and may also contribute to our understanding of soil genetic processes. Many quantitative models have so far focused on individual soil genetic processes and are difficult to extrapolate to the landscape scale. A few attempts have been made to model soil evolution as a whole from a pedologic perspective. This study develops a quantitative model of soil formation at the profile scale, taking into account major soil‐forming processes. These include physical and chemical weathering of primary minerals, strain processes, and bioturbation. The model allows the quantification of the evolution of the particle size, mineral composition and bulk density of the soil. The model is applied with varying values of input parameters, and is compared with actual soil genetic processes. Running the model results in the formation of stone‐layered soil profiles. Stone‐line formation by means of bioturbation, as already described in the literature, seems to be adequately simulated. Planned improvements of the model include implementation of other major soil genetic processes such as leaching, organic matter influence, etc. This model will then have to be implemented spatially considering particularly redistribution processes, to reproduce soil formation at the landscape scale.     

Measuring and modelling the actual energy involved in aggregate breakdown

This work investigates the distribution of soil aggregates for Vertisols and Ferrasols as a function of the actual energy involved in dispersion, known as the dispersive energy. For Vertisols showing an aggregate hierarchy, the breakdown of aggregates 2–50 μm is modelled using the aggregate liberation and dispersion characteristic curve indicating a stepwise breakdown of soil aggregates. Meanwhile, for Ferrasols, the breakdown of 2–50 μm aggregates increases monotonically with increasing dispersive energy, which is indicative of the direct release of silt and clay from the disruption of aggregates. For soils displaying an aggregate hierarchy, the relative rate constant of aggregate liberation is much larger than the relative rate constant of the aggregate dispersion. Furthermore, the redistribution of aggregates for a number of size ranges within the 2–50 μm fraction illustrates a number of different pathways in aggregate breakdown, and the assumption that aggregates follow an exponential decay may not detect the presence of a weakly expressed aggregate hierarchy. The exponential decrease in ultrasonic power over time for the Ferrasols, as opposed to the prominent drop or steps in the curves for the Vertisols, confirms the lack of a prominent aggregate hierarchy.     

Uncertainty analysis for pedotransfer functions

Both empirical and process‐simulation models are useful in predicting the outcome of agricultural management on soil quality and vice versa, and pedotransfer functions have been developed to translate readily available soil information into variables that are needed in the models. The input data are subject to error, and consequently the transfer functions can produce varied outputs. A general approach to quantifying the resulting uncertainty is to use Monte Carlo methods. By sampling repeatedly from the assumed probability distributions of the input variables and evaluating the response of the model, the statistical distribution of the outputs can be estimated. Methods for sampling the probability distribution include simple random sampling, the sectioning method, and Latin hypercube sampling. The Latin hypercube sampling is applied to the quantification of uncertainties in pedotransfer functions of soil strength and soil hydraulic properties. Hydraulic properties predicted using recently developed pedotransfer functions are also used in a model to analyse the uncertainties in the prediction of soil‐water regimes in the field. The uncertainties of hydraulic properties in soil‐water simulation show that the model is sensitive to the soil's moisture state.     

Models relating soil pH measurements in water and calcium chloride that incorporate electrolyte concentration

Soil pH is the most routinely measured soil property for assessing plant nutrient availability. Nevertheless, there are various techniques for soil pH measurement, which vary with regard to the solution used and the soil‐to‐solution ratio. Soil pH is commonly measured in water or 0.01 m CaC1₂. Soil pH in CaCl₂ is usually preferred as it is less affected by soil electrolyte concentration and provides a more consistent measurement. Therefore there is a need to convert measurement values between the two methods. Previous models reported linear and curvilinear relationships between the two measurements. However, the pH difference between measurements in water and CaCl₂ is related to the soil solution electrolyte concentration. We observed that the pH difference between the two methods became smaller with increasing soil electrical conductivity (EC). We therefore developed models that relate pH in CaCl₂ and water and incorporate EC values. We calibrated a linear and a non‐linear model (artificial neural networks, ANN) using 9817 soil samples from Queensland, Australia. Soil pH in water and CaCl₂ and EC were measured with a 1:5 soil‐to‐solution ratio. The results show that incorporating EC in the prediction model improves the prediction of pH in CaCl₂ significantly. We validated these models using 4576 independent samples obtained from a diverse range of soils across Australia. Although the linear and ANN models performed similarly, the ANN (which has a curvilinear relationship) provided a better prediction and aligns with the theory that for acid and alkaline pH values, the difference between pH in water and CaCl₂ is less than that for pHs between 4.5 and 7.     

Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution

Apparent electrical conductivity of soil (ECa) is a property frequently used as a diagnostic tool in precision agriculture, and is measured using vehicle‐mounted proximal sensors. Crop‐yield data, which is measured by harvester‐mounted sensors, is usually collected at a higher spatial density compared to ECa. ECa and crop‐yield maps frequently exhibit similar spatial patterns because ECa is primarily controlled by the soil clay content and the interrelated soil moisture content, which are often significant contributors to crop‐yield potential. By quantifying the spatial relationship between soil ECa and crop yield, it is possible to estimate the value of ECa at the spatial resolution of the crop‐yield data. This is achieved through the use of a local regression kriging approach which uses the higher‐resolution crop‐yield data as a covariate to predict ECa at a higher spatial resolution than would be prudent with the original ECa data alone. The accuracy of the local regression kriging (LRK) method is evaluated against local kriging (LK) and local regression (LR) to predict ECa. The results indicate that the performance of LRK is dependent on the performance of the inherent local regression. Over a range of ECa transect survey densities, LRK provides greater accuracy than LK and LR, except at very low density. Maps of the regression coefficients demonstrated that the relationship between ECa and crop yield varies from year to year, and across a field. The application of LRK to commercial scale ECa survey data, using crop yield as a covariate, should improve the accuracy of the resultant maps. This has implications for employing the maps in crop‐management decisions and building more robust calibrations between field‐gathered soil ECa and primary soil properties such as clay content.     

Building and testing conceptual and empirical models for predicting soil bulk density

The development of pedotransfer functions offers a potential means of alleviating cost and labour burdens associated with bulk‐density determinations. As a means of incorporating a priori knowledge into the model‐building process, we propose a conceptual model for predicting soil bulk density from other more regularly measured properties. The model considers soil bulk density to be a function of soil mineral packing structures (ρ_m) and soil structure (Δρ). Bulk‐density maxima were found for soils with approximately 80% sand. Bulk densities were also observed to increase with depth, suggesting the influence of over‐burden pressure. Residuals from the ρ_m model, hereby known as Δρ, correlated with organic carbon. All models were trained using Australian soil data, with limits set at bulk densities between 0.7 and 1.8 g cm^?3 and containing organic carbon levels below 12%. Performance of the conceptual model (r² = 0.49) was found to be comparable with a multiple linear regression model (r² = 0.49) and outperformed models developed using an artificial neural network (r² = 0.47) and a regression tree (r² = 0.43). Further development of the conceptual model should allow the inclusion of soil morphological data to improve bulk‐density predictions.     

Enrichment of gene-coding sequences in maize by genome filtration

Approximately 80% of the maize genome comprises highly repetitive sequences interspersed with single-copy, gene-rich sequences, and standard genome sequencing strategies are not readily adaptable to this type of genome. Methodologies that enrich for genic sequences might more rapidly generate useful results from complex genomes. Equivalent numbers of clones from maize selected by techniques called methylation filtering and High C0t selection were sequenced to generate approximately 200,000 reads (approximately 132 megabases), which were assembled into contigs. Combination of the two techniques resulted in a sixfold reduction in the effective genome size and a fourfold increase in the gene identification rate in comparison to a nonenriched library.