Soil‐water properties vary widely with soil composition and texture, but measurements are often time consuming and expensive to determine using traditional laboratory methods. Mid‐infrared (MIR) spectroscopy is sensitive to soil composition, allowing multivariate calibrations to be derived between volumetric soil water retention and MIR spectra. Mid‐infrared partial least squares (PLS) models can be derived from the spectra of soils and reference data, and can be used to predict the water retention solely from the MIR spectra of unknown samples. Regressions between laboratory‐determined volumetric water retentions, θv, at matric suctions from 1 to 1500 kPa and values predicted by MIR PLS analysis are presented for a broad variety of surface soils from southern Australia. Cross‐validation produced coefficient of determination values ranging from 0.67 to 0.87 and standard error of cross‐validation in the range 4.1 to 3.2. Prediction robustness was tested using an independent set of samples for values of θv at field capacity (10‐kPa suction) and permanent wilting point (1500‐kPa suction). The prediction standard error for the test set was higher than for cross‐validation. This was attributed to a mismatch between spectra for the test set and those of the calibration samples, resulting in a reduced ability of the calibration samples to model the test set spectra. The MIR PLS prediction method performed at least as well as some pedotransfer functions and was shown to be a rapid and inexpensive method for the prediction of volumetric soil moisture content for a range of soil types at a range of matric suctions.
The potential to change agricultural land use to increase soil carbon stocks has been proposed as a mechanism to offset greenhouse gas emissions. To estimate the potential carbon storage in the soil from regional surveys it is important to understand the influence of environmental variables (climate, soil type, and landscape) before land management can be assessed. A survey was done of 354 sites to determine soil organic carbon stock (SOC stock; Mg C/ha) across the Lachlan and Macquarie catchments of New South Wales, Australia. The influences of climate, soil physical and chemical properties, landscape position, and 10 years of land management information were assessed. The environmental variables described most of the regional variation compared with management. The strongest influence on SOC stock at 0–10 cm was from climatic variables, particularly 30-year average annual rainfall. At a soil depth of 20–30 cm, the proportion of silica (SiO2) determined by mid-infrared spectra (SiMIR) had a negative relationship with SOC stock, and sand and clay measured by particle size analysis also showed strong relationships at sites where measured. Of the difference in SOC stock explained by land use, cropping had lower soil carbon than pasture in rotation or permanent pasture at 0–10 cm. This relationship was consistent across a rainfall gradient, but once soil carbon was standardised per mm of average annual rainfall, there was a greater difference between cropping and permanent pasture with increasing SiMIR in soils. Land use is also regulated by climate, topography, and soil type, and the effect on SOC stock is better assessed in smaller land-management units to remove some variability due to climate and soil.
Domain analysis has a well-documented history in peer reviewed academic literature; however there are few instances of its application to facilitate the assessment of system specific navigation risk. This paper details one example of a practical approach to domain analysis for a busy section of the River Thames in Central London. The results correlate well to known high risk collision areas on the river and help to quantify and corroborate expert opinion and local knowledge. However a number of conditions must be accounted for in undertaking a robust study such as the geography of the study site, the purpose and audience of the research, and the availability of data and its limitations.K E Y WO R D S 1. Domain Analysis.2. Automatic Identification System (AIS). 3. River Thames.
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