Supplementary financial information, special data requirements, and agency functions and planning program at June 30... /

Auditors: Arthur Anderson, 1996 ; Geo S. Olive & Co., 1997 ; Olive, 1998-

Decomposition of wood and bark sawdusts in soil, nitrogen requirements and effects on plants /

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Co-kriging when the soil and ancillary data are not co-located

Data such as digitized aerial photographs, electrical conductivity and yield are intensive and relatively inexpensive to obtain compared with collecting soil data by sampling. If such ancillary data are co-regionalized with the soil data they should be suitable for co-kriging. The latter requires that information for both variables is co-located at several locations; this is rarely so for soil and ancillary data. To solve this problem, we have derived values for the ancillary variable at the soil sampling locations by averaging the values within a radius of 15 m, taking the nearest-neighbour value, kriging over 5 m blocks, and punctual kriging. The cross-variograms from these data with clay content and also the pseudo cross-variogram were used to co-krige to validation points and the root mean squared errors (RMSEs) were calculated. In general, the data averaged within 15m and the punctually kriged values resulted in more accurate predictions.

Comparing sampling needs for variograms of soil properties computed by the method of moments and residual maximum likelihood

It has been generally accepted that the method of moments (MoM) variogram, which has been widely applied in soil science, requires about 100 sites at an appropriate interval apart to describe the variation adequately. This sample size is often larger than can be afforded for soil surveys of agricultural fields or contaminated sites. Furthermore, it might be a much larger sample size than is needed where the scale of variation is large. A possible alternative in such situations is the residual maximum likelihood (REML) variogram because fewer data appear to be required. The REML method is parametric and is considered reliable where there is trend in the data because it is based on generalized increments that filter trend out and only the covariance parameters are estimated. Previous research has suggested that fewer data are needed to compute a reliable variogram using a maximum likelihood approach such as REML, however, the results can vary according to the nature of the spatial variation. There remain issues to examine: how many fewer data can be used, how should the sampling sites be distributed over the site of interest, and how do different degrees of spatial variation affect the data requirements? The soil of four field sites of different size, physiography, parent material and soil type was sampled intensively, and MoM and REML variograms were calculated for clay content. The data were then sub-sampled to give different sample sizes and distributions of sites and the variograms were computed again. The model parameters for the sets of variograms for each site were used for cross-validation. Predictions based on REML variograms were generally more accurate than those from MoM variograms with fewer than 100 sampling sites. A sample size of around 50 sites at an appropriate distance apart, possibly determined from variograms of ancillary data, appears adequate to compute REML variograms for kriging soil properties for precision agriculture and contaminated sites. (C) 2007 Elsevier B.V. All rights reserved.

Importance of soil ecology in vegetation rehabilitation on a North Stradbroke Island sand mine

Sibelco Australia Limited (SAL), a mineral sand mining operation on North Stradbroke Island, undertakes progressive rehabilitation of mined areas. Initial investigations have found that some areas at SAL’s Yarraman Mine have failed to redevelop towards approved criteria. This study, undertaken in 2010, examined ground cover rehabilitation of different aged plots at the Yarraman Mine to determine if there was a relationship between key soil and vegetation attributes. Vegetation and soil data were collected from five plots rehabilitated in 2003, 2006, 2008, 2009 and 2010, and one unmined plot. Cluster (PATN) analysis revealed that vegetation species composition, species richness and ground cover differed between plots. Principal component analysis (PCA) extracted ten soil attributes that were then correlated with vegetation data. The attributes extracted by PCA, in order of most common variance, were: water content, pH, terrolas depth, elevation, slope angle, leaf litter depth, total organic carbon, and counts of macrofauna, fungi and bacteria. All extracted attributes differed between plots, and all except bacteria correlated with at least one vegetation attribute. Water content and pH correlated most strongly with vegetation cover suggesting an increase in soil moisture and a reduction in pH are required in order to improve vegetation rehabilitation at Yarraman Mine. Further study is recommended to confirm these results using controlled experiments and to test potential solutions, such as organic amendments.

ERP stakeholders’ perceptions of data quality and utility

While data quality has been identified as a critical factor associated with enterprise resource planning (ERP) failure, the relationship between ERP stakeholders, the information they require and its relationship to ERP outcomes continues to be poorly understood. Applying stakeholder theory to the problem of ERP performance, we put forward a framework articulating the fundamental differences in the way users differentiate between ERP data quality and utility. We argue that the failure of ERPs to produce significant organisational outcomes can be attributed to conflict between stakeholder groups over whether the data contained within an ERP is of adequate ‘quality’. The framework provides guidance as how to manage data flows between stakeholders, offering insight into each of their specific data requirements. The framework provides support for the idea that stakeholder affiliation dictates the assumptions and core values held by individuals, driving their data needs and their perceptions of data quality and utility.

A life cycle assessment approach to quantifying greenhouse gas emissions from land-use change for beef production in eastern Australia

In life cycle assessment studies, greenhouse gas (GHG) emissions from direct land-use change have been estimated to make a significant contribution to the global warming potential of agricultural products. However, these estimates have a high uncertainty due to the complexity of data requirements and difficulty in attribution of land-use change. This paper presents estimates of GHG emissions from direct land-use change from native woodland to grazing land for two beef production regions in eastern Australia, which were the subject of a multi-impact life cycle assessment study for premium beef production. Spatially- and temporally consistent datasets were derived for areas of forest cover and biomass carbon stocks using published remotely sensed tree-cover data and regionally applicable allometric equations consistent with Australia's national GHG inventory report. Standard life cycle assessment methodology was used to estimate GHG emissions and removals from direct land-use change attributed to beef production. For the northern-central New South Wales region of Australia estimates ranged from a net emission of 0.03 t CO2-e ha-1 year-1 to net removal of 0.12 t CO2-e ha-1 year-1 using low and high scenarios, respectively, for sequestration in regrowing forests. For the same period (1990-2010), the study region in southern-central Queensland was estimated to have net emissions from land-use change in the range of 0.45-0.25 t CO2-e ha-1 year-1. The difference between regions reflects continuation of higher rates of deforestation in Queensland until strict regulation in 2006 whereas native vegetation protection laws were introduced earlier in New South Wales. On the basis of liveweight produced at the farm-gate, emissions from direct land-use change for 1990-2010 were comparable in magnitude to those from other on-farm sources, which were dominated by enteric methane. However, calculation of land-use change impacts for the Queensland region for a period starting 2006, gave a range from net emissions of 0.11 t CO2-e ha-1 year-1 to net removals of 0.07 t CO2-e ha-1 year-1. This study demonstrated a method for deriving spatially- and temporally consistent datasets to improve estimates for direct land-use change impacts in life cycle assessment. It identified areas of uncertainty, including rates of sequestration in woody regrowth and impacts of land-use change on soil carbon stocks in grazed woodlands, but also showed the potential for direct land-use change to represent a net sink for GHG.

Soil N availability, rather than N deposition, controls indirect N2O emissions

Ammonia volatilised and re-deposited to the landscape is an indirect N2O emission source. This study established a relationship between N2O emissions, low magnitude NH4 deposition (0–30  kg N ha − 1 ), and soil moisture content in two soils using in-vessel incubations. Emissions from the clay soil peaked ( < 0.002 g N [ g soil ] − 1 min − 1 ) from 85 to 93% WFPS (water filled pore space), increasing to a plateau as remaining mineral-N increased. Peak N2O emissions for the sandy soil were much lower ( < 5 × 10 − 5 μg N [ g soil ] − 1 min − 1 ) and occurred at about 60% WFPS, with an indistinct relationship with increasing resident mineral N due to the low rate of nitrification in that soil. Microbial community and respiration data indicated that the clay soil was dominated by denitrifiers and was more biologically active than the sandy soil. However, the clay soil also had substantial nitrifier communities even under peak emission conditions. A process-based mathematical denitrification model was well suited to the clay soil data where all mineral-N was assumed to be nitrified ( R 2 = 90 % ), providing a substrate for denitrification. This function was not well suited to the sandy soil where nitrification was much less complete. A prototype relationship representing mineral-N pool conversions (NO3− and NH4+) was proposed based on time, pool concentrations, moisture relationships, and soil rate constants (preliminary testing only). A threshold for mineral-N was observed: emission of N2O did not occur from the clay soil for mineral-N <70 mg ( kg of soil ) − 1 , suggesting that soil N availability controls indirect N2O emissions. This laboratory process investigation challenges the IPCC approach which predicts indirect emissions from atmospheric N deposition alone.

The importance of high frequency data in ecological modelling.

The purpose of this note is to discuss the role of high frequency data in ecological modelling and to identify some of the data requirements for the further development of ecological models for operational oceanography. There is a pressing requirement for the establishment of data acquisition systems for key ecological variables with a high spatial and temporal coverage. Such a system will facilitate the development of operational models. It is envisaged that both in-situ and remotely sensed measurements will need to combined to achieve this aim.

Ligand arsenic complexation and immunoperoxidase detection of metallothionein in the earthworm Lumbricus rubellus inhabiting arsenic-rich soil

Although earthworms have been found to inhabit arsenic-rich soils in the U.K., the mode of arsenic detoxification is currently unknown. Biochemical analyses and subcellular localization studies have indicated that As3+-thiol complexes may be involved; however, it is not known whether arsenic is capable of inducing the expression of metallothionein (MT) in earthworms. The specific aims of this paper were (a) to detect and gain an atomic characterization of ligand complexing by X-ray absorption spectrometry (XAS), and (b) to employ a polyclonal antibody raised against an earthworm MT isoform (w-MT2) to detect and localize the metalloprotein by immunoperoxidase histochemistry in the tissues of earthworms sampled from arsenic-rich soil. Data suggested that the proportion of arsenate to sulfur-bound species varies within specific earthworm tissues. Although some arsenic appeared to be in the form of arsenobetaine, the arsenic within the chlorogogenous tissue was predominantly coordinated with S in the form of -SH groups. This suggests the presence of an As::MT complex. Indeed, MT was detectable with a distinctly localized tissue and cellular distribution. While MT was not detectable in the surface epithelium or in the body wall musculature, immunoperoxidase histochemistry identified the presence of MT in chloragocytes around blood vessels, within the typhlosolar fold, and in the peri-intestinal region. Focal immunostaining was also detectable in a cohort of cells in the intestinal wall. The results of this study support the hypothesis that arsenic induces MT expression and is sequestered by the metalloprotein in certain target cells and tissues.

Using geostatistical Bayesian Updating to integrate airborne radiometrics and soil geochemistry to improve mapping for mineral exploration

Mineral exploration programmes around the world use data from remote sensing, geophysics and direct sampling. On a regional scale, the combination of airborne geophysics and ground-based geochemical sampling can aid geological mapping and economic minerals exploration. The fact that airborne geophysical and traditional soil-sampling data are generated at different spatial resolutions means that they are not immediately comparable due to their different sampling density. Several geostatistical techniques, including indicator cokriging and collocated cokriging, can be used to integrate different types of data into a geostatistical model. With increasing numbers of variables the inference of the cross-covariance model required for cokriging can be demanding in terms of effort and computational time. In this paper a Gaussian-based Bayesian updating approach is applied to integrate airborne radiometric data and ground-sampled geochemical soil data to maximise information generated from the soil survey, to enable more accurate geological interpretation for the exploration and development of natural resources. The Bayesian updating technique decomposes the collocated estimate into a production of two models: prior and likelihood models. The prior model is built from primary information and the likelihood model is built from secondary information. The prior model is then updated with the likelihood model to build the final model. The approach allows multiple secondary variables to be simultaneously integrated into the mapping of the primary variable. The Bayesian updating approach is demonstrated using a case study from Northern Ireland where the history of mineral prospecting for precious and base metals dates from the 18th century. Vein-hosted, strata-bound and volcanogenic occurrences of mineralisation are found. The geostatistical technique was used to improve the resolution of soil geochemistry, collected one sample per 2 km2, by integrating more closely measured airborne geophysical data from the GSNI Tellus Survey, measured over a footprint of 65 x 200 m. The directly measured geochemistry data were considered as primary data in the Bayesian approach and the airborne radiometric data were used as secondary data. The approach produced more detailed updated maps and in particular maximized information on mapped estimates of zinc, copper and lead. Greater delineation of an elongated northwest/southeast trending zone in the updated maps strengthened the potential to investigate stratabound base metal deposits.

Assessment of Polycyclic Aromatic Hydrocarbons in an Urban Soil Dataset

We compare a suite of Polycyclic Aromatic Hydrocarbons (Parent PAHs) in soils and air across an urban area (Belfast UK). Isomeric PAH ratios suggest that soil PAHs are mainly from a combustion source. Fugacity modelling across a range of soil temperatures predicts that four ring and larger PAHs from pyrene to indeno[1,2,3–cd]pyrene all partition strongly (>98%) to the soil compartment. This modelling also implies that these PAHs do not experience losses through partitioning to other phases (air, water) due to soil temperature effects. Such modelling may help in understanding the overall contaminantdistribution in soils. The air and soil data together with modelling suggests that care must be taken when considering isomeric ratios of compounds with mass lighter than 178 (i.e. phenanthrene and anthracene) in the soil phase. Comparison of duplicate and replicate samples suggest that field sampling of duplicates dominates uncertainty and validated methodologies for selection of field duplicates and lab splitting are required. As the urban soil four ring PAHs are at equilibrium in the soil phase, and have characteristic ratios that are dominated by a combustion source that is a single controlling factor over spatial distribution, methods that calculate background concentrations can be compared.

A Meteorological and Snow observational data set from Snoqualmie Pass (921 m), Washington Cascades, U.S.

We introduce a quality controlled observational atmospheric, snow, and soil data set from Snoqualmie Pass, Washington, U.S.A., to enable testing of hydrometeorological and snow process representations within a rain-snow transitional climate where existing observations are sparse and limited. Continuous meteorological forcing (including air temperature, total precipitation, wind speed, specific humidity, air pressure, short- and longwave irradiance) are provided at hourly intervals for a 24-year historical period (water years 1989-2012) and at half-hourly intervals for a more-recent period (water years 2013-2015), separated based on the availability of observations. Additional observations include 40-years of snow board new snow accumulation, multiple measurements of total snow depth, and manual snow pits, while more recent years include sub-daily surface temperature, snowpack drainage, soil moisture and temperature profiles, and eddy co-variance derived turbulent heat flux. This data set is ideal for testing hypotheses about energy balance, soil and snow processes in the rain-snow transition zone. Plots of live data can be found here: http://depts.washington.edu/mtnhydr/cgi/plot.cgi

Soil sampling design and sampling techniques for soil properties monitoring – common difficulties related to forest soils

Mathematical models and statistical analysis are key instruments in soil science scientific research as they can describe and/or predict the current state of a soil system. These tools allow us to explore the behavior of soil related processes and properties as well as to generate new hypotheses for future experimentation. A good model and analysis of soil properties variations, that permit us to extract suitable conclusions and estimating spatially correlated variables at unsampled locations, is clearly dependent on the amount and quality of data and of the robustness techniques and estimators. On the other hand, the quality of data is obviously dependent from a competent data collection procedure and from a capable laboratory analytical work. Following the standard soil sampling protocols available, soil samples should be collected according to key points such as a convenient spatial scale, landscape homogeneity (or non-homogeneity), land color, soil texture, land slope, land solar exposition. Obtaining good quality data from forest soils is predictably expensive as it is labor intensive and demands many manpower and equipment both in field work and in laboratory analysis. Also, the sampling collection scheme that should be used on a data collection procedure in forest field is not simple to design as the sampling strategies chosen are strongly dependent on soil taxonomy. In fact, a sampling grid will not be able to be followed if rocks at the predicted collecting depth are found, or no soil at all is found, or large trees bar the soil collection. Considering this, a proficient design of a soil data sampling campaign in forest field is not always a simple process and sometimes represents a truly huge challenge. In this work, we present some difficulties that have occurred during two experiments on forest soil that were conducted in order to study the spatial variation of some soil physical-chemical properties. Two different sampling protocols were considered for monitoring two types of forest soils located in NW Portugal: umbric regosol and lithosol. Two different equipments for sampling collection were also used: a manual auger and a shovel. Both scenarios were analyzed and the results achieved have allowed us to consider that monitoring forest soil in order to do some mathematical and statistical investigations needs a sampling procedure to data collection compatible to established protocols but a pre-defined grid assumption often fail when the variability of the soil property is not uniform in space. In this case, sampling grid should be conveniently adapted from one part of the landscape to another and this fact should be taken into consideration of a mathematical procedure.

Predicted soil organic carbon stocks and changes in Jordan between 2000 and 2030 made using the GEFSOC modelling system

Estimates of soil organic carbon (SOC) stocks and changes under different land use systems can help determine vulnerability to land degradation. Such information is important for countries in and areas with high susceptibility to desertification. SOC stocks, and predicted changes between 2000 and 2030, were determined at the national scale for Jordan using The Global Environment Facility Soil Organic Carbon (GEFSOC) Modelling System. For the purpose of this study, Jordan was divided into three natural regions (The Jordan Valley, the Uplands and the Badia) and three developmental regions (North, Middle and South). Based on this division, Jordan was divided into five zones (based on the dominant land use): the Jordan Valley, the North Uplands, the Middle Uplands, the South Uplands and the Badia. This information was merged using GIS, along with a map of rainfall isohyets, to produce a map with 498 polygons. Each of these was given a unique ID, a land management unit identifier and was characterized in terms of its dominant soil type. Historical land use data, current land use and future land use change scenarios were also assembled, forming major inputs of the modelling system. The GEFSOC Modelling System was then run to produce C stocks in Jordan for the years 1990, 2000 and 2030. The results were compared with conventional methods of estimating carbon stocks, such as the mapping based SOTER method. The results of these comparisons showed that the model runs are acceptable, taking into consideration the limited availability of long-term experimental soil data that can be used to validate them. The main findings of this research show that between 2000 and 2030, SOC may increase in heavily used areas under irrigation and will likely decrease in grazed rangelands that cover most of Jordan giving an overall decrease in total SOC over time if the land is indeed used under the estimated forms of land use. (C) 2007 Elsevier B.V. All rights reserved.