Patricia H. Bellamy

Carbon losses from all soils across England and Wales 1978-2003

More than twice as much carbon is held in soils as in vegetation or the atmosphere, and changes in soil carbon content can have a large effect on the global carbon budget. The possibility that climate change is being reinforced by increased carbon dioxide emissions from soils owing to rising temperature is the subject of a continuing debate. But evidence for the suggested feedback mechanism has to date come solely from small-scale laboratory and field experiments and modelling studies. Here we use data from the National Soil Inventory of England and Wales obtained between 1978 and 2003 to show that carbon was lost from soils across England and Wales over the survey period at a mean rate of 0.6% yr-1 (relative to the existing soil carbon content). We find that the relative rate of carbon loss increased with soil carbon content and was more than 2% yr-1 in soils with carbon contents greater than 100 g kg-1. The relationship between rate of carbon loss and carbon content is irrespective of land use, suggesting a link to climate change. Our findings indicate that losses of soil carbon in England and Wales—and by inference in other temperate regions—are likely to have been offsetting absorption of carbon by terrestrial sinks.

An assessment of the risk to surface water ecosystems of groundwater P in the UK and Ireland

A good quantitative understanding of phosphorus (P) delivery is essential in the design of management strategies to prevent eutrophication of terrestrial freshwaters. Most research to date has focussed on surface and near-surface hydrological pathways, under the common assumption that little P leaches to groundwater. Here we present an analysis of national patterns of groundwater phosphate concentrations in England and Wales, Scotland, and the Republic of Ireland, which shows that many groundwater bodies have median P concentrations above ecologically significant thresholds for freshwaters. The potential risk to receptor ecosystems of high observed groundwater P concentrations will depend on (1) whether the observed groundwater P concentrations are above the natural background; (2) the influence of local hydrogeological settings (pathways) on the likelihood of significant P transfers to the receptor; (3) the sensitivity of the receptor to P; and, (4) the relative magnitude of P transfers from groundwater compared to other P sources. Our research suggests that, although there is often a high degree of uncertainty in many of these factors, groundwater has the potential to trigger and/or maintain eutrophication under certain scenarios: the assumption of groundwater contribution to river flows as a ubiquitous source of dilution for P-rich surface runoff must therefore be questioned. Given the regulatory importance of P concentrations in triggering ecological quality thresholds, there is an urgent need for detailed monitoring and research to characterise the extent and magnitude of different groundwater P sources, the likelihood for P transformation and/or storage along aquifer-hyporheic zone flow paths and to identify the subsequent risk to receptor ecosystems.

Generic Issues on Broad-Scale Soil Monitoring Schemes： A Review

Numerous scientific challenges arise when designing a soil monitoring network (SMN), especially when assessing large areas and several properties that are driven by numerous controlling factors of various origins and scales. Different broad approaches to the establishment of SMNs are distinguished. It is essential to establish an adequate sampling protocol that can be applied rigorously at each sampling location and time. We make recommendations regarding the within-site sampling of soil. Different statistical methods should be associated with the different types of sampling design. We review new statistical methods that account for different sources of uncertainty. Except for those parameters for which a consensus exists, the question of testing method harmonisation remains a very difficult issue. The establishment of benchmark sites devoted to harmonisation and inter-calibration is advocated as a technical solution. However, to our present knowledge, no study has addressed crucial scientific issues such as how many calibration sites are necessary and how to locate them.

Determination of soil carbon stocks and changes

INTRODUCTION Soil carbon pools and the global carbon cycle In terrestrial ecosystems soils represent the major reservoir of organic carbon (Table 4.1), but with large and yet unquantified uncertainties in their estimates (mainly due to low soil sample numbers used for global up-scaling and assumptions on mean soil depths). At the global level, the soil organic matter (SOM) pool (estimated to 1 m depth) contains about 1580 Pg of carbon (Pg = 10 15 g), about 610 Pg are stored in the vegetation and about 750 Pg are present in the atmosphere (Schimel, 1995). Carbon is found in soils both in organic and inorganic forms (Table 4.2). Organic carbon is commonly classified into three ‘arbitrary’ pools, mostly for modelling purposes (such as in CENTURY), i.e. fast, slow and passive reflecting the rate of turnover. However, it is difficult to relate these pools to soil carbon fractions (see Section 4.1.5). The total amount of carbonate carbon to 1 m depth is estimated at 695–748 Pg carbon (Batjes, 1996). About one third of organic soil carbon occurs in forests and another third in grasslands and savannas, the rest in wetlands, croplands and other biomes (Janzen, 2004). The global soil organic carbon map (Fig. 4.1, ISLSCP II; ORNL DAAC, http://daac.ornl.gov/) shows the areas of high soil organic carbon predominantly in cold boreal (e.g. Northern Canada) and warm and humid tropical regions (e.g. South-East Asia), reflecting areas of deep organic soils (i.e. peatlands).

A new approach to evaluating the well-being of police

BACKGROUND There is a growing body of evidence that links employee well-being to organizational performance. Although police forces are under increasing pressure to improve efficiency and productivity, the evaluation of well-being in law enforcement is mostly restricted to self-report stress questionnaires that are based on questionable construction methodologies. No instrument to specifically determine the well-being of police force employees currently exists. AIMS To construct an instrument that measures the work-related well-being of officers and staff within a police force. METHODS The approach is drawn from well-established clinical models used to evaluate the well-being of patients. Potential variables were confirmed using an item selection method known as impact analysis that places keen emphasis on frequency and importance as perceived by the respondents themselves. RESULTS Analyses of 822 completed response sets showed that nine separate dimensions of police work can adversely affect well-being (advancement, facilities, home work interface, job, physical health, psychological health, relationships, organizational and workload). Overall, officers showed inferior well-being compared with their colleagues. Content validity and adequate internal reliability were confirmed. CONCLUSIONS This study considered a new robust approach to evaluating the well-being of all those working in law enforcement. The nine dimensions extended beyond conventional stress measures and may offer a practical alternative way of assessing the overall well-being status of an entire force using a systematic item selection framework.

An inter-laboratory comparison of multi-enzyme and multiple substrate-induced respiration assays to assess method consistency in soil monitoring

The use of indicators in soil monitoring schemes to detect changes in soil quality is receiving increased attention, particularly the application of soil biological methods. However, to date, the ability to compare information from different laboratories applying soil microbiological techniques in broad-scale monitoring has rarely been taken into account. This study aimed to assess the consistency and repeatability of two techniques that are being evaluated for use as microbiological indicators of soil quality: multi-enzyme activity assay and multiple substrate-induced respiration (MSIR). Data were tested for intrinsic (within-assay plate) variation, inter-laboratory repeatability (geometric mean regression and correlation coefficient) and land-use discrimination (principal components analysis). Intrinsic variation was large for both assays suggesting that high replicate numbers are required. Inter-laboratory repeatability showed diverging patterns for the enzyme assay and MSIR. Discrimination of soils was significant for both techniques with relatively consistent patterns; however, combined laboratory discrimination analyses for each technique showed inconsistent correspondence between the laboratories. These issues could be addressed through the adoption of reliable analytical standards for biological methods along with adequate replication. However, until the former is addressed, dispersed analyses are not currently advisable for monitoring schemes.

Simulating pesticides in ditches to assess ecological risk (SPIDER): I. Model description.

Risk assessment for pesticides in the aquatic environment relies on a comparison between estimated exposure concentrations in surface water bodies and endpoints from a series of effect tests. Many field- and catchment-scale models have been developed, ranging from simple empirical models to comprehensive, physically-based, distributed models that require complex parameterisation, often through inverse modelling methods. Routine use of catchment models for assessment and management of pesticides requires a tool that is comprehensive in being able to address all major routes of entry of pesticides into surface water and that has reasonable parameter requirements. Current models either focus primarily on transport of pesticides in surface runoff or are restricted in application because they require calibration against data from detailed monitoring programmes. SPIDER (Simulating Pesticides In Ditches to assess Ecological Risk) was developed to address the gap in models available to simulate pesticide exposure within networks of small surface water bodies (ditches and streams) in support of ecological risk assessment for pesticides. SPIDER is a locally distributed, capacitance-based model that accounts for pesticide entry into surface water bodies via spray drift, surface runoff, interlayer flow and drainflow and that can be used for small agricultural catchments. This paper provides a detailed description of the model.

Review and analysis of global agricultural N2O emissions relevant to the UK

As part of a UK government funded research project to update the UK N2O inventory methodology, a systematic review of published nitrous oxide (N2O) emission factors was carried out of non-UK research, for future comparison and synthesis with the UK measurement based evidence base. The aim of the study is to assess how the UK IPCC default emission factor for N2O emissions derived from synthetic or organic fertiliser inputs (EF1) compares to international values reported in published literature. The availability of data for comparing and/or refining the UK IPCC default value and the possibility of analysing sufficient auxiliary data to propose a Tier 2 EF1 reporting strategy is evaluated. The review demonstrated a lack of consistency in reporting error bounds for fertiliser-derived EFs and N2O flux data with 8% and 44% of publications reporting EF and N2O flux error bounds respectively. There was also poor description of environmental (climate and soil) and experimental design auxiliary data. This is likely to be due to differences in study objectives, however potential improvements to soil parameter reporting are proposed. The review demonstrates that emission factors for agricultural-derived N2O emissions ranged -0.34% to 37% showing high variation compared to the UK Tier 1 IPCC EF1 default values of 1.25% (IPCC 1996) and 1% (IPPC 2006). However, the majority (83%) of EFs reported for UK-relevant soils fell within the UK IPCC EF1 uncertainty range of 0.03% to 3%. Residual maximum likelihood (REML) analysis of the data collated in the review showed that the type and rate of fertiliser N applied and soil type were significant factors influencing EFs reported. Country of emission, the length of the measurement period, the number of splits, the crop type, pH and SOC did not have a significant impact on N2O emissions. A subset of publications where sufficient data was reported for meta-analysis to be conducted was identified. Meta-analysis of effect sizes of 41 treatments demonstrated that the application of fertiliser has a significant effect on N2O emissions in comparison to control plots and that emission factors were significantly different to zero. However no significant relationships between the quantity of fertiliser applied and the effect size of the amount of N2O emitted from fertilised plots compared to control plots were found. Annual addition of fertiliser of 35 to 557 kg N/ha gave a mean increase in emissions of 2.02 ± 0.28 g N2O/ha/day compared to control treatments (p<0.01). Emission factors were significantly different from zero, with a mean emission factor estimated directly from the meta analysis of 0.17 ± 0.02%. This is lower than the IPCC 2006 Tier 1 EF1 value of 1% but falling within the uncertainty bound for the IPCC 2006 Tier 1 EF1 (0.03% to 3%). As only a small number of papers were viable for meta analysis to be conducted due to lack of reporting of the key controlling factors, the estimates of EF in this paper cannot include the true variability under conditions similar to the UK. Review-derived EFs of 0.34% to 37% and mean EF from meta-analysis of 0.17 ± 0.02% highlight variability in reporting EFs depending on the method applied and sample size. A protocol of systematic reporting of N2O emissions and key auxiliary parameters in publications across disciplines is proposed. If adopted this would strengthen the community to inform IPCC Tier 2 reporting development and reduce the uncertainty surrounding reported UK N2O emissions.

Assessing employee wellbeing: is there another way?

Purpose – The purpose of this paper is to compare factor analysis (FA) with an alternative approach known as impact analysis (IA) in determining items for a questionnaire to measure employee wellbeing.Design/methodology/approach – FA and IA were conducted on a raw data set drawn from an earlier study to develop an assessment that measures the impact of work on employee wellbeing. IA is an accepted clinical methodology used to verify items in the development of health‐related quality of life instruments that evaluate patient wellbeing in clinical trials.Findings – FA and IA gave rise to considerably different assessments. IA resulted in a 51‐item scale spread across ten different domains. FA generated an eight‐factor scale with 46 items. In total, 31 variables were common to each version. The additional 20 items using IA included a number of variables that were identified by employees as being important to their wellbeing. The 15 extra items yielded by FA included six variables that were perceived by staff...

Two Methods for Using Legacy Data in Digital Soil Mapping

Legacy data are useful sources of information on the spatial variation of soil properties. There are, however, problems using legacy data, and in this paper we explore some of these problems. A common issue is often the uneven sample distribution over geographical and predictor space and the problems this generates for the subsequent modelling efforts. Furthermore legacy soil data often has a mixture qualitative and quantitative data. The current need is for quantitative data, whereas the available datasets are often qualitative; e.g. auger bores. In this paper we compare two methods:(i) a Generalized Linear modelling (GZLM) approach which uses scarce,measured soil property data and (ii) Bayesian Belief networks (BBN) which uses extensive but generic values of the soil property, linked to soil classes. We used digital soil mapping covariates such as small scale soil maps, geology, digital terrain model, climate data and landscape position in order to predict continuous surfaces for sand, silt, clay, bulk density and organic carbon. The objective is to present a qualitative comparison between the two methods, as a direct comparison was not possible due to the number and distribution of the legacy data. We found that the GZLM approach was significantly impacted by an uneven sampling of the predictor space. This study suggests that a more generalist approach such as BBN is better in the absence of few hard data but in the presence of many soft data.