Predicting microbial pollution concentrations in UK rivers in response to land use change

The Water Framework Directive has caused a paradigm shift towards the integrated management of recreational water quality through the development of drainage basin-wide programmes of measures. This has increased the need for a cost-effective diagnostic tool capable of accurately predicting riverine faecal indicator organism (FIO) concentrations. This paper outlines the application of models developed to fulfil this need, which represent the first transferrable generic FIO models to be developed for the UK to incorporate direct measures of key FIO sources (namely human and livestock population data) as predictor variables. We apply a recently developed transfer methodology, which enables the quantification of geometric mean presumptive faecal coliforms and presumptive intestinal enterococci concentrations for base- and high-flow during the summer bathing season in unmonitored UK watercourses, to predict FIO concentrations in the Humber river basin district. Because the FIO models incorporate explanatory variables which allow the effects of policy measures which influence livestock stocking rates to be assessed, we carry out empirical analysis of the differential effects of seven land use management and policy instruments (fiscal constraint, production constraint, cost intervention, area intervention, demand-side constraint, input constraint, and micro-level land use management) all of which can be used to reduce riverine FIO concentrations. This research provides insights into FIO source apportionment, explores a selection of pollution remediation strategies and the spatial differentiation of land use policies which could be implemented to deliver river quality improvements. All of the policy tools we model reduce FIO concentrations in rivers but our research suggests that the installation of streamside fencing in intensive milk producing areas may be the single most effective land management strategy to reduce riverine microbial pollution.

An overview of the current status of research into adopting high levels of renewables in Ireland

This paper provides an overview of the reduction targets that Ireland has set in the context of decarbonising their electricity generation through the use of renewables. The main challenges associated with integrating high levels (>20% of installed capacity) of non-dispatchable renewable generation are identified. The rising complexity of the challenge as renewable penetration levels increase is highlighted. A list of relevant research questions is then proposed, and an overview is given into the previous work that has gone into answering some of them. In particular, studies into the Irish energy market are identified, the current knowledge gap is described, and areas of necessary future research are suggested

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

Built environment education, research and practice: integrating diverse interests to make an impact

The role of the academic in the built environment seems generally to be not well understood or articulated. While this problem is not unique to our field, there are plenty of examples in a wide range of academic disciplines where the academic role has been fully articulated. But built environment academics have tended not to look beyond their own literature and their own vocational context in trying to give meaning to their academic work. The purpose of this keynote presentation is to explore the context of academic work generally and the connections between education, research and practice in the built environment, specifically. By drawing on ideas from the sociology of the professions, the role of universities, and the fundamentals of social science research, a case is made that helps to explain the kind of problems that routinely obstruct academic progress in our field. This discussion reveals that while there are likely to be great weaknesses in much of what is published and taught in the built environment, it is not too great a stretch to provide a more robust understanding and a good basis for developing our field in a way that would enable us collectively to make a major contribution to theory-building, theory-testing and to make a good stab at tackling some of the problems facing society at large. There is no reason to disregard the fundamental academic disciplines that underpin our knowledge of the built environment. If we contextualise our work in these more fundamental disciplines, there is every reason to think that we can have a much greater impact that we have experienced to date.

UK Food Standards Agency cis-monounsaturated fatty acid workshop report

The UK Food Standards Agency convened a group of expert scientists to review current research investigating the optimal dietary intake for n-9 cis-monounsaturated fatty acids (MUFA). The aim was to review the mechanisms underlying the reported beneficial effects of MUFA on CHD risk, and to establish priorities for future research. The issue of optimal MUFA intake is contingent upon optimal total fat intake; however, there is no consensus of opinion on what the optimal total fat intake should be. Thus, it was recommended that a large multi-centre study should look at the effects on CHD risk of MUFA replacement of saturated fatty acids in relation to varying total fat intakes; this study should be of sufficient size to take account of genetic variation, sex, physical activity and stage of life factors, as well as being of sufficient duration to account for adaptation to diets. Recommendations for studies investigating the mechanistic effects of MUFA were also made. Methods of manipulating the food chain to increase MUFA at the expense of saturated fatty acids were also discussed.

UK Food Standards Agency alpha-linolenic acid workshop report

The UK Food Standards Agency convened a group of expert scientists to review current research investigating whether n-3 polyunsaturated fatty acids (PUFA) from plant oils (a-linolenic acid; ALA) were as beneficial to cardiovascular health as the n-3 PUFA from the marine oils, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). The workshop also aimed to establish priorities for future research. Dietary intake of ALA has been associated with a beneficial effect on CHD; however, the results from studies investigating the effects of ALA supplementation on CHD risk factors have proved equivocal. The studies presented as part of the present workshop suggested little, if any, benefit of ALA, relative to linoleic acid, on risk factors for cardiovascular disease; the effects observed with fish-oil supplementation were not replicated by ALA supplementation. There is a need, therefore, to first prove the efficacy of ALA supplementation on cardiovascular disease, before further investigating effects on cardiovascular risk factors. The workshop considered that a beneficial effect of ALA on the secondary prevention of CHD still needed to be established, and there was no reason to look further at existing CHD risk factors in relation to ALA supplementation. The workshop also highlighted the possibility of feeding livestock ALA-rich oils to provide a means of increasing the dietary intake in human consumers of EPA and DHA.

Impacts of pollution and climate change on ombrotrophic Sphagnum species in the UK: analysis of uncertainties in two empirical niche models

A significant challenge in the prediction of climate change impacts on ecosystems and biodiversity is quantifying the sources of uncertainty that emerge within and between different models. Statistical species niche models have grown in popularity, yet no single best technique has been identified reflecting differing performance in different situations. Our aim was to quantify uncertainties associated with the application of 2 complimentary modelling techniques. Generalised linear mixed models (GLMM) and generalised additive mixed models (GAMM) were used to model the realised niche of ombrotrophic Sphagnum species in British peatlands. These models were then used to predict changes in Sphagnum cover between 2020 and 2050 based on projections of climate change and atmospheric deposition of nitrogen and sulphur. Over 90% of the variation in the GLMM predictions was due to niche model parameter uncertainty, dropping to 14% for the GAMM. After having covaried out other factors, average variation in predicted values of Sphagnum cover across UK peatlands was the next largest source of variation (8% for the GLMM and 86% for the GAMM). The better performance of the GAMM needs to be weighed against its tendency to overfit the training data. While our niche models are only a first approximation, we used them to undertake a preliminary evaluation of the relative importance of climate change and nitrogen and sulphur deposition and the geographic locations of the largest expected changes in Sphagnum cover. Predicted changes in cover were all small (generally <1% in an average 4 m2 unit area) but also highly uncertain. Peatlands expected to be most affected by climate change in combination with atmospheric pollution were Dartmoor, Brecon Beacons and the western Lake District.

The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: a case study over the UK

Over recent years there has been an increasing deployment of renewable energy generation technologies, particularly large-scale wind farms. As wind farm deployment increases, it is vital to gain a good understanding of how the energy produced is affected by climate variations, over a wide range of time-scales, from short (hours to weeks) to long (months to decades) periods. By relating wind speed at specific sites in the UK to a large-scale climate pattern (the North Atlantic Oscillation or "NAO"), the power generated by a modelled wind turbine under three different NAO states is calculated. It was found that the wind conditions under these NAO states may yield a difference in the mean wind power output of up to 10%. A simple model is used to demonstrate that forecasts of future NAO states can potentially be used to improve month-ahead statistical forecasts of monthly-mean wind power generation. The results confirm that the NAO has a significant impact on the hourly-, daily- and monthly-mean power output distributions from the turbine with important implications for (a) the use of meteorological data (e.g. their relationship to large scale climate patterns) in wind farm site assessment and, (b) the utilisation of seasonal-to-decadal climate forecasts to estimate future wind farm power output. This suggests that further research into the links between large-scale climate variability and wind power generation is both necessary and valuable.

Effect of intensive vs. free range production on the fat and fatty acid composition of whole birds and edible portions of retail chickens in the UK

The primary objective was to compare the fat and fatty acid contents of cooked retail chickens from intensive and free range systems. Total fat comprised approximately 14, 2.5, 8, 9 and 15 g/100 g cooked weight in whole birds, skinless breast, breast with skin, skinless leg and leg meat with skin, respectively, with no effect of intensive compared with free range systems. Free range breast and leg meat contained significantly less polyunsaturated fatty acids (n-6 and n-3) than did those from intensive rearing and had a consistently higher n-6/n-3 ratio (6.0 vs. 7.9). Generally, the concentrations of long chain n-3 fatty acids were considerably lower than those reported in earlier research studies. Overall, there was no evidence that meat from free range chickens had a fatty acid profile that would be classified as healthier than that from intensively reared birds and indeed, in some aspects, the opposite was the case. (C) 2011 Elsevier Ltd. All rights reserved.