Assessing the environmental performance of English arable and livestock holdings using data from the Farm Accountancy Data Network (FADN)

Agri-environment schemes (AESs) have been implemented across EU member states in an attempt to reconcile agricultural production methods with protection of the environment and maintenance of the countryside. To determine the extent to which such policy objectives are being fulfilled, participating countries are obliged to monitor and evaluate the environmental, agricultural and socio-economic impacts of their AESs. However, few evaluations measure precise environmental outcomes and critically, there are no agreed methodologies to evaluate the benefits of particular agri-environmental measures, or to track the environmental consequences of changing agricultural practices. In response to these issues, the Agri-Environmental Footprint project developed a common methodology for assessing the environmental impact of European AES. The Agri-Environmental Footprint Index (AFI) is a farm-level, adaptable methodology that aggregates measurements of agri-environmental indicators based on Multi-Criteria Analysis (MCA) techniques. The method was developed specifically to allow assessment of differences in the environmental performance of farms according to participation in agri-environment schemes. The AFI methodology is constructed so that high values represent good environmental performance. This paper explores the use of the AFI methodology in combination with Farm Business Survey data collected in England for the Farm Accountancy Data Network (FADN), to test whether its use could be extended for the routine surveillance of environmental performance of farming systems using established data sources. Overall, the aim was to measure the environmental impact of three different types of agriculture (arable, lowland livestock and upland livestock) in England and to identify differences in AFI due to participation in agri-environment schemes. However, because farm size, farmer age, level of education and region are also likely to influence the environmental performance of a holding, these factors were also considered. Application of the methodology revealed that only arable holdings participating in agri-environment schemes had a greater environmental performance, although responses differed between regions. Of the other explanatory variables explored, the key factors determining the environmental performance for lowland livestock holdings were farm size, farmer age and level of education. In contrast, the AFI value of upland livestock holdings differed only between regions. The paper demonstrates that the AFI methodology can be used readily with English FADN data and therefore has the potential to be applied more widely to similar data sources routinely collected across the EU-27 in a standardised manner.

Neurofuzzy design and model construction of nonlinear dynamical processes from data

A common problem in many data based modelling algorithms such as associative memory networks is the problem of the curse of dimensionality. In this paper, a new two-stage neurofuzzy system design and construction algorithm (NeuDeC) for nonlinear dynamical processes is introduced to effectively tackle this problem. A new simple preprocessing method is initially derived and applied to reduce the rule base, followed by a fine model detection process based on the reduced rule set by using forward orthogonal least squares model structure detection. In both stages, new A-optimality experimental design-based criteria we used. In the preprocessing stage, a lower bound of the A-optimality design criterion is derived and applied as a subset selection metric, but in the later stage, the A-optimality design criterion is incorporated into a new composite cost function that minimises model prediction error as well as penalises the model parameter variance. The utilisation of NeuDeC leads to unbiased model parameters with low parameter variance and the additional benefit of a parsimonious model structure. Numerical examples are included to demonstrate the effectiveness of this new modelling approach for high dimensional inputs.

Data based constructive identification - overcoming the curse of dimensionality

The modelling of a nonlinear stochastic dynamical processes from data involves solving the problems of data gathering, preprocessing, model architecture selection, learning or adaptation, parametric evaluation and model validation. For a given model architecture such as associative memory networks, a common problem in non-linear modelling is the problem of "the curse of dimensionality". A series of complementary data based constructive identification schemes, mainly based on but not limited to an operating point dependent fuzzy models, are introduced in this paper with the aim to overcome the curse of dimensionality. These include (i) a mixture of experts algorithm based on a forward constrained regression algorithm; (ii) an inherent parsimonious delaunay input space partition based piecewise local lineal modelling concept; (iii) a neurofuzzy model constructive approach based on forward orthogonal least squares and optimal experimental design and finally (iv) the neurofuzzy model construction algorithm based on basis functions that are Bézier Bernstein polynomial functions and the additive decomposition. Illustrative examples demonstrate their applicability, showing that the final major hurdle in data based modelling has almost been removed.

Evaluating the effectiveness of common structures in property portfolio construction

A good portfolio structure enables an investor to diversify more effectively and understand systematic influences on their performance. However, in the property market, the choice of structure is affected by data constraints and convenience. Using individual return data, this study tests the hypothesis that some common structures in the UK do not explain a significant amount about property returns. It is found that, in the periods studied, not all the structures were effective and, for the annual returns, no structures were significant in all periods. The results suggest that the drivers represented by the structures take some time to be reflected in individual property returns. They also confirm the results of other studies in finding property type a much stronger factor in explaining returns than regions.

Preliminary observations on the presence of sustained tendon strain and eccentric contractions of the wrist extensors during a common manual task: implications for lateral epicondylitis

Lateral epicondylitis (LE) is hypothesized to occur as a result of repetitive, strenuous and abnormal postural activities of the elbow and wrist. There is still a lack of understanding of how wrist and forearm positions contribute to this condition during common manual tasks. In this study the wrist kinematics and the wrist extensors’ musculotendon patterns were investigated during a manual task believed to elicit LE symptoms in susceptible subjects. A 42-year-old right-handed male, with no history of LE, performed a repetitive movement involving pushing and turning a spring-loaded mechanism. Motion capture data were acquired for the upper limb and an inverse kinematic and dynamic analysis was subsequently carried out. Results illustrated the presence of eccentric contractions sustained by the extensor carpi radialis longus (ECRL), together with an almost constant level of tendon strain of both extensor carpi radialis brevis (ECRB) and extensor digitorum communis lateral (EDCL) branch. It is believed that these factors may partly contribute to the onset of LE as they are both responsible for the creation of microtears at the tendons’ origins. The methodology of this study can be used to explore muscle actions during movements that might cause or exacerbate LE.

Forecasting annual inflation with seasonal monthly data: using levels versus logs of the underlying price index

This paper investigates whether using natural logarithms (logs) of price indices for forecasting inflation rates is preferable to employing the original series. Univariate forecasts for annual inflation rates for a number of European countries and the USA based on monthly seasonal consumer price indices are considered. Stochastic seasonality and deterministic seasonality models are used. In many cases, the forecasts based on the original variables result in substantially smaller root mean squared errors than models based on logs. In turn, if forecasts based on logs are superior, the gains are typically small. This outcome sheds doubt on the common practice in the academic literature to forecast inflation rates based on differences of logs.

Analysis of physiological data from the International Clonal Trial (CFC)

A range of physiological parameters (canopy light transmission, canopy shape, leaf size, flowering and flushing intensity) were measured from the International Clone Trial, typically over the course of two years. Data were collected from six locations, these being: Brazil, Ecuador, Trinidad, Venezuela, Côte d’Ivoire and Ghana. Canopy shape varied significantly between clones, although it showed little variation between locations. Genotypic variation in leaf size was differentially affected by the growth location; such differences appeared to underlie a genotype by environment interaction in relation to canopy light transmission. Flushing data were recorded at monthly intervals over the course of a year. Within each location, a significant interaction was observed between genotype and time of year, suggesting that some genotypes respond to a greater extent than others to environmental stimuli. A similar interaction was observed for flowering data, where significant correlations were found between flowering intensity and temperature in Brazil and flowering intensity and rainfall in Côte d’Ivoire. The results demonstrate the need for local evaluation of cocoa clones and also suggest that the management practices for particular planting material may need to be fine-tuned to the location in which they are cultivated.

Advances in sequential data assimilation and numerical weather forecasting: an Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic Ensemble Square Root Filters, and a test of a new time stepping scheme in an atmospheric model

This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn’t represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion in the mean value of the function. Using statistical significance tests both at the local and field level, it is shown that the climatology of the SPEEDY model is not modified by the changed time stepping scheme; hence, no retuning of the parameterizations is required. It is found the accuracy of the medium-term forecasts is increased by using the RAW filter.

Multi-target out-of-sequence data association: tracking using graphical models

When performing data fusion, one often measures where targets were and then wishes to deduce where targets currently are. There has been recent research on the processing of such out-of-sequence data. This research has culminated in the development of a number of algorithms for solving the associated tracking problem. This paper reviews these different approaches in a common Bayesian framework and proposes an architecture that orthogonalises the data association and out-of-sequence problems such that any combination of solutions to these two problems can be used together. The emphasis is not on advocating one approach over another on the basis of computational expense, but rather on understanding the relationships among the algorithms so that any approximations made are explicit. Results for a multi-sensor scenario involving out-of-sequence data association are used to illustrate the utility of this approach in a specific context.

Multi-target out-of-sequence data association

In data fusion systems, one often encounters measurements of past target locations and then wishes to deduce where the targets are currently located. Recent research on the processing of such out-of-sequence data has culminated in the development of a number of algorithms for solving the associated tracking problem. This paper reviews these different approaches in a common Bayesian framework and proposes an architecture that orthogonalises the data association and out-of-sequence problems such that any combination of solutions to these two problems can be used together. The emphasis is not on advocating one approach over another on the basis of computational expense, but rather on understanding the relationships between the algorithms so that any approximations made are explicit.

Fine-root turnover rates of European forests revisited: an analysis of data from sequential coring and ingrowth cores

Background and Aims Forest trees directly contribute to carbon cycling in forest soils through the turnover of their fine roots. In this study we aimed to calculate root turnover rates of common European forest tree species and to compare them with most frequently published values. Methods We compiled available European data and applied various turnover rate calculation methods to the resulting database. We used Decision Matrix and Maximum-Minimum formula as suggested in the literature. Results Mean turnover rates obtained by the combination of sequential coring and Decision Matrix were 0.86 yr−1 for Fagus sylvatica and 0.88 yr−1 for Picea abies when maximum biomass data were used for the calculation, and 1.11 yr−1 for both species when mean biomass data were used. Using mean biomass rather than maximum resulted in about 30 % higher values of root turnover. Using the Decision Matrix to calculate turnover rate doubled the rates when compared to the Maximum-Minimum formula. The Decision Matrix, however, makes use of more input information than the Maximum-Minimum formula. Conclusions We propose that calculations using the Decision Matrix with mean biomass give the most reliable estimates of root turnover rates in European forests and should preferentially be used in models and C reporting.

The METAFOR project: preserving data through metadata standards for climate models and simulations

Climate modeling is a complex process, requiring accurate and complete metadata in order to identify, assess and use climate data stored in digital repositories. The preservation of such data is increasingly important given the development of ever-increasingly complex models to predict the effects of global climate change. The EU METAFOR project has developed a Common Information Model (CIM) to describe climate data and the models and modelling environments that produce this data. There is a wide degree of variability between different climate models and modelling groups. To accommodate this, the CIM has been designed to be highly generic and flexible, with extensibility built in. METAFOR describes the climate modelling process simply as "an activity undertaken using software on computers to produce data." This process has been described as separate UML packages (and, ultimately, XML schemas). This fairly generic structure canbe paired with more specific "controlled vocabularies" in order to restrict the range of valid CIM instances. The CIM will aid digital preservation of climate models as it will provide an accepted standard structure for the model metadata. Tools to write and manage CIM instances, and to allow convenient and powerful searches of CIM databases,. Are also under development. Community buy-in of the CIM has been achieved through a continual process of consultation with the climate modelling community, and through the METAFOR team’s development of a questionnaire that will be used to collect the metadata for the Intergovernmental Panel on Climate Change’s (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) model runs.

Grid warehousing of molecular dynamics protein unfolding data

With the increasing awareness of protein folding disorders, the explosion of genomic information, and the need for efficient ways to predict protein structure, protein folding and unfolding has become a central issue in molecular sciences research. Molecular dynamics computer simulations are increasingly employed to understand the folding and unfolding of proteins. Running protein unfolding simulations is computationally expensive and finding ways to enhance performance is a grid issue on its own. However, more and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. This paper describes efforts to provide a grid-enabled data warehouse for protein unfolding data. We outline the challenge and present first results in the design and implementation of the data warehouse.

P-found: grid-enabling distributed repositories of protein folding and unfolding simulations for data mining

The P-found protein folding and unfolding simulation repository is designed to allow scientists to perform data mining and other analyses across large, distributed simulation data sets. There are two storage components in P-found: a primary repository of simulation data that is used to populate the second component, and a data warehouse that contains important molecular properties. These properties may be used for data mining studies. Here we demonstrate how grid technologies can support multiple, distributed P-found installations. In particular, we look at two aspects: firstly, how grid data management technologies can be used to access the distributed data warehouses; and secondly, how the grid can be used to transfer analysis programs to the primary repositories — this is an important and challenging aspect of P-found, due to the large data volumes involved and the desire of scientists to maintain control of their own data. The grid technologies we are developing with the P-found system will allow new large data sets of protein folding simulations to be accessed and analysed in novel ways, with significant potential for enabling scientific discovery.

File Transfer Protocol

The discourse surrounding the virtual has moved away from the utopian thinking accompanying the rise of the Internet in the 1990s. The Cyber-gurus of the last decades promised a technotopia removed from materiality and the confines of the flesh and the built environment, a liberation from old institutions and power structures. But since then, the virtual has grown into a distinct yet related sphere of cultural and political production that both parallels and occasionally flows over into the old world of material objects. The strict dichotomy of matter and digital purity has been replaced more recently with a more complex model where both the world of stuff and the world of knowledge support, resist and at the same time contain each other. Online social networks amplify and extend existing ones; other cultural interfaces like youtube have not replaced the communal experience of watching moving images in a semi-public space (the cinema) or the semi-private space (the family living room). Rather the experience of viewing is very much about sharing and communicating, offering interpretations and comments. Many of the web’s strongest entities (Amazon, eBay, Gumtree etc.) sit exactly at this juncture of applying tools taken from the knowledge management industry to organize the chaos of the material world along (post-)Fordist rationality. Since the early 1990s there have been many artistic and curatorial attempts to use the Internet as a platform of producing and exhibiting art, but a lot of these were reluctant to let go of the fantasy of digital freedom. Storage Room collapses the binary opposition of real and virtual space by using online data storage as a conduit for IRL art production. The artworks here will not be available for viewing online in a 'screen' environment but only as part of a downloadable package with the intention that the exhibition could be displayed (in a physical space) by any interested party and realised as ambitiously or minimally as the downloader wishes, based on their means. The artists will therefore also supply a set of instructions for the physical installation of the work alongside the digital files. In response to this curatorial initiative, File Transfer Protocol invites seven UK based artists to produce digital art for a physical environment, addressing the intersection between the virtual and the material. The files range from sound, video, digital prints and net art, blueprints for an action to take place, something to be made, a conceptual text piece, etc. About the works and artists: Polly Fibre is the pseudonym of London-based artist Christine Ellison. Ellison creates live music using domestic devices such as sewing machines, irons and slide projectors. Her costumes and stage sets propose a physical manifestation of the virtual space that is created inside software like Photoshop. For this exhibition, Polly Fibre invites the audience to create a musical composition using a pair of amplified scissors and a turntable. http://www.pollyfibre.com John Russell, a founding member of 1990s art group Bank, is an artist, curator and writer who explores in his work the contemporary political conditions of the work of art. In his digital print, Russell collages together visual representations of abstract philosophical ideas and transforms them into a post apocalyptic landscape that is complex and banal at the same time. www.john-russell.org The work of Bristol based artist Jem Nobel opens up a dialogue between the contemporary and the legacy of 20th century conceptual art around questions of collectivism and participation, authorship and individualism. His print SPACE concretizes the representation of the most common piece of Unicode: the vacant space between words. In this way, the gap itself turns from invisible cipher to sign. www.jemnoble.com Annabel Frearson is rewriting Mary Shelley's Frankenstein using all and only the words from the original text. Frankenstein 2, or the Monster of Main Stream, is read in parts by different performers, embodying the psychotic character of the protagonist, a mongrel hybrid of used language. www.annabelfrearson.com Darren Banks uses fragments of effect laden Holywood films to create an impossible space. The fictitious parts don't add up to a convincing material reality, leaving the viewer with a failed amalgamation of simulations of sophisticated technologies. www.darrenbanks.co.uk FIELDCLUB is collaboration between artist Paul Chaney and researcher Kenna Hernly. Chaney and Hernly developed together a project that critically examines various proposals for the management of sustainable ecological systems. Their FIELDMACHINE invites the public to design an ideal agricultural field. By playing with different types of crops that are found in the south west of England, it is possible for the user, for example, to create a balanced, but protein poor, diet or to simply decide to 'get rid' of half the population. The meeting point of the Platonic field and it physical consequences, generates a geometric abstraction that investigates the relationship between modernist utopianism and contemporary actuality. www.fieldclub.co.uk Pil and Galia Kollectiv, who have also curated the exhibition are London-based artists and run the xero, kline & coma gallery. Here they present a dialogue between two computers. The conversation opens with a simple text book problem in business studies. But gradually the language, mimicking the application of game theory in the business sector, becomes more abstract. The two interlocutors become adversaries trapped forever in a competition without winners. www.kollectiv.co.uk