Consideration of landscape in the framework documentation during the evolution of the Rural Environment Protection Scheme (REPS) in the Republic of Ireland.

working paperThis paper looks at the changing concept of landscape during the evolution of REPS. It reviews and groups definitions of landscape and identifies their agri-environmental relevance. Descriptions were devised to amplify each grouping with reference to an Irish context and were used as an analytical framework to categorise each landscape reference in REPS documentation. There was an increase in the use of the term landscape with each version of the scheme and expansion in the range of different landscape categories to which this apparently applied. However there has been no coherence in its use. This paper makes recommendations to improve the framework for the treatment of landscape issues in REPS and its future evolution

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

Habermasian accounting colonization and its effect on the schools’ sector

As a social activity, accounting has the potential to affect the behaviour of individuals in organizations. Habermasian colonization theory has been used to investigate the way in which accounting affects these organizational micro-practices. The aim of this study is to develop the understanding of Habermasian accounting colonization and to critically evaluate its impact on the schools’ sector. Qualitative field studies at three secondary schools were used to gather empirical detail in the form of interview data and documentary evidence. The research approach was underpinned by a theoretical perspective drawn from Habermasian colonization theory. The findings reveal that accounting can have both immediate and subtle effects on organizational micro-practices. These effects can be more complex than is suggested by much of the existing literature. Contrasting accounting disturbances lead to variation in the prevalence of the organizational symptoms of accounting colonization; those that are constitutive-transactional and external to the organization have the greatest impact on behaviour. The findings suggest that absorbing groups, headteacher type and the cumulative effect of exposure to multiple accounting disturbances are also influential. As well as the effect of accounting on organizational micro-practices, this study also provides insights into its broader impact on policy in the schools’ sector. A closer alignment with Habermas’s social ontology and the organizational symptoms of Habermasian accounting colonization is presented than is found in the existing body of research. Such alignment contributes to the understanding and development of Habermasian accounting colonization as a theoretical framework. A deeper comprehension of the impact of accounting on behaviour is enabled by the application of a developed model of accounting colonization. Knowledge of how accounting affects organizational micro-practices also has practical implications for both the development of policy and the work of accounting professionals

Investigation into the bio-physical constraints on farmer turn-out-date decisions using remote sensing and meteorological data.

ThesisDoctoral thesisAccepted versionGrass is the most common landcover in Ireland and covers a bigger percentage (52%) of the country than any other in Europe. Grass as fodder is Ireland’s most important crop and is the foundation of its most important indigenous industry, agriculture. Yet knowledge of its distribution, performance and yield is scant. How grass is nationally, on a farm by farm, year by year basis managed is not known. In this thesis the gaps in knowledge about grassland performance across Ireland are presented along with arguments on why these knowledge gaps should be closed. As an example the need for high spatial resolution animal stocking rate data in European temperate grassland systems is shown. The effect of high stocking density on grass management is most apparent early in the growing season, and a 250m scale characterization of early spring vegetation growth from 2003-2012, based on MODIS NDVI time series products, is constructed. The average rate of growth is determined as a simple linear model for each pixel, using only the highest quality data for the period. These decadal spring growth model coefficients, start of season cover and growth rate, are regressed against log of stocking rate (r2 19 = 0.75, p<0.001). This model stocking rate is used to create a map of grassland use intensity in Ireland, which, when tested against an independent set of stocking data, is shown to be successful with an RMSE of 0.13 Livestock Unit/ha for a range of stocking densities from 0.1 to 3.3 Livestock Unit/ha. This model provides the first validated high resolution approach to mapping stocking rates in intensively managed European grassland systems. There is a demonstrated a need for a system to estimate current growing conditions. Using the spring growth model constructed for estimating stocking density a new style of grass growth progress anomaly map in the time-domain was developed. Using the developed satellite dataset 1 and 12 years of ground climate station data in Ireland, NDVI was modelled against time as a proxy for grass growth This model is the reference for estimating current seasonal progress of grass growth against a ten year average. The model is developed to estimate Seasonal Progress Anomalies in the Time domain (SPAT), giving a result in terms of “days behind” and “days ahead” of the norm. SPAT estimates for 2012 and 2013 are compared to ground based estimates from 30 climate stations and have a correlation coefficient of 0.897 and RMSE of 15days. The method can successfully map current grass growth trends compared to the average and present this information to the farmer in simple everyday language. This is understood by the author to be the first validated growth anomaly service, and the first for intensive European grasslands. The decisions on when to turn out cattle (the turn out date (TOD)) from winter housing to spring grazing is an important one on Irish dairy farms which has significant impacts on operating costs on the farm. To examine the relationship of TOD to conditions, the National Farm Survey (NFS) of Ireland database was geocoded and the data on turn out dates from 199 farms across Ireland over five years was used. A fixed effects linear panel data model was employed to explore the association between TOD and conditions, as it allows for unobserved variation between farmers to be ignored in favour of modelling the variance year on year. The environmental variables used in the analysis account for 38% of the variance in the turn out dates on farms nationwide. National seasonal conditions dominate over local variation, and for every week earlier grass grows in spring, farmers gain 3.7 days in grazing season but ignore 3.3 days of growth that could have been used. Every 100mm extra rain in spring means TOD is a day later and every dry day leads to turn out being half a day earlier. A well-drained soil makes TOD 2.5 days earlier compared to a poorly drained soil and TOD gets a day later for every 16km north form the south coast. This work demonstrates that precision agriculture 1 driven by optical and radar satellite data is closer to being a reality in Europe driven by enormous amounts of free imagery from NASA and the ESA Sentinel programs coupled with open source meteorological data and models and new developments in data analytics

Personal Imagery and Paint

None provided

Consuming Linked Data within a Large Educational Organization

Large universities tend to spread their services across several departments to serve their substantial student base. It is very common for this to result in developing different systems, which end up in creating many disconnected data silos within the organization. Data isolation is one of the main bottlenecks that prevent unlocking the full potential behind exploiting such data, to provide a better experience at the level of application deployment and data analysis. The Open University is in the process of connecting their data silos by relying on the Linked Data principles within the LUCERO project. We discuss in this paper three use-cases through which we consume Linked Data produced at the Open University: (1) a student services use-case showing how we exploit data connections to deliver learning material related to courses through the university's main course information website; (2) a mobile course application that enables students to easily explore courses by subject, qualification or research topic; and (3) a Leanback TV service that provides students the ability to watch, with a degree of control, a set of podcasts grouped in different channels. Through these use cases, we highlight in this paper the advantages and effects of consuming Linked Data within an organization

Is There Too Much Criminal Law?

Book review of "Overcriminalization: The Limits of the Criminal Law" by Douglas Husa

Operating room first case start times: a metric to assess systems-based practice milestones?

BACKGROUND: Resident competence in peri-operative care is a reflection on education and cost-efficiency. Inspecting pre-existing operating room metrics for performance outliers may be a potential solution for assessing competence. Statistical correlation of problematic benchmarks may reveal future opportunities for educational intervention. METHODS: Case-log database review yielded 3071 surgical cases involving residents over the course of 5 years. Surgery anticipated and actual start times were evaluated for delays and residents were assessed using the days of resident training performed at the time of each corresponding case. Other variables recorded included day of week, attending anesthesiologist name, attending surgeon name, patient age, sex, American Society of Anesthesiologists physical status classification (ASA PS), and in-patient versus day surgery status. Mixed-effect, multi-variable, linear regression determined independent determinants of delay time. RESULTS: The analysis identified day of the week (F = 25.65, P \u3c 0.0001), days of training (F = 8.39, P = 0.0038), attending surgeon (F = 2.67, P \u3c 0.0001), and anesthesiology resident (F = 1.67, P = 0.0012) as independent predictors of delay time for first-start cases, with an overall regression model F = 3.09, r2 = 0.186, and P \u3c 0.0001. CONCLUSIONS: The day of the week and attending surgeon demonstrated significant impact of case delay compared to resident days trained. If a learning curve for first-case start punctuality exists for anesthesiology residents, it is subtle and irrelevant to operating room efficiency. The regression model accounted for only 19% of the variability in the outcome of delay time, indicating a multitude of additional unidentified factors contributing to operating room efficiency