Towards a European tephrochronological framework for Termination 1 and the Early Holocene

The record of deposition of tephras in Europe and the North Atlantic during the period 18.5–8.0 ¹⁴C ka BP (the Last Termination and Early Holocene) is reviewed. Altogether, 34 tephras originating from four main volcanic provinces (Iceland, the Eifel district, the Massif Central and Italy) have been identified so far in geological sequences spanning this time–interval. Most of the records have been based, until very recently, on observations of visible layers of tephras. Here, we report on the potential for extending the areas over which some of the tephras can be traced by the search for layers of micro–tephra, which are not visible to the naked eye, and on the use of geochemical methods to correlate them with known tephra horizons. This approach has greatly extended the area in Northern Europe over which the Vedde Ash can be traced. The same potential exists in southern Europe, which is demonstrated for the first time by the discovery of a distinct layer of micro–tephra of the Neapolitan Yellow Tuff in a site in the Northern Apennines in Italy, far to the north of the occurrences of visible records of this tephra. The paper closes by considering the potential for developing a robust European tephrostratigraphy to underpin the chronology of records of the Last Termination and Early Holocene, thereby promoting a better understanding of the nature, timing and environmental effects of the abrupt climatic changes that characterized this period.

Towards an improved understanding of farmers' behaviour: the integrative agent-centred (IAC) framework

An effective approach to research on farmers' behaviour is based on: i) an explicit and well-motivated behavioural theory; ii) an integrative approach; and iii) understanding feedback processes and dynamics. While current approaches may effectively tackle some of them, they often fail to combine them together. The paper presents the integrative agent-centred (IAC) framework, which aims at filling this gap. It functions in accordance with these three pillars and provides a conceptual structure to understand farmers' behaviour in agricultural systems. The IAC framework is agent-centred and supports the understanding of farmers' behavior consistently with the perspective of agricultural systems as complex social-ecological systems. It combines different behavioural drivers, bridges between micro and macro levels, and depicts a potentially varied model of human agency. The use of the framework in practice is illustrated through two studies on pesticide use among smallholders in Colombia. The examples show how the framework can be implemented to derive policy implications to foster a transition towards more sustainable agricultural practices. The paper finally suggests that the framework can support different research designs for the study of agents' behaviour in agricultural and social-ecological systems.

An analytical framework for appraising Integrated Arable Farming Systems

Integrated Arable Farming Systems are examined from the perspective of the farmer considering the use of such techniques, and data are presented which suggest that the uptake of the approach may expose the manager to a greater degree of risk. Observations are made about the possible uptake of such systems in the UK and the implications this may have for agricultural and environmental policy in general.

Panzootics and the poor: devising a global livestock disease prioritisation framework for poverty alleviation

Panzootics such as highly pathogenic avian influenza and Rift Valley fever have originated from the South, largely among poor communities. On a global level, approximately two-thirds of those individuals living on less than US$2 per day keep livestock. Consequently, there is a need to better target animal health interventions for poverty reduction using an evidence-based approach. Therefore, the paper offers a three-step prioritisation framework using calculations derived from standard poverty measures: the poverty gap and the head count ratio. Data from 265 poor livestock-keeping households in Kenya informed the study. The results demonstrate that, across a spectrum of producers, the dependence upon particular species varies. Furthermore, the same livestock disease has differing impacts on the depth and severity of poverty. Consequently, animal health interventions need to

Modeling data with structural and temporal correlation using lower level and higher level multilevel models

Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter-relationships, but the most appropriate statistical analysis for handling this data is often uncertain - precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two-level and four-level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied.

A data framework for measuring the energy consumption of the non-domestic building stock

The transition to a low-carbon economy urgently demands better information on the drivers of energy consumption. UK government policy has prioritized energy efficiency in the built stock as a means of carbon reduction, but the sector is historically information poor, particularly the non-domestic building stock. This paper presents the results of a pilot study that investigated whether and how property and energy consumption data might be combined for non-domestic energy analysis. These data were combined in a ‘Non-Domestic Energy Efficiency Database’ to describe the location and physical attributes of each property and its energy consumption. The aim was to support the generation of a range of energy-efficiency statistics for the industrial, commercial and institutional sectors of the non-domestic building stock, and to provide robust evidence for national energy-efficiency and carbon-reduction policy development and monitoring. The work has brought together non-domestic energy data, property data and mapping in a ‘data framework’ for the first time. The results show what is possible when these data are integrated and the associated difficulties. A data framework offers the potential to inform energy-efficiency policy formation and to support its monitoring at a level of detail not previously possible.