Modelling risk perception and trust in food safety information within the theory of planned behaviour

This study suggests a statistical strategy for explaining how food purchasing intentions are influenced by different levels of risk perception and trust in food safety information. The modelling process is based on Ajzen's Theory of Planned Behaviour and includes trust and risk perception as additional explanatory factors. Interaction and endogeneity across these determinants is explored through a system of simultaneous equations, while the SPARTA equation is estimated through an ordered probit model. Furthermore, parameters are allowed to vary as a function of socio-demographic variables. The application explores chicken purchasing intentions both in a standard situation and conditional to an hypothetical salmonella scare. Data were collected through a nationally representative UK wide survey of 533 UK respondents in face-to-face, in-home interviews. Empirical findings show that interactions exist among the determinants of planned behaviour and socio-demographic variables improve the model's performance. Attitudes emerge as the key determinant of intention to purchase chicken, while trust in food safety information provided by media reduces the likelihood to purchase. (C) 2006 Elsevier Ltd. All rights reserved.

A structural time series approach to modelling multiple and resurgent meat scares in Italy

This paper exploits a structural time series approach to model the time pattern of multiple and resurgent food scares and their direct and cross-product impacts on consumer response. A structural time series Almost Ideal Demand System (STS-AIDS) is embedded in a vector error correction framework to allow for dynamic effects (VEC-STS-AIDS). Italian aggregate household data on meat demand is used to assess the time-varying impact of a resurgent BSE crisis (1996 and 2000) and the 1999 Dioxin crisis. The VEC-STS-AIDS model monitors the short-run impacts and performs satisfactorily in terms of residuals diagnostics, overcoming the major problems encountered by the customary vector error correction approach.

Predictive empirical modelling of Orobanche life cycle and seed ecology

Abstract 1.7.4

Modelling simultaneously water content and dry matter dynamics of wheat grains

A model was devised to describe simultaneously the grain masses of water and dry matter against thermal time during grain filling and maturation of winter wheat. The model accounted for a linear increase in water mass of duration anthesis-m(1) (end of rapid water assimilation phase) and rate a, followed by a more stable water mass until in,, after which water mass declined rapidly at rate e. Grain dry matter was described as a linear increase of rate bgf until a maximum size (maxgf) was attained at m(2).The model was fitted to plot data from weekly samples of grains taken from replicated field experiments investigating effects of grain position (apical or medial), fungicide (five contrasting treatments), sowing date (early or late), cultivar (Malacca or Shamrock) and season (2001/2002 and 2002/2003) on grain filling. The model accounted for between 83 and 99% of the variation ( 2) when fitted to data from individual plots, and between 97 and 99% when fitted to treatment means. Endosperm cell number of grains from early-sown plots in the first season were also counted. Differences in maxgf between grain positions and also between cultivars were mostly the result of effects on bgf and were empirically associated with water mass at nil. Fungicide application controlled S. tritici and powdery mildew infection, delayed flag leaf senescence, increased water mass at m(1) (wm(1)), and also increased m(2), bgf and maxgf. Fungicide effects on water mass were detected before fungicide effects on dry matter, but comparison of the effects of individual fungicide treatments showed no evidence that effects on wm(1), nor on endosperm cell numbers at about m(1), were required for fungicide effects on maxgf, (c) 2005 Elsevier B.V. All rights reserved.

Trophic interactions in a changing world: modelling aboveground-belowground interactions

The rate and scale of human-driven changes can exert profound impacts on ecosystems, the species that make them up and the services they provide that sustain humanity. Given the speed at which these changes are occurring, one of society's major challenges is to coexist within ecosystems and to manage ecosystem services in a sustainable way. The effect of possible scenarios of global change on ecosystem services can be explored using ecosystem models. Such models should adequately represent ecosystem processes above and below the soil surface (aboveground and belowground) and the interactions between them. We explore possibilities to include such interactions into ecosystem models at scales that range from global to local. At the regional to global scale we suggest to expand the plant functional type concept (aggregating plants into groups according to their physiological attributes) to include functional types of aboveground-belowground interactions. At the scale of discrete plant communities, process-based and organism-oriented models could be combined into "hybrid approaches" that include organism-oriented mechanistic representation of a limited number of trophic interactions in an otherwise process - oriented approach. Under global change the density and activity of organisms determining the processes may change non-linearly and therefore explicit knowledge of the organisms and their responses should ideally be included. At the individual plant scale a common organism-based conceptual model of aboveground-belowground interactions has emerged. This conceptual model facilitates the formulation of research questions to guide experiments aiming to identify patterns that are common within, but differ between, ecosystem types and biomes. Such experiments inform modelling approaches at larger scales. Future ecosystem models should better include this evolving knowledge of common patterns of aboveground-belowground interactions. Improved ecosystem models are necessary toots to reduce the uncertainty in the information that assists us in the sustainable management of our environment in a changing world. (C) 2004 Elsevier GmbH. All rights reserved.

Modelling the partitioning of evapotranspiration in a maize-sunflower intercrop

The primary purpose of this study was to model the partitioning of evapotranspiration in a maize-sunflower intercrop at various canopy covers. The Shuttleworth-Wallace (SW) model was extended for intercropping systems to include both crop transpiration and soil evaporation and allowing interaction between the two. To test the accuracy of the extended SW model, two field experiments of maize-sunflower intercrop were conducted in 1998 and 1999. Plant transpiration and soil evaporation were measured using sap flow gauges and lysimeters, respectively. The mean prediction error (simulated minus measured values) for transpiration was zero (which indicated no overall bias in estimation error), and its accuracy was not affected by the plant growth stages, but simulated transpiration during high measured transpiration rates tended to be slightly underestimated. Overall, the predictions for daily soil evaporation were also accurate. Model estimation errors were probably due to the simplified modelling of soil water content, stomatal resistances and soil heat flux as well as due to the uncertainties in characterising the 2 micrometeorological conditions. The SW’s prediction of transpiration was most sensitive to parameters most directly related to the canopy characteristics such as the partitioning of captured solar radiation, canopy resistance, and bulk boundary layer resistance.

Bayesian clustering of farm types using the mixtures model

A Bayesian method of classifying observations that are assumed to come from a number of distinct subpopulations is outlined. The method is illustrated with simulated data and applied to the classification of farms according to their level and variability of income. The resultant classification shows a greater diversity of technical charactersitics within farm types than is conventionally the case. The range of mean farm income between groups in the new classification is wider than that of the conventional method and the variability of income within groups is narrower. Results show that the highest income group in 2000 included large specialist dairy farmers and pig and poultry producers, whilst in 2001 it included large and small specialist dairy farms and large mixed dairy and arable farms. In both years the lowest income group is dominated by non-milk producing livestock farms.

Stock assessment for fishery management - A framework guide to the stock assessment tools of the Fisheries Management Science Programme (FMSP)

This document provides guidelines for fish stock assessment and fishery management using the software tools and other outputs developed by the United Kingdom's Department for International Development's Fisheries Management Science Programme (FMSP) from 1992 to 2004. It explains some key elements of the precautionary approach to fisheries management and outlines a range of alternative stock assessment approaches that can provide the information needed for such precautionary management. Four FMSP software tools, LFDA (Length Frequency Data Analysis), CEDA (Catch Effort Data Analysis), YIELD and ParFish (Participatory Fisheries Stock Assessment), are described with which intermediary parameters, performance indicators and reference points may be estimated. The document also contains examples of the assessment and management of multispecies fisheries, the use of Bayesian methodologies, the use of empirical modelling approaches for estimating yields and in analysing fishery systems, and the assessment and management of inland fisheries. It also provides a comparison of length- and age-based stock assessment methods. A CD-ROM with the FMSP software packages CEDA, LFDA, YIELD and ParFish is included.