Integrating socio-economics and ecology: a taxonomy of quantitative methods and a review of their use in agro-ecology

Answering many of the critical questions in conservation, development and environmental management requires integrating the social and natural sciences. However, understanding the array of available quantitative methods and their associated terminology presents a major barrier to successful collaboration. We provide an overview of quantitative socio-economic methods that distils their complexity into a simple taxonomy. We outline how each has been used in conjunction with ecological models to address questions relating to the management of socio-ecological systems. We review the application of social and ecological quantitative concepts to agro-ecology and classify the approaches used to integrate the two disciplines. Our review included all published integrated models from 2003 to 2008 in 27 journals that publish agricultural modelling research. Although our focus is on agro-ecology, many of the results are broadly applicable to other fields involving an interaction between human activities and ecology. We found 36 papers that integrated social and ecological concepts in a quantitative model. Four different approaches to integration were used, depending on the scale at which human welfare was quantified. Most models viewed humans as pure profit maximizers, both when calculating welfare and predicting behaviour. Synthesis and applications. We reached two main conclusions based on our taxonomy and review. The first is that quantitative methods that extend predictions of behaviour and measurements of welfare beyond a simple market value basis are underutilized by integrated models. The second is that the accuracy of prediction for integrated models remains largely unquantified. Addressing both problems requires researchers to reach a common understanding of modelling goals and data requirements during the early stages of a project.

Abstracts of communications: Proceedings of the thirty-nineth meeting of the Agricultural Research Modellers' Group

This group, which is concerned with the applications of mathematics to agricultural science, was formed in 1970 and has since met at approximately yearly intervals in London for one-day meetings. The thirty-ninth meeting of the group, chaired by Professor N. Crout of the University of Nottingham, was held in the Kohn Centre at the Royal Society, 6 Carlton House Terrace, London on Friday, 30 March 2007 when the following papers were read.

Domestically produced food: Consumer perceptions of origin, safety and the issue of trust

It is becoming increasingly difficult for the public to attempt to assess risks using traditional methods such as smell, taste or other physical attributes of food. The existence of extrinsic cues such as the country of origin (COO) of food can help to make food purchase decisions easier for consumers. However, the use of extrinsic cues depends heavily on the extent to which consumers trust such signals to be indicative of quality or safety, which in turn depends on the credibility behind that cue. This paper aims to examine consumers association of domestically produced food with increased food safety standards and the association of COO and food safety information with socio-demographics and other aspects of consumer psychology such as attitudes, risk perception and trust. Using an ordered probit model, domestic production is examined as an extrinsic cue for food safety by looking at the relationship with trust in food safety information provided by national food standards agencies (NFSAs) and other socio-demographic characteristics, based on nationally representative data from 2725 face-to-face interviews across five European countries. Results suggest that domestic production of food is an extrinsic cue for food safety and as consumers place increasing importance on food safety they are more interested in food produced in their own country. This, coupled with consumer trust in a strong, and independent national food standards agency, suggests the potential exists for the increased consumption of domestically produced foods.

Features of the Atlantic diet and its healthiness: Theory and evidence from social science

In order to understand diets, why and how they change and can be influenced, it is important to understand how food choices are made. The has been the subject of, considerable study within many of the social science disciplines and the humanities. The paper draws on the theoretical and empirical work of psychologists, sociologists, economists, market researchers, anthropologists, geographers and historians to understand better the forces behind food choice, derive some general empirical messages from the literature, to shed light on food choice in a European context and to address the question of whether there is, or has been, a recognisably Atlantic diet. The paper proceeds to analyse the characteristics of the food consumption patterns in the Atlantic diet countries, examines whether their food consumption patterns are homogenous (i.e. similar across the countries of this group), whether they are specific (i.e. different from the ones in other country groups) and finally evaluates the nutritional composition of the Atlantic diet against the WHO/FAO recommendations for a healthy and wholesome diet.

Bayesian decision procedures for dose-escalation based on evidence of undesirable events and therapeutic benefit.

In this paper, Bayesian decision procedures are developed for dose-escalation studies based on bivariate observations of undesirable events and signs of therapeutic benefit. The methods generalize earlier approaches taking into account only the undesirable outcomes. Logistic regression models are used to model the two responses, which are both assumed to take a binary form. A prior distribution for the unknown model parameters is suggested and an optional safety constraint can be included. Gain functions to be maximized are formulated in terms of accurate estimation of the limits of a therapeutic window or optimal treatment of the next cohort of subjects, although the approach could be applied to achieve any of a wide variety of objectives. The designs introduced are illustrated through simulation and retrospective implementation to a completed dose-escalation study. Copyright © 2006 John Wiley & Sons, Ltd.

Efficient and cost-effective experimental determination of kinetic constants and data: the success of a Bayesian systematic approach to drug transport, receptor binding, continuous culture and cell transport kinetics

Details about the parameters of kinetic systems are crucial for progress in both medical and industrial research, including drug development, clinical diagnosis and biotechnology applications. Such details must be collected by a series of kinetic experiments and investigations. The correct design of the experiment is essential to collecting data suitable for analysis, modelling and deriving the correct information. We have developed a systematic and iterative Bayesian method and sets of rules for the design of enzyme kinetic experiments. Our method selects the optimum design to collect data suitable for accurate modelling and analysis and minimises the error in the parameters estimated. The rules select features of the design such as the substrate range and the number of measurements. We show here that this method can be directly applied to the study of other important kinetic systems, including drug transport, receptor binding, microbial culture and cell transport kinetics. It is possible to reduce the errors in the estimated parameters and, most importantly, increase the efficiency and cost-effectiveness by reducing the necessary amount of experiments and data points measured. (C) 2003 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

The climate response to CO2 of the Hadley Centre coupled AOGCM with and without flux adjustment

Coupled atmosphere‐ocean general circulation models have a tendency to drift away from a realistic climatology. The modelled climate response to an increase of CO2 concentration may be incorrect if the simulation of the current climate has significant errors, so in many models, including ours, the drift is counteracted by applying prescribed fluxes of heat and fresh water at the ocean‐atmosphere interface in addition to the calculated surface exchanges. Since the additional fluxes do not have a physical basis, the use of this technique of “flux adjustment” itself introduces some uncertainty in the simulated response to increased CO2. We find that the global‐average temperature response of our model to CO2 increasing at 1% per year is about 30% less without flux adjustment than with flux adjustment. The geographical patterns of the response are similar, indicating that flux adjustment is not causing any gross distortion. The reduced size of the response is due to more effective vertical transport of heat into the ocean, and a somewhat smaller climate sensitivity. Although the response in both cases lies within the generally accepted range for the climate sensitivity, systematic uncertainties of this size are clearly undesirable, and the best strategy for future development is to improve the climate model in order to reduce the need for flux adjustment.