RICHPIG: a semantic model to assess enrichment materials for pigs

Abstract

AbstractA computer-based model was constructed to assess enrichment materials (EMats) for intensively-farmed weaned, growing and fattening pigs on a scale from 0 to 10. This model, called RICHPIG, was constructed in order to support the further implementation of EC Directive 2001/93/EC, which states that “pigs must have permanent access to a sufficient quantity of material to enable proper investigation and manipulation activities”. This paper describes the underlying conceptual framework for assessing EMats and explains the concepts, procedures and calculation rules used for semantic modelling. A (parsimonious) weighted average calculation rule was used to calculate enrichment scores from assessment criteria scores (which specify welfare relevant material properties of EMats) and weighting factors (WFs, which specify the relative importance of the assessment criteria). In total, 30 assessment criteria were identified and classified as object design criteria (eg novelty and accessibility), behavioural elements (eg nose, root, chew), biological functions (explore and forage), manipulations (ie object-directed behaviours), other (non-manipulative) consequences (eg aggression and stress) and object performance criteria (eg changeability/destructibility and hygiene). WFs were calculated from a systematic analysis of 573 scientific statements collected in the database, using 11 so-called weighting categories (Wcat, ie scientific paradigms to assess welfare such as the study of natural behaviour, consumer demand studies and stress-physiology) to assign Wcat level scores (which indicate the intensity, duration and incidence of a welfare impact) to the assessment criteria. The main advantages of the RICHPIG model are that it is based explicitly on available scientific information, that it has an explicitly formulated conceptual framework, is transparent, disputable, upgradeable, robust and reasonably in accordance with expert opinion. Major scope for improvements exist in the form of the need for further upgrading with new knowledge, empirical validation and (further) implementation in political decision-making processes.