Published online by Cambridge University Press: 16 November 2017
European pig production continues to encounter economic and environmental challenges. To address these issues, methods have been developed to assess performances of pig production systems. Recent studies indicate that considering variability in performances among pigs improves the accuracy and reliability of results compared with modelling an average animal. Our objective was to develop a pig fattening unit model able to (i) simulate individual pig performances, including their variability in interaction with farmers’ practices and management, and (ii) assess their effects on technical, economic and environmental performances. Farmer practices included in the model were chosen from a typology generated from on-farm surveys focused on batch management, pig allocation to pens, pig feeding practices, practices of shipping to the slaughterhouse, and management of the remaining pigs. Pigs are represented using an individual-based model adapted from the InraPorc® model. To illustrate the model’s abilities, four scenarios were simulated that combine two feed rationing plans (ad libitum, restricted to 2.5 kg/day) and two feed sequence plans (two-phase, 10-phase). Analysis of variance was performed on the simulated technical, economic and environmental indicators (calculated via Life Cycle Assessment). The feed rationing plan and feed sequence plan significantly affected all indicators except for the premium per pig, for which the feed sequence plan did not have a significant effect. The ‘restricted 10-phase’ scenario maximised gross margin of the fattening unit (14.2 €/pig) and minimised environmental impacts per kg of pig produced. In contrast, the ‘ad libitum two-phase’ scenario generated the lowest margin (8.20 €/pig) and the highest environmental impacts. The model appears to be a promising tool to assess effects of farmers’ practices, pig characteristics and farm infrastructure on technical, economic and environmental performances of the fattening unit, and to investigate the potential of improvement. However, further work is needed, based on virtual experiments, in order to evaluate the effects of a larger diversity of practices.