Florence Garcia-Launay

Effect of feeding strategy on environmental impacts of pig fattening in different contexts of production: evaluation through life cycle assessment1

Life cycle assessment (LCA) has been used in many studies to evaluate the effect of feeding strategy on the environmental impact of pig production. However, because most studies have been conducted in European conditions, the question of possible interactions with the context of production is still under debate. The objective of this study was to evaluate these effects in 2 contrasted geographic contexts of production, South America (Brazil) and Europe (France). The LCA considered the process of pig fattening, including production and transport of feed ingredients and feed, raising of fattening pigs, and manure storage, transport, and spreading. Impacts were calculated at the farm gate, and the functional unit considered was 1 kg of BW gain over the fattening period. The performances of pigs were simulated for each scenario using the InraPorc population model (2,000 pigs per scenario considering between-animal variability). The LCA calculations were performed for each pig according to its own performance and excretion, and the results were subjected to variance analysis. The results indicate that for some impacts there are clear interactions between the effects of the feeding program, the origin of soybean, and the location of production. For climate change, interest in phase feeding and incorporation of crystalline AA (CAA) is limited and even counterproductive in Brazil with soybeans from the South (without deforestation), whereas they appear to be efficient strategies with soybeans from the Center West (with deforestation), especially in France. Rather similar effects, as those for climate change, were observed for cumulative energy demand. Conversely, potential eutrophication and acidification impacts were reduced by phase feeding and CAA addition in a rather similar way in all situations. Individual daily feeding, the only strategy that took into account between-animal variability, was the most effective approach for reducing the life cycle impact of pig fattening in all situations, whereas the potential of phase feeding programs and CAA was dependent on soybean origin and the geographical context of pig production, in contrast with previous results.

Towards a decision support tool with an individual-based model of a pig fattening unit

Abstract European pig production is encountering many economic and environmental challenges. To address these challenges, farmers need tools to assess the sustainability of their production systems and to make changes to ensure their sustainability. Decision support tools can help farmers to simulate and understand the influence of their management practices on production system performance. In a previous article, we described a dynamic pig fattening unit model that considers individual variability in pig performance, farmers’ practices and animal management and estimated environmental impacts (through Life Cycle Assessment) and economic results of the unit. This model is intended to be included in a decision support tool, which requires appropriate parameterisation for on-farm application and assessment to guarantee the quality of predictions. The objective of the present article is to develop a process to adequately parameterise a model for on-farm use, apply it to the pig unit model, and evaluate it using external data from commercial farms. Twenty-one pig farms were surveyed in western France in 2015 to collect data on animal performance, batch and shipping management, and farming practices. The parameterisation process was divided into six steps which correspond to incremental parameterisation of the model using data collected in the survey. The first step consists of parameterising the inputs related to farm infrastructure and management. The second step consists of setting initial mean weight and age of pigs at the beginning of fattening equal to those observed on each farm. The third step consists of three successive parameterisations for targeted slaughter weight, mean protein deposition, and mean feed intake. Steps four, five and six are iterations of step three. Each input parameterisation step improved predictions, with a decrease in the squared bias, non-unity slope and lack of correlation between predicted and observed data. For slaughter weight (SW), the root mean squared error (RMSE) decreased from 3.25 to 0.83 kg (i.e. from 2.8 to 0.7% of mean SW). For average daily gain (ADG), the RMSE decreased from 58.9 to 14.3 g live weight (LW)/day (i.e. from 7.3 to 1.8% of ADG). For the feed conversion ratio (FCR), the RMSE decreased from 0.22 to 0.03 kg feed/kg LW (i.e. from 7.8 to 1.1% of mean FCR). Considering the final RMSE values, the parameterisation process developed appears suitable for calibrating the model for future use in a decision support system.

Modelling nutrient requirements for pigs to optimize feed efficiency

Improvement of feed efficiency is crucial if pig production is to meet the challenge of sustainability in terms of production costs and environmental impact. . This implies to precisely know the nutrient requirements of sows and growing pigs to develop adapted feeding strategies and thus optimize performance. This chapter describes existing modelling approaches developed to predict the nutrient requirement of a single individual animal (growing pig or sow) in terms of protein / amino acids, energy and minerals, and depending on characteristics of the pig and the feed, and environmental conditions. The chapter proposes and explains the integration of individual variability among animals into models for pig feeding, its application in precision feeding, and illustrates via a case study the relevance of the application of these models for improving feed efficiency.