The economic and environmental value of genetic improvements in fattening pigs: An integrated dynamic model approach

Comparison of the Faecal Microbiota Composition Following a Dairy By-Product Supplemented Diet in Nero Siciliano and Large White × Landrace Pig Breeds

The current study compared the faecal microbiota composition of two pig breeds (autochthonous vs. commercial) to understand what happens after the integration of liquid whey in the diet and what the role of the host genetic is. The trial was conducted for 60 days, and the faecal microbiota composition was investigated at three time points, T0, T1 (after 30 days) and T2 (after 60 days) in 30 female pigs (20 commercial crossbred and 10 Nero Siciliano pigs). The animals were divided into four groups (two control and two treatment groups). Generally, in both breeds, Firmicutes (51%) and Bacteroidota (36%) were the most abundant phylum whereas Prevotella, Treponema and Lactobacillus were the most abundant genera. The two breeds have a different reaction to a liquid whey diet. In fact, as shown by PERMANOVA analysis, the liquid whey significantly (p < 0.001) affects the microbiota composition of crossbreeds while not having an effect on the microbiota of the Nero Siciliano. Despite this, in both breeds Bifidobacterium and Ruminococcus have been positively influenced by liquid whey and they promote intestinal health, improve immunity, increase performance, and feed efficiency. In conclusion, the integration of liquid whey had a different effect on the Nero Siciliano and crossbred pig breeds, emphasizing the importance of the host genetic profile in determining the faecal bacterial composition.

Economic feasibility of interventions targeted at decreasing piglet perinatal and pre-weaning mortality across European countries

BACKGROUND

Piglet perinatal and pre-weaning mortality is a welfare problem causing economic losses in pig production. In this study, the effects of housing and management interventions on the economic result of sow enterprises representing six European countries (Belgium, Denmark, Finland, Germany, The Netherlands and Spain) were tested. Interventions concerned: (1) installing mechanical ventilation, (2) re-designing of the gestation unit, (3) drying and warming newborn piglets, (4) providing enrichment for gestating sows, including high-fiber dietary supplementation and point-source objects, and (5) music provision and backscratching of sows in the farrowing unit. A bio-economic model was used to determine the effects of interventions on economic outcomes during the nursery phase and to calculate a maximum cost of 1%-point reduction in perinatal and pre-weaning mortality, irrespective of the intervention type. Biological parameters were set according to previous observational and experimental studies. Interventions 1-4 were expected to decrease perinatal mortality, defined as stillbirths and deaths occurring within the first 48 h of postnatal life. Intervention 5 was expected to decrease pre-weaning mortality. Interventions increased fixed (1-3) and variable costs (3-5). We hypothesized that housing and management interventions would have a positive economic effect.

RESULTS

Piglet mortality can be decreased in various ways. Interventions concerning ventilation and re-designing of the gestation unit (1 and 2) were the most beneficial in countries with low housing costs and high perinatal mortality. Drying and warming newborn piglets (3) resulted in varying economic results, with the highest increase in profits obtained in a country with low labor costs and high litter size. Interventions providing sows with enrichment and human-animal interaction (4 and 5) were effective across varying conditions. Regardless of intervention type, policies aiming at 1%-point reduction in perinatal and pre-weaning mortality could cost from €0.2 to €0.5 (average €0.4) and from €0.4 to €0.5 (average €0.5) per piglet, respectively, depending on productions conditions.

CONCLUSIONS

To decrease piglet mortality, farmers should consider low input interventions, such as those targeting appropriate behavior. Our results suggest that providing enrichment or increasing human-animal interaction pays off and brings positive economic result even when piglet mortality is marginally reduced.

Environmental Impacts and Their Association With Performance and Excretion Traits in Growing Pigs

The selection of pigs for improved production traits has been, for a long time, the major driver of pig breeding. More recently, because of the increasing concern with the environment, new selection criteria have been explored, such as nitrogen (N) excretion. However, many studies indicate that life cycle assessment (LCA) provides much better indicators of environmental impacts than excretion. Therefore, the objective of this study was to investigate, using a modeling approach, the relationships between production traits and LCA impacts of individual growing pigs calculated at the farm gate for 1 kg of body weight gain. Performances of pigs were simulated for 2-phase (2P) and precision feeding (PR), using the InraPorc population model (on 1,000 pigs). Nitrogen excretion was positively correlated with feed conversion ratio (FCR; r = +0.96), climate change (CC; r = +0.96), acidification potential (AC; r = +0.97), eutrophication potential (EU; r = +0.97), and land occupation (LO; r = +0.96), whatever the feeding program. However, FCR appeared to be a better indicator of LCA impacts, with very high and positive correlations (r > +0.99) with CC, AC, EU, and LO for both feeding programs. The CC, AC, and EU impacts of pig production for PR feeding were 1.3, 10, and 7.5% lower than for 2P, respectively, but the correlations within each outcome were very similar among feeding programs. It was concluded that the use of FCR as a selection criterion in pig breeding seems to be a promising approach to associate improved performance and low environmental impact of pig fattening.

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

Frequent BW monitoring of growing pigs can be useful for identifying production (e.g. feeding), health and welfare problems. However, in order to construct a tool which will properly recognize abnormalities in pigs' growth a precise description of the growth process should be used. In this study we proposed a new model of pig growth accounting for daily fluctuations in BW. Body weight measurements of 1710 pigs (865 gilts and 843 barrows) originating from five consecutive batches from a Danish commercial farm were collected. Pigs were inserted into a large pen (maximum capacity=400) between November 2014 and September 2015. On average, each pig was observed for 42 days and weighed 3.6 times a day when passing from the resting to feeding area. Altogether, 243,160 BW measurements were recorded. A multilevel model of pig growth was constructed and fitted to available data. The BW of pigs was modeled as a quadratic function of time. A diurnal pattern was incorporated into the model by a cosine wave with known length (24 h). The model included pig effect which was defined as a random autoregressive process with exponential correlation. Variance of within-pigs error was assumed to increase with time. Because only five batches were observed, it was not possible to obtain the random effect for batch. However, in order to account for the batch effect the model included interactions between batch and fixed parameters: intercept, time, square value of time and cosine wave. The gender effect was not significant and was removed from the final model. For all batches, morning and afternoon peaks in the frequency of visits to the feeding area could be distinguished. According to results, pigs were lighter in the morning and heavier in the evening (minimum BW was reached around 1000 h and maximum around 2200 h). However, the exact time of obtaining maximum and minimum BW during the day differed between batches. Pigs had access to natural light and, therefore, existing differences could be explained by varying daylight level during observations periods. Because the diurnal amplitude for pig growth varied between batches from 0.9 to 1.4 kg, BW monitoring tools based on frequent measurements should account for diurnal variation in BW of pigs. This proposed description of growth will be built into a monitoring tool (a dynamic linear model) and applied to farm data in future studies.

Monitoring growth in finishers by weighing selected groups of pigs - A dynamic approach

Application of BW monitoring methods for the whole batch of pigs is not common in commercial herds. Instead, farm managers may regularly weigh a chosen subset of pigs (observed group) and use the obtained information for monitoring, forecasting, and decision support. The objective of this study was to construct a model for growth monitoring and forecasting in pig fattening herds and use the developed model framework to quantify the value of information on BW. The dynamic process of pig growing was described by means of a dynamic linear model (DLM) with Kalman filtering. For this study, data from 9 fattening cycles with the total registration for 9,800 pigs were used. The variance components were estimated by fitting a mixed-effects linear model on selected BW measurements. The obtained model was evaluated on its performance in forecasting the number of pigs ready to deliver from the whole batch and from a particular pen given the level of information on a reference data set consisting of 2 batches (Batch 3 [B1] and Batch 4 [B2]). Scenarios with a different frequency of observations (only 1 selected week, every second week, or weekly) on individual and aggregated levels for an observed group comprising 1 pen (36 pigs, which constitute 7.5% of pigs in a batch) or 2 pens (15.5% of pigs) were analyzed. Moreover, results with only initial herd information and insertion BW at the batch, pen, and pig level were presented. The model can be used for growth monitoring of the batch and for prediction of the number of pigs ready for slaughter in a given week (i.e., with a BW exceeding a threshold, which, in this study, is set to 105 kg). With an increased level of information, both accuracy (measured by the mean absolute deviation [MAD] of actual number of pigs above 105 kg from predicted number) and precision (measured by CV) of the model continue to improve. When monitoring all pigs at insertion and the observed groups every week (15.5% of pigs) compared with predictions based on only initial herd information, the MAD between the observed and predicted number of pigs above 105 kg in a single pen decreased by 1.4 and 2 pigs whereas CV was reduced by 147 and 78% for B1 and B2, respectively. The DLM was able to detect variation between pens already at insertion; therefore, data on initial BW had high value for the prediction procedure. Moreover, the aggregation had a marginal effect on model performance.