The choice of animal-based traits to identify and deal with production diseases is often a challenge for pig farmers, researchers and other related professionals. This systematic review focused on production diseases, that is, the diseases that arise from management practices, affecting the digestive, locomotory and respiratory system of pigs. The aim was to classify all traits that have been measured and conduct a meta-analysis to quantify the impact of diseases on these traits so that these can be used as indicators for intervention. Data were extracted from 67 peer-reviewed publications selected from 2339 records. Traits were classified as productive (performance and carcass composition), behavioural, biochemical and molecular traits. A meta-analysis based on mixed models was performed on traits assessed more than five times across studies, using the package metafor of the R software. A total of 524 unique traits were recorded 1 to 31 times in a variety of sample material including blood, muscle, articular cartilage, bone or at the level of whole animal. No behavioural traits were recorded from the included experiments. Only 14 traits were measured on more than five occasions across studies. Traits within the biochemical, molecular and productive trait groups were reported most frequently in the published literature and were most affected by production diseases; among these were some cytokines (interleukin (IL) 1-β, IL6, IL8 and tumour necrosis factor-α), acute phase proteins (haptoglobin) and daily weight gain. Quantification of the influence of factors relating to animal characteristics or husbandry practices was not possible, due to the low frequency of reporting throughout the literature. To conclude, this study has permitted a holistic assessment of traits measured in the published literature to study production diseases occurring in various stages of the production cycle of pigs. It shows the lack of consensus and common measurements of traits to characterise production diseases within the scientific literature. Specific traits, most of them relating to performance characteristics or immunological response of pigs, are proposed for further study as potential tools for the prognosis and study of production diseases.

A plethora of sensors and information technologies with applications to the precision nutrition of herbivores have been developed and continue to be developed. The nutritional processes start outside of the animal body with the available feed (quantity and quality) and continue inside it once the feed is consumed, degraded in the gastrointestinal tract and metabolised by organs and tissues. Finally, some nutrients are wasted via urination, defecation and gaseous emissions through breathing and belching whereas remaining nutrients ensure maintenance and production. Nowadays, several processes can be monitored in real-time using new technologies, but although these provide valuable data ‘as is’, further gains could be obtained using this information as inputs to nutrition simulation models to predict unmeasurable variables in real-time and to forecast outcomes of interest. Data provided by sensors can create synergies with simulation models and this approach has the potential to expand current applications. In addition, data provided by sensors could be used with advanced analytical techniques such as data fusion, optimisation techniques and machine learning to improve their value for applications in precision animal nutrition. The present paper reviews technologies that can monitor different nutritional processes relevant to animal production, profitability, environmental management and welfare. We discussed the model-data fusion approach in which data provided by sensor technologies can be used as input of nutrition simulation models in near-real time to produce more accurate, certain and timely predictions. We also discuss some examples that have taken this model-data fusion approach to complement the capabilities of both models and sensor data, and provided examples such as predicting feed intake and methane emissions. Challenges with automatising the nutritional management of individual animals include monitoring and predicting of the flow of nutrients including nutrient intake, quantity and composition of body growth and milk production, gestation, maintenance and physical activities at the individual animal level. We concluded that the livestock industries are already seeing benefits from the development of sensor and information technologies, and this benefit is expected to grow exponentially soon with the integration of nutrition simulation models and techniques for big data analysis. However, this approach may need re-evaluating or performing new empirical research in both fields of animal nutrition and simulation modelling to accommodate a new type of data provided by the sensor technologies.

Animal welfare standards have been incorporated in EU legislation and in farm assurance schemes, based on scientific information and aiming to safeguard the welfare of the species concerned. Recently, emphasis has shifted from resource-based measures of welfare to animal-based measures, which are considered to assess more accurately the welfare status. The data used in this analysis were collected from April 2013 to May 2016 through the ‘Real Welfare’ scheme in order to assess on-farm pig welfare, as required for those finishing pigs under the UK Red Tractor Assurance scheme. The assessment involved five main measures (percentage of pigs requiring hospitalization, percentage of lame pigs, percentage of pigs with severe tail lesions, percentage of pigs with severe body marks and enrichment use ratio) and optional secondary measures (percentage of pigs with mild tail lesions, percentage of pigs with dirty tails, percentage of pigs with mild body marks, percentage of pigs with dirty bodies), with associated information about the environment and the enrichment in the farms. For the complete database, a sample of pens was assessed from 1928 farm units. Repeated measures were taken in the same farm unit over time, giving 112 240 records at pen level. These concerned a total of 13 480 289 pigs present on the farm during the assessments, with 5 463 348 pigs directly assessed using the ‘Real Welfare’ protocol. The three most common enrichment types were straw, chain and plastic objects. The main substrate was straw which was present in 67.9% of the farms. Compared with 2013, a significant increase of pens with undocked-tail pigs, substrates and objects was observed over time (P<0.05). The upper quartile prevalence was <0.2% for all of the four main physical outcomes, and 15% for mild body marks. The percentage of pigs that would benefit from being in a hospital pen was positively correlated to the percentage of lame pigs, and the absence of tail lesions was positively correlated with the absence of body marks (P<0.05, R>0.3). The results from the first 3 years of the scheme demonstrate a reduction of the prevalence of animal-based measures of welfare problems and highlight the value of this initiative.

In reproducing ewes, a periparturient breakdown of immunity is often observed to result in increased fecal egg excretion, making them the main source of infection for their immunologically naive lambs. In this study, we expanded a simulation model previously developed for growing lambs to explore the impact of the genotype (performance and resistance traits) and host nutrition on the performance and parasitism of both growing lambs and reproducing ewes naturally infected with Teladorsagia circumcincta. Our model accounted for nutrient-demanding phases, such as gestation and lactation, and included a supplementary module to manage the age structure of the ewe flock. The model was validated by comparison with published data. Because model parameters were unknown or poorly estimated, detailed sensitivity analysis of the model was performed for the sheep mortality and the level of infection, following a preliminary screening step. The parameters with the greatest effect on parasite-related outputs were those driving animal growth and milk yield. Our model enables different parasite-control strategies (host nutrition, breeding for resistance and anthelmintic treatments) to be assessed on the long term in a sheep flock. To optimize in silico exploration, the parameters highlighted by the sensitivity analysis should be refined with real data.

The objective of this study was to develop a novel methodology that enables pig diets to be formulated explicitly for environmental impact objectives using a Life Cycle Assessment (LCA) approach. To achieve this, the following methodological issues had to be addressed: (1) account for environmental impacts caused by both ingredient choice and nutrient excretion, (2) formulate diets for multiple environmental impact objectives and (3) allow flexibility to identify the optimal nutritional composition for each environmental impact objective. An LCA model based on Canadian pig farms was integrated into a diet formulation tool to compare the use of different ingredients in Eastern and Western Canada. By allowing the feed energy content to vary, it was possible to identify the optimum energy density for different environmental impact objectives, while accounting for the expected effect of energy density on feed intake. A least-cost diet was compared with diets formulated to minimise the following objectives: non-renewable resource use, acidification potential, eutrophication potential, global warming potential and a combined environmental impact score (using these four categories). The resulting environmental impacts were compared using parallel Monte Carlo simulations to account for shared uncertainty. When optimising diets to minimise a single environmental impact category, reductions in the said category were observed in all cases. However, this was at the expense of increasing the impact in other categories and higher dietary costs. The methodology can identify nutritional strategies to minimise environmental impacts, such as increasing the nutritional density of the diets, compared with the least-cost formulation.

Simulation models of nutrient utilisation ignore that variation in pig system components can influence the predicted mean and variance of the performance of a group of pigs. The objective of this study was to develop a methodology to investigate how variation in feed composition would (a) affect the outputs of a nutrient utilisation model and (b) interact with variation that arises from the traits of individual pigs. We used a P intake and utilisation model to address these characteristics. Introduction of stochasticity gave rise to a number of methodological challenges – for example, how to generate variation in both feed composition and pigs and account for correlations between ingredients when modelling variation associated with feed mixing efficiency. Introducing variation in feed composition and pig phenotype resulted in moderate decreases in mean digested, retained and excreted P predicted for a population of pigs and an increase in their associated CV. A lower percentage of pigs in the population were predicted to meet their requirements during the feeding period considered, by comparison with the no-variation scenario. Variation in feed ingredient composition contributed more to performance variation than variation due to mixing efficiency. When variations in both feed composition and pig traits were considered, it was the former rather than the latter that had the dominant influence on variability in pig performance. The developed framework emphasises the consequences of random variability on the predictions of nutrient utilisation models. Such consequences will have a significant impact on decisions about management strategies such as feeding that are subject to variation.

There is a global imperative to reduce phosphorous (P) excretion from pig systems. In this study, a previously validated deterministic model was modified to be stochastic, in order to investigate the consequences of different management strategies on P excretion by a group of growing pigs. The model predicts P digestion, retention and excretion from feed composition and growth parameters that describe a specified pig phenotype. Stochasticity was achieved by introducing random variation in the latter. The strategies investigated were: (1) changing feed composition frequently in order to match more closely pig digestible P (digP) requirements to feed composition (phase feeding) and (2) grouping pigs into light and heavy groups and feeding each group according to the requirements of their group average BW (sorting). Phase feeding reduced P excretion as the number of feeding phases increased. The effect was most pronounced as feeding phases increased from 1 to 2, with a 7.5% decrease achieved; the increase in phases from 2 to 3 was associated with a further 2.0% reduction. Similarly, the effect was more pronounced when the feed targeted the population requirements for digP at the average BW of the first third, rather than the average requirements at the mid-point BW of each feeding sequence plan. Increasing the number of feeding phases increased the percentage of pigs that met their digP requirements during the early stages of growth and reduced the percentage of pigs that were supplied <85% of their digP requirements at any stage of their growth; the latter may have welfare implications. Sorting of pigs reduced P excretion to a lesser extent; the reduction was greater as the percentage of pigs in the light group increased from 10% to 30% (from 1.5% to 3.0% reduction, respectively). This resulted from an increase in the P excreted by the light group, accompanied by a decrease in the P excreted by the remaining pigs. Sorting increased the percentage of light pigs that met their dig P requirements, but only slightly decreased the percentage of heavy pigs that met these requirements at any point of their growth. Exactly the converse was the case as far as the percentage of pigs that were supplied <85% of their digP requirements were concerned. The developed model is flexible and can be used to investigate the effectiveness of other management strategies in reducing P excretion from groups of pigs, including precision livestock feeding.

A meta-analysis on the effects of management and animal-based factors on the performance and feed efficiency of growing pigs can provide information on single factor and interaction effects absent in individual studies. This study analysed the effects of such factors on average daily gain (ADG), feed intake (FI) and feed conversion ratio (FCR) of grower and finisher pigs. The multivariate models identified significant effects of: (1) bedding (P<0.01), stage of growth (P<0.001) and the interaction bedding×lysine (P<0.001) on ADG. ADG was higher on straw compared with no bedding (710 v. 605 g/day). (2) FI was significantly affected by stage of growth (P<0.01), bedding (P<0.01), group composition (P<0.05), group size (P<0.01), feed CP content (P<0.01), ambient temperature (P<0.01) and the interaction between floor space and feed energy content (P<0.001). Pigs housed on straw had a lower FI in comparison with those without (1.44 v. 2.04 kg/day); a higher FI was seen for pigs separated by gender in comparison with mixed groups (2.05 v. 1.65 kg/day); FI had a negative linear relationship with group size, the CP content of the feed and ambient temperature. (3) Stage of growth (P<0.001), feed CP (P<0.001) and lysine content (P<0.001), ambient temperature (P<0.001) and feed crude fibre (CF) content (P<0.01) significantly affected FCR; there were no significant interactions between any factors on this trait. There was an improvement in FCR at higher ambient temperatures, increased feed CP and lysine content, but a deterioration of FCR at higher CF contents. For ADG, the interaction of bedding×lysine was caused by pigs housed without bedding (straw) having higher ADG when on a feed lower in lysine, whereas those with bedding had a higher ADG when on a feed higher in lysine. Interaction effects on FI were caused by animals with the least amount of floor space having a higher FI when given a feed with a low metabolisable energy (ME) content, in contrast to all other pigs, which showed a higher FI with increased ME content. The meta-analysis confirmed the significant effect of several well-known factors on the performance and efficiency of grower and finisher pigs, the effects of some less established ones and, importantly, the interactions between such factors.

A deterministic, dynamic model was developed, to enable predictions of phosphorus (P) digested, retained and excreted for different pig genotypes and under different dietary conditions. Before confidence can be placed on the predictions of the model, its evaluation was required. A sensitivity analysis of model predictions to ±20% changes in the model parameters was undertaken using a basal UK industry standard diet and a pig genotype characterized by British Society Animal Science as being of ‘intermediate growth’. Model outputs were most sensitive to the values of the efficiency of digestible P utilization for growth and the non-phytate P absorption coefficient from the small intestine into the bloodstream; all other model parameters influenced model outputs by <10%, with the majority of the parameters influencing outputs by <5%. Independent data sets of published experiments were used to evaluate model performance based on graphical comparisons and statistical analysis. The literature studies were selected on the basis of the following criteria: they were within the BW range of 20 to 120 kg, pigs grew in a thermo-neutral environment; and they provided information on P intake, retention and excretion. In general, the model predicted satisfactorily the quantitative pig responses, in terms of P digested, retained and excreted, to variation in dietary inorganic P supply, Ca and phytase supplementation. The model performed well with ‘conventional’, European feed ingredients and poorly with ‘less conventional’ ones, such as dried distillers grains with solubles and canola meal. Explanations for these inconsistencies in the predictions are offered in the paper and they are expected to lead to further model development and improvement. The latter would include the characterization of the origin of phytate in pig diets.

Low phosphorus (P) digestibility combined with intensive pig production can increase P diffuse pollution and environmental load. The aim of this paper was to develop a deterministic, dynamic model able to represent P digestion, retention and ultimately excretion in growing and finishing pigs of different genotypes, offered access to diets of different composition. The model represented the limited ability of pig endogenous phytase activity to dephosphorylate phytate as a linear function of dietary calcium (Ca). Phytate dephosphorylation in the stomach by exogenous microbial phytase enzymes was expressed by a first order kinetics relationship. The absorption of non-phytate P from the lumen of the small intestine into the blood stream was set at 0.8 and the dephosphorylated phytate from the large intestine was assumed to be indigestible. The net efficiency of using digested P was set at 0.94 and assumed to be independent of BW, and constant across genotype and sex. P requirements for both maintenance and growth were made simple functions of body protein mass, and hence functions of animal genotype. Undigested P was assumed to be excreted in the feaces in both soluble and insoluble forms. If digestible P exceeded the requirements for P then the excess digestible P was excreted through the urinary flow; thus the model represented both forms of P excretion (soluble and insoluble) into the environment. Using a UK industry standard diet, model behaviour was investigated for its predictions of P digestibility, retention and excretion under different levels of inclusion of microbial phytase and dietary Ca, and different non-phytate P : phytate ratios in the diet, thus covering a broad space of potential diet compositions. Model predictions were consistent with our understanding of P digestion, metabolism and excretion. Uncertainties associated with the underlying assumptions of the model were identified. Their consequences on model predictions, as well as the model evaluation are assessed in a companion paper.

A 2 × 2 factorial experiment was conducted to determine the effects of rearing environment (indoor (In) v. outdoor (Out)) and dietary zinc oxide (ZnO) supplementation (0 (−Zn) v. 3100 (+Zn) mg/kg feed) on the response of weaned pigs to a challenge infection with enterotoxigenic Escherichia coli (ETEC). Pigs from the two rearing environments were weaned onto trial diets at 4 weeks of age, moved into conventional accommodation and infected 3 days later with 109 CFU ETEC per os. Faecal ETEC shedding was determined before and after challenge. After 7 days of ETEC infection, all pigs were euthanized for gut lactic acid bacteria (LAB)-to-coliform ratio, pH and small intestine morphological measurements. Both ZnO and outdoor rearing reduced ETEC excretion, and these effects were additive. Outdoor rearing increased small intestine and colon tissue weight. ZnO increased villus height and goblet cell number in the upper small intestine, LAB-to-coliform ratio (through reduced coliforms) in the lower small intestine and proximal colon, and improved growth performance. There were interactive effects of rearing environment and ZnO supplementation on upper small intestine villus height and daily gain, as outdoor rearing conferred advantages on these variables only with ZnO dietary supplementation. Daily gains were 233, 174, 277 and 347 (s.e.m. 27.2) g/day for the In − Zn, Out − Zn, In + Zn and Out + Zn, respectively. These results suggest different, but complementary mechanisms of intestinal health and performance in outdoor-reared pigs and those offered ZnO supplemented diets. The results indicate that the benefits of ZnO to the weaned pig extend beyond suppression of ETEC and appear mediated through altered development of the small intestine mucosa.

Efficiency of food and nutrient (including energy) use are considered the key factors in the economic and environmental performance of livestock systems. The aim of this paper is to consider the basis of genetic variation in the components that constitute dietary nutrient efficiency; and to conclude whether there would be benefit, in any relevant terms, in including these components in breeding programmes that aim to improve nutrient efficiency within pig and poultry systems of production. The components considered are (i) external, pre-ingestion losses, such as food spillage and its relation to feeding behaviour traits, (ii) digestive efficiency, (iii) maintenance requirements, (iv) net efficiency of energy and nutrient utilisation and (v) partitioning of scarce resources within productive and between productive and fitness functions. It is concluded that opportunities to exploit genetic variation exist mainly in the potential to improve the digestive efficiency of pigs and to reduce the maintenance requirements for resources mainly in hens, but also potentially in pigs. Current evidence suggests that there are very weak genetic and phenotypic correlations between components of feeding behaviour and productive traits, and little genetic variation in the net efficiency of nutrient utilisation among poultry and pig genotypes. The implication of the latter is that there would be little exploitable genetic variation in the partitioning of scarce nutrients between productive functions. Currently, there is a lack of understanding of the genetic basis of the partitioning of scarce nutrients between productive and fitness functions, and how this may impact upon the efficiency of nutrient use in pig and poultry systems. This is an area of research to which further effort might usefully be devoted.