Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy

Use of the linear regression method to evaluate population accuracy of predictions from non-linear models

Background

To address the limitations of commonly used cross-validation methods, the linear regression method (LR) was proposed to estimate population accuracy of predictions based on the implicit assumption that the fitted model is correct. This method also provides two statistics to determine the adequacy of the fitted model. The validity and behavior of the LR method have been provided and studied for linear predictions but not for nonlinear predictions. The objectives of this study were to 1) provide a mathematical proof for the validity of the LR method when predictions are based on conditional means, regardless of whether the predictions are linear or non-linear 2) investigate the ability of the LR method to detect whether the fitted model is adequate or inadequate, and 3) provide guidelines on how to appropriately partition the data into training and validation such that the LR method can identify an inadequate model.

Results

We present a mathematical proof for the validity of the LR method to estimate population accuracy and to determine whether the fitted model is adequate or inadequate when the predictor is the conditional mean, which may be a non-linear function of the phenotype. Using three partitioning scenarios of simulated data, we show that the one of the LR statistics can detect an inadequate model only when the data are partitioned such that the values of relevant predictor variables differ between the training and validation sets. In contrast, we observed that the other LR statistic was able to detect an inadequate model for all three scenarios.

Conclusion

The LR method has been proposed to address some limitations of the traditional approach of cross-validation in genetic evaluation. In this paper, we showed that the LR method is valid when the model is adequate and the conditional mean is the predictor, even when it is a non-linear function of the phenotype. We found one of the two LR statistics is superior because it was able to detect an inadequate model for all three partitioning scenarios (i.e., between animals, by age within animals, and between animals and by age) that were studied.

Classification of light Yorkshire pigs at different production stages using ordinary least squares and machine learning methods

Pig homogeneity and growth are major concerns for the pig industry today. Variability in pigs' size has a strong impact on profitability as uniformity plays a key role in the overall economic value of pigs produced. This research focused on statistical methods to identify pigs at risk of growth retardation at different stages of production. Data from 125 083 Yorkshire pigs at weaning (18-28 d), 59 533 pigs at the end of the nursery period (70-82 d) and 48 862 pigs at slaughter (155-170 d) were analyzed under three different cut-points (lowest 10, 20 and 30%) to characterize light animals. Records were randomly split into 2:1 training:testing sets, and each training data set was analyzed through an ordinary least squares approach and four machine learning algorithms (decision tree, random forest, and two alternative boosting approaches). A wide range of weighting functions were applied to give increased relevance to lighter pigs. Each resulting classification norm was used to classify light pigs in the testing data set. Both sensitivity and specificity were retained to construct the receiver operating characteristic curve, and the statistical performance of each analytical approach was evaluated by the area under the curve (AUC). In all production stages and cut-points, the random forest machine learning algorithm provided the highest AUC, closely followed by boosting procedures. For weaning BW (WW), factors related to birth BW and litter size accounted for more than 75% of the important prediction factors for light pigs. BW at the end of the nursery period and slaughter BW analyses revealed a similar pattern where WW and BW at the end of the nursery period accounted for more than 40 and 50% of statistical importance among the prediction factors, respectively. Machine learning algorithms are useful tools to easily evaluate the risk factors affecting the efficiency and homogeneity in swine. Since the BW at birth and weaning are key factors, sow nutrition and feeding management during gestation and lactation, along with piglet management during lactation, are identified as important influences on pig weight variability.

Blood and faecal biomarkers to assess dietary energy, protein and amino acid efficiency of utilization by growing and finishing pigs

Background

Diet evaluation and optimization is a slow and expensive process and it is not possible to do it at a farm level. This study aimed to use the blood serum metabolite (BSM) and faecal volatile fatty acid (VFA) profiles as potential biomarkers to identify changes in protein, amino acid and energy dietary content in growing and finishing pig diets at farm level.

Results

Two studies were conducted. The first study (S1) included 20 pens of 11 pigs (87.0 ± 4.10 kg; 18 weeks old) assigned to 5 diets: control (C1), high or low crude protein (HP1 and LP1, respectively), and high or low net energy (HE1 and LE1, respectively). The second study (S2) included 28 pens of 11 pigs (41.3 ± 2.60 kg; 12 weeks old) assigned to 7 diets: control (C2), high or low crude protein (HP2 and LP2, respectively), high or low amino acid (HA2 and LA2, respectively), and high or low net energy (HE2 and LE2, respectively). Pigs were followed for 10 (S1) and 20 (S2) days, and blood and faecal samples were collected at 20 (S1) and 14 (S2) weeks of age. Data were analysed using general linear models and receiver operating characteristic curve analysis. Urea nitrogen showed the best results as a biomarker. Urea nitrogen was higher in pigs fed high protein diets, HP1 (13.6 ± 0.95 mg/dL) and HP2 (11.6 ± 0.61), compared to those fed low protein diets, LP1 (6.0 ± 0.95) and LP2 (5.2 ± 0.61; P < 0.001), showing good discrimination ability (Area under the curve (AUC) = 98.4 and 100%, respectively). These differences were not observed between diets LA2 (6.5 ± 0.61) and HA2 (8.7 ± 0.61; P > 0.05; AUC = 71.9%), which were formulated based on the ideal protein profile but with no excess of protein. Creatinine, triglycerides, branched-chain fatty acids, albumin, propionic acid, and cholesterol showed differences between at least 2 diets but only in one of the studies.

Conclusions

Urea nitrogen showed high accuracy to detect excess of crude protein in growing and finishing pig diets. Other biomarkers like BCFA showed promising results and need to be further studied.

ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Opportunities and Challenges of Confined and Extensive Precision Livestock Production

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.

Limiting Dietary Lysine Increases Body Weight Variability by Restricting Growth Potential of the Lightest Growing Pigs

Simple Summary

Finding strategies to manage variability in growing pig operations is of major importance to avoid the extra costs associated with inefficient barn usage or penalties at the processing plants when pigs are not correctly sorted. The aim of this study was to determine whether dietary amino acid density can have an impact on body weight variability in a population of growing pigs. Feeding diets with a reduced amino acid density had a negative effect because they increased body weight variability of pens composed of the smallest pigs. For instance, in those pigs, the coefficient of variation, which is used as a measure of variability, increased significantly at the end of the experiment when dietary amino acid density was reduced. It was observed that this effect was explained by a growth restriction that was more severe the lightest the pigs were at the start of the trial. Hypothesizing that posteriorly there would be no differences in growth between those restricted pigs and the normally fed ones, the differences in growth would result in around 5–10 additional days to reach marketing weight. In conclusion, severe nutritional restrictions can negatively affect growing pigs’ body weight variability.

Abstract

The goal of this experiment was to determine the implications of dietary standardized ileal digestible lysine (SID Lys:NE) on body weight variability of growing pigs grouped in three initial body weight categories (BWCAT). Animals (N = 1170) were individually weighed and classified in 3 BWCAT (Lp: 32.1 ± 2.8 kg, Mp: 27.5 ± 2.3 kg, and Sp: 23.4 ± 2.9 kg). Afterward, pens were randomly allocated to five dietary SID Lys:NE treatments (3.25 to 4.88 g/Mcal) that were fed over 47 days. Pen coefficient of variation of Sp at day 47 was linearly increased when reducing SID Lys:NE (p < 0.01), from 9.8% to 15.4% between the two extreme dietary levels. The linear effect was different in Sp compared to Lp (p < 0.05) as no effect of SID Lys:NE was reported in the latter BWCAT (p = 0.992). Further analysis showed that this effect was explained by a growth restriction that was more severe the lightest the pigs were at the start of the trial. To summarize, swine body weight variability can be negatively affected when SID Lys requirements are not fulfilled.

Feeding Strategies to Reduce Nutrient Losses and Improve the Sustainability of Growing Pigs

The efficiency of pig production using nutrients has increased over the years. Still, better efficiency of nutrient utilization can be achieved by feeding pigs with diets adjusted to their estimated requirements. An increase in nutrient efficiency of utilization represents economic gains while maximizing environmental performance. The objective of this paper is to review the impact of different methods of diet formulation that provide farm animals with the amount of nutrients to satisfy their needs while minimizing nutrient excretion and greenhouse gas emissions. Diet formulation is one tool that can help to maximize nitrogen and energy utilization by decreasing crude protein content in diets. The use of local feedstuff and non-human-edible products (e.g., canola meal) associated with synthetic amino acid inclusion in the diet are valuable techniques to reduce carbon footprint. Precision feeding and nutrition is another powerful tool that allows not only daily tailoring of diets for maximal nutrient efficiency of utilization but also to reduce costs and improve nitrogen efficiency of utilization. In this review, we simulated through mathematical models the nitrogen and energy efficiency of utilization resulting from crude protein reduction in the diet. An 8% crude protein reduction in the diet can increase nitrogen efficiency of utilization by 54% while costing 11% less than a control diet without synthetic amino acids. The same reduction in crude protein represented a major improvement in available energy due to the decrease of energetic losses linked to protein deamination. Urinary and hindgut fermentation energy losses were 24% lower for pigs fed with low-protein diets when compared to control diets. In terms of modern feeding techniques and strategies, precision feeding and nutrition can decrease nitrogen excretion by 30% when compared to group phase feeding. The benefits of feeding pigs with low-protein diets and precision feeding techniques are additive and might result in a 61% nitrogen efficiency of utilization. There is room for improvement in the way nutrient requirements are estimated in pigs. Improving the understanding of the variation of nutrient utilization among pigs can contribute to further environmental gains.

A Novel Estimation of Unobserved Pig Growth Traits for the Purposes of Precision Feeding Methods

Recent technological advances make it possible to deliver feeding strategies that can be tailored to the needs of individual pigs in order to optimise the allocation of nutrient resources and contribute toward reducing excess nutrient excretion. However, these efforts are currently hampered by the challenges associated with: (1) estimation of unobserved traits from the available data on bodyweight and feed consumption; and (2) characterisation of the distributions and correlations of these unobserved traits to generate accurate estimates of individual level variation among pigs. Here, alternative quantitative approaches to these challenges, based on the principles of inverse modelling and separately inferring individual level distributions within a Bayesian context were developed and incorporated in a proposed precision feeding modelling framework. The objectives were to: (i) determine the average and distribution of individual traits characterising growth potential and body composition in an empirical population of growing-finishing barrows and gilts; (ii) simulate the growth and excretion of nitrogen and phosphorus of the average pig offered either a commercial two-phase feeding plan, or a precision feeding plan with daily adjustments; and (iii) simulate the growth and excretion of nitrogen and phosphorus across the pig population under two scenarios: a two-phase feeding plan formulated to meet the nutrient requirements of the average pig or a precision feeding plan with daily adjustments for each and every animal in the population. The distributions of mature bodyweight and ratio of lipid to protein weights at maturity had median (IQR) values of 203 (47.8) kg and 2.23 (0.814) kg/kg, respectively; these estimates were obtained without any prior assumptions concerning correlations between the traits. Overall, it was found that a proposed precision feeding strategy could result in considerable reductions in excretion of nitrogen and phosphorus (average pig: 8.07 and 9.17% reduction, respectively; heterogenous pig population: 22.5 and 22.9% reduction, respectively) during the growing-finishing period from 35 to 120 kg bodyweight. This precision feeding modelling framework is anticipated to be a starting point toward more accurate estimation of individual level nutrient requirements, with the general aim of improving the economic and environmental sustainability of future pig production systems.

How Does Protein Nutrition Affect the Epigenetic Changes in Pig? A Review

Simple Summary

Epigenetic mechanisms regulate gene expression and depend of nutrition. In farm animals, and concretely, in pigs, some papers on protein nutrition have been realized to improve several productive traits. Changes in protein diet influence on epigenetic mechanisms that could affect productive and reproductive traits in individuals and their offspring. The purpose of this review was to update the current knowledge about the effects of these nutritional changes on epigenetic mechanisms in pigs.

Abstract

Epigenetic changes regulate gene expression and depend of external factors, such as environment and nutrition. In pigs, several studies on protein nutrition have been performed to improve productive and reproductive traits. Indeed, these studies aimed not only to determine broad protein requirements but also pigs’ essential amino acids requirements. Moreover, recent studies tried to determine these nutritional requirements for each individual, which is known as protein precision nutrition. However, nutritional changes could affect different epigenetic mechanisms, modifying metabolic pathways both in a given individual and its offspring. Modifications in protein nutrition, such as change in the amino acid profile, increase or decrease in protein levels, or the addition of metabolites that condition protein requirements, could affect the regulation of some genes, such as myostatin, insulin growth factor, or genes controlling cholesterol and glucose metabolism pathways. This review summarizes the impact of most common protein nutritional strategies on epigenetic changes and describes their effects on regulation of gene expression in pigs. In a context where animal nutrition is shifting towards precision protein nutrition (PPN), further studies evaluating the effects of PPN on animal epigenetic are necessary.

Increasing Dietary Lysine Impacts Differently Growth Performance of Growing Pigs Sorted by Body Weight

An experiment was conducted analyzing whether growing pigs classified in different initial body weight categories (BWCAT) have a different response to increasing standardized ileal digestible lysine to net energy ratio (SID Lys:NE), to assess whether light pigs might benefit from being differentially fed. A total of 1170 pigs in pens of 13 were individually weighed, classified in 3 BWCAT (Lp: 32.1 ± 2.8 kg, Mp: 27.5 ± 2.3 kg, and Sp: 23.4 ± 2.9 kg), and afterwards pens were randomly allocated to 5 dietary SID Lys:NE treatments (3.25 to 4.88 g/Mcal) fed over 47 days. Results reported a greater linear improvement of growth and feed efficiency of Sp compared to Lp when increasing SID Lys:NE. Modelling the response to SID Lys:NE using quadratic polynomial models showed that the levels to reach 98% of maximum growth from day 0-47 were 3.67, 3.88, 4.06 g SID Lys/Mcal NE for Lp, Mp, and Sp, respectively. However, due to the overlapping SID Lys:NE confidence intervals at maximum performance, it was not possible to determine if requirements were different between BWCAT. Summarizing, the results suggested that feeding small pigs greater SID Lys:NE than large pigs can improve their performance and increase the efficiency of the overall production system.

Effects of interactions between feeding practices, animal health and farm infrastructure on technical, economic and environmental performances of a pig-fattening unit

European pig production faces economic and environmental challenges. Modelling can help farmers simulate and understand how changes in their management practices affect the efficiency of their production system. We developed an individual-based model of a pig-fattening unit that considers individual variability in performance among pigs, farmers' feeding practices and animal management and estimates environmental impacts (using life cycle assessment) and economic results of the unit. We previously demonstrated that this model provides reliable estimates of farm performance for different combinations of management practices, pig types and building characteristics. The objectives of this study were to quantify how interactions between feeding practices and animal management influence fattening unit results in healthy or impaired health conditions using the model. A virtual experiment was designed to evaluate effects of interactions between feeding practices, health status of the pig herd and infrastructure constraints on the technical performance, economic results and environmental impacts of the unit. The virtual experiment consisted of 96 scenarios, which combined chosen values of 6 input parameters of the model: batch interval (35 days and 7 days), use or non-use of a buffer room to manage the lightest pigs, feed rationing (ad libitum and restricted) and sequence plans (two-phase (2P), daily-phase (DP)), scale at which the feeding plan is applied (i.e. room, pen and individual) and health status of the pig herd (i.e. healthy v. impaired). Variance analysis was used to test effects of the factors in these 96 scenarios, and multivariate data analyses were used to classify the scenarios. Healthy populations obtained on average higher economic results (e.g. gross margin of 11.20 v. 1.50 €/pig) and lower environmental impacts (e.g. 2.24 v. 2.38 kg CO2-eq/kg pig live weight gain) than the population with impaired health. With 35 days batch interval and DP feeding, populations with impaired health reached gross margin similar to healthy populations with 2P ad libitum feeding and 7 days batch interval. Restricted, DP and individual feeding plans improved the economic and environmental performances of the unit for both health statuses. This study highlighted that health status of the pig herd is the main factor that affects technical, economic and environmental performances of a pig-fattening unit, and that adequate feeding strategies and animal management can compensate, to some extent, the effects of impaired health on environmental impacts but not on gross margin.

Simulated amino acid requirements of growing pigs differ between current factorial methods

Significant differences in the estimation of amino acid requirements exist between the available factorial methods. This study aimed to compare current factorial models used to estimate the individual and population standardised ileal digestible (SID) lysine (Lys) requirements of growing pigs during a 26-day feeding phase. Individual daily feed intake and BW data from 40 high-performance pigs (25-kg initial BW) were smoothed by linear regression. Body weight gain was constant (regression slope not different from 0) for all the pigs. The CV of the SID Lys requirements ranged from 22% at the beginning of the trial to 8% at the end. The population Brazilian tables (BT-2017) and National Research Council (NRC-2012) SID Lys requirements for the average pig were 16% higher than the average requirement estimated by the individual precision-feeding model (IPF), but similar to the estimated for the population assuming that population requirements are those of the 80th-percentile pig of the population (IPF-80). Meaning that, the IPF-80, BT-2017, and NRC-2012 models would yield similar recommendations when pigs are group-fed in conventional multi-phase systems. Additionally, the IPF-80 estimates are independent of the phase length, whereas the BT-2017 and NRC-2012 models use average population values in the middle of the feeding phase for the calculations and therefore, conventional requirement estimations decrease as the length of the feeding phase increases. In conclusion, the BT-2017 and NRC-2012 methods were calibrated for maximum population responses, which explains why these methods yield higher values than those estimated for the average pig by the IPF model. This study shows the limitations of conventional factorial methods to estimate amino acid requirements for precision-feeding systems.

Dynamic modeling of nutrient use and individual requirements of lactating sows1

Nutrient requirements of sows during lactation are related mainly to their milk yield and feed intake, and vary greatly among individuals. In practice, nutrient requirements are generally determined at the population level based on average performance. The objective of the present modeling approach was to explore the variability in nutrient requirements among sows by combining current knowledge about nutrient use with on-farm data available on sows at farrowing [parity, BW, backfat thickness (BT)] and their individual performance (litter size, litter average daily gain, daily sow feed intake) to estimate nutrient requirements. The approach was tested on a database of 1,450 lactations from 2 farms. The effects of farm (A, B), week of lactation (W1: week 1, W2: week 2, W3+: week 3 and beyond), and parity (P1: 1, P2: 2, P3+: 3 and beyond) on sow performance and their nutrient requirements were evaluated. The mean daily ME requirement was strongly correlated with litter growth (R2 = 0.95; P < 0.001) and varied slightly according to sow BW, which influenced the maintenance cost. The mean daily standardized ileal digestible (SID) lysine requirement was influenced by farm, week of lactation, and parity. Variability in SID lysine requirement per kg feed was related mainly to feed intake (R2 = 0.51; P < 0.001) and, to a smaller extent, litter growth (R2 = 0.27; P < 0.001). It was lowest in W1 (7.0 g/kg), greatest in W2 (7.9 g/kg), and intermediate in W3+ (7.5 g/kg; P < 0.001) because milk production increased faster than feed intake capacity did. It was lower for P3+ (6.7 g/kg) and P2 sows (7.3 g/kg) than P1 sows (8.3 g/kg) due to the greater feed intake of multiparous sows. The SID lysine requirement per kg of feed was met for 80% of sows when supplies were 112 and 120% of the mean population requirement on farm A and B, respectively, indicating higher variability in requirements on farm B. Other amino acid and mineral requirements were influenced in the same way as SID lysine. The present modeling approach allows to capture individual variability in the performance of sows and litters according to farm, stage of lactation, and parity. It is an initial step in the development of new types of models able to process historical farm data (e.g., for ex post assessment of nutrient requirements) and real-time data (e.g., to control precision feeding).

Environmental impacts of precision feeding programs applied in pig production

This study was undertaken to evaluate the effect that switching from conventional to precision feeding systems during the growing-finishing phase would have on the potential environmental impact of Brazilian pig production. Standard life-cycle assessment procedures were used, with a cradle-to-farm gate boundary. The inputs and outputs of each interface of the life cycle (production of feed ingredients, processing in the feed industry, transportation and animal rearing) were organized in a model. Grain production was independently characterized in the Central-West and South regions of Brazil, whereas the pigs were raised in the South region. Three feeding programs were applied for growing-finishing pigs: conventional phase feeding by group (CON); precision daily feeding by group (PFG) (whole herd fed the same daily adjusted diet); and precision daily feeding by individual (PFI) (diets adjusted daily to match individual nutrient requirements). Raising pigs (1 t pig BW at farm gate) in South Brazil under the CON feeding program using grain cultivated in the same region led to emissions of 1840 kg of CO2-eq, 13.1 kg of PO4-eq and 32.2 kg of SO2-eq. Simulations using grain from the Central-West region showed a greater climate change impact. Compared with the previous scenario, a 17% increase in climate change impact was found when simulating with soybeans produced in Central-West Brazil, whereas a 28% increase was observed when simulating with corn and soybeans from Central-West Brazil. Compared with the CON feeding program, the PFG and PFI programs reduced the potential environmental impact. Applying the PFG program mitigated the potential climate change impact and eutrophication by up to 4%, and acidification impact by up to 3% compared with the CON program. Making a further adjustment by feeding pigs according to their individual nutrient requirements mitigated the potential climate change impact by up to 6% and the potential eutrophication and acidification impact by up to 5% compared with the CON program. The greatest environmental gains associated with the adoption of precision feeding were observed when the diet combined soybeans from Central-West Brazil with corn produced in Southern Brazil. The results clearly show that precision feeding is an effective approach for improving the environmental sustainability of Brazilian pig production.

Effects of a daily three-meal pattern with different dietary protein contents on pig growth performance, carcass and muscle quality traits

BACKGROUND

Recent research related to phase-feeding programmes for pig nutrition do not always account for the variation among individuals, and feeds are usually formulated to optimise the performance of the whole pig population. This study aimed at measuring the effects of a daily three-meal pattern with different dietary protein contents on pig growth performance, carcass and muscle quality traits.

RESULTS

The results showed that compared with the 3C treatment, average daily gain (ADG) of pigs in the HCL treatment increased by 14.75% (P < 0.05) during period 1. The carcass weight (P = 0.006) and slaughter weight (P = 0.021) in the HCL group increased when compared with those in the 3C and LCH treatments. Moreover, the LCH feeding sequences contributed to reduce the drip loss in longissimus dorsi (LD) muscle (P < 0.05) when compared with the 3C treatment. The HCL or LCH feeding sequence contributed to increase the meat quality when compared with those receiving the 3C treatment.

CONCLUSION

Collectively, our results indicate that feeding high protein meal in the morning and a gradual reduction of the protein content in meals over the day may improve muscle quality characteristics, maximise performance, and reduce the pig feed cost. © 2017 Society of Chemical Industry.

Modelling interactions between farmer practices and fattening pig performances with an individual-based model

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.

Quantifying the consequences of nutritional strategies aimed at decreasing phosphorus excretion from pig populations: a modeling approach

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.

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

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.

Precision feeding can significantly reduce lysine intake and nitrogen excretion without compromising the performance of growing pigs

This study was developed to assess the impact on performance, nutrient balance, serum parameters and feeding costs resulting from the switching of conventional to precision-feeding programs for growing-finishing pigs. A total of 70 pigs (30.4±2.2 kg BW) were used in a performance trial (84 days). The five treatments used in this experiment were a three-phase group-feeding program (control) obtained with fixed blending proportions of feeds A (high nutrient density) and B (low nutrient density); against four individual daily-phase feeding programs in which the blending proportions of feeds A and B were updated daily to meet 110%, 100%, 90% or 80% of the lysine requirements estimated using a mathematical model. Feed intake was recorded automatically by a computerized device in the feeders, and the pigs were weighed weekly during the project. Body composition traits were estimated by scanning with an ultrasound device and densitometer every 28 days. Nitrogen and phosphorus excretions were calculated by the difference between retention (obtained from densitometer measurements) and intake. Feeding costs were assessed using 2013 ingredient cost data. Feed intake, feed efficiency, back fat thickness, body fat mass and serum contents of total protein and phosphorus were similar among treatments. Feeding pigs in a daily-basis program providing 110%, 100% or 90% of the estimated individual lysine requirements also did not influence BW, body protein mass, weight gain and nitrogen retention in comparison with the animals in the group-feeding program. However, feeding pigs individually with diets tailored to match 100% of nutrient requirements made it possible to reduce (P<0.05) digestible lysine intake by 26%, estimated nitrogen excretion by 30% and feeding costs by US$7.60/pig (-10%) relative to group feeding. Precision feeding is an effective approach to make pig production more sustainable without compromising growth performance.

Transcriptome analysis of genetic mechanism of growth curve inflection point using a pig model

Animal growth curves play an important role for animal breeders to optimize feeding and management strategies (De Lange et al., 2001 [1]; Brossard et al., 2009 [2]; Strathe et al., 2010 [3]). However, the genetic mechanism of the phenotypic difference between the inflection point and noninflection points of the growth curve remains unclear. Here, we report the differentially expressed gene pattern in pig longissimus dorsi among three typical time points of the growth curve, inflection point (IP), before inflection point (BIP) and after inflection point (AIP). The whole genome RNA-seq data was deposited at GenBank under the accession number PRJNA2284587. The RNA-seq libraries generated 117 million reads of 5.89 gigabases in length. Totals of 21,331, 20,996 and 20,139 expressed transcripts were identified in IP, UIP and AIP, respectively. Furthermore, we identified 757 differentially expressed genes (DEGs) between IP and UIP, and 271 DEGs between AIP and IP. Function enrichment analysis of DEGs found that the highly expressed genes in IP were mainly enriched in energy metabolism, global transcriptional activity and bone development intensity. This study contributes to reveal the genetic mechanism of growth curve inflection point.

The consequences of introducing stochasticity in nutrient utilisation models: the case of phosphorus utilisation by pigs

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.

Transcriptome Analysis of Liangshan Pig Muscle Development at the Growth Curve Inflection Point and Asymptotic Stages Using Digital Gene Expression Profiling

Animal growth curves can provide essential information for animal breeders to optimize feeding and management strategies. However, the genetic mechanism underlying the phenotypic differentiation between the inflection point and asymptotic stages of the growth curve is not well characterized. Here, we employed Liangshan pigs in stages of growth at the inflection point (under inflection point: UIP) and the two asymptotic stages (before the inflection point: BIP, after the inflection point: AIP) as models to survey global gene expression in the longissimus dorsi muscle using digital gene expression (DGE) tag profiling. We found Liangshan pigs reached maximum growth rate (UIP) at 163.6 days of age and a weight of 134.6 kg. The DGE libraries generated 117 million reads of 5.89 gigabases in length. 21,331, 20,996 and 20,139 expressed transcripts were identified BIP, UIP and AIP, respectively. Among them, we identified 757 differentially expressed genes (DEGs) between BIP and UIP, and 271 DEGs between AIP and UIP. An enrichment analysis of DEGs proved the immune system was strengthened in the AIP stage. Energy metabolism rate, global transcriptional activity and bone development intensity were highest UIP. Meat from Liangshan pigs had the highest intramuscular fat content and most favorable fatty acid composition in the AIP. Three hundred eighty (27.70%) specific expression genes were highly enriched in QTL regions for growth and meat quality traits. This study completed a comprehensive analysis of diverse genetic mechanisms underlying the inflection point and asymptotic stages of growth. Our findings will serve as an important resource in the understanding of animal growth and development in indigenous pig breeds.

Concept and application of ideal protein for pigs

Knowledge about the amino acid requirements and the response of pigs to the amino acid supply is essential in feed formulation. A deficient AA supply results in a reduction in performance while an oversupply is costly and leads to excessive nitrogen excretion with a potentially negative environmental impact. Amino acid requirements are determined to a large extent by the protein deposition in the body and, for lactating sows, by the protein exported in the milk. The concept of ideal protein was developed more than 50 years ago and refers to a protein with an amino acid profile that exactly meets the animal’s requirement so that all amino acids are equally limiting for performance. Because Lys typically is the first-limiting amino acid, the ideal amino acid profile is often expressed relative to Lys. Although the ideal protein profile is often assumed to be constant for a given production stage, (small) changes in the ideal protein profile can occur within a production stage. This can be caused by changes in the relative contribution of the different components of amino acid requirements during the productive life on the animal (e.g. changes in the relative contribution of growth and maintenance). Amino acids requirements can be determined experimentally using dose–response studies. The design of the study, the chosen response criterion, and the statistical model affect the requirement estimate. Although considerable experimental work has been carried out to determine the requirements for Lys, Met, Thr, and Trp in growing pigs (and to a lesser extent in sows), little is known about the requirements for the other essential amino acids. Experimental dose–response studies generally focus on the requirement and less on the overall response (i.e. what are the consequences of an amino acid deficiency?). This latter aspect is, to some extent, accounted for in modelling approaches that quantify the response of the animal to the amino acid supply in a dynamic way. The paper describes the origin of ideal protein and illustrates how fundamental concepts of amino acid nutrition have been integrated in practical modeling approaches for the nutrition of growing pigs and sows.

Characterization of the Fecal Microbial Communities of Duroc Pigs Using 16S rRNA Gene Pyrosequencing

This study characterized the fecal bacterial community structure and inter-individual variation in 30-week-old Duroc pigs, which are known for their excellent meat quality. Pyrosequencing of the V1–V3 hypervariable regions of the 16S rRNA genes generated 108,254 valid reads and 508 operational taxonomic units at a 95% identity cut-off (genus level). Bacterial diversity and species richness as measured by the Shannon diversity index were significantly greater than those reported previously using denaturation gradient gel electrophoresis; thus, this study provides substantial information related to both known bacteria and the untapped portion of unclassified bacteria in the population. The bacterial composition of Duroc pig fecal samples was investigated at the phylum, class, family, and genus levels. Firmicutes and Bacteroidetes predominated at the phylum level, while Clostridia and Bacteroidia were most abundant at the class level. This study also detected prominent inter-individual variation starting at the family level. Among the core microbiome, which was observed at the genus level, Prevotella was consistently dominant, as well as a bacterial phylotype related to Oscillibacter valericigenes, a valerate producer. This study found high bacterial diversity and compositional variation among individuals of the same breed line, as well as high abundance of unclassified bacterial phylotypes that may have important functions in the growth performance of Duroc pigs.

The impact of feeding growing-finishing pigs with daily tailored diets using precision feeding techniques on animal performance, nutrient utilization, and body and carcass composition

The impact of moving from conventional to precision feeding systems in growing-finishing pig operations on animal performance, nutrient utilization, and body and carcass composition was studied. Fifteen animals per treatment for a total of 60 pigs of 41.2 (SE = 0.5) kg of BW were used in a performance trial (84 d) with 4 treatments: a 3-phase (3P) feeding program obtained by blending fixed proportions of feeds A (high nutrient density) and B (low nutrient density); a 3-phase commercial (COM) feeding program; and 2 daily-phase feeding programs in which the blended proportions of feeds A and B were adjusted daily to meet the estimated nutritional requirements of the group (multiphase-group feeding, MPG) or of each pig individually (multiphase-individual feeding, MPI). Daily feed intake was recorded each day and pigs were weighed weekly during the trial. Body composition was assessed at the beginning of the trial and every 28 d by dual-energy X-ray densitometry. Nitrogen and phosphorus excretion was estimated as the difference between retention and intake. Organ, carcass, and primal cut measurements were taken after slaughter. The COM feeding program reduced (P < 0.05) ADFI and improved G:F rate in relation to other treatments. The MPG and MPI programs showed values for ADFI, ADG, G:F, final BW, and nitrogen and phosphorus retention that were similar to those obtained for the 3P feeding program. However, compared with the 3P treatment, the MPI feeding program reduced the standardized ileal digestible lysine intake by 27%, the estimated nitrogen excretion by 22%, and the estimated phosphorus excretion by 27% (P < 0.05). Organs, carcass, and primal cut weights did not differ among treatments. Feeding growing-finishing pigs with daily tailored diets using precision feeding techniques is an effective approach to reduce nutrient excretion without compromising pig performance or carcass composition.

Estimation of the tryptophan requirement in piglets by meta-analysis

There is no consensus concerning the Trp requirement for piglets expressed relative to Lys on a standardized ileal digestible basis (SID Trp : Lys). A meta-analysis was performed to estimate the SID Trp : Lys ratio that maximizes performance of weaned piglets between 7 and 25 kg of BW. A database comprising 130 experiments on the Trp requirement in piglets was established. The nutritional values of the diets were calculated from the composition of feed ingredients. Among all experiments, 37 experiments were selected to be used in the meta-analysis because they were designed to express the Trp requirement relative to Lys (e.g. Lys was the second-limiting amino acid in the diet) while testing at least three levels of Trp. The linear-plateau (LP), curvilinear-plateau (CLP) and asymptotic (ASY) models were tested to estimate the SID Trp : Lys requirement using average daily gain (ADG), average daily feed intake (ADFI) and gain-to-feed ratio (G : F) as response criteria. A multiplicative trial effect was included in the models on the plateau value, assuming that the experimental conditions affected only this parameter and not the requirement or the shape of the response to Trp. Model choice appeared to have an important impact on the estimated requirement. Using ADG and ADFI as response criteria, the SID Trp : Lys requirement was estimated at 17% with the LP model, at 22% with the CLP model and at 26% with the ASY model. Requirement estimates were slightly lower when G : F was used as response criterion. The Trp requirement was not affected by the composition of the diet (corn v. a mixture of cereals). The CLP model appeared to be the best-adapted model to describe the response curve of a population. This model predicted that increasing the SID Trp : Lys ratio from 17% to 22% resulted in an increase in ADG by 8%.

Precision pork production: predicting the impact of nutritional strategies on carcass quality

Variation is inherent to living systems. Because feeding strategies are applied to groups of pigs, it contributes to the inefficient use of natural resources and may even amplify the variation among pigs at slaughter. Precision pork production and precision feeding, through management of the variation among individuals, may contribute to improving the efficiency of animal production systems. This approach relies on the prediction of the response of the animal to the nutrient supply, the continuous monitoring of the response, and a system to control nutrient supply. Most nutritional models of pig growth are based on the partitioning of nutrients between energy expenditure, and protein and lipid deposition. However, the link between chemical body composition and tissue growth, tissue composition and thus carcass quality remains a challenge in modeling. The potential of precision pork production also depends on the (real-time) information that can be obtained to control growth and carcass quality.

Development of sustainable precision farming systems for swine: estimating real-time individual amino acid requirements in growing-finishing pigs

The objective of this study was to develop and evaluate a mathematical model used to estimate the daily amino acid requirements of individual growing-finishing pigs. The model includes empirical and mechanistic model components. The empirical component estimates daily feed intake (DFI), BW, and daily gain (DG) based on individual pig information collected in real time. Based on DFI, BW, and DG estimates, the mechanistic component uses classic factorial equations to estimate the optimal concentration of amino acids that must be offered to each pig to meet its requirements. The model was evaluated with data from a study that investigated the effect of feeding pigs with a 3-phase or daily multiphase system. The DFI and BW values measured in this study were compared with those estimated by the empirical component of the model. The coherence of the values estimated by the mechanistic component was evaluated by analyzing if it followed a normal pattern of requirements. Lastly, the proposed model was evaluated by comparing its estimates with those generated by the existing growth model (InraPorc). The precision of the proposed model and InraPorc in estimating DFI and BW was evaluated through the mean absolute error. The empirical component results indicated that the DFI and BW trajectories of individual pigs fed ad libitum could be predicted 1 d (DFI) or 7 d (BW) ahead with the average mean absolute error of 12.45 and 1.85%, respectively. The average mean absolute error obtained with the InraPorc for the average individual of the population was 14.72% for DFI and 5.38% for BW. Major differences were observed when estimates from InraPorc were compared with individual observations. The proposed model, however, was effective in tracking the change in DFI and BW for each individual pig. The mechanistic model component estimated the optimal standardized ileal digestible Lys to NE ratio with reasonable between animal (average CV = 7%) and overtime (average CV = 14%) variation. Thus, the amino acid requirements estimated by model are animal- and time-dependent and follow, in real time, the individual DFI and BW growth patterns. The proposed model can follow the average feed intake and feed weight trajectory of each individual pig in real time with good accuracy. Based on these trajectories and using classical factorial equations, the model makes it possible to estimate dynamically the AA requirements of each animal, taking into account the intake and growth changes of the animal.