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With an ongoing transition towards the use of Light Emitting Diodes, more knowledge is needed on which light settings optimise sustainability parameters in pig production. We studied the effects of four light intensities on social, environmental and economic sustainability indicators, including ammonia emissions, space use, pen fouling, weight gain, carcass quality, perception of the stockkeeper, costs of the light system, and use of drinking water, electricity and medicines. Light treatments included a low (45 lux), medium (198 lux) and high (968 lux) uniform intensity, and a spatial gradient treatment ranging from 71 lux in the front to 330 lux in the back of each pen. The latter treatment aimed to improve the space use of functional areas. A total of 448 growing-finishing pigs were studied on a commercial farm using two consecutive batches of four rooms containing eight pens with seven pigs. Light intensity influenced some aspects of space use and pen fouling. For example, the proportion of pigs lying in the resting area was higher in the high and medium light intensity treatment than in the low intensity and gradient treatment. Moreover, the high-intensity treatment resulted in more fouling with faeces in the feeding area compared with the low-intensity and the gradient treatment. Ammonia emissions were higher in the gradient than in the low intensity treatment (not measured in medium and high intensity treatment). Furthermore, light intensity did not affect weight gain, carcass quality, water use and medicine use. The stockkeeper was content to work in all light conditions, but slightly preferred the medium intensity due to optimal visibility. Concerning economic performance, the costs of the light system and electricity use increased in the following order: low intensity, gradient, medium intensity, and high intensity. In conclusion, contrary to expectation the spatial gradient did not notably improve space use or reduce pen fouling, but rather increased ammonia emissions in comparison with uniform light. This is likely because the gradient could not be applied in an optimal way in the existing housing conditions. Among the other sustainability indicators, mainly electricity use and costs of the light system differed per treatment. These aspects can be improved by further optimising the number of light sources needed per pen to achieve the targeted intensities.

This study was conducted to explore the regulatory mechanism of leucine (Leu) on lipid metabolism of finishing pigs. Twenty-four Duroc × Landrace × Large cross pigs with an average body weight of 68.33 ± 0.97 kg were randomly allocated into 3 treatment groups with 8 replicates per group (1 pig per replicate). The dietary treatments were as follows: control group (CON), 0.25% Leu group and 0.50% Leu group. The experimental period was 42 d. The results showed as follows. (1) Compared with the CON, 0.25% and 0.50% Leu increased (P

The feeding behaviour of individual growing-finishing pigs can be continuously monitored using sensors such as electronic feeding stations (EFSs), and this could be further used to monitor pig welfare. To make accurate conclusions about individual pig welfare, however, it is important to know whether deviations in feeding behaviour in response to welfare issues are shown only on average or by each individual pig. Therefore, this study aimed (1) to quantify the individual variation in feeding behaviour changes in response to a range of welfare issues, and (2) to explain this individual variation by quantifying the responses to welfare issues for specific subgroups of pigs. We monitored four rounds of 110 growing-finishing pigs each (3–4 months per round). We collected feeding behaviour data using IVOG® EFSs and identified health issues and heat stress using climate sensors and twice-weekly health observations. For each pig, a generalised additive model was fitted, which modelled feeding behaviour through time and estimated the effect of each welfare issue that the pig had suffered from. The range of these effect estimates was compared between pigs to study the individual variation in responses. Subsequently, pigs were repeatedly grouped using physical and feeding characteristics, and, with meta–subset analysis, it was determined for each group whether a deviation in response to the welfare issue (i.e. their combined effect estimates) was present. We found that the range in effect estimates was very large, approaching normal distributions for most combinations of welfare issues and feeding variables. This indicates that most pigs did not show feeding behaviour deviations during the welfare issue, while those that did could show both increases and reductions. One exception was heat stress, for which almost all pigs showed reductions in their feed intake, feeding duration and feeding frequency. When looking at subgroups of pigs, it was seen that especially for lameness and tail damage pigs with certain physical characteristics or feeding strategies did consistently deviate on some feeding components during welfare issues (e.g. only relatively heavier pigs reduced their feeding frequency during lameness). In conclusion, while detection of individual pigs suffering from heat stress using feeding variables should be feasible, detection of (mild) health issues would be difficult due to pigs responding differently, if at all, to a given health issue. For some pigs with specific physical or behavioural characteristics, nevertheless, detection of some health issues, such as lameness or tail damage, may be possible.

Three studies were conducted evaluating the use of benzoic acid in swine diets. In experiment 1, 350 weanling barrows (DNA 200 × 400; initially 5.9 ± 0.04 kg) were allotted to one of the five dietary treatments with 14 pens per treatment. Diets were fed in three phases: phase 1 from weaning to day 10, phase 2 from days 10 to 18, and phase 3 from days 18 to 38. Treatment 1 contained no benzoic acid throughout all three phases (weaning to day 42). Treatment 2 included 0.50% benzoic acid throughout all three phases. Treatment 3 contained 0.50% benzoic acid in phases 1 and 2, and 0.25% benzoic acid in phase 3. Treatment 4 contained 0.50% benzoic acid in phases 1 and 2, and no benzoic acid in phase 3. Treatment 5 contained 0.50% benzoic acid in phase 1, 0.25% benzoic acid in phase 2, and no benzoic acid in phase 3. For the overall period, pigs fed 0.50% in the first two phases and 0.25% benzoic acid in the final phase had greater ( P  < 0.05) average daily gain (average daily gain) than pigs fed no benzoic acid through all three phases, or pigs fed 0.50% in the first two phases and no benzoic acid in the final phase, with pigs fed the other treatments intermediate. Pigs fed 0.50% in the first two phases and 0.25% benzoic acid in the final phase had improved ( P  < 0.05) gain-to-feed ratio (G:F) compared with pigs fed no benzoic acid throughout all three phases, pigs fed 0.50% in the first two phases and no benzoic acid in the third phase, or pigs fed 0.50%, 0.25%, and no benzoic acid, respectively. For experiment 2, a 101-d trial was conducted using two groups of 1,053 finishing pigs (2,106 total pigs; PIC 337 × 1,050; initially 33.3 ± 1.9 kg). Dietary treatments were corn-soybean meal-dried distillers grains with solubles-based with the addition of none, 0.25%, or 0.50% benzoic acid. Overall, pigs fed increasing benzoic acid had a tendency for increased average daily feed intake (linear, P  = 0.083) but decreased G:F (linear, P  < 0.05). In experiment 3, 2,162 finishing pigs (DNA 600 × PIC 1050; initially 31.4 ± 2.2 kg) were used in a 109-d trial. Dietary treatments were formulated with or without 0.25% benzoic acid. For the overall experimental period, pigs fed benzoic acid had increased ( P  < 0.05) G:F. In summary, feeding benzoic acid elicits improved growth performance when fed throughout the entire nursery period while improved G:F in growing-finishing pigs was observed in one experiment, but not in the other., Competing Interests: The authors declare no conflict of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

This study aims to investigate the effect of synbiotic-glyconutrients (SB-GLN) additive on growth performance, fatty acid profile, sensory characteristics, and texture profile analysis in finishing pig. Landrace × Yorkshire ♀ × (Duroc ♂) (n = 60) pigs with average body weight of 54.88 ± 1 kg were allocated into one of three dietary treatment groups in a complete randomized block design with four replicates of five pigs (two barrows and three gilts) per pen. The test treatments (TRT) were CON—corn-soybean meal basal diet; TRT 1—CON+ 0.25% SB-GLN; and TRT 2—CON + 0.5% SB-GLN. SB-GLN contains 1 × 107 CFU/g each of: L. plantarum, B. subtilis, and S. Cerevisiae, and 5% yeast cell wall β-Glucans (from S. Cerevisiae), and 14% of glyconutrients (N-acetylglucosamine, D-xylose, and Fucose). Pigs fed SB-GLN supplement showed linearly increased (p < 0.05) body weight, daily gain, and daily feed at the end of week 5, 10, and the overall experimental period. In addition, G:F showed a tendency to decrease (p < 0.1) at the end of week 10 and the overall experimental period. In addition, pigs that received a graded level of SB-GLN showed a tendency to increase (p < 0.1) their longiness muscle area and decreased (p < 0.05) cooking loss. The sensory results of pork belly (tenderness and juiciness) and loin (flavor) meat, and the texture profile analysis parameters of hardness 1, cohesiveness, and gumminess (belly), and hardness 2, chewiness, and springiness (loin) meat were linearly higher (p < 0.05) in the SB-GLN group. The values of fatty acid like butyric acid, caproic acid, undecylic acid, tridecylic acid, myristic acid, pentadecyclic acid, palmitic acid, margaric acid, stearic acid, eicosapentaenoic acid, and lignoceric acid were higher in pork belly fat of the SB-GLN-treated group compared to CON. Moreover, pigs that received SB-GLN exhibited higher crude fat and lauric acid, myristic acid, pentacyclic acid, palmitic acid, margaric acid, Octadecanoic acid, Oleic acid, linoleic acid, and eicosapentaenoic acid FA profiles in belly-lean meat. Also, the FA profile of the SB-GLN-treated group loin-lean meat showed increased lauric acid, myristic acid, palmitic acid, margaric acid, stearic acid, oleic acid, linoleic acid, alpha-linoleic acid, and eicosapentaenoic acid. The SB-GLN-treated group pork belly fat, belly lean meat, and loin-lean meat showed linearly increased docosahexaenoic acid, nervonic acid, omega 3, omega 6, ω-6: ω-3, Σ saturated FA, Σ un-SFA, Σ mono-USFA, Σ poly-USFA, MUFA/SFA, and PUFA/SFA. Therefore, we infer that the inclusion of 0.5% SB-GLN additive to finishing pig diet would be more beneficial to enhance their performance, and to increase the essential FA profile of pork meat for human consumption.

Supplemental methionine (Met) is widely used within the swine industry; however, data are limited regarding the effect of Met sources on carcass cutability and meat quality. The objective was to determine the effects of L-Met (LM, 99%), DL-Met (DLM, 99%), or calcium salt of DL-Met hydroxyl analog (MHA, 84%) in finishing pig diets on carcass characteristics and meat quality. At 9 weeks of age, pigs ( N  = 240) were allocated to 60 single-sex pens for a four-phase finishing trial that lasted 104 d. Pigs were fed a common grower diet until day 56 where pens were randomly allotted to one of the three experimental diets. For the remaining 7 wk of the finisher phase, pigs (BW = 79.9 ± 0.80 kg) were fed diets containing LM, DLM, or MHA, with the supplemental Met source providing 25% of standardized ileal digestible (SID) Met + cysteine (Cys) requirement based on 65% bioefficacy for MHA in comparison with LM or DLM. One pig per pen was slaughtered at the study conclusion (on day 104), and the left sides of carcasses were fabricated into subprimal cuts to determine carcass-cutting yields. Loin quality including proximate composition and shear force were measured. Hot carcass weight was not different ( P  = 0.34) between treatments (LM 104.5 kg; DLM 103.0 kg; MHA 101.5 kg), moreover, loin eye area was not different ( P  = 0.98) between treatments (LM 52.65 cm²; DLM 52.49 cm²; MHA 52.81 cm²). Boneless carcass-cutting yield was not different ( P  = 0.56) between treatments (LM 54.97 kg; DLM 54.82 kg; MHA 54.52 kg). Loin pH was not different ( P  = 0.24) between treatments (LM 5.45; DLM 5.48; MHA 5.45). However, drip loss tended to be reduced ( P  = 0.11) by the DLM treatment (5.58%) compared with LM (7.03%) and MHA (6.68%) treatments. Shear force was not different ( P  = 0.85) between treatments (LM 3.03 kg; DLM 3.06 kg; MHA 3.10 kg). However, cook loss tended to be reduced ( P  = 0.06) by the DLM treatment (16.20%) compared with LM (18.18%) and MHA (18.50%) treatments. These data suggest that only minimal differences in carcass cutability and meat quality can be attributed to Met source in finishing pig diets when using 65% bioefficacy for MHA relative to L-Met or DL-Met., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Feed additives have shown benefits throughout the literature in improving grow–finish pigs’ growth performance and carcass characteristics. However, the results have not been well summarized. Therefore, this review summarizes the available research (402 articles) on 14 feed additive categories fed to grow–finish pigs. The categories were acidifiers, betaine, Cr, conjugated linoleic acids, Cu, direct-fed microbials, carbohydrases, proteases, phytases, multi-enzymes, essential oils, L-carnitine, yeasts, and Zn. Qualified articles were collected and selected based on inclusion and exclusion criteria from online databases. The percentage difference for each response variable between the treatment and control group was calculated and summarized. Most results were positive for each feed additive; however, the magnitude of improvement varied, and most were not statistically significant. For ADG, DFM, Cu, L-carnitine, and multi-enzymes showed relatively large positive effects (>2.1% improvement) across a reasonable number of articles. Acidifiers, betaine, CLA, multi-enzymes, DFM, L-carnitine, and yeasts showed relatively large positive effects (>2.5% improvement) on improving G:F. Moreover, except for betaine, Cr, CLA, and L-carnitine, most feed additives showed little and non-significant effects on BF thickness (

The selection of animals to be marketed is largely completed by their visual assessment, solely relying on the skill level of the animal caretaker. Real-time monitoring of the weight of farm animals would provide important information for not only marketing, but also for the assessment of health and well-being issues. The objective of this study was to develop and evaluate a method based on 3D Convolutional Neural Network to predict weight from point clouds. Intel Real Sense D435 stereo depth camera placed at 2.7 m height was used to capture the 3D videos of a single finishing pig freely walking in a holding pen ranging in weight between 20–120 kg. The animal weight and 3D videos were collected from 249 Landrace × Large White pigs in farm facilities of the FZEA-USP (Faculty of Animal Science and Food Engineering, University of Sao Paulo) between 5 August and 9 November 2021. Point clouds were manually extracted from the recorded 3D video and applied for modeling. A total of 1186 point clouds were used for model training and validating using PointNet framework in Python with a 9:1 split and 112 randomly selected point clouds were reserved for testing. The volume between the body surface points and a constant plane resembling the ground was calculated and correlated with weight to make a comparison with results from the PointNet method. The coefficient of determination (R2 = 0.94) was achieved with PointNet regression model on test point clouds compared to the coefficient of determination (R2 = 0.76) achieved from the volume of the same animal. The validation RMSE of the model was 6.79 kg with a test RMSE of 6.88 kg. Further, to analyze model performance based on weight range the pigs were divided into three different weight ranges: below 55 kg, between 55 and 90 kg, and above 90 kg. For different weight groups, pigs weighing below 55 kg were best predicted with the model. The results clearly showed that 3D deep learning on point sets has a good potential for accurate weight prediction even with a limited training dataset. Therefore, this study confirms the usability of 3D deep learning on point sets for farm animals’ weight prediction, while a larger data set needs to be used to ensure the most accurate predictions.

This study aims to investigate the effect of synbiotic-glyconutrients (SB-GLN) additive on growth performance, fatty acid profile, sensory characteristics, and texture profile analysis in finishing pig. Landrace × Yorkshire ♀ × (Duroc ♂) ( n = 60) pigs with average body weight of 54.88 ± 1 kg were allocated into one of three dietary treatment groups in a complete randomized block design with four replicates of five pigs (two barrows and three gilts) per pen. The test treatments (TRT) were CON-corn-soybean meal basal diet; TRT 1-CON+ 0.25% SB-GLN; and TRT 2-CON + 0.5% SB-GLN. SB-GLN contains 1 × 10 7 CFU/g each of: L. plantarum , B. subtilis , and S. Cerevisiae , and 5% yeast cell wall β-Glucans (from S. Cerevisiae), and 14% of glyconutrients (N-acetylglucosamine, D-xylose, and Fucose). Pigs fed SB-GLN supplement showed linearly increased ( p < 0.05) body weight, daily gain, and daily feed at the end of week 5, 10, and the overall experimental period. In addition, G:F showed a tendency to decrease ( p < 0.1) at the end of week 10 and the overall experimental period. In addition, pigs that received a graded level of SB-GLN showed a tendency to increase ( p < 0.1) their longiness muscle area and decreased ( p < 0.05) cooking loss. The sensory results of pork belly (tenderness and juiciness) and loin (flavor) meat, and the texture profile analysis parameters of hardness 1, cohesiveness, and gumminess (belly), and hardness 2, chewiness, and springiness (loin) meat were linearly higher ( p < 0.05) in the SB-GLN group. The values of fatty acid like butyric acid, caproic acid, undecylic acid, tridecylic acid, myristic acid, pentadecyclic acid, palmitic acid, margaric acid, stearic acid, eicosapentaenoic acid, and lignoceric acid were higher in pork belly fat of the SB-GLN-treated group compared to CON. Moreover, pigs that received SB-GLN exhibited higher crude fat and lauric acid, myristic acid, pentacyclic acid, palmitic acid, margaric acid, Octadecanoic acid, Oleic acid, linoleic acid, and eicosapentaenoic acid FA profiles in belly-lean meat. Also, the FA profile of the SB-GLN-treated group loin-lean meat showed increased lauric acid, myristic acid, palmitic acid, margaric acid, stearic acid, oleic acid, linoleic acid, alpha-linoleic acid, and eicosapentaenoic acid. The SB-GLN-treated group pork belly fat, belly lean meat, and loin-lean meat showed linearly increased docosahexaenoic acid, nervonic acid, omega 3, omega 6, ω-6: ω-3, Σ saturated FA, Σ un-SFA, Σ mono-USFA, Σ poly-USFA, MUFA/SFA, and PUFA/SFA. Therefore, we infer that the inclusion of 0.5% SB-GLN additive to finishing pig diet would be more beneficial to enhance their performance, and to increase the essential FA profile of pork meat for human consumption.

The selection of animals to be marketed is largely completed by their visual assessment, solely relying on the skill level of the animal caretaker. Real-time monitoring of the weight of farm animals would provide important information for not only marketing, but also for the assessment of health and well-being issues. The objective of this study was to develop and evaluate a method based on 3D Convolutional Neural Network to predict weight from point clouds. Intel Real Sense D435 stereo depth camera placed at 2.7 m height was used to capture the 3D videos of a single finishing pig freely walking in a holding pen ranging in weight between 20-120 kg. The animal weight and 3D videos were collected from 249 Landrace × Large White pigs in farm facilities of the FZEA-USP (Faculty of Animal Science and Food Engineering, University of Sao Paulo) between 5 August and 9 November 2021. Point clouds were manually extracted from the recorded 3D video and applied for modeling. A total of 1186 point clouds were used for model training and validating using PointNet framework in Python with a 9:1 split and 112 randomly selected point clouds were reserved for testing. The volume between the body surface points and a constant plane resembling the ground was calculated and correlated with weight to make a comparison with results from the PointNet method. The coefficient of determination (R 2 = 0.94) was achieved with PointNet regression model on test point clouds compared to the coefficient of determination (R 2 = 0.76) achieved from the volume of the same animal. The validation RMSE of the model was 6.79 kg with a test RMSE of 6.88 kg. Further, to analyze model performance based on weight range the pigs were divided into three different weight ranges: below 55 kg, between 55 and 90 kg, and above 90 kg. For different weight groups, pigs weighing below 55 kg were best predicted with the model. The results clearly showed that 3D deep learning on point sets has a good potential for accurate weight prediction even with a limited training dataset. Therefore, this study confirms the usability of 3D deep learning on point sets for farm animals' weight prediction, while a larger data set needs to be used to ensure the most accurate predictions.

Simple Summary: Monitoring the weight of farm pigs is crucial for their well-being. Implementing Cameras and Machine Vision Systems shows promise for automating this process. Traditionally, studies have focused on RGB and depth images for weight prediction, using measurements like volume and body area. However, these methods prove less robust in fluctuating environmental conditions, especially lighting. This study reveals that PointNet, a 3D deep learning architecture trained on point cloud data (3D points), outperforms the conventional approach, and demonstrates stability in varying light conditions due to its ability to learn on spatial information. This finding underscores the potential for PointNet to significantly improve the accuracy and reliability of weight monitoring in farm settings. The selection of animals to be marketed is largely completed by their visual assessment, solely relying on the skill level of the animal caretaker. Real-time monitoring of the weight of farm animals would provide important information for not only marketing, but also for the assessment of health and well-being issues. The objective of this study was to develop and evaluate a method based on 3D Convolutional Neural Network to predict weight from point clouds. Intel Real Sense D435 stereo depth camera placed at 2.7 m height was used to capture the 3D videos of a single finishing pig freely walking in a holding pen ranging in weight between 20–120 kg. The animal weight and 3D videos were collected from 249 Landrace × Large White pigs in farm facilities of the FZEA-USP (Faculty of Animal Science and Food Engineering, University of Sao Paulo) between 5 August and 9 November 2021. Point clouds were manually extracted from the recorded 3D video and applied for modeling. A total of 1186 point clouds were used for model training and validating using PointNet framework in Python with a 9:1 split and 112 randomly selected point clouds were reserved for testing. The volume between the body surface points and a constant plane resembling the ground was calculated and correlated with weight to make a comparison with results from the PointNet method. The coefficient of determination (R2 = 0.94) was achieved with PointNet regression model on test point clouds compared to the coefficient of determination (R2 = 0.76) achieved from the volume of the same animal. The validation RMSE of the model was 6.79 kg with a test RMSE of 6.88 kg. Further, to analyze model performance based on weight range the pigs were divided into three different weight ranges: below 55 kg, between 55 and 90 kg, and above 90 kg. For different weight groups, pigs weighing below 55 kg were best predicted with the model. The results clearly showed that 3D deep learning on point sets has a good potential for accurate weight prediction even with a limited training dataset. Therefore, this study confirms the usability of 3D deep learning on point sets for farm animals' weight prediction, while a larger data set needs to be used to ensure the most accurate predictions. [ABSTRACT FROM AUTHOR]

Using agricultural by-products such as dehydrated orange pulp (DOP) in animal feeds is of interest to increase pig sector sustainability. With this aim, an assay was carried out to assess the effects of increasing inclusion levels of DOP in pig diets regarding animal performance, carcass quality, and environmental impact. Four experimental diets were designed, a control diet (T1) and three more diets with increasing levels of DOP with 80, 160, and 240 g/kg of DOP for diets T2, T3, and T4, respectively. One hundred and sixty growing pigs were used in the experiment. Growth performance (average daily gain, ADG; average daily feed intake, ADFI and feed conversion ratio, FCR) and in vivo backfat thickness (BF) and loin depth (LD) gain were recorded during the finishing phase (from 70 to 130 kg BW). Faecal samples were incubated for bacteria enumeration. At slaughter, carcass characteristics and meat quality traits were measured, and subcutaneous fat was sampled to analyse the fatty acid (FA) profile. Additionally, the slurry excreted by the animals was measured, characterised and subjected to a gaseous emission assay during its storage. The final BW and overall ADFI, ADG and FCR were similar among treatments. In vivo final LD and BF gain decreased (P ≤ 0.10) as the inclusion level of DOP increased. No differences were observed in carcass characteristics with the inclusion of DOP, except carcass weight that decreased linearly (P = 0.05) with DOP. Regarding the FA profile of the subcutaneous fat, the ratio of total monounsaturated to saturated FA increased with the inclusion level of DOP. Neither slurry excretion and characterisation nor bacterial counts from faeces showed any significant difference among treatments. The inclusion of DOP led to greater CH4 emissions in mg per L of slurry and hour, whereas these differences disappeared when expressed in mg per animal and day. In all, it has been demonstrated that the inclusion of DOP up to 240 mg/kg in pig diets had minor effects on growth performance, carcass quality traits or gaseous emissions from slurry, favouring the circular economy strategy and pig sector sustainability.

This study was conducted to explore the regulatory mechanism of leucine (Leu) on lipid metabolism of finishing pigs. Twenty-four Duroc × Landrace × Large cross pigs with an average body weight of 68.33 ± 0.97 kg were randomly allocated into 3 treatment groups with 8 replicates per group (1 pig per replicate). The dietary treatments were as follows: control group (CON), 0.25% Leu group and 0.50% Leu group. The experimental period was 42 d. The results showed as follows. (1) Compared with the CON, 0.25% and 0.50% Leu increased ( P  < 0.01) the average daily gain (ADG), while the average backfat thickness (ABT) and the ratio of feed intake to body weight gain (F:G ratio) were decreased ( P  < 0.05). (2) In the 0.25% Leu group, the relative mRNA expression levels of sterol regulatory element binding protein-1c ( SREBP1c ), recombinant fatty acid transport protein 1 ( FATP1 ), chemerin and peroxisome proliferator-activated receptor γ ( PPARγ ) were decreased but the level of fatty acid binding protein 4 ( FABP4 ) and fatty acid translocase ( FAT/CD36 ) were increased in backfat tissue. In the 0.25% Leu group, the protein levels of p-Rictor, p-Raptor, p-eIF4E-binding protein 1 (p-4EBP1), p-silent mating type information regulator 2 homolog 1 (p-SIRT1) and acetylation ribosome s6 protein kinase 1 (Ac-S6K1) were increased ( P  < 0.05). (3) Compared to the CON, the diversity of gut microbiota in the 0.25% Leu group was increased. Principal component analysis showed that the relative abundance of Bacteroidetes, Lactobacillus and Desulfovibrio was higher in the 0.25% Leu group than the CON, but the relative abundance of Firmicutes, Treponema and Shigella was lower than in the CON ( P  < 0.05). (4) Four different metabolites were screened out from the serum of finishing pigs including allolithocholic acid (alloLCA), isolithocholic acid (isoLCA), ursodeoxycholic acid (UDCA) and hyodeoxycholic acid (HDCA), which correlate to various degrees with the above microorganisms. In conclusion, Leu could promote adipose tissue lipolysis of finishing pigs through the mTOR-SIRT1 signaling pathway, and S6K1 is acetylated at the same time, and the interaction between gut microbiota and bile acid metabolism is also involved., Competing Interests: We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the content of this paper., (© 2024 The Authors.)

Branched-chain amino acids (BCAAs) play a regulatory role in adipogenesis and energy balance. Therefore, this study aimed to investigate the impact of BCAA supplements, especially leucine (Leu) and valine (Val) supplementation, on lipid metabolism and related disorders in a finishing pig model. The results demonstrated that Leu (1%) and Val decreased serum as well as hepatic lipid accumulation. Moreover, metabolomics and lipidomics analyses revealed that Leu and Val markedly downregulated the level of various lipid species in the liver. This outcome may be explained by Leu and Val promoting cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA)/hormone-sensitive triglyceride lipase (HSL) signaling pathways. Leu and Val altered the fatty acid composition in distinct adipose tissues and decreased the levels of inflammatory factors. Additionally, they significantly decreased back fat thickness, and the results of the fatty acid profiles demonstrated that Leu and Val significantly increased the levels of monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) while decreasing those of saturated fatty acids (SFAs), especially in back fat and abdominal fat. Besides, Leu and Val restored glucose homeostasis by suppressing gluconeogenesis through the serine/threonine protein kinase (AKT)/transcription factor forkhead box O1 (FOXO1) signaling pathway in the liver and back fat. In summary, these results suggest that Leu and Val may serve as key regulators for modulating lipid metabolism and steatosis.

Livestock manure is an important component of agricultural organic waste, and in recent years, with the development of research on the bioconversion of manure, BSFs have been proven to be useful in the treatment of a variety of livestock wastes. In-depth research on the composition of manure and its effect on the development of BSFL is, however, very scarce. The purpose of this study was to identify the parameters that influenced the growth of BSFL that was fed fattening pig manure. The pH, moisture, and nutrients of the fattening manures (namely, nursery, growing, and finishing pig manures) were measured. To examine the influence of manure types on larval growth, 100 larvae were inoculated in 100 g of each type of manure in triplicate. According to the findings, larvae fed finishing pig manure had the lowest dry weight (30.2 ± 6.1 mg) compared to those fed growing (58.2 ± 7.3 mg) or nursery (65.5 ± 6.2 mg) pig manure. The correlation coefficients (r) between the nutrients in the manure and the weight of the larvae were calculated. Hemicellulose had the greatest |r| value (0.9569). Further research revealed that larvae raised on hemicellulase-pretreated finishing pig manure frequently weighed 21–30% (days 2–8) more than larvae raised on control manure. In conclusion, hemicellulose was a significant component that might hinder larval growth. The results of this study could be used to improve the system before it is put into use.

The aim of this study was to determine how different doses of phytase in diets with extruded soybean seeds and rapeseed meal affected pigs’ growth performance, meat quality, bone mineralization, and fatty acid profiles. Sixty pigs were divided into three treatments by sex and body mass. Pigs were divided into starter (25 days), grower (36 days), and finisher (33 days) periods and fed with mash diets. No phytase was used in the control group diet, whereas in Phy1 and Phy2, 100 g and 400 g of phytase per ton of mixture were used, respectively. The feed conversion ratio and meat color were significantly correlated with phytase. Phytase supplementation had no effect on the growth of pigs, but total phosphorus was significantly increased in the bones and meat of the pigs. The enzyme additive reduced the C22:4 n-6 acid content in the meat, whereas other results were not significantly affected. The data suggest that the addition of phytase at a dosage of 100 g/ton to diets with extruded full-fat soya seeds and rapeseed meal can be valuable, as it reduces the FCR and increases the P content in the meat and bones.

One of the key factors to improve swine production sustainability is the use of agro-industrial by-products in feeds, such as olive by-products. However, it is necessary to assess its effects on the overall production process, including the animal and the environment. With this aim, an experiment was conducted to determine the effects of including a partially defatted olive cake (PDOC) in pig diets on growth performance, faecal microbiota, carcass quality and gas emission from the slurry. Two finishing diets were formulated, a control (C) diet and a diet with PDOC included at 120 g/kg. Eighty finishing male pigs Duroc-Danbred × (Landrace × Large White) of 60.4 ± 7.00 kg BW were divided between these two treatments. During the finishing period (60 to 110 kg BW, 55 days) average daily gain, average daily feed intake and feed conversion ratio were recorded. Faecal samples from the rectum of 16 animals per treatment were incubated for bacteria enumeration. At the end of finishing period, backfat thickness and loin depth (LD) were measured. Animals were slaughtered to obtain carcass weight and carcass composition parameters, and subcutaneous fat was sampled to analyse the fatty acid (FA) profile. In addition greenhouse gas and ammonia emissions were measured during pig slurry storage using the methodology of dynamic flux chambers. An initial slurry characterisation and biochemical methane potential (B0) were also determined. No significant differences between treatments were found in performance, carcass quality and microbial counts with the exception of LD, which was lower in PDOC compared with C animals (45.5 v. 47.5 mm, SEM: 0.62; P = 0.020). The FA profile of the subcutaneous fat did not differ between treatments, but the monounsaturated FA (MUFA) concentration was higher and the polyunsaturated FA was lower in the animals fed PDOC (50.9 v. 48.3, SEM: 0.48, P < 0.001; 17.6 v. 19.3, SEM: 0.30, P < 0.001 in mg/100 g of Total FA, for PDOC and C animals, respectively). The initial pig slurry characterisation only showed differences in ADF concentration that was higher (P < 0.05) in the slurry from PDOC treatment. Regarding gas emission, slurries from both treatments emitted similar amounts of ammonia (NH3), carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as B0 values. The results obtained suggest that PDOC may be included in balanced pig diets at rates of up to 120 g/kg without negative effects on performance, carcass quality, gut microflora and slurry gas emission, while improving the MUFA concentration of subcutaneous fat.

Feed conversion ratio (FCR) in grow-finishing pigs is one of the most important determinants of pig farm profitability and production efficiency. In its simplest form, FCR represents the amount of feed used per unit weight gain of the pig. Yet, this approach entails various limitations hampering its practical applicability such as availability of accurate data and large variation in ways to adapt FCR values for different starting and end weight as well as mortality rates. Various stakeholders are using their own formulas to determine FCR creating a ‘definition nonconformity’ when comparing FCRs among farms. This study aimed to optimize the calculation of FCR through the use of participatory qualitative research. A multidisciplinary research group of 9 persons (animal scientists, veterinarians and agricultural economists) and a consulting group of 31 stakeholders (representing the Flemish primary sector, feed industry, pharma, genetic companies, large retailers, academia and policy institutions) were involved. The decision problem analysis started with a literature review, followed by 25 in-depth interviews and their analyses (NVivo 11™). This led to an additional literature review and the formation of focus (expert) groups that helped to formulate preliminary FCR formulas. Revision rounds between the research team and the stakeholders further fine-tuned the formulas with the final result being two distinct complimentary formulas that are fit for purpose. Both refer to carcass gain per kg feed intake (plain (CGF) and standardized (CGFstandardized)). The first formula (CGF), namely number delivered pigs × average warm carcass weight−number stocked piglets × average piglet weight × piglet carcass yieldfeed consumption is an objective representation of the animals’ performance. The second formula (CGFstandardized) was developed for farm benchmarking, incorporating a seven-step standardization process that corrects for mortality and ‘standardizes’ for a fixed (yet fictive) live weight trajectory of 25 to 115 kg. This second formula allows to compare farms (or batches of fattening pigs) with different weight trajectories and different mortality rates. A webtool was designed to ease this standardization process (https://varkensloket.be/tools/CGF).

The aim of this study was to determine how different doses of phytase in diets with extruded soybean seeds and rapeseed meal affected pigs' growth performance, meat quality, bone mineralization, and fatty acid profiles. Sixty pigs were divided into three treatments by sex and body mass. Pigs were divided into starter (25 days), grower (36 days), and finisher (33 days) periods and fed with mash diets. No phytase was used in the control group diet, whereas in Phy1 and Phy2, 100 g and 400 g of phytase per ton of mixture were used, respectively. The feed conversion ratio and meat color were significantly correlated with phytase. Phytase supplementation had no effect on the growth of pigs, but total phosphorus was significantly increased in the bones and meat of the pigs. The enzyme additive reduced the C22:4 n-6 acid content in the meat, whereas other results were not significantly affected. The data suggest that the addition of phytase at a dosage of 100 g/ton to diets with extruded full-fat soya seeds and rapeseed meal can be valuable, as it reduces the FCR and increases the P content in the meat and bones.

Feed additives have shown benefits throughout the literature in improving grow−finish pigs’ growth performance and carcass characteristics. However, the results have not been well summarized. Therefore, this review summarizes the available research (402 articles) on 14 feed additive categories fed to grow−finish pigs. The categories were acidifiers, betaine, Cr, conjugated linoleic acids, Cu, direct-fed microbials, carbohydrases, proteases, phytases, multi-enzymes, essential oils, L-carnitine, yeasts, and Zn. Qualified articles were collected and selected based on inclusion and exclusion criteria from online databases. The percentage difference for each response variable between the treatment and control group was calculated and summarized. Most results were positive for each feed additive; however, the magnitude of improvement varied, and most were not statistically significant. For ADG, DFM, Cu, L-carnitine, and multi-enzymes showed relatively large positive effects (>2.1% improvement) across a reasonable number of articles. Acidifiers, betaine, CLA, multi-enzymes, DFM, L-carnitine, and yeasts showed relatively large positive effects (>2.5% improvement) on improving G:F. Moreover, except for betaine, Cr, CLA, and L-carnitine, most feed additives showed little and non-significant effects on BF thickness (<1.7% improvement). This review provides a descriptive analysis for commonly used feed additives in the hope of better understanding feed additives’ effects on grow−finish pigs.

Copper (Cu) and zinc (Zn) are essential nutrients for swine. However, as pigs have a low retention rate for these elements, more than 90% of ingested Cu and Zn are excreted, mainly in feces. After spreading manure, these trace elements could accumulate in the soil and potentially have negative environmental effects. Moreover, Cu and Zn are also limited natural resources that should be preserved. A better understanding of the behavior of these elements throughout the feed-animal-effluent-treatment continuum according to feed composition and manure management chain is thus required to propose alternative ways to reduce these environmental impacts. Three feeding strategies differing in their Cu and Zn contents were compared in 72 growing finishing pigs raised in individual pens [from 24.3 ± 3.3 up to 110 ± 9.0 kg body weight (BW), one-half males; and one-half females] a first diet with Cu and Zn at maximum EU regulation levels (20 and 100 ppm of Cu and Zn, respectively; REG), a second diet without any Cu and Zn supplementation (5 ppm Cu, 30 ppm Zn; WS) and a third diet with Cu and Zn at an intermediary level (10 and 50 ppm of Cu and Zn; INT). Cu and Zn were supplemented as Cu2O (CoRouge, Animine) and ZnO (HiZox, Animine), respectively. Eighteen additional finishing pigs (6 per diet) were raised in digestibility cages for excreta collection over 10 d, and their feces were submitted to two types of manure treatment: anaerobic digestion (AD) and composting with straw. Over the entire experimental period, the dietary Cu and Zn content did not influence growth performance of pigs. The Cu and Zn concentrations of the feces decreased linearly (P < 0.001) with dietary content from 191 and 834 (REG) up to 63 and 359 (INT) and 40 and 213 (WS) mg Cu and Zn/kg dry matter (DM) respectively. After both manure treatments, the degradation of the organic matter resulted in a significant increase (twice as much) in Cu and Zn concentration per kg of DM. Feeding strategy appears to be the main lever to reduce the Cu and Zn content of effluents. However, in the context of the diversification of manure management strategies, it is also important to take into account the effect of the effluent collection and treatment chain (e.g., spreading of raw slurry, phase separation, anaerobic digestion, composting), which has also an impact on the behavior of Cu and Zn and consequently on the risk for the environment. [ABSTRACT FROM AUTHOR]

Simple Summary: With the increasing consumption of pork, pig manure has become an important pollution problem in the pig livestock industry. It is worth noting that the amount of manure from fattening pigs far exceeds that from breeding herds. During the fattening period, the manure's composition may vary depending on the stage of the pig's growth, from nursery to finishing, and on the variety of feed. The treatment of manure by black soldier fly larvae (BSFL), Hermetia illucens (L.), (Diptera: Stratiomydiae) is an emerging waste management technology. Although studies have shown that BSFL could be used in swine manure treatment, there is little literature discussing the nutrient discrepancies of swine manure types and their influences on BSFL growth. Thus, this study analyzed the nutrient composition of different fattening pig manure types. Furthermore, the factors that influence larval growth were identified. This could be used in further waste treatment and formulation diets for BSFL. Livestock manure is an important component of agricultural organic waste, and in recent years, with the development of research on the bioconversion of manure, BSFs have been proven to be useful in the treatment of a variety of livestock wastes. In-depth research on the composition of manure and its effect on the development of BSFL is, however, very scarce. The purpose of this study was to identify the parameters that influenced the growth of BSFL that was fed fattening pig manure. The pH, moisture, and nutrients of the fattening manures (namely, nursery, growing, and finishing pig manures) were measured. To examine the influence of manure types on larval growth, 100 larvae were inoculated in 100 g of each type of manure in triplicate. According to the findings, larvae fed finishing pig manure had the lowest dry weight (30.2 ± 6.1 mg) compared to those fed growing (58.2 ± 7.3 mg) or nursery (65.5 ± 6.2 mg) pig manure. The correlation coefficients (r) between the nutrients in the manure and the weight of the larvae were calculated. Hemicellulose had the greatest |r| value (0.9569). Further research revealed that larvae raised on hemicellulase-pretreated finishing pig manure frequently weighed 21–30% (days 2–8) more than larvae raised on control manure. In conclusion, hemicellulose was a significant component that might hinder larval growth. The results of this study could be used to improve the system before it is put into use. [ABSTRACT FROM AUTHOR]

ABSTRACT: The objective of this study was to explore the substitution of ractopamine by coconut or safflower oil in finishing pig diets. The study included 24 crossbred barrows weighing 78.00 ± 8.76 kg distributed in a randomized block design with four treatments and six replicates composed of: basal ration (BR), BR + 10 ppm ractopamine, BR + four 1 g capsules of safflower oil, and BR + four 1 g capsules of coconut oil. Performance evaluation showed that safflower oil, ractopamine, and coconut oil supplementation had a significant effect (P < 0.05) on weight gain and feed conversion. Carcass-related variables were also affected by the treatments (P < 0.05), with fat thickness 3 (FT3) reduced by the use of safflower oil, ractopamine, and coconut oil. Rib eye area was positively affected (P < 0.05) by diet, with ractopamine, coconut oil, and safflower oil supplementation treatments showing higher values than control diet treatment. The diets also affected fatty acid profiles (P < 0.05), with decreased myristic acid content in animals supplemented with ractopamine and safflower oil and increased deposition of palmitoleic and oleic acids in animals supplemented with coconut oil and safflower oil, respectively. Results suggested that both safflower oil and coconut oil can be used as substitutes for ractopamine.

Farm animal welfare is an increasingly important issue, leading to the need for an efficient methodology to deliver accurate benchmarking. The “Real Welfare” project developed a methodology based on regular recording of a limited number of animal-based measures, so-called welfare outcomes, which allows faster and easier on-farm assessment of finishing pig welfare. The objective of this paper is to estimate, with sufficient robustness and confidence, the prevalence of different mandatory and optional welfare outcomes in the mainstream herd of the finishing farms in the UK based on the “Real Welfare” scheme data and to assess the changes in prevalence over time, inspection visits and seasons. The mean overall prevalence of the four mandatory welfare outcomes (pigs requiring hospitalization, lame pigs, pigs with severe body marks, and pigs with severe tail lesions) was very low (≤0.2%) and a significant decreasing trend was observed for the first three of these mandatory welfare outcomes since the inception of the scheme. This result might reflect either a reduction in factors giving rise to welfare problems in the mainstream herd or increasing awareness about management of compromised pigs. Additional data are required to clarify these possibilities, but both represent improved pig welfare.

In order to determine the standardized ileal digestible (SID) Ile:Lys requirement in finishing diets containing 20% DDGS, a 56-d study was conducted utilizing 2,268 pigs (DNA 600 x Topigs Norsvin 70, initially 82.3 kg). A total of six dietary treatments were fed, including a standard corn-soybean meal (SBM) diet and five diets containing 20% DDGS with SID Ile:Lys ratios of 55%, 60%, 65%, 70%, and 75%. Dietary treatments were assigned to pens, incompletely balancing for previous treatment, with each treatment being replicated 14 times. Pair-wise comparisons were used to evaluate the impact of dietary treatment on performance and carcass traits, whereas single degree of freedom orthogonal polynomials were used to evaluate dose response of SID Ile:Lys in 20% DDGS diets. Increasing the SID Ile:Lys ratio in diets containing 20% DDGS did not impact pig growth performance criteria in a quadratic or linear fashion (P ≥ 0.18). However, increasing the SID Ile:Lys ratio in 20% DDGS diets resulted in decreased back fat (BF; Quadratic, P = 0.01), increased loin depth (Quadratic, P = 0.03), and tended to increase percent lean (Quadratic, P = 0.07) with optimal carcass parameters occurring when 65% SID Ile:Lys was supplied in 20% DDGS diets. Pigs fed the corn-SBM diet had a similar final body weight (BW; P = 0.26) and cumulative average daily gain (ADG; P = 0.12) compared to pigs fed a 20% DDGS diet containing 70% SID Ile:Lys ratio and 3% greater cumulative average daily feed intake (ADFI) compared to pigs receiving diets with SID Ile:Lys ratios of 65% and 75% (P ≤ 0.01). In conclusion, these results suggest that when feeding 20% DDGS in late finishing swine diets, a SID Ile:Lys ratio of 70% should be utilized when attempting to achieve similar overall growth performance relative to a corn-SBM diet., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)