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Methionine is an essential dietary amino acid that can be supplied in different chemical forms: DL-methionine, L-methionine, and OH-methionine. It has been hypothesized that L-methionine might be used with greater efficacy for average daily gain (ADG) in weaning pigs due to their decreased enzymatic capacity to convert the other methionine forms. This study aimed to compare DL-methionine, L-methionine, and OH-methionine efficiency of utilization for ADG in weaned pigs through meta-analysis.The systematic review resulted in 2,167 papers screened for the title and abstract, with 24 papers meeting the inclusion criteria. The ADG was regressed as a function of the standardized ileal digestible (SID) methionine intake (Meti). The study was used as a random intercept, and regressions were weighted by the square root of the number of observations. Non-linear models were used to determine the Meti requirements. The average Meti requirement for pigs of 5 to 25 kg body weight (BW) was 2.09 g/d. The slopes of linear mixed-effects regression models were compared for the different methionine sources. Average initial BW was 7.99 (DL-methionine), 8.82 (L-methionine), and 7.06 kg (OH-methionine). Meti was 1.5 g/d for all sources whereas SID cysteine intake was 1.90, 2.23, and 1.93 g/d, for DL-methionine, L-methionine, and OH-methionine, respectively . Increase in Meti below the estimated requirement linearly increased ADG (ADG = 107 + 138 × Meti; P < 0.01). No difference between the slopes of methionine sources was observed, rejecting the hypothesis that L-methionine is used with greater efficiency in weaned pigs. Pigs receiving DL-methionine, L-methionine, and OH-methionine presented an estimated ADG of 319, 317, and 311 (SE = 19, 25 and, 26, respectively) g/d, respectively. In conclusion, methionine efficiency of utilization for ADG in post-weaned pigs is not affected by methionine sources.

Made available in DSpace on 2020-12-12T01:18:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-04-01 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. Sherbrooke Research and Development Centre Agriculture and Agri-Food Canada Department of Animal Science School of Agricultural and Veterinary Studies São Paulo State University (Unesp) Department of Animal Science School of Agricultural and Veterinary Studies São Paulo State University (Unesp)

Made available in DSpace on 2020-12-12T02:16:18Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01 Precision feeding using real-time models to estimate daily tailored diets can potentially increase nutrient utilization efficiency. However, to improve the estimation of amino acid requirements for growing-finishing pigs, it is necessary to accurately estimate the real-time body protein (BP) mass. The aim of this study was to predict individual BP over time in order to obtain individual daily protein content of the gain (i.e., protein deposition/daily gain, PD/DG) to be integrated into a real-time model used for precision feeding. Two databases were used in this study: one for the development of the equations for the model and the other for model evaluation. For the equations, data from 79 barrows (25 to 144 kg BW) were used to estimate the parameters for a Gompertz function and a mixed linear-quadratic regression. Individual BP predictions obtained by dual X-ray absorptiometry were regressed as a function of BW. Individual pig BP estimates were obtained by linear-quadratic regression using the MIXED procedure of SAS, considering pig measurements repeated in time. Individual Gompertz curves were obtained using the NLMIXED procedure of SAS. Both procedures generate an average or a general model, which was assessed for accuracy with the database used to generate the equations. Coefficients of concordance and determination were both 0.99, and the RMSE was 0.21 kg for the linear-quadratic regression. The Gompertz curve coefficients of concordance and determination were both 0.99, and the RMSE was 0.36 kg. In sequence, the linear-quadratic regression and Gompertz curve were evaluated in an independent data set (488 observations; 21 to 126 kg BW). The linear-quadratic regression to predict BP mass was accurate (mean absolute percentage error (MAPE) = 2.5%; bias = 0.03); the Gompertz model performed worse (MAPE = 3.9%; bias = 0.04) than the linear-quadratic regression. When using the derivative of these equations to predict PD/DG, the linear-quadratic regression was more accurate (MAPE = 4.8%, bias = 0.17%) compared to the Gompertz (MAPE = 10.6%, bias = -0.99%) mainly due to the linear decrease in PD/DG in the observed data. Further analysis using individual pig data showed that the goodness of fit of PD/DG curve depends on the individual shape of the growth curve, with either the Gompertz or the linear-quadratic regression being more accurate for specific individuals. Therefore, both approaches are provided to allow end users to select the model that best fits their needs. The proposed update of the empirical component of the original model, using either linear-quadratic regression or the Gompertz function, is able to predict BP in real-time with good accuracy. Sherbrooke Research and Development Centre Agriculture and Agri-Food Canada, 2000 College Street Animal Science Department Faculdade de Ciências Agrárias e Veterinárias Universidade Estadual Paulista (Unesp), Via de Acesso Prof. Paulo Donato Castelane Animal Science Department Faculdade de Ciências Agrárias e Veterinárias Universidade Estadual Paulista (Unesp), Via de Acesso Prof. Paulo Donato Castelane

Precision feeding requires a mathematical model to estimate standardized ileal digestible (SID) lysine (Lys) requirements (SIDLysR) in real time. However, this type of model requires constant calibration updates. The objective of this study was to review the calibration of the model used to estimate the real-time Lys requirements of individual growing-finishing pigs. A digestibility trial (n = 10) was conducted to evaluate amino acids digestibility during the growing and finishing phases. Additionally, 120 pigs were used in two 28-day growth experiments conducted as completely randomized design with growing (25 ± 2.1 kg BW, n = 60; 10 pigs per treatment) or finishing barrows (68.1 ± 6 kg BW, n = 60; 10 pigs per treatment). In each experiment, the pigs were divided into six equal treatment groups and fed 60%, 70%, 80%, 90%, 100% or 110% of their estimated individual SIDLysR. The Lys requirement of each pig was estimated daily using a real-time model. Body composition was measured with dual-energy X-ray densitometry on day 1 and 28 of the experiments. Average daily feed intake increased quadratically (P < 0.05) during both growth phases. Maximum average daily gain (ADG) (0.98 kg) and maximum protein deposition (PD; 170 g/day) were observed in growing pigs fed 100% of the estimated SIDLysR (P < 0.001). During the growing period, PD in BW gain (17% to 19%) and N efficiency (52% to 65%) increased linearly (P < 0.01) with increasing inclusion rates of SID Lys. Finishing pigs had maximum ADG (1.2 kg/day) when they were fed 100% of the requirements. However, the amount of protein in BW gain (13% to 16%) and N efficiency (40% to 55%) increased linearly (P < 0.01) with increasing inclusion rates of SID Lys. In conclusion, the model proposed for precision feeding is correctly calibrated to predict SIDLysR that maximize PD and ADG of average pigs from 25 to 50 kg BW. Still, there is an opportunity to improve the estimation of SIDLysR and N retention in individual pigs by better representing the individual proportion of protein in BW gain and the factors controlling the efficiency of Lys utilization in individual pigs.

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.Interest and investment in precision technologies are growing within the livestock sector. Though these technologies offer many promises of increased efficiency and reduced inputs, there is a need to assess the opportunities and challenges associated with precision technology implementation in livestock production systems. In this review, precision livestock measurement and management tools are explained in the context of a logical and iterative five-step process that highlights the need for systems computer modeling to realize anticipated benefits from these technologies and avoid unintended consequences. This review includes key case studies to highlight past challenges and current opportunities within operations that house animals in a central area or building with sufficient infrastructure (confined livestock production systems) and other operation settings that utilize large grasslands that contain far less infrastructure (extensive livestock production systems). The key to precision livestock management success is training the next generation of animal scientists in computer modeling, precision technologies, computer programming, and data science while still being grounded in traditional animal science principles.

Simple Summary Precision feeding considers the difference in amino acid requirements among pigs and over time by providing daily tailored diets. This practice allows improving environmental and economic performances. Future systems should focus on maximizing nutrient use efficiency to move towards “green” pig production. This study explored a new method of providing amino acids to maximize their use, mainly focusing on understanding variations in the protein metabolism response among individuals to minimize variation in growth response. This study showed that even pigs fed the same amino acid level might use these nutrients differently, especially in protein deposition. Logically, pigs with the greatest protein deposition are the ones that use amino acids the most efficiently, thus exhibiting the lowest nitrogen excretion. This study helped identify some of the factors affecting the efficiency of nitrogen use in pigs. By improving the understanding of pigs’ nutrient response, pig production can become more resource-efficient. Abstract This study aimed to measure protein deposition (PD) in pigs fed with daily tailored diets where either dietary lysine (Lys) or threonine (Thr) were provided at independent levels (ignoring an ideal ratio). A total of 95 growing pigs (35 kg body weight (BW)) with electronic ear tags granting them access to automatic feeders were randomly assigned to treatments. The setup was an unbalanced 2 × 5 factorial arrangement with Lys and Thr provided at five levels (i.e., 60%, 80%, 100%, 120%, and 140% of the estimated individual requirements of Lys and Thr), resulting in 25 treatments for 21 days. The observed PD variation to Lys and Thr provisions was large, with Lys and Thr intake explaining only 11% of the variation. Cluster analysis discriminated pigs with low (167 g/d, n = 16), medium (191 g/d, n = 38), and high (213 g/d, n = 37) PD, but with a similar amino acid intake. Differences in PD were associated with differences in nutrient efficiency of utilization. Providing Lys and Thr in a factorial mode, ignoring an ideal ratio, did not decrease the variability in PD. Future research efforts should focus on identifying and investigating the sources of interindividual variability—a necessary step before final recommendations can be made for AA in precision-fed pigs.

Variations in body fat (BF) among pigs can be associated with differences in insulin sensitivity given the insulin anabolic effect in lipid synthesis. The study objectives were to characterize this association and compare the relative mRNA abundance of genes associated with insulin resistance and de novo lipogenesis in the adipose tissue of fat and lean pigs. Thirty 95 kg pigs, catheterized in the jugular vein, received an oral dose of 1.75 g glucose/kg of BW after 18 hours of fasting. Blood samples were collected at -20, -10, 5, 10, 15, 20, 25, 30, 45, 60, 90, 120, 150, 180, 210, 240, 300 and 360 minutes following glucose ingestion. Insulin sensitivity indexes were calculated and analyzed. The BF (%) was estimated by dual X-ray densitometry. The 8 fattest (22 % BF) and the 8 leanest pigs (17.2 % BF) were used to determine the relative mRNA abundance of studied genes using real-time qPCR analyses. Insulin sensitivity was determined using QUICKI and Matsuda indexes, respectively, and their association with body fat was studied with Spearman correlations. Differences in gene expression and insulin sensitivity between fat and lean pigs were studied with a one-way ANOVA. The QUICKI and Matsuda indexes negatively correlated with BF (r = -0.67 and r = -0.59; P < 0.001). Fat pigs had reduced insulin sensitivity and higher relative mRNA abundance of lipogenic enzymes (ACACA, ACLY, FASN; P < 0.05) than lean pigs. The higher expression level of glucose-6-phosphate dehydrogenase (G6PD) combined with the trend (P < 0.10) of lower expression of peroxisome proliferator-activated receptor-gamma (PPAR-γ) in fat pigs may explain part of their reduced insulin sensitivity. These results suggest that an increased BF is associated with reduced insulin sensitivity and greater expression of lipogenic enzymes in pig adipose tissue.

Pig and poultry production systems have reached high-performance levels over the last few decades. However, there is still room for improvement when it comes to their environmental sustainability. This issue is even more relevant due to the growing demand for food demand since this surplus food production needs to be met at an affordable cost with minimum impact on the environment. This study presents a systematic review of peer-reviewed manuscripts that investigated the environmental impacts associated with pig and poultry production. For this purpose, independent reviews were performed and two databases were constructed, one for each production system. Previous studies published in peer-reviewed journals were considered for the databases if the method of life cycle assessment (LCA) was applied to pig (pork meat) or poultry (broiler meat or table eggs) production to estimate at least the potential effects of climate change, measured as CO2-eq. Studies considering the cradle-to-farm gate were considered, as well as those evaluating processes up to the slaughterhouse or processor gate. The pig database comprised 55 studies, while 30 publications were selected for the poultry database. These studies confirmed feeding (which includes the crop cultivation phase, manufacturing processes, and transportation) as the main contributor to the environmental impact associated with pig and poultry production systems. Several studies evaluated feeding strategies, which were indicated as viable alternatives to mitigate the environmental footprint associated with both production chains. In this study, precision feeding techniques are highlighted given their applicability to modern pig and poultry farming. These novel feeding strategies are good examples of innovative strategies needed to break paradigms, improve resource-use efficiency, and effectively move the current productive scenario toward more sustainable livestock systems.

Little is still known about the environmental impacts of exogenous enzyme supplementation in pig and poultry feeding programs. Thus, this study aimed to assess the potential environmental impacts of producing feeds for pigs and broilers by simulating the effects of β-mannanase Hemicell™ HT supplementation through energy savings during diet formulation. Life-cycle assessment standards were applied to simulate a cradle-to-feed mill gate scope. The functional units used were the production of 1 kg of the enzyme and 1 kg of feed at a feed mill gate located in Concórdia, Santa Catarina, Brazil. Climate change, eutrophication, and acidification were the chosen environmental impact categories. Energy savings through β-mannanase supplementation were assessed by different metabolizable energy (ME) matrices (45 or 90 kcal of ME/kg of feed) during diet formulation in different grain production scenarios (Southern and/or Central-West origin). A total of 28 feeds were formulated based on the nutritional requirements and feeding programs described in the Brazilian Tables for Poultry and Swine. The least-cost formulation method was used based on real price averages practiced in a local industry over 12 months. The production of 1 kg of β-mannanase was associated with the emission of 1,800 g of CO2-eq, 4.53 g of PO4-eq, and 7.89 g of SO2-eq. For pig feeds, β-mannanase supplementation mitigated both climate change and eutrophication impacts up to 8.5 and 1.4% (45 kcal of ME/kg of feed) or up to 16.2 and 2.7% (90 kcal of ME/kg of feed) compared to control diets formulated without the enzyme. For broiler feeds, these impacts were mitigated up to 5.6 and 1.1% (45 kcal of ME/kg of feed), respectively. On the other hand, the effect of using β-mannanase on the acidification impact was not consistent among feeds/species. Overall, β-mannanase supplementation reduced the amount of soybean oil in feed formulas, which is associated with high environmental impacts. Consequently, the potential impacts of climate change and eutrophication associated with producing feeds for pigs and broilers were substantially mitigated. These results suggest that β-mannanase supplementation is an eco-friendly feed strategy to reduce the environmental impacts of pig and poultry feeding programs.

Simple Summary A better understanding of pig feeding behavior can provide critical information for improving feeding strategies, productivity, and animal well-being. However, the availability of information is very limited in this research field. Data collected using electronic feeders were used in this study to generate information on pig feeding behavior, such as the time, size, and duration of each visit and meal. Later, data were used to calculate prandial correlations that could be interpreted as hunger or satiety-regulating mechanisms. The results indicated that the hunger-regulating mechanisms were slightly stronger than the satiety-regulation mechanisms in the studied animals. A decrease in both regulating mechanisms was observed during animal growth. Feeding programs showed little influence on the feeding regulating mechanisms, with conventional feeding systems (a group of animals fed diets adjusted by phase) slightly differing from precision feeding programs (animals receiving diets adjusted daily to meet the individual nutritional requirements). The use of electronic feeders in research and field conditions is increasing, as well the variety of sensors available in the market. Thus, more studies focusing on pig feeding behavior must be developed in the coming years to further understand such a complex and interesting animal. Abstract The feeding behavior of growing-finishing pigs was analyzed to study prandial correlations and the probability of starting a new feeding event. The data were collected in real-time based on 157,632 visits by a group of 70 growing-finishing pigs (from 30.4 to 115.5 kg body weight, BW) to automatic feeders. The data were collected over 84 days, during which period the pigs were kept in conventional (by phase and by group) or precision (with daily and individual adjustments) feeding programs. A criterion to delimit each meal was then defined based on the probability of an animal starting a new feeding event within the next minute since the last visit. Prandial correlations were established between meal size and interval before meal (pre-prandial) or interval after meal (post-prandial) using Pearson correlation analysis. Post-prandial correlations (which can be interpreted as hunger-regulating mechanisms) were slightly stronger than pre-prandial correlations (which can be interpreted as satiety regulation mechanisms). Both correlations decreased as the animals’ age increased but were little influenced by the feeding programs. The information generated in this study allows a better understanding of pigs’ feeding behavior regulation mechanisms and could be used in the future to improve precision feeding programs.

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.

Dietary energy and nutrient losses associated with its conversion into animal products increase production costs and contribute to the environmental footprint of farms with the excessive application of nitrogen, phosphorus, or trace minerals from manure or carbon and methane losses. Formulating diets with the appropriate levels of minerals and amino acids can help improve dietary protein and energy efficiency and reduce nutrient losses. For example, an 8% reduction in dietary crude protein in pig feeds is estimated to increase nitrogen efficiency by more than 50%, while costing 11% less than a control diet without industrial amino acids. This reduction in protein intake also increases energy availability due to reduced energy losses associated with protein deamination. Urinary and intestinal fermentation energy losses can be 24% lower for pigs fed low-protein diets compared to control diets. Nonetheless, determining the optimal level of dietary amino acid remains a difficult challenge in conventional phase feeding systems. Therefore, group or individual precision feeding is another powerful tool to increase nutrient efficiency. By feeding individual growing-finishing pigs with diets tailored to their requirements, precision feeding can decrease nitrogen excretion by 30% and greenhouse gas emissions by 22% compared to conventional 3-phase feeding. The benefits of feeding pigs with low-protein diets and precision feeding techniques are additive and might result in a 61% protein efficiency of utilization. The formulation of very-low-protein diets and the implementation of precision feeding techniques rely on sound nutritional concepts and comprehensive biological models developed to precisely estimate individual real-time nutrient requirements and animal responses. Understanding the metabolic processes responsible for the observed variation between individual animals in their ability to utilize dietary nutrients is challenging, but there is a need to further improve nutrient efficiency and reduce the environmental impact of livestock production systems.

Describing the feeding behavior of pigs is difficult given the large day-to-day variations observed for a given animal. The objectives of this study were to create an index that integrates the information from several components of feeding behavior in order to account for intra-animal variation within a day and between days, and to evaluate the capability of this index to study the impact of relevant nutritional factors affecting feeding behavior. Feed intake information from 160 pigs during the last 28 d of the growing phase from three studies was used. For each pig, the sum of the absolute values of the deviation areas between the regression line of the relative cumulative feed intake and the observed cumulative feed intake was used to calculate the weekly index measuring the irregularity of feed intake (IIFI). Spearman's correlations of IIFI with the number of daily meals (r = -0.42; P < 0.001), meal duration (r = 0.38; P < 0.01), and feed intake per meal (r = 0.41; P < 0.01) indicate that pigs with high IIFI have fewer meals of longer duration and higher feed intake compared to pigs with low IIFI. This shows that IIFI captures information from several components of feeding behavior. Analysis of variance showed no effect of treatment on feeding behavior for datasets 1 and 2. However, the correlation between IIFI and dietary levels of CP (r = 0.34) indicates that diets high in CP were associated with pigs having more irregular meals. In dataset 3, pig feeding behavior was more regular in control diets than in pigs fed fibrous diets (IIFI; 164 vs. 197, respectively; P < 0.05). Additionally, IIFI was smaller in pigs fed canola by-product diets than in pigs fed wheat by-product diets, indicating that the source of fiber may also influence the feeding behavior of pigs. In most cases, IIFI was more effective at identifying differences in feeding behavior between dietary treatments than conventional feeding behavior variables. These results show the ability of IIFI to integrate information from several conventional components of the feeding behavior of pigs and its potential to successfully evaluate the effect of nutritional factors on feeding behavior.

Made available in DSpace on 2019-10-06T16:17:22Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-02-22 Background: There is large variation in amino acids requirements among pigs, hence feeding pigs individually with daily tailored diets or in groups with a single feed may require different levels of nutrients. Thus, the response to different threonine levels (70%, 85%, 100%, 115%, and 130% of the ideal threonine:lysine protein ratio of 0.65) was studied in growing pigs raised in a conventional group phase-feeding (GPF) system or fed individually using individual precision-feeding (IPF) techniques. In a 21-day trial, 110 barrows (25 ± 0.80 kg body weight) were housed in the same room and fed using electronic feeders. Five pigs per treatment were slaughtered at the end of the trial. Results: Threonine intake increased linearly for the IPF and GPF pigs (P < 0.05). Lysine intake was similar across the treatments. Average daily gain, gain:feed ratio, and protein deposition were affected linearly by threonine level (P < 0.05) in both feeding systems. Protein deposition in the GPF pigs was maximized at 150 g/d and a 0.65 threonine:lysine ratio, whereas protein deposition increased linearly in the IPF pigs. Plasma Met and serine levels were 11 and 7% higher, respectively, in the IPF pigs than in the GPF pigs (P < 0.05). Dietary threonine increased (P < 0.05) threonine concentration in the longissimus dorsi in a quadratic manner in the IPF pigs, whereas there was no effect in the GPF pigs. Longissimus dorsi collagen decreased as dietary threonine increased in the IPF and GPF pigs (P < 0.10). Carcass muscle crude protein was 2% higher in the GPF pigs than in the IPF pigs (P < 0.05). Conclusions: Individual pigs are able to modulate growth and the composition of growth according to threonine intake. The average amino acid ratio value that is currently used for GPF cannot be used for IPF. Sherbrooke Research and Development Centre Agriculture and Agri-Food Canada, Sherbrooke Département des Sciences Animales Université Laval School of Agricultural and Veterinary Studies (FCAV) Department of Animal Science University of São Paulo State (UNESP) Ajinomoto Animal Nutrition Europe School of Agricultural and Veterinary Studies (FCAV) Department of Animal Science University of São Paulo State (UNESP)

The use of radioactive isotopes to measure de novo lipogenesis in pigs has been well established. Different from radioactive isotopes, stable isotopes present little or no risk to human and animal subjects. Therefore, the objective of this study was to adapt the method of bolus injection of radioactive glucose (14C) to use 13C-labeled glucose to estimate de novo lipogenesis in finishing pigs. Five vein-catheterized gilts received 3.0 kg/d of a commercial diet for 2 wk. On the last day, the pigs received a bolus injection of [U-13C]glucose (12 mg/kg body weight). A serial of blood samples was taken for 4 h to determine the glucose rate of disappearance (Rd) from plasma glucose isotopic enrichment (IE). The 13C IE of lipids was determined from adipose tissue biopsies collected at 1, 2, and 3 h after the bolus injection and from adipose tissue collected after pig euthanasia 4 h after the bolus. Lipogenesis was estimated from the incorporation of 13C from glucose into adipose tissue lipids. Glucose Rd, estimated using a double-exponential function, averaged 5.4 ± 1.4 mmol/min. The IE of lipids increased linearly during the 4 h following the bolus injection (P < 0.05). The rate of incorporation of glucose into lipids, estimating lipogenesis, averaged 9.0 µg glucose/(min × g of lipids) 4 h after the bolus injection. In conclusion, the in vivo method using a bolus injection of [U-13C]glucose allows a successful estimation of de novo lipogenesis in finishing pigs.

Background: Large variability in body composition is observed in pigs fed and raised in similar conditions. Part of this variation might be explained by the feeding behavior of pigs, given its effect on metabolic hormones associated with energy regulation and protein metabolism. The objective of this study was to elucidate this relationship by examining body composition with a new index describing feeding behavior in finishing pigs. Feeding behavior and body composition information on 138 pigs during the last 28 d of the growing phase from three trials were used. For each pig, the sum of the areas between the observed relative cumulative feed intake and the regression line for the evolution of relative feed intake over time was used to calculate the new feeding behavior index. This index quantifies the irregularity of feed intake (IIFI) by integrating different components of feeding behavior into a single measurement. Body lipids and protein composition were estimated by dual X-ray densitometry at the beginning (day 1) and end of the finishing phase (day 28). Results: Weak to moderate correlations (r = ±0.31 to ±0.44; P < 0.05) of IIFI with body composition and performance variables were found only in datasets 1 and 2. However, in dataset 2, IIFI was also correlated with daily feed intake (r = - 0.54; P < 0.001). Irregular feeding behavior is associated to increased proportion of protein (PdDG) and decreased proportion of lipids (LdDG) in body gain. However, IIFI only accounted for 8% to 14% and 10% to 12% of the total variation of PdDG and LdDG in datasets 2 and 1, respectively. Additionally, factor analysis showed that the number of meals and IIFI had high loadings in a factor separate from factors associated with body composition or performance, suggesting that body composition and performance are independent of feeding behavior. Conclusion: Factors other than feeding behavior might be involved in modulating body composition of finishing pigs fed ad libitum.

Precision livestock farming (PLF) involves the use of sensors that captures large amounts of real-time information at the building, herd or animal level, which are later processed to control the system. Data processing can be accomplished using mathematical models (MM), artificial intelligence algorithms (AI) or a combination of these and other methods. The choice of the method must be made according to the volume of data to be processed, its nature and the relationship between the available information and the desired control of the system. Several components of PLF such as precision nutrition, early disease detection, animal welfare among others may require sophisticated data processing methods. MM is today the preferred method to estimate nutrient requirements in the precision nutrition component of PLF. Conventional MM estimate average population responses using historical population information. Important limitations of these models are the assumption that all the individuals of the population have the same response to a given nutrient provision and that they have not been developed for real-time estimations using up-to-date available information. Therefore, MM have to be developed specifically for PLF and operate in real-time at individual or small group level, considering the between and within-animal variation. Growth patterns, nutrient utilization and behavior vary among animals and herds. There are opportunities to combine data-driven AI with knowledge-driven MM to control more complex PLF components. AI thrive in large complex datasets, where establishing connections can be otherwise difficult due to data complexity, volume and where flexibility is needed to process real-time data from individuals. In contrast, knowledge-driven MM can simplify complex biological systems based on well-established concepts and information. In both cases, PLF models must be flexible enough to consider changes over time for the same animal or herd, and among animals and herds, acknowledging the method limitation while using its strength.

Made available in DSpace on 2021-06-25T23:53:20Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-10-01 Agriculture and Agri-Food Canada Ajinomoto Animal Nutrition Europe Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Swine Innovation Porc Aliments Breton Sherbrooke Research and Development Centre Feeding behavior is an important aspect of pig husbandry as it can affect protein deposition (PD) in pigs. A decrease in plasma threonine (Thr) levels may influence feed intake (FI) due to amino acid imbalance. We set out to study whether different Thr inclusion rates of 70%, 85%, 100%, 115%, and 130% of the ideal Thr:lysine (Lys) ratio of 0.65 in two different feeding programs (individual precision feeding and group-phase feeding could affect pig feeding behavior and consequently PD. Two 21-d trials were performed in a 2 x 5 factorial setup (feeding systems x Thr levels) with 110 pigs in the growing phase [25.0 +/- 0.8 kg of body weight (BW)] and 110 pigs in the finishing phase (110.0 +/- 7.0 kg BW), which correspond to 11 pigs per treatment in each trial. Pigs were housed in the same room and fed using computerized feeding stations. The total lean content was estimated by dual x-ray absorptiometry at the beginning (day 1) and the end (day 21) of the trial. Multivariate exploratory factor analysis was performed to identify related variables. Confirmatory analysis was performed by orthogonal contrasts and Pearson correlation analysis. Graphical analysis showed no difference in feeding patterns between feeding systems during the growing or finishing phase. Pigs exhibited a predominant diurnal feeding, with most meals (73% on average) consumed between 0600 and 1800 h. Exploratory factor analysis indicated that feeding behavior was not related to growth performance or PD in growing or finishing pigs. Changes in feeding behavior were observed during the growing phase, where increasing dietary Thr resulted in a linear increase in the FI rate (P < 0.05). During the finishing phase, the duration of the meal and FI rate increased linearly as dietary Thr increased in the diet (P < 0.05). These changes in feeding behavior are, however, correlated to BW. In conclusion, the exploratory factor analysis indicated that feeding behavior had no correlation with growth performance or protein and lipid deposition in growing or finishing pigs. Dietary Thr levels and feeding systems had no direct effect on FI. Sao Paulo State Univ, Sch Agr & Vet Studies, Dept Anim Sci, BR-14883108 Sao Paulo, SP, Brazil Univ Laval, Dept Anim Sci, Quebec City, PQ G1V 0A6, Canada Agr & Agri Food Canada, Sherbrooke Res & Dev Ctr, Sherbrooke, PQ J1M 0C8, Canada Univ Fed Rio Grande do Sul, Sch Agron, Dept Anim Sci, BR-91540000 Porto Alegre, RS, Brazil Sao Paulo State Univ, Sch Agr & Vet Studies, Dept Anim Sci, BR-14883108 Sao Paulo, SP, Brazil FAPESP: 2014/25075-6 FAPESP: 233118/2014-4 FAPESP: 18/15559-7 CNPq: 132530/2013-9

Made available in DSpace on 2020-12-12T02:04:51Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-07-01 Swine Innovation Porc Agriculture and Agri-Food Canada Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Optimal amino acid ratios for pigs have been established for conventional phase feeding systems, but these ratios may differ for precision feeding systems. Our objective was to assess the impact of different dietary Thr inclusion levels (70, 85, 100, 115, or 130% of the estimated ideal 0.65 Thr:Lys ratio) on growth performance, plasma biochemical parameters, and splanchnic and muscle AA composition in pigs raised in a conventional group phase feeding (GPF) system and in an individual precision feeding (IPF) system. A total of 110 finishing pigs (110 ± 7.02 kg initial body weight; 11 pigs per treatment) were housed in the same room for 21 d. Pigs were randomly assigned to a 2 × 5 factorial arrangement and fed using automatic feeders. Individual pigs were considered the experimental units. Five pigs per treatment were slaughtered at the end of the trial. The gain:feed ratio was not affected by feeding system, but there was a quadratic effect of Thr inclusion rate (P < 0.05). Lysine intake was 16% greater and Thr intake was 15% greater for the GPF pigs than for the IPF pigs (P < 0.05). Protein deposition (g/d) was not affected by any treatment. Protein deposition in daily gain was affected by the interaction between Thr inclusion rate and feeding system, with a quadratic (P < 0.05) effect for the GPF pigs and a cubic (P < 0.10) effect for the IPF pigs. The pigs in IPF consumed 14% less (P < 0.05) crude protein and excreted 17% less (P < 0.05) N than the GPF pigs did. The pigs in IPF retained 9% more (P < 0.05) N than the GPF pigs. Plasma urea values were 9% higher (P < 0.05) in GPF than those in IPF. The gamma-glutamyl transferase enzyme decreased (P < 0.05) in a quadratic manner within IPF but increased (P < 0.05) in a quadratic manner within GPF. In the IPF systems, the average concentration of albumin and C-reactive protein tended (P < 0.10) to be 2 and 22% lower, respectively, than in GPF. Changes in amino acid concentrations occurred mainly in the liver of the IPF pigs. The pigs in IPF had higher (P < 0.05) concentrations of collagen in the longissimus dorsi than the pigs in GPF. Lastly, IPF allowed standardized ileal digestible Lys and Thr intakes to decrease by 16 and 15%, respectively, without differences in average daily gain and gain:feed ratio in comparison with GPF. Sherbrooke Research and Development Centre Agriculture and Agri-Food Canada Department of Animal Science School of Agricultural and Veterinary Sciences (FCAV) São Paulo State University (UNESP) Département des sciences animales Université Laval Ajinomoto Animal Nutrition Europe Department of Animal Science School of Agricultural and Veterinary Sciences (FCAV) São Paulo State University (UNESP) FAPESP: 2012/03781-0 FAPESP: 2014/25075-6 FAPESP: 233118/2014-4

Made available in DSpace on 2020-12-12T01:28:23Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01 Pigs exposed to stressors might change their daily typical feeding intake pattern. The objective of this study was to develop a method for the early identification of deviations from an individual pig's typical feeding patterns. In addition, a general approach was proposed to model feed intake and real-time individual nutrient requirements for pigs with atypical feeding patterns. First, a dynamic linear model (DLM) was proposed to model the typical daily feed intake (DFI) and daily gain (DG) patterns of pigs. Individual DFI and DG dynamics are described by a univariate DLM in conjunction with Kalman filtering. A standardized tabular cumulative sum (CUMSUM) control chart was applied to the forecast errors generated by DLM to activate an alarm when a pig showed deviations from its typical feeding patterns. The relative feed intake (RFI) during a challenge period was calculated. For that, the forecasted individual pig DFI is expressed as its highest DFI relative to the intake during pre-challenge period. Finally, the DLM and RFI approaches were integrated into the actual precision-feeding model (original model) to estimate real-time individual nutrient requirements for pigs with atypical feeding patterns. This general approach was evaluated with data from two studies (130 pigs, at 35.25 ± 3.9 kg of initial BW) that investigated during 84 days the effect of precision-feeding systems for growing-finishing pigs. The proposed general approach to estimating real-time individual nutrient requirements (updated model) was evaluated by comparing its estimates with those generated by the original model. For 11 individuals out of 130, the DLM did not fit the observed data well in a specific period, resulting in an increase in the sum of standardized forecast errors and in the number of time steps that the model needed to adapt to the new patterns. This poor fit can be identified by the increase in the CUMSUM with a consequent alarm generated. The results of this study show that the updated model made it possible to reduce intra-individual variation for the estimated lysine requirements in comparison with the original model, especially for individuals with atypical feeding patterns. In conclusion, the DLM in conjunction with CUMSUM could be used as a tool for the online monitoring of DFI for growing-finishing pigs. Moreover, the proposed general approach allows the estimation of real-time amino acid requirements and accounts for the reduced feed intake and growth potential of pigs with atypical feeding patterns. Department of Animal Science School of Agricultural and Veterinary Sciences São Paulo State University (UNESP) Department of Veterinary and Animal Sciences Faculty of Health and Medical Sciences University of Copenhagen, Grønnegårdsvej 2 Faculty of Agronomy Federal University of Rio Grande Do sul Sherbrooke Research and Development Centre Agriculture and Agri-Food Canada, 2000 College Street Department of Animal Nutrition and Pastures Federal Rural University of Rio de Janeiro Department of Animal Science School of Agricultural and Veterinary Sciences São Paulo State University (UNESP)

Feeding behavior is an area of study that links the nutritional and behavioral sciences. However, describing the feeding behavior of pigs is difficult given the diversity of variables available and the large day-to-day intra-animal variation. The objectives of this study were to create an index integrating information from several components of feeding behavior that accounts for intra-animal variation, and to evaluate the capability of this index to study the impact of different feeding strategies in pigs’ feeding behavior. Feed intake information from 160 pigs during the last 28 days of the finishing phase from 3 datasets was used. For each pig, the sum of the absolute values of the deviation areas between the regression line of the relative cumulative feed intake and the observed cumulative feed intake was used to calculate the weekly index measuring the irregularity of feed intake (IIFI). Spearman’s correlations of IIFI with the number of daily meals (r = -0.42; P < 0.001), meal duration (r = 0.38; P < 0.01), and feed intake per meal (r = 0.41; P < 0.01) indicate that pigs with high IIFI have fewer meals of longer duration and higher feed intake compared to pigs with low IIFI. Analysis of variance in datasets 1 and 2 showed that moving from conventional to feeding precision systems does not interfere with the feeding behavior of finishing pigs (P > 0.05). In dataset 3, pig feeding behavior was more regular in control diets than for pigs fed fibrous diets (IIFI; 164 vs. 197, respectively; P < 0.05). Additionally, IIFI was smaller in pigs fed canola than in pigs fed wheat by-product diets. These results indicate that IIFI integrates information from several components of the feeding behavior of pigs and demonstrates its potential to evaluate the effect of feeding strategies on feeding behavior.

Large variability in body composition is observed in pigs fed and raised in similar conditions. Part of this variation might be explained by the feeding behaviour of pigs, given its effect on metabolic hormones associated with energy regulation and protein metabolism. The objective of this study was to elucidate this relationship by examining body composition with a new index describing feeding behaviour in finishing pigs. Feeding behaviour and body composition information on 138 pigs during the last 28 d of the finishing phase from three trials were used. For each pig, the sum of the areas between the observed relative cumulative feed intake and the regression line for the evolution of relative feed intake over time was used to calculate the new feeding behaviour index. This index quantifies the irregularity of feed intake (IIFI) by integrating different components of feeding behaviour into a single measurement. Body lipids and protein composition were estimated by dual X-ray densitometry at the beginning (day 1) and end of the finishing phase (day 28). The relationship of IIFI with performance and body composition variables was examined using correlation tests and factor analysis within each dataset. In datasets where IIFI was correlated with body composition, linear models were used to quantify the contribution of IIFI to the total variation of body composition. Weak to moderate correlations (r = ±0.31 to ±0.44; P

Growing pigs’ body composition variation can be associated with differences in insulin sensitivity given the insulin anabolic effect on protein and lipid synthesis. The objective of this study was to elucidate this association by relating the individual insulin response to the oral glucose tolerance test (OGTT) with the body composition of growing pigs. Thirty 95 kg jugular vein catheterized pigs received an oral dose of 1.75 g of glucose/kg of BW after 18 hours of fasting. Blood samples were collected at -20, -10, 5, 10, 15, 20, 25, 30, 45, 60, 90, 120, 150, 180, 210, 240, 300 and 360 min following glucose ingestion. Insulin sensitivity indexes were calculated and analyzed. Body lipids (LB, %) and protein (PB, %) composition were estimated by dual X-ray densitometry. Association between body composition and insulin sensitivity were studied by using partial least squares and correlations. Average LB and PB were 19.7% (CV = 7.6 %) and 16.2% (CV = 2.2%), respectively. Basal insulin blood concentration and area-under-the-curve (AUC) CV (51.9 % and 26.9 %, respectively) were larger than those for basal glucose and AUC (5.52 and 5.48 %, respectively). Additionally, insulin sensitivity (%S), steady-state beta cell function (%B), and insulin resistance (HOMA-IR) estimated with the Homeostasis Model Assessment (HOMA 2) and whole-body insulin sensitivity index (ISI) were highly variable between pigs which CV ranged from 30.1 % to 54.5 %. These results can indicate an early stage of insulin resistance in an important part of the studied pig population. LB and PB were affected by insulin sensitivity indexes (P < 0.05) which accounted, respectively, for 48% and 44% of the observed variation. In conclusion, lower insulin sensitivity was associated with higher body fat in growing pigs raised under similar conditions.

Little is known about the toxicity of concomitantly occurring mycotoxins in pig diets. This study was conducted to evaluate, through meta-analysis, the individual and the combined effects of mycotoxins on pig performance. The meta-analysis followed three sequential analyses (graphical, correlation, and variance-covariance) based on a database composed of 85 published papers, 1,012 treatments and 13,196 animals. Contamination of diets with individual mycotoxins reduced (p < 0.05) feed intake by 14 % and weight gain by 17 %, while combined mycotoxins reduced the same responses by 42 % and 45 %, respectively, in comparison with the non-challenged group. The correlation (p < 0.05) between reduction in weight gain (ΔG) and reduction in feed intake (ΔFI) was 0.67 in individual challenges and 0.93 in combined challenges. The estimated ΔG was –6 % in individual challenges and –7 % in combined challenges when ΔFI was zero, suggesting an increase in the maintenance requirements of challenged animals. Most of ΔG (58 % in individual challenges and 84 % in combined challenges) was attributed to the changes in feed efficiency. The association of mycotoxins enhances individual toxic effects and the ΔFI is important in explaining the deleterious effects on the growth of challenged pigs.

This study was developed to model the pellet quality to identify influential factors in an industrial pelleting process of feeds for broilers and pigs. Two independent databases were used to calibrate and to validate the models. Each column of the spreadsheet represented a descriptive variable of the manufacturing process (yield, amperage, pressure in the conditioner, and temperatures of the environment, the conditioner, and the cooler), feed characteristics (inclusion of the ingredients in the feed formula and bromatological composition of the main ingredients), and pellet quality (percentage of fines and pellet durability index - PDI). Each row of the spreadsheet represented one observation, or the equivalent of a lot of feed produced. The data were submitted to graphical analysis, descriptive statistics, and regression analysis by step-wise procedure. Three models were developed for each variable (yield, percentage of fines, and PDI): Model I, characteristics of the manufacturing process and inclusion of the ingredients in the formula; Model II, characteristics of the manufacturing process and weighted bromatological composition; and Model III, characteristics of the manufacturing process, inclusion of the ingredients in the formula, and weighted bromatological composition. The accuracy of the models (validation) was evaluated by the mean square of the predicted error (MSPE). The models obtained in this study differed from each other in the number of predictors selected in the statistical procedure. However, the main factors have been found repeatedly in the models. The amperage represented at least 22.84 % of the total variance, the cooling temperature responded by at least 2.93 %, and the inclusion of soybean oil in the feed formula accounted for at least 4.21 %. The models that considered characteristics of the manufacturing process and the inclusion of the ingredients in the formula (Models I) were the most accurate (lower MSPE) in relation to the Models II and III of each response. The pelletizing process requires constant monitoring in the feed factories and the models generated in this study are useful in the quality assurance sectors, providing a better definition of the monitoring parameters.

Made available in DSpace on 2018-11-26T17:48:51Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-07-01. Added 1 bitstream(s) on 2021-07-15T15:01:48Z : No. of bitstreams: 1 S0103-90162018000400296.pdf: 220849 bytes, checksum: 87c4a999afd9998deaef89b51758822d (MD5) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Several empirical models were proposed to predict feed intake (FI) of growing-finishing pigs reared under high environmental temperatures. However, these models have not been evaluated under conditions different from those in which they were developed. Twelve empirical models were evaluated using a database built after systematic literature review (observed data: 28 studies in which the FI was evaluated in pigs under high environmental temperatures). Model accuracy was assessed using the mean squared of prediction error (MSPE). Analyses were performed considering two scenarios: (1) general population, where all observed data were used in the simulation; (2) reference population, where data were filtered in order to simulate only scenarios with environment (temperature range) and animals (body weight and sex) similar to that used in the model development. Six models estimated FI values similar (p > 0.05) to those observed in the general population, while four models produced estimates similar to the observed values in the reference populations. Most models were more accurate when they were simulated using the reference population than when the simulation considered the general database. Moving the simulation from the general database to the reference population reduced up to 98 % of the MSPE, depending on the equation. Empirical models allow to accurately predict FI of growing-finishing pigs exposed to high environmental temperatures, especially in scenarios similar to the ones used for model development. Thus, population characteristics (body weight and sex) and environment (temperature range) must be considered in the model assessment. Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, Via Acesso Prof Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, Brazil Univ Fed Rio Grande do Sul, Fac Agron, Dept Zootecnia, Av Bento Goncalves 7712, BR-91540000 Porto Alegre, RS, Brazil Agr & Agri Food Canada, Sherbrooke Res & Dev Ctr, 2000 Coll St, Sherbrooke, PQ J1M 1Z3, Canada Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, Via Acesso Prof Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, Brazil FAPESP: 2012/03781-0

The objective of this study was to review the calibration of the model estimating real-time Lys requirements individual in growing-finishing pigs. Two 28 d growth experiments were performed with 110 pigs distributed in a complete randomized design with growing (25 kg BW ± 2.1, n = 60; 10 pigs per treatment) and finishing (68 kg BW ± 1.8, n = 60; 10 pigs per treatment) barrows. Pigs were fed with 60, 70, 80, 90, 100, or 110% of the pig’s estimated individual standardized ileal digestible (SID) Lys (SIDLys) requirements. Body composition was measured by dual-energy X-ray densitometry on days 1 and 28 of the trial. Pigs were housed in the same pen but fed individually using computerized feeding stations. The Mixed and NLIN procedures of SAS were used to analyze the data and estimate optimal SIDLys requirements. In the growing and finishing trials protein in gain (17 to 19%; 13 to 16%) and N efficiency (52 to 65%; 40 to 55%) increased linearly (P < 0.01) with the increasing levels of SIDLys. Maximum ADG (0.98 kg/d) and protein deposition (PD; 170 g/d) were observed in growing pigs fed at 100% of the estimated SIDLys requirements (P < 0.001). Finishing pigs had maximal ADG (1.2 kg/d) when fed at 100% of the estimated SIDLys requirements, but PD increased linearly (P < 0.05) with SIDLys levels without reaching a plateau at the higher levels of Lys intake. The proposed model correctly estimates the level of SIDLys that maximizes PD and ADG in growing (25 to 50 kg of BW), and ADG in finishing pigs (65 to 100 kg BW); still, there is an opportunity for increasing PD in older pigs fed using precision feeding techniques.

Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-27T11:30:05Z No. of bitstreams: 0 Made available in DSpace on 2014-05-27T11:30:05Z (GMT). No. of bitstreams: 0 Previous issue date: 2013-08-01 The meta-analysis was used to evaluate the performance of piglets in post-weaning period, without imposition of sanitary challenge and fed diets containing blood plasma, obtained by spray-dried process (SDBP). Piglets are faced with normal challenges in post-weaning period such as environmental stress and the substitution of the liquid diet to a solid one. References regarding sanitary challenges were disregarded in this study. Only data regarding normal and expected challenges were considered. Data were obtained from indexed journals with information extracted from the material, methods and results sections of pre-selected scientific articles. First, the database was analyzed graphically to observe the distribution of data and presence of outliers. Afterwards correlation analysis and variance-covariance analyses were carried out. The database contained a total of 23 articles. The average initial weight of the piglets was 8.02. kg (4.00-9.28. kg) and the average initial age was 27 days (14-32 days). The average duration of feeding diets containing spray-dried blood plasma (SDBP) was 9 days (6-28 days). SDBP increased the feed conversion by 20.2% (P0.05) during the entire period, but average daily gain was higher (P