A Systematic Review and Meta-Analysis of the Effects of Various Sources and Amounts of Copper on Nursery Piglets.

This study evaluated the impact of different dietary levels and sources of copper on the growth performance of nursery piglets through a combination of systematic review and meta-analysis. The database for this study was created using articles selected from major electronic databases. Data analysis involved forest plots and analysis of variance using mixed-effects models. The database included 63 articles published between 1990 and 2021, comprising 21,113 piglets in 946 treatments. Positive effects of supranutritional levels of copper from both inorganic and organic sources on the growth performance of nursery piglets were detected using Forest plots and analysis of variance (p < 0.001). Using mixed models, it was observed that piglet performance is influenced by body weight (p < 0.001), age (p < 0.001), and copper intake (p < 0.001). Both organic and inorganic sources of copper at supranutritional levels (>81 mg Cu/kg of diet) improved the performance of nursery piglets, but levels higher than 201 mg Cu/kg of diet did not further improve growth performance compared to 80-200 mg Cu/kg of diet. The feed conversion was worse in piglets fed with inorganic Cu sources (p < 0.001). In conclusion, dietary Cu supplementation influenced the weight gain and feed conversion rate in weaned piglets, particularly during the first few weeks post-weaning. Levels of 81 and 200 mg Cu/kg improved growth performance, but no further benefits were obtained for higher levels.

Review: Fundamentals, limitations and pitfalls on the development and application of precision nutrition techniques for precision livestock farming.

Precision livestock farming (PLF) concerns the management of livestock using the principles and technologies of process engineering. Precision nutrition (PN) is part of the PLF approach and involves the use of feeding techniques that allow the proper amount of feed with the suitable composition to be supplied in a timely manner to individual animals or groups of animals. Automatic data collection, data processing, and control actions are required activities for PN applications. Despite the benefits that PN offers to producers, few systems have been successfully implemented so far. Besides the economical and logistical challenges, there are conceptual limitations and pitfalls that threaten the widespread adoption of PN. Developers have to avoid the temptation of looking for the application of available sensors and instead concentrate on identifying the most appropriate and relevant information needed for the optimal functioning of PN applications. Efficient PN applications are obtained by controlling the nutrient requirement variations occurring between animals and over time. The utilization of feedback control algorithms for the automatic determination of optimal nutrient supply is not recommended. Mathematical models are the preferred data processing method for PN, but these models have to be designed to operate in real time using up-to-date information. These models are therefore structurally different than traditional nutrition or growth models. Combining knowledge- and data-driven models using machine learning and deep learning algorithms will enhance our ability to use real-time farm data, thus opening up new opportunities for PN. To facilitate the implementation of PN in farms, different experts and stakeholders should be involved in the development of the fully integrated and automatic PLF system. Precision livestock farming and PN should not be seen as just being a question of technology, but a successful marriage between knowledge and technology.

Insulin sensitivity is associated with the observed variation of de novo lipid synthesis and body composition in finishing pigs.

Variations in body composition among pigs can be associated with insulin sensitivity given the insulin anabolic effect. The study objectives were to characterize this association and to compare de novo lipogenesis and the gene expression in the adipose tissue of pigs of the same genetic background. Thirty 30-95 kg of body weight (BW) pigs, catheterized in the jugular vein participated into an oral glucose tolerance test (OGTT; 1.75 g glucose/kg of BW) to calculate insulin-related indexes. The 8 fattest and the 8 leanest pigs were used to determine the relative mRNA abundance of studied genes. The rate of lipogenesis was assessed by incorporation of [U-13C]glucose into lipids. The QUICKI and Matsuda indexes negatively correlated with total body lipids (r = - 0.67 and r = - 0.59; P < 0.01) and de novo lipogenesis (r = - 0.58; P < 0.01). Fat pigs had a higher expression level of lipogenic enzymes (ACACA, ACLY; P < 0.05) than lean pigs. The reduced insulin sensitivity in fat pigs was associated with a higher expression level of glucose-6-phosphate dehydrogenase (G6PD) and a lower expression of peroxisome proliferator-activated receptor-gamma (PPAR-γ). In conclusion, pigs with increased body lipids have lower insulin sensitivity which is associated with increased de novo lipogenesis.

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production.

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

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.

ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences.

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams ("big data") and the exponential increase in computing power have allowed the appearance of "new" modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine "old" and "new" modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.

Modeling in the animal sciences has received a boost by large-scale adoption of sensor technology, increased computing power, and the further development of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL) models. Together with open-source programming languages, extensive modeling libraries, and heavy marketing, modeling reached a larger audience via AI. However, like most technological innovations, AI overpromised. By adopting an almost singular model-centric view to solving business needs, models failed to integrate with existing business processes. Models, especially AI, need data and both need humans. Together, they need room to learn and fail and by offering them as the end-solution to a problem, they are unable to act as sparring partners for all relevant stakeholders. In this article, we highlight fundamental model limitations exemplified via AI, and we offer solutions toward a more sustainable adoption of AI as a catalyst for modeling. This means sharing data and code and placing a more realistic view on models. Universities and industry both play a fundamental role in offering technological prowess and business experience to the future modeler. People, not technology, are the key to a more successful adoption of models.

Estimating Amino Acid Requirements in Real-Time for Precision-Fed Pigs: The Challenge of Variability among Individuals.

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.

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

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

Environmental Impacts of Pig and Poultry Production: Insights From a Systematic Review.

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.

Prandial Correlations and Structure of the Ingestive Behavior of Pigs in Precision Feeding Programs.

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.

ß-Mannanase Supplementation as an Eco-Friendly Feed Strategy to Reduce the Environmental Impacts of Pig and Poultry Feeding Programs.

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 ß-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 ß-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 ß-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, ß-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 ß-mannanase on the acidification impact was not consistent among feeds/species. Overall, ß-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 ß-mannanase supplementation is an eco-friendly feed strategy to reduce the environmental impacts of pig and poultry feeding programs.

Technical Note: In vivo estimation of lipogenesis using a bolus injection of [U-13C]glucose in pigs.

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.

Feeding behavior of growing and finishing pigs fed different dietary threonine levels in a group-phase feeding and individual precision feeding system.

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 × 5 factorial setup (feeding systems × 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.

Pigs receiving daily tailored diets using precision-feeding techniques have different threonine requirements than pigs fed in conventional phase-feeding systems.

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.

Criação intensiva de suínos em confinamento ou ao ar livre: estudo meta-analítico do desempenho zootécnico nas fases de crescimento e terminação e avaliação de carcaça e carne no Longissimus dorsi / Intensive pig production in confinement or outdoor systems: a meta-analytical study of the production performance in the growing and finishing phases and carcass and meat evaluation from the Longissimus dorsi

Uma meta-análise foi realizada para avaliar o desempenho e a qualidade de carcaça e carne de suínos em função do sistema de alojamento nas fases de crescimento/terminação. Foram selecionadas publicações indexadas com resultados de experimentos comparando variáveis de desempenho e de pós-abate de suínos alojados em sistemas intensivos convencionais ou ao ar livre. A base de dados contemplou 27 artigos publicados entre 1997 e 2009. Ao total, foram considerados 2.985 animais e 701 médias na análise de dados. A meta-análise foi realizada através de duas análises sequenciais: gráfica e de variância-covariância. O sistema de criação de suínos ao ar livre aumentou em 9% (P<0,05) o consumo de ração, reduziu em 2% (P<0,01) o ganho de peso diário e piorou em 3% (P<0,05) a conversão alimentar dos animais em relação ao sistema confinado. As características de carcaça (peso de carcaça quente, rendimento, espessura de toucinho e percentual de carne magra) e de carne no Longissimus dorsi (perda por gotejamento e cozimento, pH, temperatura inicial, força de cisalhamento, marmoreio dureza, maciez e suculência) não foram influenciadas (P>0,05) pelo sistema de alojamento dos suínos. Concluiu-se que o sistema de criação influencia no desempenho dos suínos, mas não interfere nas características de carcaça e carne estudadas.

A meta-analysis was carried out to study performance and characteristics of carcass and meat in pigs in function of housing system during the growing/finishing period. Indexed publications with results of experiments comparing performance and post-slaughter variables of pigs housed in outdoor or conventional intensive systems were selected. In total, 2.985 animals and 701 averages were considered in the data analysis. Meta-analysis followed two sequential analyses: graphic and variance. The outdoor productive system increased in 9% (P<0.05) feed intake, reduced in 2% (P<0.01) daily weight gain and worsened in 3% (P<0.05) feed conversion ratio of pigs in comparison to animals in the confined system. Characteristics of carcass (hot carcass weight, yield, backfat thickness and lean meat percentage) and Longissimus dorsi meat (drip and cooking loss, pH, the initial temperature, shear force, marbling, hardness, juiciness and tenderness) were not affected (P>0.05) by the housing system. It is concluded that the housing system influences the pig performance, but does not interfere in characteristics of carcass and meat.