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Threonine (Thr) is generally the second limiting amino acid (AA) in growing pig diets, and it has a vital role in supporting intestinal functions and the immune system. Additionally, providing pigs with the correct AA requirements decreases nitrogen excretion and improves growth performance. This study aimed to determine standardized ileal digestibility (SID) Thr requirements for individual post-weaned pigs. Additionally, the Thr supplementation effect on average daily gain (ADG) of weaning and growing [7.3 - 51.5 kg of body weight (BW)] pigs under a sanitary challenge was evaluated. Eleven papers published from 1993 to 2015 composed the dataset used to determine the SID Thr requirement in the nursery phase, and five studies conducted from 2014 to 2020 composed the enteric sanitary challenge dataset. The SID Thr was obtained from publications when available or estimated based on the ingredient composition using EvaPig software. The average individual pig SID Thr requirements were estimated by bootstrapping 1,000 repetitions of a linear-plateau mixed model where all parameters were considered random as a function of the trial code. A general positive-definite matrix was used to represent the between-factors covariance using the nlme package of the R software. The effect of supplementing SID Thr above the requirement to attenuate ADG losses during a challenge was evaluated by an effect size analysis using a mixed model of the R Metafor package. Due to the low number of studies assessing pigs' response to enteric challenges, a P < 0.25 was considered significant. In the post-weaning (14 kg average BW), the estimated ADG was 457 g/d (411 to 493 g/d) as a function of SID Thr individual requirement, which was 4.71 g/d for the average pig, with the least demanding pig requiring 4.4 g/d and the most demanding one requiring 5.1 g/d. For challenged pigs, the across-study heterogeneity was moderate (Higgs index = 36%). Increasing Thr intake by 18% above the estimated requirement attenuated (P = 0.15) ADG losses by 0.7 standard deviations (60 g) compared with pigs fed diets meeting Thr requirements. For pigs in individual precision feeding, the SID Thr requirement is estimated to be 1.03 g per 100 g of ADG for post-weaned pigs. The increase of SID Thr intake by 18% attenuated the ADG losses by 19% compared with non-supplemented pigs with enteric challenges. [ABSTRACT FROM AUTHOR]

Ionophores are a separate class of nonmedically important antibiotics used in animal production to increase growth rate. Narasin (Skycis; Elanco Animal Health, Greenfield, IN) is an ionophore labeled for increased rate of body weight gain and improved feed efficiency in growing-finishing swine. The mode of action of this ionophore is to increase energy availability by altering volatile fatty acid production in the hindgut in favor of propionate, which is the most efficient product of fermentation. Therefore, a metaregression analysis was conducted to evaluate the effects of narasin inclusion in growing-finishing pig diets to predict average daily gain and feed efficiency. A database was developed that contained 21 papers from 2012 to 2021 representing 308 observations including both individual period data as well as overall trial data. For statistical analysis two models were created. The individual period model considered only data with single continuous narasin feeding periods while the overall trial model considered complete data sets representing periods with and without narasin feeding. Regression model equations were developed with the GLIMMIX procedure of SAS (Version 9.4, SAS institute, Cary, NC). Predictor variables were assessed with a stepwise manual forward selection for model inclusion. Predictor variables were required to provide an improvement of at least 2 Bayesian information criterion units to be included in the final model. For individual period data for G:F, the model selected included concentration of narasin in the feed, ADG and ADFI of the control group not provided narasin, and average BW of the control group categorized into greater than 105 kg or less than 105 kg (Table 1). There were 2 competing models to predict improvement in overall G:F. For model 1, significant predictors included concentration of narasin in feed and ADG of the control group. Model 2 for overall G:F included concentration of narasin in feed, ADG and ADFI of the control group. For the individual period model that predicted ADG, the predictors were concentration of narasin in the feed, ADFI, G:F of the control group and BW category for feeding period. The best fitting ADG model using the overall trial data included concentration of narasin in feed, ADFI and G:F of the control group, and feeding duration categorized as longer than 65 days or shorter than 65 days. Together, these models can be used to predict the response to narasin supplementation in finishing pigs. Based on the results, the overall response to narasin inclusion rate for ADG and G:F is quadratic in nature. Additionally, as ADG and G:F increase, the response to narasin supplementation decreases. In summary, using average values from the database for predictor variables, this meta-analysis demonstrates narasin would be expected to improve overall G:F from 0.74 to 1.13% and ADG from 1 to 3%. [ABSTRACT FROM AUTHOR]

Several papers report greater bioavailability of dietary trace mineral fed as organic in pigs compared with inorganic sources. However, only a few studies have tried to assess the variability of the trace mineral bioavailability among different organic sources. Growing barrows (n = 54; initial BW = 28.9 ± 1.3 kg) were used to determine the effects of trace mineral source on growth performance, bones mineralization and mineral status over the entire fattening period (d0-d73). Pigs were blocked by BW and randomly assigned to supplementation of trace minerals as inorganic (ITM) or one of 2 organic sources (OTM): glycine complex (GLY) or amino acids complex (AA). During the two feeding phases (growing: d0-d35; finishing: d35-d73), the experimental diets were supplemented with 5, 35 and 5 mg/kg of Cu, Zn and Mn, respectively. Pigs were fed restricted all through the experimental period. Feed intake was therefore comparable all through the experimental period for all pigs. Growth performance did not differ among the three experimental groups during the growing phase. However, Feed:Gain ratio measured during the finishing period tended to be affected by the dietary treatments (P=0.10). When grouping the GLY and AA pigs, the Feed:Gain ratio measured from d35 to d73 for the OTM pigs was significantly lower compared with ITM pigs (-0.07, P = 0.03). There was no significant effect of the dietary treatment on plasma trace mineral concentrations and alkaline phosphatase activity measured at day 34. At the end of the growing phase, plasma ceruloplasmin activity tended to be greater for the GLY and AA groups in comparison with the ITM pigs (+6.9%, P = 0.06). The zinc deposition in the proximal phalanx was significantly increased when the trace minerals were offered as organic compared with the group ITM (+10.9%, P = 0.06). Zinc content in the liver was greater for the pigs fed the OTM sources compared with the others (+6.0%, P = 0.10). The quantity of copper deposited in the liver did not differ significantly among the 3 trace mineral sources. This study indicates that the total replacement of inorganic Zn, Cu and Mn by organic sources brings benefits in terms of feed efficiency in fattening pigs. A better bioavailability as suggested by greater deposition levels in bone and liver as well as the increased J. Anim. Sci Vol. 101, Suppl. S2 103 circulating ceruloplasmin activity could explain the improvement of performance during the finishing period. Regardless of the variable considered, no significant differences were noticed between the two organic trace mineral sources. [ABSTRACT FROM AUTHOR]

The USDA-NIFA is developing a vision for agricultural genomics to phenomics research (AG2P) through two programs. In 2017, the Functional Annotation of Animal Genomes (FAANG) request for proposals was released, which has provided over $9 Million in funding to groups over the past five years to create public resources for the study of regulatory and other functional elements in agriculturally important species. This effort has been extended by other countries who have contributed another $20 Million+ (equivalent). These initial resource-building efforts are providing extensive new epigenetic data that are used to better understand the function of animal genomes and to enhance animal genetic improvement. However, such research demonstrates new infrastructure is needed across the agricultural enterprise to predict phenotypes from genome information. In 2020, NIFA inaugurated the Agricultural Genome to Phenome Initiative program (AG2PI), and an important objective of this presentation is to encourage strong participation of animal genomic researchers and industry in the AG2PI project. The purpose of AG2PI is to assemble and prepare a community to conduct AG2P research across the plant and animal kingdoms. The focus of the AG2PI is: a) identifying research gaps and opportunities, b) fostering community solutions to these challenges, and c) rapidly disseminating findings. Training workshops, field days, conferences, community surveys, and seed grants have all contributed to these goals. Opinions and ideas from the crop, livestock and aquaculture genetics communities are key to advancing G2P science and related work. The AG2PI uses online community surveys and in-person conferences to gather these and to facilitate transdisciplinary communications. In September 2022, AG2PI held a conference entitled "Thinking Big: Visualizing the Future of AG2PI", in which attendees focused small group discussions to identify which agricultural G2P-related topics are most critical for future R&D funding and which may be the most difficult to achieve. These discussions have been summarized in a concept paper for the community and shared with USDA-NIFA. A follow-up conference is planned for June 2023 in Kansas City, which we hope many members from animal genetics research and industry will attend. The future of agricultural genomics will be shaped by both the participants in AG2PI and the solutions developed by the AG2PI community. USDA NIFA awards 2022-70412-38454, 2021-70412-35233, and 2020-70412-32615. [ABSTRACT FROM AUTHOR]

Precision feeding (PF) with the daily mixing of 2 diets with different lysine content (high (H) or low (L)) was previously reported for growing pigs to reduce protein intake and N excretion compared with a conventional feeding (CF) based on a single diet (C). Using a simulation approach based on farm data, the objective of the present paper was to describe and evaluate a decision support system for the PF of gestating sows allowing the daily distribution of a tailored ration to each sow. Two datasets, 1 of 2,511 gestations (farm A) and 1 of 2,528 gestations (farm B), reporting sows' characteristics at insemination and objectives at farrowing were used as inputs for a Python model. This model, mainly based on InraPorc, calculates the nutrient requirements of each sow over gestation and simulates the impact of PF in comparison to CF. Simulated diets L, H, and C contained 3.0, 6.5, and 4.8 g/kg of standardized ileal digestible lysine (SID Lys) and 2.0, 3.3, and 2.5 g/kg of standardized total tract digestible phosphorus (STTD-P), respectively. The influence of farm, parity, gestation week, and their interactions, on calculated SID Lys and STTD-P requirements was analyzed applying a mixed model. The calculated SID Lys and STTD-P requirements increased markedly in the last third of gestation (P < 0.01) and were higher for primiparous than for multiparous sows, unless after week 14 for STTD-P requirement. The calculated SID AA and mineral requirements were lower for farm B than farm A (respectively, 2.94 vs. 3.08 g/kg for SID Lys and 1.30 vs. 1.35 g/kg for STTD-P, P < 0.01). On average, feed L represented 86% and 92% of the feed projected to be delivered by the PF strategy in farms A and B, respectively. Compared to CF, average calculated dietary SID Lys content was lowered by 27% and 32% with PF, for farms A and B, respectively, while average calculated dietary phosphorus content was lowered by 13% and 16%. The simulated proportions of sows in excess and deficient in SID Lys were reduced with PF. Compared to CF, the PF strategy allowed for a 3.6% reduction in simulated feed cost per sow during gestation, and reduced nitrogen and phosphorus intake (by 11.0% and 13.8%, respectively) and excretion (by 16.7% and 15.4%, respectively). To conclude, these simulations indicate that PF of gestating sow appears to be relevant to meet the amino acid requirement while reducing feed cost, and supplies and excretion of nitrogen and phosphorus. [ABSTRACT FROM AUTHOR]

Traditional feed composition tables have been a useful tool in the field of animal nutrition throughout the last 70 yr. The objective of this paper is to discuss the challenges and opportunities associated with creating large feed ingredient composition tables. This manuscript will focus on three topics discussed during the National Animal Nutrition Program (NANP) Symposium in ruminant and nonruminant nutrition carried out at the American Society of Animal Science Annual Meeting in Austin, TX, on July 11, 2019, namely: 1) Using large datasets in feed composition tables and the importance of standard deviation in nutrient composition as well as different methods to obtain accurate standard deviation values, 2) Discussing the importance of fiber in animal nutrition and the evaluation of different methods to estimate fiber content of feeds, and 3) Description of novel feed sources, such as insects, algae, and single-cell protein, and challenges associated with the inclusion of such feeds in feed composition tables. Development of feed composition tables presents important challenges. For instance, large datasets provided by different sources tend to have errors and misclassifications. In addition, data are in different file formats, data structures, and feed classifications. Managing such large databases requires computers with high processing power and software that are also able to run automated procedures to consolidate files, to screen out outlying observations, and to detect misclassified records. Complex algorithms are necessary to identify misclassified samples and outliers aimed to obtain accurate nutrient composition values. Fiber is an important nutrient for both monogastrics and ruminants. Currently, there are several methods available to estimate the fiber content of feeds. However, many of them do not estimate fiber accurately. Total dietary fiber should be used as the standard method to estimate fiber concentrations in feeds. Finally, novel feed sources are a viable option to replace traditional feed sources from a nutritional perspective, but the large variation in nutrient composition among batches makes it difficult to provide reliable nutrient information to be tabulated. Further communication and cooperation among different stakeholders in the animal industry are required to produce reliable data on the nutrient composition to be published in feed composition tables. [ABSTRACT FROM AUTHOR]

Selecting on increased breast meat yield and body weight (BW) is thought to be associated with the increasing prevalence of leg disorders. A disorder of particular importance is tibial dyschondroplasia (TD), which is characterized as uncalcified cartilage in the proximal area of the tibiotarsal bone, leading to increased lameness, reduced feed and water intake, and subsequently poorer production efficiency and increased pre-slaughter mortality. Although it is hypothesized that TD is more prevalent in fast-growing poultry, this relationship can vary across studies making it challenging to distill the relationship between BW and TD, especially as studies of TD span several decades. Therefore, a systematic literature search and meta-analysis was conducted to identify factors influencing TD incidence. To date, the systematic literature search has resulted in 21 eligible papers, although data are still being extracted from the literature. Data extracted from each study included TD incidence (%), BW (kg), species (turkeys vs. broilers), sex (male vs. female), and age (weeks) of the animals. Preliminary univariable models have been developed based on the current database. Univariable generalized linear mixed models were developed in SAS (PROC GLIMMIX) including study as a random effect to evaluate the relationship between TD incidence and BW, species, sex, and age. Turkeys (27.3 ± 0.08%) had a greater TD incidence than broilers (19.3 ± 0.03%); however, this difference was not significant (P = 0.3481). There was a tendency for age to influence TD incidence (P = 0.0553) and a significant effect of BW (P < 0.0001). TD incidence also tended to differ between the sexes, with greater incidence in males (22.9 ± 0.07%) than females (11.3 ± 0.05%; P = 0.0657). After extracting all available data from the literature, further analysis will be conducted to consider multivariable models to predict TD. As such, caution should be used when generalizing these results due to the preliminary nature of the database. With the currently available data, the results of this study support the relationship between BW/size and TD. Furthermore, this study illustrates that TD is a prevalent challenge in both major poultry species. Reducing TD incidence could significantly improve animal welfare and mitigate economic losses associated with pre-slaughter mortality. [ABSTRACT FROM AUTHOR]

The growing emphasis on ensuring the sustainability of animal agriculture is providing an impetus for the adoption of new approaches to structuring and conducting research. Sustainability is a complex topic involving many considerations related to the economic, social, and environmental impacts of production systems. Successfully addressing this topic requires multidisciplinary research as well as a high degree of communication with food system stakeholders to ensure that the research results contribute to informed decision making. In this paper, we provide an overview of a public–private partnership, the Coalition for Sustainable Egg Supply (CSES), which was formed to support research evaluating the sustainability of laying hen housing systems. Because of increasing public concerns about the behavioral restriction imposed on laying hens housed in conventional cages, the U.S. egg industry is faced with a need to transition to alternative systems. However, before the CSES project, there was limited information available about how this transition might affect trade-offs related to the sustainability of egg production. The goal of the CSES project was to provide this information by conducting holistic research on a commercial farm that had 3 different hen housing systems. The CSES members represented a variety of stakeholders, including food retailers and distributors, egg producers, universities, and governmental (USDA ARS) and nongovernmental organizations. The CSES was facilitated by a not-for-profit intermediary, the Center for Food Integrity, which was also responsible for communicating the research results to food system stakeholders, including via quantitative and qualitative consumer research. In this paper, we describe the structural aspects of the CSES that were responsible for the successful completion and dissemination of the research as well as the insights that were gained regarding multidisciplinary and multi-institutional collaboration, conducting commercial-scale research, fostering and maintaining stakeholder interaction, and communicating research results. Although not without limitations, this project demonstrates that public–private partnerships can be effective strategies for addressing sustainability questions related to animal agriculture and, thus, serves as a useful model for the other animal industries. [ABSTRACT FROM AUTHOR]

The objective of this paper is to report observations from a 10-week summer training program on Data Management in Animal and Plant Sciences. A total of six undergraduate students from three universities in the Texas A&M University System [Texas A&M University (TAMU)-College Station, TAMUKingsville, and TAMU-Corpus Christi] representing disciplines of Animal Science, Plant Science, and Computer Science learned handling and interpretation of sensor-based data derived from both animal sensors and UAV images of plant fields. Students attended weekly training sessions on data collection, management, processing, and visualization software including ArcGIS, MySQL and power BI. In addition, students also worked as a team and developed a database project and gave an oral presentation on their team project. On the pretraining survey, two students indicated that they had a background in animal science, two students indicated a background in plant science, one student in computer science, and one student with experience in spatial data. To begin with, the students had a mean score of 4.3 (range 0-8; on a scale of 0-10) for knowledge of data management in agriculture. One third of the students indicated that they did not have data handling experience, and one half of the students had some data analysis experience whereas one student indicated partial experience of working with data. In general, students indicated that they were very satisfied with the internship experience. On a scale of 0-10, the mean satisfaction score was 8.75 (range 7.5-10). Students indicated that they were more confident to work with and talk about data from animal systems (mean 8.5; range 7-10) and plant systems (mean = 8.4; range = 7-10). All students agreed that they learned at least one new concept related to animal and plant data ecosystems. Students indicated that the program was a good start for understanding the overall data architecture, indicated progress on data handling and thought helpful to understand opportunities in agriculture. All students agreed that their understanding of data management in agriculture changed significantly because of the course. Consequently, four students indicated that they were interested in career opportunities related to data in agriculture whereas two students indicated their interest in application of the developed tools for the on-farm use. When evaluating effect of the cross disciplinary training, students agreed that the training was helpful in learning concepts outside of their own discipline with the mean score 9.4(range 8-10) on a scale of 0-10. All students indicated that they learned team management skills and skills working with people that are different from their own. Students suggested improvement on the communications, prerecorded videos, in-person meetings, weekly reporting reviews in the future iterations for students to benefit more from the program. [ABSTRACT FROM AUTHOR]

A meta-analysis was conducted to evaluate the effects of branched-chain amino acids (BCAA), their interactions, and interactions with large neutral amino acids (LNAA) to develop prediction equations for growth performance of pigs. Data from 25 papers, published from 1995 to 2018, for a total of 44 trials and 210 observations were recorded in a database. Diets were reformulated using the NRC (2012) loading values to estimate nutrient concentrations. The response variables were average daily gain (ADG), average daily feed intake (ADFI), and gain-to-feed ratio (G:F). The predictor variables tested included average body weight (BW), crude protein, neutral detergent fiber, Ile:Lys, Leu:Lys, Val:Lys, BCAA:Lys, Ile:Leu, Val:Leu, Ile:Val, (Ile+Val):Leu, Trp:Lys, Leu:Trp, Ile:Trp, Val:Trp, BCAA:Trp, Met:Lys, Leu:Met, Ile:Met, Val:Met, BCAA:Met, His:Lys, Leu:His, Ile:His, Val:His, BCAA:His, Thr:Lys, Leu:Thr, Ile:Thr, Val:Thr, BCAA:Thr, (Phe+Tyr):Lys, Leu:(Phe+Tyr), Ile:(Phe+Tyr), Val:(Phe+Tyr), BCAA:(Phe+Tyr), LNAA:Lys, Leu:LNAA, Ile:LNAA, Val:LNAA, and BCAA:LNAA. Amino acids were expressed on standardized ileal digestible basis. The MIXED procedure of SAS (SAS Institute Inc. Cary, NC) was used to develop the equations. The inverse of squared SEM was used to account for heterogeneous errors using the WEIGHT statement. Models were selected with a step-wise manual forward selection. In order to be included in the final model, predictor variables had to be statistically significant (P < 0.05) and provide an improvement of at least 2 points in Bayesian information criterion. The optimum equations were: ADG, g = – 985.94 + (15.2499 × average BW (kg)) – (0.08885 × average BW × average BW) + (1.063 × Leu:Lys) + (20.2659 × Ile:Lys) – (0.1479 × Ile:Lys × Ile:Lys) + (9.2243 × (Ile+Val):Leu) – (0.03321 × (Ile+Val):Leu × (Ile+Val):Leu) – (0.4413 × Ile:Trp); G:F, g/kg = 648.3 – (6.2974 × average BW (kg)) + (0.02051 × average BW × average BW) + (0.5396 × Ile:Lys) + (1.7284 × Val:Lys) – (0.00795 × Val:Lys × Val:Lys) – (1.7594 × Met:Lys); and ADFI, kg = predicted ADG/predicted G:F. Overall, the prediction equations suggest that increasing Leu:Lys negatively impacts ADG due to a reduction in G:F and ADFI caused by insufficient levels of other BCAA and LNAA relative to Leu. According to the model, the addition of Val, Ile, and Trp, alone or in combination, has the potential to counteract the negative effects of high dietary Leu concentrations on growth performance. [ABSTRACT FROM AUTHOR]

Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. System dynamics methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity, and focuses on feedback processes arising from endogenous system elements. This paper highlights the process of SD modeling using 2 examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model illustrates the benefits of the SD approach in modeling rumen fill and animal performance. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing, and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The paper concludes with recommendations about how to increase awareness and training in SD methods to enhance their appropriate use in research and instruction. [ABSTRACT FROM AUTHOR]

This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler's ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence (AI) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. Artificial intelligence might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies. [ABSTRACT FROM AUTHOR]

For genomic predictors to be of use in genetic evaluation, their predicted accuracy must be a reliable indicator of their utility, and thus unbiased. The objective of this paper was to evaluate the accuracy of prediction of genomic breeding values (GBV) using different clustering strategies and response variables. Red Angus genotypes (n = 9,763) were imputed to a reference 50K panel. The influence of clustering method [k-means, k-medoids, principal component (PC) analysis on the numerator relationship matrix (A) and the identical-by-state genomic relationship matrix (G) as both data and covariance matrices, and random] and response variables [deregressed estimated breeding values (DEBV) and adjusted phenotypes] were evaluated for cross-validation. The GBV were estimated using a Bayes C model for all traits. Traits for DEBV included birth weight (BWT), marbling (MARB), rib-eye area (REA), and yearling weight (YWT). Adjusted phenotypes included BWT, YWT, and ultrasonically measured intramuscular fat percentage and REA. Prediction accuracies were estimated using the genetic correlation between GBV and associated response variable using a bivariate animal model. A simulation mimicking a cattle population, replicated 5 times, was conducted to quantify differences between true and estimated accuracies. The simulation used the same clustering methods and response variables, with the addition of 2 genotyping strategies (random and top 25% of individuals), and forward validation. The prediction accuracies were estimated similarly, and true accuracies were estimated as the correlation between the residuals of a bivariate model including true breeding value (TBV) and GBV. Using the adjusted Rand index, random clusters were clearly different from relationship-based clustering methods. In both real and simulated data, random clustering consistently led to the largest estimates of accuracy, while no method was consistently associated with more or less bias than other methods. In simulation, random genotyping led to higher estimated accuracies than selection of the top 25% of individuals. Interestingly, random genotyping seemed to overpredict true accuracy while selective genotyping tended to underpredict accuracy. When forward in time validation was used, DEBV led to less biased estimates of GBV accuracy. Results suggest the highest, least biased GBV accuracies are associated with random genotyping and DEBV. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present a systematic review and meta-analysis of research efforts aimed at recovering cattle production losses attributed to toxic endophyte-infected [Neotyphodium coenophialum (Morgan-Jones & Gams.) Glenn, Bacon, & Hanlin comb. Nov.] tall fescue [Lolium arundinaceum (Schreb.) Darbysh.]. The strategies presented include those 1) applied with forage systems, 2) based on pharmacological compounds and functional foods, and 3) based on supplemental dietary nutrients. Cattle BW gain and DM intake was the dependent response evaluated. Among the forage systems reviewed, studies with nontoxic, endophyte-infected tall fescue as a total replacement forage system demonstrated the greatest improvement in per-hectare (152 ± 27.5 kg/ha) and per-animal (0.29 ± 0.03 kg/d) BW gain. Studies with interseeded legumes have exhibited a small and highly variable BW gain effect size per hectare (52 ± 24.1 kg/ha) and per animal (0.11 ± 0.03 kg/d). The legume response was seasonal, with summer exhibiting the greatest benefit. Studies with chemicals that suppress plant growth demonstrated BW gain responses (0.17 ± 0.06 kg/d) equal to or greater than the response observed with legume studies. Cattle grazing toxic tall fescue responded well to anthelmentics, antimicrobial feed additives, and steroid implants, and the use of these technologies may additively help recover production losses. As a group, functional foods have not improved BW gain (P = 0.85). Studies with cattle supplemented with highly digestible fiber supplements observed a 0.15 kg greater BW gain compared with studies using starch- and sugar-based supplements (P < 0.05). Weight gain was positively impacted by the level of supplementation (0.06 kg/DM intake as percent BW0.75). Supplement feed conversion was estimated at 6:1 for the highly digestible fiber supplements compared with 11:1 for starch-based supplements. Tall fescue forage DM intake was predicted to maximize at a supplemental feeding rate of 0.24% BW with a breakpoint at 0.5% BW, and total maximum DM intake (forage plus supplement) occurred at 2.7% BW when supplemental feeding approached 0.9% BW. Results from this meta-analysis should be useful for 1) establishing and comparing measured responses to theoretical improvements in BW gain when additive strategies are considered, 2) research planning, and 3) producer education. [ABSTRACT FROM AUTHOR]

Previous research identified that a review of the current industry New Zealand lamb survival trait was necessary as its recording accuracy was reliant on farmers notifying their Sheep Improvement Limited bureau of lamb deaths. This paper reports the decision rules and genetic parameters for a new lamb survival trait for the New Zealand sheep industry. These rules define the new lamb survival trait (NEWSUR) using lamb birth fate (BFATE) codes and the presence/absence of lamb weight measurements. Six univariate animal models were tested and used to estimate variance or covariance components and the resulting direct and maternal heritabilities for NEWSUR. The models differed in the way they adjust for the effect of day of birth, the exclusion or inclusion of a litter (dam/year of birth) random effect, and the application or not of a logit transformation of the phenotypes. For both the linear and logistic methods, models including the random effect of litter provided the best fit for NEWSUR according to log-likelihood values. Log-likelihoods for the linear and logistic models cannot be compared; therefore, a cross-validation method was used to assess whether the logit transformation was appropriate by analyzing the predictive ability of the models. The mean square errors were slightly lower for the linear compared with the logistic model, and therefore, the linear model was recommended for industry use. The heritability attributed to direct effects ranged from 2 to 5.5%. A direct heritability of 5.5% resulted from a linear model without litter effect and omitting the effect of day of birth on survival, whereas a direct heritability of 2% resulted from the logistic model fitting a random litter effect. The heritability attributed to maternal genetic effects ranged from 1.9 to 7.7%. A maternal genetic heritability of 7.7% resulted from the logistic model omitting the litter effect, whereas a maternal genetic heritability of 1.9% resulted from the linear model fitting a random litter effect. The addition of the litter random effect substantially decreased the maternal heritabilities in all cases and was recommended for industry use to avoid overestimation of the maternal genetic variance. Sheep Improvement Limited has implemented NEWSUR and the associated genetic evaluation model based on information described in this paper. Industry-wide implementation will enable sheep breeders to produce more accurate genetic evaluations to their commercial clients. [ABSTRACT FROM AUTHOR]

Data from existing literature were used to generate equations to predict finishing pig back, belly, and jowl fat iodine values (IV) and an experiment was conducted to evaluate these equations. The final database included 24, 21, and 29 papers for back, belly, and jowl fat IV, respectively. For experiments that changed dietary fatty acid composition, initial (INT) diets were defined as those fed before the change in diet composition and final (FIN) diets were those fed after. The predictor variables tested were divided into 5 groups: 1) diet fat composition (dietary percent C16:1, C18:1, C18:2, C18:3, EFA, unsaturated fatty acids, and IV product) for both INT and FIN diets, 2) day feeding the INT and FIN diets, 3) ME or NE of the INT and FIN diet, 4) live performance criteria (initial BW, final BW, ADG, ADFI, and G:F), and 5) carcass criteria (HCW and backfat thickness). The PROC MIXED procedure of SAS (SAS Inst., Inc., Cary, NC) was used to develop regression equations. Evaluation of models with significant terms was then conducted based on the Bayesian information criterion. The optimum equations to predict back, belly, and jowl fat IV were backfat IV = 84.83 + (6.87 × INT EFA) - (3.90 × FIN EFA) - (0.12 × INT days) - (1.30 × FIN days) - (0.11 × INT EFA × FIN days) + (0.048 × FIN EFA × INT days) + (0.12 × FIN EFA × FIN days) - (0.0060 × FIN NE) + (0.0005 × FIN NE × FIN days) - (0.26 × backfat depth); belly fat IV = 106.16 + (6.21 × INT EFA) - (1.50 × FIN days) - (0.11 × INT EFA × FIN days) - (0.012 × INT NE) + (0.00069 × INT NE × FIN days) - (0.18 × HCW) - (0.25 × backfat depth); and jowl fat IV = 85.50 + (1.08 × INT EFA) + (0.87 × FIN EFA) - (0.014 × INT days) - (0.050 × FIN days) + (0.038 × INT EFA × INT days) + (0.054 × FIN EFA × FIN days) - (0.0066 × INT NE) + (0.071 × INT BW) - (2.19 × ADFI) - (0.29 × backfat depth). Dietary treatments from the evaluation experiment consisted of a corn-soybean meal control diet with no added fat or a 3 × 3 factorial arrangement with main effects of fat source (4% tallow, 4% soybean oil, or a blend of 2% tallow and 2% soybean oil) and feeding duration (d 0 to 42, 42 to 84, or 0 to 84). The back, belly, and jowl fat IV equations tended to overestimate IV when observed IV were less than approximately 65 g/100 g and underestimate belly fat IV when actual IV are greater than approximately 74 g/100 g or when the fat blend was fed from d 0 to 84 or 42 to 84. Overall, with the exceptions noted, the regression equations were an accurate tool for predicting carcass fat quality based on dietary and pig performance factors. [ABSTRACT FROM AUTHOR]

A short survey was conducted in 2019 to determine knowledge of bioactive forages for parasite control, including specifically Sericea Lespedeza (Lespedeza cuneata), before or during small ruminant workshops (also available online via SurveyMonkey® - link sent to a small ruminant email list). Questions also included livestock owned, hay grown and/or fed, and interest in bioactive forages, among others. Descriptive statistics were obtained by using weighted (by region for goat numbers) SURVEYMEANS procedures (SAS). A total of 151 surveys were completed, including paper (n = 45) and online responses (n = 106). Response rate for paper surveys was 90%. Twenty-six of the fifty states were represented and assigned to regions: Southeast (SE), Midwest (MW), Northeast (NE), and southwestern and western combined (SWW). The highest responses came from the SE (unweighted), but after weighting, the SWW region was represented at the highest level (P < 0.001). Nearly 100% of respondents had small ruminants on their farm, most fed both grass and legume hay (P < 0.001), 36.5 ± 8.6% raised hay, but very few sold hay (4.9 ± 1.5%; less than those that did not, P < 0.001). The most popular method of marketing for hay was "word of mouth" (P < 0.001). Prior knowledge of forage plants with bioactivity was noted for 78.0 ± 8.1% (P < 0.001) vs those without. Approximately a third of respondents had used or grown sericea lespedeza (32.8 ± 8.4%; P < 0.05) and most (tendency, P < 0.07) thought of it favorably. Almost half of respondents indicated they were willing to grow sericea lespedeza (P < 0.02). Other sericea lespedeza products of interest to respondents included primarily pellets (around 85.3 ± 8.3%), with little interest in silage (2.2 ± 1.1%; P < 0.0001). Producer surveys, such as this, help inform efforts for researchers and Extension staff. [ABSTRACT FROM AUTHOR]

Perennial grass pastures provide the basis for beef production systems across the Southeast United States. One common management practice that is widely recommended among agronomists is interseeding cool-season legumes. Legumes can serve as a complementary resource for filling in production gaps, reducing supplemental feedstuffs, reducing the need for nitrogen fertilization, increasing the nutritional quality of forage available, increase biomass production, improve animal performance, and can reduce the toxic effects of endophyte-infected tall fescue through dilution. A simulated economic analysis was developed to further the economic understanding of the cost of implementation, the subsequent animal and forage performance benefits, and net returns from the inclusion of legumes over many research trials and years. Data from 15 peer-reviewed papers was used to simulate the economic benefits of implementing this production practice. Cost of production and revenue for each paper were calculated using the 10-year average from 2010 to 2019. This analysis provides users with a further understanding of the net returns, critical breakeven areas, and return on investment that is necessary in order to successfully implement the inclusion of cool-season legumes in perennial grass-based systems. [ABSTRACT FROM AUTHOR]

As input costs increase, producers need to be mindful of management practices to maintain profit margins and it is important to understand current management practices. Therefore, the objective of this study was to investigate current beef marketing strategies and beliefs of beef producers in the state of Mississippi in 2019. This survey was distributed to cattle producers in Mississippi in the June/July issue of "Cattle Business in Mississippi" magazine with return postage. There also was a link included in the "Monday Memo" email sent to members of the Mississippi Cattlemen's Association. The survey consisted of 22 questions that were divided into three sections: current practices, current cow-calf marketing practice, and general information. Responses from the survey were anonymous and confidential and were received via paper mail or electronic responses. Responses were stored and analyzed using Qualtrics. Data collected from this study were compared with 1999 data from a similar study conducted by Little et al. (2000). Out of 148 respondents, there was a 95% completion rate of surveys taken. Most respondents were 51 years and older with 54% of the respondents being part of the cattle industry for over 31 years. Ninety percent of respondents were members of the Mississippi Cattlemen's Association and almost 60% were certified in Beef Quality Assurance. The majority, 122 of 148 producers, stated their cattle operation was not the primary source of income for their household. The average number of cows, bulls, stockers/yearling, replacement heifers and cattle on-feed on an operation was greater in the 2019 data compared with 1999. On average, the number of cows per operation increased from 40 to 68 animals, bulls increased from 2 to 5 animals, and replacement heifers increased from 7 to 17 animals from 1999 to 2019. There was also an average increase from 30 to76 animals of stocker/yearling and an increase from 2 to 27 animals of on-feed cattle. Most producers vaccinate (81%) and deworm (85%) according to recommendations and indicated a lack of labor and time as reasons for not implementing some common management practices such as preconditioning. Respondents mainly purchased calves directly from a producer (61%) or from special sales (59%). In 1999, over one-half of respondents purchased from an auction. When it came to selling cattle, 58% of respondents sold at auction regularly in 2019. In 1999, 73% of respondents sold at auctions. In 2019, 72% of respondents stated they did not retain ownership while in 1999 only 58% of respondents did not retain ownership. In conclusion, our results suggest there have been key changes within the cattle industry regarding herd size increases and a shift in selling and purchasing habits since 1999. [ABSTRACT FROM AUTHOR]

Optimal lean tissue accretion is contingent on the ability of the pig to efficiently establish and maintain host defenses, and to digest and absorb nutrients and water. Epithelial cells in the mucosa of the gastrointestinal tract interconnect with each other by tight junctions, adherens junctions, and desmosomes, forming a complete intestinal barrier. Of the secretory cells in this epithelial barrier, mucus secreting goblet cells and hormone producing enteroendocrine cells can be found interspersed, while Paneth cells lie in the base of the crypts and secrete antimicrobial proteins/peptides into the mucus layer. In the small intestine, enterocytes lining the villi allow for efficient nutrient transport. Maintenance of the epithelial barrier and its function is dependent on intestinal stem cells residing at the crypt base. Two major signaling pathways controlling intestinal stem cell proliferation and differentiation are the β-catenin/Wnt and Notch signaling pathways. Thus, achieving and maintaining optimal nutrient absorptive capacity requires an intricate balance amongst β-catenin/Wnt and Notch signaling pathways, wherein dysregulation of either can result in inefficiency of nutrient absorption. Weaning transition, off-feed events, hypophagia, and enteric and respiratory diseases can all compromise the structural and secretory barrier of the gastrointestinal tract mucosa via changes in tight and adherens junction proteins and mucin production. As a result, these stressors can increase the incidence of gastric ulcers, villous atrophy, and colitis in pigs. Depending on the severity and duration of the mucosal damage, repair and restitution occurs due to increased stem cell proliferation, migration, and repolarization. Thus, efficiency of digestive and absorptive function depends on several factors, including the types of epithelial cells present and their functionality, the rate of cell renewal, and epithelial surface area. This paper will discuss what we know about the regulation and repair of the mucosal epithelium in growing pigs during health, stress, and disease. [ABSTRACT FROM AUTHOR]

Data from existing literature examining the influence of floor space allowance on the growth of finishing pigs was used to develop prediction equations for ADG, ADFI, and G:F. Two databases were used: the first included information from studies examining the influence of floor space allowance, and the second included the aforementioned papers along with papers examining the impact of floor space after pigs were removed from the pen. The first database included 27,25, and 25 papers for ADG, ADFI, and G:F, respectively, and the second database contained 30, 28, and 28 papers for ADG, ADFI, and G:F, respectively. The predictor variables tested were floor space (m2/pig), k (floor space/final BW067), initial BW, final BW, feed space (pigs per feeder hole), water space (pigs per waterer), group size (pigs per pen), gender, floor type, and study length (d). Floor space treatments within each experiment were the experimental unit and random effects of decade, paper within decade, and experiment within paper x decade interactions were included in the statistical model. A weighted variance term was included in the statistical model to account for heterogeneity of experimental designs and replication across the existing literature. The statistical significance for inclusion of terms in the model was determined at P < 0.10. Further evaluation of models with significant terms was then conducted based on the Bayesian information criterion (BIC). Once the ADG and ADFI models for each respective database were determined, then the G:F model was evaluated as the predicted ADG/predicted ADFI. The optimum equations to predict finishing ADG, ADFI, and G:F for the first database were ADG, g = 395.57 + (15,727 x k) - (221,705 x k2) - (3.6478 x initial BW, kg) + (2.209 x final BW, kg) + (67.6294 xkx initial BW, kg); ADFI, g = 802.07 + (20,121 x k) - (301,210 x P) - (1.5985 x initial BW, kg) + (11.8907 x final BW, kg) + (159.79 xjx initial BW, kg); and G:F = predicted ADG/predicted ADFI. The optimum equations to predict ADG, ADFI, and G:F for the second database were ADG, g = 337.57 + (16,468 x k) - (237,350 x P) - (3.1209 x initial BW, kg) + (2.569 x final BW, kg) + (71.6918 xkx initial BW, kg); ADFI, g = 833.41 + (24,785 x k) - (388,998 x P) - (3.0027 x initial BW, kg) + (11.246 x final BW, kg) + (187.61 x k x initial B W, kg); and G:F = predicted ADG/predicted ADFI. All multi-term models improved BIC values compared with single-term predictor models, signifying that multiterm models proved to better fit their respective databases. [ABSTRACT FROM AUTHOR]

Correct pedigree is essential to produce accurate genetic evaluations of livestock populations. Pedigree validation has traditionally been undertaken using microsatellites and more recently, based on checks on opposing homozygotes using single nucleotide polymorphisms (SNPs). In this study, the genomic relationship matrix was examined to see whether it was a useful tool to forensically validate pedigree and discover unknown pedigree. Using 5,993 genotyped Limousin animals which were imputed to a core set of 38,907 SNPs, the genomic relationships between animals were assessed to validate the reported pedigree. Using already pedigree-verified animals, the genomic relationships between animals of different relationships were shown to be on average 0.58, 0.59, 0.32, 0.32, 0.19, and 0.14 between animals and their parents, full siblings, half siblings, grandparents, great grandparents, and great great grandparents, respectively. Threshold values were defined based on the minimum genomic relationship reported between already pedigree-verified animals; 0.46, 0.41, 0.17, 0.17, 0.07, and 0.05, respectively for animals and their parents, full siblings, half siblings, grandparents, great grandparents, and great great grandparents. Using the wider population and the above genomic relationship threshold values, potential pedigree conflicts were identified within each relationship type. Pedigree error rates of between 0.9% (animal and great great grandparent) and 4.0% (full siblings) were identified. A forensic genomic pedigree validation and discovery system was developed to enable pedigree to be verified for individual genotyped animals. This system verifies not just the parents, but also a wide number of other genotyped relatives and can therefore identify more potential errors in the pedigree than current conventional methods. A novel aspect to this algorithm is that it can also be used to discover closely related animals on the basis of their genomic relationships although they are not recorded as such in the pedigree. This functionality enables missing pedigree information to be discovered and corrected in the pedigree of livestock populations. The methods in this paper demonstrate that the genomic relationship matrix can be a useful tool in the validation and discovery of pedigree in livestock populations. However, the method does rely on being able to define threshold values appropriate to the specific livestock population, which will require sufficient number of animals to be genotyped and pedigree validated before it can be used. [ABSTRACT FROM AUTHOR]

The weight of a pig and the rate of its growth are key elements in pig production. In particular, predicting future growth is extremely useful, since it can help in determining feed costs, pen space requirements, and the age at which a pig reaches a desired slaughter weight. However, making these predictions is challenging, due to the natural variation in how individual pigs grow, and the different causes of this variation. In this paper, we used machine learning, namely random forest (RF) regression, for predicting the age at which the slaughter weight of 120 kg is reached. Additionally, we used the variable importance score from RF to quantify the importance of different types of input data for that prediction. Data of 32,979 purebred Large White pigs were provided by Topigs Norsvin, consisting of phenotypic data, estimated breeding values (EBVs), along with pedigree and pedigree-genetic relationships. Moreover, we presented a 2-step data reduction procedure, based on random projections (RPs) and principal component analysis (PCA), to extract features from the pedigree and genetic similarity matrices for use as inputs in the prediction models. Our results showed that relevant phenotypic features were the most effective in predicting the output (age at 120 kg), explaining approximately 62% of its variance (i.e., R2 = 0.62). Estimated breeding value, pedigree, or pedigree-genetic features interchangeably explain 2% of additional variance when added to the phenotypic features, while explaining, respectively, 38%, 39%, and 34% of the variance when used separately. [ABSTRACT FROM AUTHOR]

Advancements in technology have ushered in a new era of sensor-based measurement and management of livestock production systems. These sensor-based technologies have the ability to automatically monitor feeding, growth, and enteric emissions for individual animals across confined and extensive production systems. One challenge with sensor-based technologies is the large amount of data generated, which can be difficult to access, process, visualize, and monitor information in real time to ensure equipment is working properly and animals are utilizing it correctly. A solution to this problem is the development of application programming interfaces (APIs) to automate downloading, visualizing, and summarizing datasets generated from precision livestock technology. For this methods paper, we develop three APIs and accompanying processes for rapid data acquisition, visualization, systems tracking, and summary statistics for three technologies (SmartScale, SmartFeed, and GreenFeed) manufactured by C-Lock Inc (Rapid City, SD). Program R markdown documents and example datasets are provided to facilitate greater adoption of these techniques and to further advance precision livestock technology. The methodology presented successfully downloaded data from the cloud and generated a series of visualizations to conduct systems checks, animal usage rates, and calculate summary statistics. These tools will be essential for further adoption of precision technology. There is huge potential to further leverage APIs to incorporate a wide range of datasets such as weather data, animal locations, and sensor data to facilitate decision-making on times scales relevant to researchers and livestock managers. [ABSTRACT FROM AUTHOR]

The circadian rhythm alters forage chemical composition by balancing processes of photosynthesis, respiration, and transpiration. Timing of moving animals to a new paddock in a rotationally stocked system may take advantage of potential improvements in forage quality. The objective of this study was to collate available peer-reviewed papers aiming at comparing the forage quality and grazing dairy cattle performance when moved to a new paddock either in the morning (a.m.) or afternoon (p.m.). Twenty-four studies were gathered in total, and the number of observations per variable varied according to its availability within the studies. Among forage chemical composition parameters, contents of dry matter (DM), total nonstructural carbohydrates (TNC), soluble carbohydrates (SC), starch, neutral-detergent fiber (NDF), aciddetergent fiber (ADF), acid-detergent lignin (ADL), ether extract (EE), crude protein (CP), and digestibility were assessed. The animal performance parameters included dry matter intake (DMI), milk yield (MY), energy-corrected milk yield (ECM), milk fat (MF) and milk protein (MP) contents, fat yield (FY), and protein yield (PY). A meta-analysis was performed according to a random effects model weighted by inverse variance (Comprehensive Meta-Analysis V3.3.070). Means, standard deviations or p-values, and number of observations were used to compute the effect sizes. Heterogeneity between observed effect sizes was examined with the Cochran’s Q test and the I2 statistic. The standardized mean difference was reported with its 95% confidence interval (CI), where positive values indicate an increment of p.m. compared with a.m., and negative values indicate a reduction. Statistical differences were declared significant at P ≤ 0.05, and trends were declared at P ≤ 0.10. Forage sampled in the p.m. had greater (P < 0.001) contents of DM, TNC, SC, starch, and digestibility by 19.8, 16.3, 21.3, 12.1, and 1.0%, while NDF (P < 0.001), ADF (P < 0.001), ADL (P < 0.001), and CP (P < 0.01) were decreased by 1.8, 2.4, 2.4, and 1.7% relative to a.m. forage, respectively (Table 1). Content of EE was not influenced (P > 0.05) by time. The ECM was 3.4% greater (P < 0.001) and the PY tended to be 3.7% greater (P = 0.07) for dairy cows moved in the p.m. compared with those moved in the a.m (Table 2). Dry matter intake, MY, MF, MP, and FY were not affected (P > 0.05) by time. Results indicate that even with considerable improvements of forage nutritive value in the afternoon, the benefits in terms of animal performance are small. Future studies assessing potential benefits of moving animals to a new paddock during the afternoon, should consider statistical power analysis and sample size calculation when designing the experiment. [ABSTRACT FROM AUTHOR]

This meta-analysis aimed to evaluate the effect of feeding butyric acid (BA) on growth performance and metabolic indicators in dairy calves. A literature search was conducted to identify in vivo research studies carried out on BA supplementation in dairy calves. Inclusion criteria were in vivo studies, dairy calves, weaning, BA, and inclusion doses of BA. Twenty-three papers with 80 data and 1,132 calves were selected. Data extracted included the number of calves in control and treatment groups and measures of variance of responses (standard error or standard deviation). The response variables were production performance including body weight (BW), average daily gain (ADG) feed efficiency (FE), and metabolic indicators including blood glucose and beta-hydroxybutyrate (BHB) concentrations. A random effect of model was used to examine the raw mean difference and standardized mean difference between BA supplementation and the control group. The analysis was conducted using the metafor package of Rstudio. Butyric acid moderately improved the growth performance and metabolic indicators responses of calves with substantial heterogeneity (59 < I² < 73%, P < 0.01). Final BW and ADG were increased by 2.7 kg and 48.1 g/day, or > 0.6 and > 0.4 standard deviation units (P < 0.05), respectively. However, FE was increased by BA supplementation but not significantly different. For metabolic indicators, glucose concentration was greater with BA supplementation with > 0.4 standard deviation unit, (P < 0.05). However, BA supplementation did not affect the blood concentration of BHB (P > 0.05). In conclusion, the meta-analysis results demonstrated that BA constitutes an important energy source for dairy calves. Butyric acid is effective in promoting growth performance and intestinal health and, yet, increasing production profits [ABSTRACT FROM AUTHOR]

Cattle health management in the feedlot sector continues to evolve and improve cattle welfare by promoting an understanding of illness and injury to minimize their effects. Many papers have focused on treating individual ill or injured cattle in cattle feeding facilities. However, care, treatment, and outcomes for chronically ill or injured cattle (“chronic cattle”) have received little attention in the literature. The purpose of this descriptive study was to present demographics, diagnoses, and outcomes of chronic cattle in 17 feedlots in the U.S. and Canada and discuss potential risk factors and welfare implications. Individual chronic cattle information and treatment records from 17 feedlots (U.S.: 8; Canada: 9) over 7 yr (2014 – 2020) were retrospectively retrieved from a large feedlot consulting and data-management service (Feedlot Health Management Services, a division of TELUS Agriculture Solutions Inc.). Chronic cattle were defined as those that had spent time in a feedlot’s designated chronic pen. Descriptive results are presented as means ± standard deviations. U.S. feedlots averaged 31,280 ± 23,231 cattle/yr, had an average chronicity rate of 2.24 ± 2.42 % (median 1.53%), and contributed 52,709 chronic cattle to the dataset. Canadian feedlots averaged 21,913 ± 10,497 cattle/yr, had an average chronicity rate of 1.41 ± 0.82% (median 1.10%), and contributed 15,940 chronic cattle to the dataset. U.S. chronic cattle were sourced across all seasons primarily from auctions and ranches, had an average arrival weight of 163 ± 59 kg, and an average days on feed (DOF) of 362 ± 172 d. Canadian chronic cattle were primarily auction-sourced in the fall, weighed 272 ± 95 kg at arrival, and averaged 246 ± 141 DOF. Chronic cattle were treated at least once for respiratory issues (U.S.: 46.3%; Canada: 26.9%), bullers (U.S.: 34.5%; Canada: 5.8%), musculoskeletal issues (U.S.: 2.2%; Canada: 1.4%), lameness (U.S.: 1.7%; Canada: 28.1%), metabolic issues (U.S.: 0.5%; Canada: 4.5%), or other diagnoses (U.S.: 2.2%; Canada: 6.3%), with many cattle treated for multiple issues (U.S.: 12.5%; Canada: 27.0%). Outcomes for chronic cattle were shipped (U.S.: 73.1%; Canada: 52.5%), railed (U.S.: 8.1%; Canada: 20.7%), euthanized (U.S.: 0.7%; Canada: 9.9%) died unassisted (U.S.: 18.1%; Canada: 16.2%), or transferred (U.S.: < 0.1%; Canada: 0.8%). These results are some of the first to describe chronic cattle populations and present interesting trends for further analysis. For example, differences in buller and lameness rates could reflect actual differences but may be artifices of the dataset and feedlot definitions. Additionally, high marketed cattle rates (shipped and railed) indicate positive outcomes for most cattle, but variable mortality rates merit further scrutiny [ABSTRACT FROM AUTHOR]

The objective of this paper was to determine the relationship and repeatability of between-evaluator scores for claw set (CS), hoof angle (HA), and rear leg side view (RSV). Repeatability of feet and legs scoring has implications on scorer bias and subsequently on the estimation of genetic parameters, expected progeny differences (EPD), and accuracy. Data were collected on 112 13-15-month-old Simmental heifers by 4 evaluators, where scorer 1 (S1) was a trained and experienced evaluator and subsequently trained scorers 2 (S2), 3 (S3), and 4 (S4) for approximately 20 minutes before all 4 evaluators sequentially scored every female at the same time. Training was conducted using feet and leg rubrics provided by the American Simmental Association (ASA) on a 1-9 scale, with 5 being optimum for each trait. Scorers were provided an online evaluation instrument, requiring responses for each evaluator for each animal, and included identification, CS, HA, and RSV. Pearson correlations were computed pairwise, with each new scorer compared to the trained scorer. For partial linear regression, the three new evaluators were fit individually to S1. Pearson correlation values ranged from 0.088 to 0.397. The correlation between S1 and S3 was the strongest for all traits. The weakest correlation of 0.088 was in rear leg side view between S1 and S2. Partial linear regression adjusted R-squared values ranged from 0.00 to 0.15. The correlation between S1 and S3 was the strongest for all traits. The weakest correlation of 0.00 was in rear leg side view between S1 and S2. The correlations between S1 and S2 was significant for CS and HA (P = 0.002, P = 0.039), but not for RSV (P = 0.355). The correlation between S1 and S3 was significant for CS, HA, and RSV [P = 1.5x10^(-5), P = 0.006, P = 0.02]. The correlation between S1 and S4 was not significant for CS, HA, and RSV (P = 0.276, P = 0.085, P = 0.223). Overall, the scores had weak correlations, which illustrates the differences in variability across scorers. The weak correlation also illustrates the need for more robust and intensive training of evaluators. Overall, while the scores are not highly correlated between evaluators, scorer bias should not affect estimation of genetic parameters long as each contemporary group is scored by one evaluator. [ABSTRACT FROM AUTHOR]

Objective: The experiment was conducted to study the effects of selenium-enriched fermented Chinese herbal feed additive on growth performance, immune performance and meat quality of fowl. Method: A total of 180 Hyline brown chicks of 7-day-old were randomly divided into 3 groups with 3 replicates per group and 20 chicks per replicate. These three groups were control group (basal diet), experimental group I (basal diet &Chinese herbal feed additive) and experimental group II (basal diet &selenium-enriched fermented Chinese herbal feed additive). The experiment lasted for 42d. Results: The ADG (average daily gain) of experimental groups II were significantly greater than those of control group and experimental groups I (P< 0.05). The ADFI (average daily feed intake) of experimental groups I and II were significantly greater than those of control group, and the F/G (ratio of feed to gain) was respectively decreased by 5.3% and 7.7% (P< 0.01). The content of GSH-Px in serum of experimental group II was significantly greater than that of experimental group I and control group and the contents of IgG and SOD in serum of experimental group I and II were significantly greater than that of control group (P< 0.05). The contents of 1-octen-3-ol and hexanal in chicken breast of experimental group II were greater than those of experimental group I and control group (P< 0.05). Conclusion: It was concluded that adding selenium-enriched fermented Chinese herbal feed additive in the basal diet can enhance the growth performance and immune performance of Hyline brown. It also helped to improve the flavor of the meat. Innovation: In this paper, seleniumenriched probiotic was used to ferment Chinese herbal medicine to research the effects of selenium-enriched fermented Chinese herbal medicine feed additives on fowl. Meanwhile, similar reports have not been found internationally. [ABSTRACT FROM AUTHOR]

The scientific publication system should evolve into practices that enhance free dissemination and full access to research findings. At the same time, it should ensure reproducibility and transparency and safeguard scientific integrity from the detrimental effects of the current "publish or perish" culture. The objective of this contribution is to introduce the Peer Community In (PCI) Animal Science initiative (https://animsci.peercommunityin.org/), which represents an alternative to the current publication system under the umbrella of the "Peer Community In" project (https://peercommunityin.org/). PCI Animal Science is an international community of researchers working in animal science and related areas and it promotes open science and research transparency. Although PCI Animal Science is not a scientific journal, it operates similarly with editors (here: recommenders) and reviewers. Currently, the PCI Animal Science community has 64 recommenders from 20 countries. PCI Animal Science is a non-profit initiative, run and managed by researchers. The PCI Animal Science community performs, at no cost, rigorous open reviews of preprints that have been deposited on repositories such as bioRxiv and Zenodo from a wide range of research areas related to animal science. Based on independent reviews, a recommender decides whether a paper is recommended or not. Recommended preprints are peer-reviewed and citable stand-alone articles of high scientific value that do not need publication in traditional journals. However, if the authors wish, they can also publish their recommended preprint in the Diamond Open Access Peer Community Journal (https://peercommunityjournal.org/section/animsci/) at no cost. Authors can also submit their recommended manuscript to PCI-friendly journals (i.e., journals that consider the PCI evaluation in their own review processes) or to other journals. This contribution shows the workflow of the evaluation of manuscripts by PCI Animal Science and the advantages of adopting this new publishing model. [ABSTRACT FROM AUTHOR]

Shade or shelter as protection from extremes of weather is required for horses at agricultural research and teaching facilities and is recommended or required by many states, professional organizations, and industry groups. The focus of this paper is the recent research on the responses of horses to hot, sunny weather, which has begun to provide scientific evidence that characterizes how and when shade is used and any benefits shade confers on horses. These behavioral and physiological findings support provision of shade as a resource for thermal comfort and the expression of normal behavior that should be included as a standard of best care practices for healthy adult horses living in the environmental conditions reviewed, rather than an absolute minimum care requirement. Additional research is required for horses living under other environmental conditions, for very young or old horses, horses in very poor body condition, or those with compromised health to determine if their responses to hot, sunny weather differ from those presented here. [ABSTRACT FROM AUTHOR]

Pig and poultry production relies on crossbreeding of purebred populations to produce production animals. In those breeding schemes, selection takes place within the purebred population to improve crossbred performance (CB performance). The genetic correlation between purebred performance (PB performance) and CB performance (rpc) is, however, lower than unity for many traits. When rpc is low, the use of CB performance in selection is required to achieve sizable genetic progress. The objectives of this paper were to describe the different components and importance of rpc, and to review existing literature that report rpc estimates in pigs. The rpc has 3 components: 1) genotype by genotype interactions, 2) genotype by environment interactions, and 3) differences in trait measurements. We theoretically showed that direct selection for CB performance reduces the response to selection in purebreds for PB performance by a factor 1/r2PC , when achieving the same crossbred response as obtained with indirect selection based on PB performance. This implies that direct selection for CB performance leads to less extreme PB performances and thus potentially easier management in the nucleus, especially for traits with low rpc. In the review, 201 rpc estimates from 27 studies were considered, published between 1964 and 2017. The average rpc estimate was 0.63, with 50% of the estimates between 0.45 and 0.87. Standard errors of the estimates were on average 0.16, with 50% of the standard errors between 0.06 and 0.19. For all different trait categories, e.g., Growth, Meat amount, Meat quality, Feed, and Fertility, the average rpc was around 0.6. Genotype by environment interactions appeared to have a smaller contribution to rpc than genotype by genotype interactions. More research regarding the impact of the different components on the rpc can help to improve breeding programs. Future studies are advised to report characteristics of the herd environments in detail, to report estimated h2 and additive genetic variances for purebreds and crossbreds, to report the estimated rpc with standard errors or confidence intervals, to estimate separate rpc for different pure lines, and to genotype the animals under study. [ABSTRACT FROM AUTHOR]

The intramuscular fat (IMF) and oleic acid (OL) content have been favorably related to pork quality and human health. This influences the purchasing behavior of consumers and, therefore, also shifts the attention of breeding companies toward whether these traits are included into the breeding goal of the lines producing for high-valued markets. Because IMF and OL are unfavorably associated with lean content, a key economic trait, the real challenge for the industry is not simply to increase IMF and OL, but rather to come up with the right trade-off between them and lean content. In this paper we review the efforts performed to genetically improve IMF and OL, with particular reference to the research we conducted in a Duroc line aimed at producing high quality fresh and dry-cured pork products. Based on this research, we conclude that there are selection strategies that lead to response scenarios where IMF, OL, and lean content can be simultaneously improved. Such scenarios involve regular recording of IMF and OL, so that developing a cost-efficient phenotyping system for these traits is paramount. With the economic benefits of genomic selection needing further assessment in pigs, selection on a combination of pedigree-connected phenotypes and genotypes from a panel of selected genetic markers is presented as a suitable alternative. Evidence is provided supporting that at least a polymorphism in the leptin receptor and another in the stearoyl-CoA desaturase genes should be in that panel. Selection for IMF and OL results in an opportunity cost on lean growth. The extent to which it is affordable relies on the consumers' willingness to pay for premium products and on the cost to benefit ratio of alternative management strategies, such as specific dietary manipulations. How the genotype can influence the effect of the diet on IMF and OL remains a topic for further research. [ABSTRACT FROM AUTHOR]

Managing the demands of an academic appointment in extension can be a challenging task. Demands from constituent groups, expectations of supervisors, and rigors of promotion and tenure processes can create pressures that young faculty did not expect. Throw in spousal and family duties and you have created a situation that many will find hard to navigate. However, there are ways to cope and, even better news, there are ways to excel in meeting the demands of an academic appointment and enjoying life. Because many new extension faculty members do not have prior experience in extension, best practices in documenting programs and extension scholarship over the pretenure period are provided in this paper. Appointments that include both research and extension are quite common at many land grant universities. The advantages of joint appointments are numerous and include the fact that more and more grant agencies are seeking integrated research, teaching, and/or extension projects. However, the time demands of joint appointments can be challenging. Joint appointments can be designed to help faculty members conduct important translational research and have it be applied in a production setting. By seeking commonalities in research and extension efforts, joint appointments can be very synergistic. Development of highly successful programs requires planning on the front end with an emphasis on an in-depth needs assessment to determine stakeholder needs for both research and extension. Impact assessment should be part of this planning effort. Performing as a successful extension faculty member while maintaining relationships outside of work is challenging and requires deliberate effort on the part of employees and supervisors to realize there is more to life than work. Some authors have referred to this as work-life balance, but it may be more helpful to think of it as work-life effectiveness. To do this, one needs to 1) define what success looks like, 2) set boundaries and maintain control including control of your schedule, and 3) find time to ensure your physical, emotional, and spiritual well-being are nurtured in addition to your professional development. In summary, extension careers can be challenging at times as demands and expectations of stakeholders, supervisors, and rigors of the tenure system create formidable obstacles. However, by keeping a focus on the priorities of the position and looking for synergy in research and extension work, they can actually be quite enjoyable and very rewarding. [ABSTRACT FROM AUTHOR]

Some popular press articles and social media discussions continue to cite the erroneous data presented in the "Livestock's Long Shadow" publication (FAO, 2006). While this data has subsequently been shown to be incorrect (Pitesky et al, 2009), on-going discussions about the high level of impact of livestock on global GHG emissions continue to be shared as fact nearly 15 years later. There is inherent complexity in studying the biology of livestock for meat and milk production. Efforts to make this complexity more understandable can lead to incorrect assumptions and create an unfavorable and incorrect view of dairy and livestock production for the general public. An unfavorable image of animal production creates added challenge for dairy and livestock producers. If the general public is to believe information about the environmental impact of livestock production, it is critical that that information is both accurate and easily understood. The following paper that highlights sustainable animal science and practices by Capper and Cady (2020) provides a review using modeling techniques for more recent updates (2007–2017), GHG emissions and overall impact of dairy production on environmental sustainability. Additionally, the contribution of ruminants to overall GHG emissions has also been more recently been summarized by Mitloehner (2018.) [ABSTRACT FROM AUTHOR]

This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals (A22-1) by a vector (q). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix (A-1) including genotyped animals and their ancestors. The elements of A-1 were rapidly calculated with the Henderson's rule and stored as sparse matrices in memory. Implementation of A22-1 q was by a series of sparse matrix–vector multiplications. Diagonal elements of A22-1, which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of A22-1 q was compared with explicit inversion of A22 with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When the equations in ssGBLUP are solved with the PCG algorithm, A22-1 is no longer a limiting factor in the computations. [ABSTRACT FROM AUTHOR]

Macro- and microminerals are required for all physiological processes in beef cattle. When mineral deficiencies occur in pasture-based production systems, a multitude of deficiency symptoms may occur, and most often, these deficiencies result in hidden losses to include reduced production potential, reduced immunity, and reproductive failure. More severe deficiencies will result in specific deficiency symptoms, depending on the specific mineral deficiency. In pasture-based beef operations, specific deficiency symptoms are seldom observed. Forage mineral supply is highly variable and dependent on the forage type, stage of forage growth, and soil fertility. Grazing cattle mineral requirements change throughout the production cycle. With the variability of forage mineral supply and the changing mineral requirements, care must be taken when developing mineral supplementation programs for grazing cattle. The macrominerals function in nerve transmission, bone development, and metabolism and as electrolytes. These are Ca, P, Mg, Na, K, Cl, and S. The microminerals function in various areas of metabolism, immune responsiveness, reproductive efficiency, and many other functions. These minerals are Zn, Cu, Fe, I, Mn, Se, Mo, and Co. Of these macro- and microminerals, those that are important for supplementation to pasture-based cattle include Ca, P, Mg, Na, Cl, Zn, Cu, I, Mn, Se, and Co. However, the amounts to be supplemented will depend on the specific mineral requirements and the forage supply of the minerals. Consequently, the development of free-choice mineral supplementation programs is a moving target. Generally, this entangled mess is sorted by taking one mineral at a time into consideration. However, in doing so, one has to be cognizant of the many interactions that occur when changing intake of just 1 mineral in the supplement with respect to the amount supplied in the base forage. Often times, mineral nutrition problems of one or more minerals are created when trying to correct a deficiency of another mineral. This paper will address specific factors that are important to consider when developing and delivering mineral supplementation programs in pasture-based production systems in the Southeastern United States. [ABSTRACT FROM AUTHOR]

Beef cow efficiency is a century’s old debate on what the criteria, certain phenotypic traits, and definition of an “efficient” cow. However, we do know that energy utilization by the cow herd is proportionally large compared to the rest of the sector, which accounts up to 70 to 75% of dietary energy for maintenance with the residual used for pregnancy, lactation, activity and adaptation to the environment. Therefore, leveraging genetic variation in cow energy efficiency by selecting cows that require less energy for maintenance potentially reduces total energy utilization for beef cattle production, which will ultimately improve production efficiency and profitability. For livestock producers, optimizing both economic and biological efficiency is critical. The continued viability of production systems utilizing rangelands requires more rapid adoption of innovative management practices and selection tools that lend to increased profitability through optimization of nutrient utilization and increased reproductive performance. However, the implementation of a gold standard to identify energy efficient beef cows has not yet been fully realized for the beef industry. Rangeland beef cow herds are required to be biologically and reproductively efficient in an array of ever changing environmental conditions. The most efficient cows are those that easily adapt to environmental changes and have the ability to conserve or reset their maintenance requirements to match current environmental conditions. To achieve this metabolic adaptability or flexibility, beef cows need to be suited to their environment and have the ability to acclimate to environmental changes. The purpose of this proceedings paper is to identify and describe factors that influence range cow efficiency and ultimately contributes to the sustainability (both from a productivity and economic view point) of range cows in extensive enterprises. [ABSTRACT FROM AUTHOR]

Many factors can affect animal feed efficiency which is one of the important economic traits in the beef industry. To date, our understanding of the role of the rumen microbiota and its impact on feed efficiency is largely unknown. The objective of this paper was to determine if the rumen microbiota is associated with residual feed intake (RFI) by studying the metatranscriptome of the rumen microbiome from cattle with different RFI values. The microbial transcriptome profiles revealed that Succinivibrionaceae and Methanomassiliicoccales were the most active microbial phylotypes, occupying approximately 44.1% and 35.6% of the bacterial and archaeal communities in the rumen of steers fed with barley-based diet, respectively. Further, the relative abundance of Lachnospiraceae, Veillonellaceae, and p-2534–18B5 bacterial families and Methanomassiliicoccales archaeal taxa were different between low RFI (L-RFI; efficient) and high RFI (H-RFI; inefficient) beef steers. The microbiome of L-RFI animals possessed characteristics associated with higher metabolic function and cell survivability, suggesting that enhanced fermentation capacity and adaptability of the microbiome may be key factors to improve nutrient availability. Our results provide evidence that the rumen microbiome could be one of the biological factors associated with variation in cattle feed efficiency. [ABSTRACT FROM AUTHOR]

This paper deals with the use of the Choquet integral to identify breeding objectives and construct an aggregate genotype. The Choquet integral can be interpreted as an extension of the aggregate genotype based on profit equations, substituting the vector of economic weights by a monotone function, called capacity, which allows the aggregation of traits based, for instance, on the preferences of decision makers. It allows the aggregation of traits with or without economic value, taking into account not only the importance of the breeding value of each trait but also the interaction among them. Two examples have been worked out for pig and dairy cattle breeding scenarios to illustrate its application. It is shown that the expression of stakeholders’ or decision makers’ preferences, as a single ranking of animals or groups of animals, could be sufficient to extract information to derive breeding objectives. It is also shown that coalitions among traits can be identified to evaluate whether a linear additive function, equivalent of the Hazel aggregate genotype where economic values are replaced by Shapley values, could be adequate to define the net merit of breeding animals. [ABSTRACT FROM AUTHOR]

A 300-d study was conducted to evaluate storage stability of 4 commercially available phytase products under varied environmental conditions. Products used were: 1) Quantum Blue G (AB Vista, Plantation, FL), 2) Ronozyme Hi Phos GT (DSM Nutritional Products, Parsippany, NJ), 3) Axtra Phy TPT (Dupont, Wilmington, DE), and 4) Microtech 5000 Plus (Guangdong Vtr Bio-Tech Co., Ltd., Guangdong, China). Products were stored as pure forms, in a vitamin premix, or a vitamin trace mineral (VTM) premix. Pure products were stored at -20°C, 4°C, 22°C, or 35°C (75% humidity). Vitamin and VTM premixes were stored at 22°C or 35°C (75% humidity). Samples were stored in open-topped paper bags and sampled on d 30, 60, 90, 120, 210, and 300. Stability was determined as the amount of residual phytase activity (% of initial) at each sampling point. For the pure forms, all interactive and main effects of phytase product, time, and storage temperature were significant (P < 0.05). From d 30 to 300, products had similar reductions in phytase activity at the 3 highest temperatures; however, Quantum Blue G, Ronozyme HiPhos GT, and Axtra Phy TPT had reduced (P < 0.05) phytase activity as compared to Microtech 5000 at -20°C. In general, as storage time increased, residual phytase activity decreased (P < 0.05) regardless of product and storage temperature. When product was stored at 4°C and 22°C, phytase activity was greater than that of product stored at -20°C or 35°C, and Microtech 5000 Plus had greater (P < 0.05) stability regardless of time and temperature as compared to the other 3 products. For vitamin and VTM premixes, a time x temperature x product interaction (P < 0.05) was observed. When stored at 22°C, Axtra Phy TPT and Microtech 5000 Plus had reduced residual phytase activity (P < 0.05) when compared to the other 2 products; however, when stored at 35°C Axtra Phy had even further reduced (P < 0.05) activity than the other 3 products regardless of which form the products were stored in. From d 30 to 300 Axtra Phy TPT and Microtech 5000 Plus had the lowest (P < 0.05) residual phytase activity when compared to the others regardless of storage form or temperature. Phytase products stored in VTM premix had decreased (P < 0.05) residual phytase activity when compared to pure products and vitamin premixes. In conclusion, phytase stored for longer than 90 to 120 d, at both high (35°C) and low (-20°C) temperatures when in pure form or as a VTM premix had reduced residual phytase activity. [ABSTRACT FROM AUTHOR]

The porcine model has been utilized by researchers to study human developmental aspects, surgical procedures, disease impacts, and nutritional interventions for over 60 yr. However, when it comes to investigating how nutrients regulate human gastrointestinal tract (GIT) function and health, and nutritional research evolves from dietary composition to potential nutraceutical applications, the question needs to be asked: is the pig an appropriate translational animal model? Morphologically and functionally, the porcine GIT has more similarity to the human GIT than commonly used models such as rats. Food intake related to BW is much lower in humans than in pigs, which explains the higher and faster growth and earlier maturation in the latter species. In addition, the digestion and metabolism of carbohydrates, prebiotics, lipids, and amino acids produce similar metabolites and subsequent effects on GIT development and health in pigs and humans, making the pig a more comparable model to study human GIT issues than other nonprimate species. However, there are key divergences. Pigs have a functional cecum, larger colon, and the endogenous microbiota of the GIT, while similar at the phylum level is vastly different at the genera level. One notable microbial species that is low to nonexistent in the porcine GIT are the Bifidobacterium, which is a major probiotic species in humans. Several porcine models have been developed to overcome these issues, but they are new and have yet to be fully tested. This paper will provide a comparative analysis between the human and porcine GIT in regards to structure, function, and health and how various nutritional factors influence these characteristics and future developments of the porcine model. [ABSTRACT FROM AUTHOR]

This paper provides a review on the effects of functional ingredients and processing of ingredients and diets on the functions of the gastrointestinal tract (GIT) and health of the GIT in pigs. There is increasing attention for these topics since the ban of in-feed growth promoting antibiotics in many parts of the world and due to the pressure on the use of antibiotics in animal production. A prime function of the GIT is to digest and absorb nutrients. In addition, it has a complex barrier function of which the intestinal microbiota and the residing local immune system are important components. The health of the GIT is related to the capacity of the GIT to exert these functions. Ingredient and nutrient composition of the diet affect the various functions of the GIT and, therefore, also influence GIT health. Feed and feed ingredient processing can affect the extent and site of enzymatic nutrient digestion within the small intestine but can also induce changes in the extent of fermentation of dietary constituents by intestinal microbiota and induce changes in microbiota composition in various segments of the GIT. Further understanding of the mechanisms involved in the complex interactions among the diet, intestinal microbiota, and intestinal tissue can assist in supporting functions of the GIT and health of the GIT via targeted modifications of the diet. [ABSTRACT FROM AUTHOR]