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In recent times, there has been a growing focus on the well-being and healthcare of small ruminants, particularly in relation to the issue of anemia due to infection with blood-feeding gastrointestinal nematodes, such as Haemonchus contortus. The objective of this study was to assess the hematocrit levels in blood samples obtained from small ruminants, specifically goats. Additionally, the study is an attempt to design and create a quick sensor for identifying anemia, which could be conveniently used on farms. A total of 75 mature, intact male Spanish goats were subjected to hematocrit analysis to ascertain their spectrum of hematocrit values and association with anemic conditions. Simultaneously, a unique sensor with user-friendly features was developed to promptly provide farmers with feedback on the anemic status of animals using AI-based machine learning algorithms, enabling timely intervention. The sensor utilized a semi-invasive approach with minimal blood sample requirement. 30 µL of blood was dropped on glycerol soaked Whatman filter paper No. 1, and images were taken at 90 sec, 150 sec, and 270 sec to capture the blood pattern on filter paper soaked in glycerol using a cell phone. These images were correlated with actual hematocrit value obtained from conventional hematocrit analysis. A RMSprop with adjusted learning rate convolution neural network (RMSprop-CNN) based images classification model was developed for the classification of different patterns at different levels of hematocrit. The model was trained on a total of 1,000 images with a training and testing split of 80:20 ratio. For the optimization process, the Adam optimizer with a learning rate of 0.001 is employed. The model is compiled with a categorical cross-entropy loss function, aiming to improve its accuracy metric over training iterations. The initial studies demonstrated a detection accuracy level of 70.31 % at 10 epochs in identifying different hematocrit levels (Level 1; healthy, to Level 5; severely anemic) but improved significantly up to 94.89 % by 100 epochs, resulting in a notable reduction in the time and level of knowledge previously necessary for conducting such evaluations. The present study not only provides valuable insights into the hematological characteristics of small ruminants, but also establishes a foundation for the development of a straightforward and efficient method for detecting anemia. Consequently, this advancement has the potential to enhance the overall care and well-being of animals within agricultural environments.

Development of useful gene editing technologies, such as ZFN, TALEN and CRISPR/Cas9 systems, has made it possible to efficiently produce knock-out and knock-in mammals, including domestic animals. In particular, the number of scientific papers on gene editing by using the CRISPR/Cas9 system has increased rapidly since 2013, although it has begun to decline from 2022. Among those researchers, the contribution of Japanese scientists accounts for 5 to 10% of the total number of published papers in many species, including laboratory and domestic animals, while those in rats account for nearly 20 % of the total. Whereas research results in rodents, such as mice and rats, have been published from many Japanese institutions, those in domestic animals, such as pigs, appears to be from only limited numbers. With regard to the application of gene editing technology to livestock species, only pigs have different objectives compared with other species, and are used for xenotransplantation and disease models rather than for industrial applications. Whereas this trend is also true for research in Japan, publications on the attempts of new methodologies seem to stand out. A distinctive feature of Japanese research on mammalian gene editing may be the development of methods to introduce gene editors into oocytes with electroporation, for example, GEEP and iGonad, and the applications. These methods seem to be used to generate knock-out animals efficiently, and production of pigs with simultaneous knockout of two or three genes has been reported. To produce knock-in pigs, however, it seems that cloned animals still need to be produced by nuclear transplantation of somatic cells in which the interested gene has been previously modified. Although the attempts to introduce editing tools directly into zygotes by microinjection, which is much simpler than somatic cell nuclear transplantation, have been reported, it has not been controlled yet to reduce the occurrence of mosaics, and it would be necessary to wait for further improvement. In addition, some methods for gene editing chemically or by lipofection have also been attempted, but the efficiencies may still need to be improved. Arrangements for the proper handling of gene-edited animals and food products by Japanese government, especially whether or not such animals and food products are genetically modified organisms, etc. as defined by law, will have a significant impact on the direction and promotion of research on gene editing in Japan.

Enrichment increases the ability of a sow to express innate behavior in a barren environment and can reduce aggressive behaviors, stress, and promote animal welfare. The objective of this study was to determine sow interaction with different types of enrichment materials provided before farrowing. A total of 11 sows were randomly assigned an enrichment combination of cardboard, paper, and banana leaves. Sows received two enrichment types, then enrichment was switched for the following day, until all combinations were completed. The enrichment material hung from 2x4 wood using ropes that hung the enrichment and chains placed in the ceiling of the farrowing room that held the 2x4 above the pen of each sow. Therefore, enrichment was placed within the nose reach of each sow. Enrichment material (113 g) was placed in the pens at 0700 h and left for 24 h each day before changing with another enrichment combination. Any enrichment material remaining was weighed. Final weight varied by treatment (P= 0.01), with sows consuming the least of cardboard (24.9 ± 10.0 g) and having an equal preference for banana leaves and paper (74.1 ± 10.0 g and 50.1 ± 11.3 g, respectively). Scan sampling was utilized to measure the frequency of interaction with the enrichments. The scans were designed with the first hour recording every minute a sow interacted with enrichment and the following 3 hours recording every 10 minutes. Continuous observation for the first hour of placement was performed for count data. Each time the sow made contact with the enrichment with her nose, the observation was recorded. The results of the behavior observation suggested that time in contact with enrichment did not differ (P> 0.05), nor did the number of times the pig-initiated contact with enrichment (P> 0.05). In conclusion, sow preference showed that bananas and papers were preferred over cardboard. However, sows will engage with any enrichment material provided before farrowing. Future research should be done to see if any environmental factors play a role in sow’s preference.

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.

Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers the different modeling approaches taken in the prediction of dairy cattle CH4 emissions and highlights their individual strengths and limitations. Following the guidelines set out by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA); Scopus, EBSCO, Web of Science, PubMed and PubAg were each queried for papers with titles that contained search terms related to a population of “Bovine,” exposure of “Statistical Analysis or Machine Learning,” and outcome of “Methane Emissions”. The search was executed in December 2022 with no publication date range set. Eligible papers were those that investigated the prediction of CH4 emissions in dairy cattle via statistical or machine learning (ML) methods and were available in English. 299 papers were returned from the initial search, 55 of which, were eligible for inclusion in the discussion. Data from the 55 papers was synthesized by the CH4 emission prediction approach explored, including mechanistic modeling, empirical modeling, and machine learning. Mechanistic models were found to be highly accurate, yet they require difficult-to-obtain input data, which, if imprecise, can produce misleading results. Empirical models remain more versatile by comparison, yet suffer greatly when applied outside of their original developmental range. The prediction of CH4 emissions on commercial dairy farms can utilize any approach, however, the traits they use must be procurable in a commercial farm setting. Milk fatty acids (MFA) appear to be the most popular commercially accessible trait under investigation, however, MFA-based models have produced ambivalent results and should be consolidated before robust accuracies can be achieved. ML models provide a novel methodology for the prediction of dairy cattle CH4 emissions through a diverse range of advanced algorithms, and can facilitate the combination of heterogenous data types via hybridization or stacking techniques. In addition to this, they also offer the ability to improve dataset complexity through imputation strategies. These opportunities allow ML models to address the limitations faced by traditional prediction approaches, as well as enhance prediction on commercial farms.This systematic review outlines the strengths and limitations of traditional dairy cattle methane emission prediction approaches, as well as highlights the promising potential of machine learning in methane emission prediction. Special consideration is also given to the prediction of dairy cattle methane emissions on commercial farms.This review provides a comprehensive overview of the different modeling approaches taken in the prediction of dairy cattle methane emissions.Mechanistic models, which mathematically simulate the methane production process of the dairy cattle rumen, are both accurate and adaptable, yet their necessary input data is difficult to obtain and if imprecise, can produce misinformative results.Empirical models, which statistically quantify the relationships between methane emissions and production factors, are a more accessible alternative to mechanistic models, yet their accessible structure limits them to the same range of data on which they were originally developed.Machine learning models, which are based on a particular learning pattern, can be trained to identify trends in methane production and use these lessons to make accurate predictions. Their application in the prediction of dairy cattle methane emissions remains scarce, yet those that have been show promising potential.Commercially deployable models can utilize any of the previous approaches, as long as the traits they use are obtainable in a commercial farm setting. Those developed favor the use of milk fatty acids, yet the variation in their results needs to be consolidated before robust predictions of methane emissions on commercial farms can be achieved.

Automated Milking Systems (AMS) have undergone significant evolution over the past 30 yr, and their adoption continues to increase, as evidenced by the growing scientific literature. These systems offer advantages such as a reduced milking workload and increased milk yield per cow. However, given concerns about the welfare of farmed animals, studying the effects of AMS on the health and welfare of animals becomes crucial for the overall sustainability of the dairy sector. In the last few years, some analysis conducted through text mining (TM) and topic analysis (TA) approaches have become increasingly widespread in the livestock sector. The aim of the study was to analyze the scientific literature on the impact of AMS on dairy cow health, welfare, and behavior: the paper aimed to produce a comprehensive analysis on this topic using TM and TA approaches. After a preprocessing phase, a dataset of 427 documents was analyzed. The abstracts of the selected papers were analyzed by TM and a TA using Software R 4.3.1. A Term Frequency-Inverse Document Frequency (TFIDF) technique was used to assign a relative weight to each term. According to the results of the TM, the ten most important terms, both words and roots, were feed, farm, teat, concentr, mastiti, group, SCC (somatic cell count), herd, lame and pasture. The 10 most important terms showed TFIDF values greater than 3.5, with feed showing a value of TFIDF of 5.43 and pasture of 3.66. Eight topics were selected with TA, namely: 1) Cow traffic and time budget, 2) Farm management, 3) Udder health, 4) Comparison with conventional milking, 5) Milk production, 6) Analysis of AMS data, 7) Disease detection, 8) Feeding management. Over the years, the focus of documents has shifted from cow traffic, udder health and cow feeding to the analysis of data recorded by the robot to monitor animal conditions and welfare and promptly identify the onset of stress or diseases. The analysis reveals the complex nature of the relationship between AMS and animal welfare, health, and behavior: on one hand, the robot offers interesting opportunities to safeguard animal welfare and health, especially for the possibility of early identification of anomalous conditions using sensors and data; on the other hand, it poses potential risks, which requires further investigations. TM offers an alternative approach to information retrieval in livestock science, especially when dealing with a substantial volume of documents.According to the results of the text mining, the ten most important terms, both words and roots, were feed, farm, teat, concentr, mastitis, group, SCC (somatic cell count), herd, lame, and pasture.Eight topics were selected with topic analysis, namely: 1) Cow traffic and time budget, 2) Farm management, 3) Udder health, 4) Comparison with conventional milking, 5) Milk production, 6) Analysis of AMS data, 7) Disease detection, and 8) Feeding management.Milking robots have revolutionized the cow milking, reducing dependence on human labor and increasing milk yield per cow. However, addressing concerns about farmed animal welfare and overall sustainability is crucial. This paper presents a text-mining analysis of the scientific literature to explore the effects of robotic milking on cow health, welfare, and behavior. The analysis revealed a growing body of research studies on these subjects, highlighting the complex nature of the relationship between automated milking, welfare, health, and cow behavior. Robotic milking has the potential to enhance animal health and living conditions, but the associated risks require further investigation.

This paper presents a non-contact method for the detection of changes in sow vulva size in a group pen. The traditional approach to estrus detection is manually pressing down on the back of the sow to elicit standing responses; however, this method causes undue distress for sows not in estrus. When a sow is in estrus, the vulva is red and swollen due to the presence of endocrine. Monitoring changes in vulva size to detect estrus with as little impact on the sow as possible is the focus of this study. This is achieved using a single camera combined with a deep learning framework. Our approach comprises two steps: vulva detection and vulva size conversion. Images of sows of Yorkshire, Landrace, and Duroc breeds were collected in group housing, and the vulva was detected through artificial markers and the network architecture of YOLO v4. Based on the internal and external parameters of the camera, the detected size was converted into millimeters and the results of manual measurement (MM) and automatic calculation combined to calculate the size of the vulva. Analysis of the calculated size compared with MM indicates that the object recognition rate of the system exceeds 97.06%, with a size error of only + 1.70 to −4.47 mm and high-calculation efficiency (>2.8 frames/s). Directions for future research include the automatic detection of pig width.This paper uses a single camera to accurately grasp changes in the size of sow vulva in a group pen. A fixed camera combined with deep learning detects the position and size of the vulva. Pixel size is converted into actual size through the internal and external parameters of the camera, with a size error of <4.5 mm.The size of a sow’s vulva is an important indicator of sow estrus. Non-contact means of monitoring size changes for estrus timing would represent a significant contribution to the field of pig farming. This paper thus focuses on development of a system for the automatic detection of sow vulva size using a single camera combined with a deep learning framework. Experiments showed that the object recognition rate of the system exceeds 97.06%, the vulva size error is +1.70 to −4.47 mm, and the calculation efficiency is high (>2.8 frames/s).

Prediction models for estimating feed intake (FI) or metabolizable energy intake (MEI) of pigs are available in the literature. During the recent two decades, several reports have addressed the influence of heat stress on FI. The objectives of the present work were to validate the accuracy of prediction equations for estimating FI or MEI of pigs in the literature using data from heat stress experiments and to develop novel equations for estimating FI and MEI of pigs under thermoneutral or heat stress environment. A total of 123 data from 25 research papers measuring the effects of heat stress on FI of pigs published between 2001 and 2021 were used for the validation of previous equations and the development of novel equations. In the 25 papers, the thermoneutral temperature (T) ranged from 17 to 24℃ and the heat stress T ranged from 21 to 35℃. The body weight (BW) of pigs ranged from 19 to 119 kg. In the first validation study, the equation for estimating FI was evaluated: FI, g/day = –1,264 + 73.6×BW – 0.26×BW2+ 117×T – 2.40×T2– 0.95×T×BW. The slope representing a linear bias did not differ from 0 but the intercept representing a mean bias was greater than 0 (109 ± 36; P< 0.01), meaning that the predicted mean underestimated the FI. In the second validation study, the equation for estimating MEI using equations for lower critical temperature (LCT) and fraction of MEI: LCT, ℃ = 17.9 – 0.0375×BW and fraction of MEI = 1 – 0.012914 × [T – (LCT + 3)] – 0.001179 × [T – (LCT + 3)]2. The slope representing a linear bias did not differ from 0 but the intercept representing a mean bias was greater than 0 (731 ± 68; P< 0.001), meaning that the predicted mean underestimated the MEI. A novel equation for estimating MEI under thermoneutral or heat stress environment was developed: MEI, kcal/day = 2,603 + 137×BW – 0.189×BW2– 38.4×T – 0.556×T2– 1.165×T×BW (P< 0.001; R2= 0.83). An equation for estimating fraction of MEI under thermoneutral or heat stress environment was also developed: fraction of MEI = 1 – 0.00677 × [T – (LCT + 3)] – 0.000668 × [T – (LCT + 3)]2(P< 0.001; R2= 0.98). In conclusion, previously published equations underestimate FI and MEI and novel equations for estimating MEI under thermoneutral or heat stress environment have been developed in the present work. However, the novel equations have not been validated under cold stress.

Genetic improvement of animals plays an important role in improving the economic and environmental sustainability of livestock production systems. This paper proposes a method to incorporate mitigation of environmental impacts and risk preferences of producers into a breeding objective via economic values (EVs). The paper assesses the effects of using these alternative EVs of breeding goal traits on discounted economic response to selection and on environmental impacts at commercial farm level. The application focuses on a Brazilian pig production system. Separate dam- and sire-line breeding programs that supply parents in a 3-tier production system for producing crossbreds (fattening pigs) at commercial level were assumed. Using EVs that are derived from utility functions by incorporating risk aversion increases the cumulative discounted economic response to selection in sire-line selection (6%) while reducing response in dam-line selection (12%) compared with the use of traditional EVs. The use of EVs that include environmental costs increases the cumulative discounted social response to selection in both dam-line (5%) and sire-line (10%) selections. Emission of greenhouse gases, and excretion of nitrogen and phosphorus can be reduced more with genetic improvements of production traits than reproduction traits for the typical Brazilian farrow-to-finish pig farm. Reductions in environmental impacts do not, however, depend on the use of the different EVs (i.e., with and without taking into account environmental costs and risk). Both environmental costs and risk preferences of producers need to be considered in sire-line selection, and only environmental costs in dam-line selection to improve, at the same time, the economic and environmental sustainability of the Brazilian pig production system.

The public controversy around genetically engineered (GE) products, aka genetically modified organisms (GMO), has been ongoing for almost 40 yr fueled in part by well-funded misinformation campaigns disseminated by activist organizations. Polls reveal that public perception of GE animals is generally negative, with biomedical applications being more positively perceived than agricultural applications. Opposition to GE animals is often conflated with opposition to use of animals in research in general, as well as opposition to aspects of intensive animal agriculture. In general, concerns about animal biotechnology are influenced by views around the moral status of animals, the boundary between “natural” and “unnatural,” and perceived risks and benefits of GE animals to health and the environment (personal and cultural characteristics); the purpose of the application (e.g., disease resistance > fast growth), the method(s) being used, and the motivation of the research group making the genetic modification (public or private); and the species being modified (animal characteristics). There were three factors that aggravated opposition to GE products including 1) a lengthy and expensive regulatory step uniquely associated with commercializing products developed using that breeding method, 2) competing business interests (e.g., organic, non-GMO project) that were able to monetarize fear around the method to extract value (rent seeking) and selling their value-added (more expensive) product that avoided the use of GE, and 3) there is a mandatory labeling requirement to track/differentiate products produced with (or without) that breeding method to enable value-added marketing. Even so, when GE products that people wanted were allowed to reach the market (e.g., Impossible Burger with GE heme, GloFish, AquAdvantage salmon), they met with commercial acceptance. Now it has been suggested that the public will similarly reject animal products derived from gene edited (GnEd) animals, but that idea has only recently been put to the test as such food products were not previously available. A 2022 Japanese paper noted that with regard to GnEd food (gene knock-out tomatoes and fish) there was a “gap between anticipated and actual public response”. They noted that “even though there were some petitions against the use of genome-editing made by consumer groups; they did not develop into a mass mobilization, and media coverage was mostly positive.” These GnEd food products were developed by startups from Japanese universities, required no unique regulatory step before commercialization or mandatory labeling with Japanese regulatory agencies transparently sharing a summary of the information and notification on their websites, and businesses were encouraged to share information on products to respect The right of consumers to know. Successful commercialization of GnEd animal products has so far occurred in countries (e.g., Argentina, Brazil, Japan) where regulations treat simple GnEd products with no “foreign DNA” to be “non-GMO” and analogous to conventional breeding.

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.

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.

Ionophores are a separate class of non-medically 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 meta-regression 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 step-wise 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%.

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 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.

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.

To meet the challenges of an increasing global population and multiple environmental stressors on agricultural production, it is essential to better understand how genotype (G) and environment (E) influence phenotype for traits of economic importance in agriculture. To accomplish this requires interdisciplinary teams consisting of researchers from crop and livestock sciences, genetics, genomics, computational and data sciences, and engineering. Towards this end, in 2020, Congress established the Agricultural Genome to Phenome Initiative (AG2PI). An important objective of this presentation is to encourage strong participation of animal science researchers and industry in ongoing AG2PI activities. The purpose of the initial AG2PI project, awarded to Iowa State University and the Universities of Arizona, Idaho and Nebraska, was to assemble and prepare a community to conduct AG2P research across the plant and animal kingdoms. The AG2PI project aims to: a) identify research gaps and opportunities, b) foster community solutions to these challenges, and c) rapidly disseminate findings for community use. Training workshops, field days, conferences, community surveys, and seed grants have all contributed to these goals. Opinions and ideas from the crop and livestock genetics communities are key to advancing AG2P science and related work. AG2PI has used online community surveys and in-person conferences to gather these and to facilitate transdisciplinary communications. In September 2022, AG2PI held a conference titled “Thinking Big: Visualizing the Future of AG2PI”, in which attendees participated in 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 are summarized in a concept paper for the community, shared with USDA-NIFA, and submitted for publication. A follow-up conference was held June 14-15, 2023, in Kansas City, where people working in both the animal and plant genetics research communities and industry stakeholders attended. Outcomes from this conference will be summarized in this presentation, and requests for animal scientists to provide further input will be made. The future of agricultural genomics will be shaped by both the participants in AG2PI and the solutions developed by the AG2PI community. Funding for this work comes from USDA NIFA awards 2022-70412-38454, 2021-70412-35233, and 2020-70412-32615.

Claw lesions in dairy cows are a primary contributor to lameness, posing severe welfare concerns due to the distress and discomfort experienced by affected cows. Claw disorders have a significant negative impact on herd efficiency, milk yield, reproductive performance, and involuntary culling. Despite increased awareness of the detrimental effects of lameness, its incidence has continued to rise. Reducing the occurrence of lameness remains a major challenge, especially in defining and recording of claw traits. In South Africa (SA), claw data is limited to private hoof trimmers with paper-based recording. The objective of this research was to examine the occurrence and recording of claw lesions in Holstein cattle from the central region of South Africa. Claw-trimming data from five intensively managed dairy farms (average herd size: 1,073) using both dirt lot (DL) and free-stall (FS) housing systems were analyzed over a six-year period (2014 to 2019). The Claw Lesion Identification in Dairy Cattle brochure, co-developed by Zinpro Corporation and the International Lameness Committee, was used as the reference for lesion identification by a local hoof trimmer. Digital dermatitis (D) showed the greatest prevalence (64.02% of all recorded lesions), followed by sole ulcers (U; 8.59%), white line disease (W; 6.27%), and sole hemorrhage (H; 4.28%), with most lesions occurring in the rear feet. A chi-square test of independence between lesion and housing was rejected (P <0.0001). Correspondence analysis (CA) was then used to explore these dependencies. The first dimension of the graphical display explained 90.7% of the variation in the data. The CA indicated that the prevalence of D and F are strongly associated, and closely associated with U. The prevalence of these lesions is also associated with housing systems DL and FS. The CA also confirmed a close association between W and H, and the prevalence of these lesions in housing system DL+FS. Improving the precision of claw lesion identification and recording, especially with regard to lesions that tend to occur together, can furnish essential data to identify the most prevalent lesions in a population. This will enhance our comprehension of the causal factors behind these significant lesions and offer producers practical recommendations to use these data for prevention, early detection, management, and improvement of lameness.

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.

Constructing dynamic mathematical models of biological systems requires estimating unknown parameters from available experimental data, usually using a statistical fitting procedure. This procedure is usually called parameter identification, parameter estimation, model fitting, or model calibration. In animal science, parameter identification is often performed without analytic considerations on the possibility of determining unique values of the model parameters. These analytical studies are related to the mathematical property of structural identifiability, which refers to the theoretical ability to recover unique values of the model parameters from the measures defined in an experimental setup and use the model structure as the sole basis. The structural identifiability analysis is a powerful tool for model construction because it informs whether the parameter identification problem is well-posed (i.e., the problem has a unique solution). Structural identifiability analysis is helpful to determine which actions (e.g., model reparameterization, choice of new data measurements, and change of the model structure) are needed to render the model parameters identifiable (when possible). The mathematical technicalities associated with structural identifiability analysis are very sophisticated. However, the development of dedicated, freely available software tools enables the application of identifiability analysis without needing to be an expert in mathematics and computer programming. We refer to such a non-expert user as a practitioner for hands-on purposes. However, a practitioner should be familiar with the model construction and software implementation process. In this paper, we propose to adopt a practitioner approach that takes advantage of available software tools to integrate identifiability analysis in the modeling practice in the animal science field. The application of structural identifiability implies switching our regard of the parameter identification problem as a downstream process (after data collection) to an upstream process (before data collection) where experiment design is applied to guarantee identifiability. This upstream approach will substantially improve the workflow of model construction toward robust and valuable models in animal science. Illustrative examples with different levels of complexity support our work. The source codes of the examples were provided for learning purposes and to promote open science practices.Identifiability analysis is a powerful, valuable tool for developing robust predictive models. With the help of freely available software, modelers in animal science can easily integrate this analysis into their model developments.When modeling biological systems, one major step of the modeling exercise is connecting the theory (the model) with the reality (the data). Such a connection passes through the resolution of the parameter identification (model calibration) problem, which aims at finding a set of parameters that best fits the variables predicted by the model to the data. Traditionally, the parameter identification step is often addressed like a downstream process (after data collection). Using this traditional approach, the modeler has minimal room for maneuvering to improve the model’s accuracy. This paper discusses the benefits of adopting an upstream approach (before data collection) during the model construction phase. This approach capitalizes on the identifiability analysis, a powerful tool seldom applied in dynamic models of the animal science domain, likely because of the lack of awareness or the specialized mathematical technicalities involved in the identifiability analysis. In this paper, we illustrate that the modeling community in animal science can easily integrate identifiability analysis in their model developments following a practitioner approach taking advantage of a variety of freely available software tools dedicated to identifiability testing.

Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the “per animal” approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.Precision livestock farming (PLF) needs current technologies to adapt to suit its unique needs. We analyze cutting-edge sensor, networking, communication, and analytics advancements from the perspective of PLF.Precision technologies are revolutionizing animal agriculture by enhancing the management of animal welfare and productivity. To fully realize the potential benefits of precision livestock farming (PLF), the development and application of digital technologies are needed to facilitate the responsible and sustainable intensification of livestock production over the next several decades. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring audibility in value chains while assuaging concerns associated with labor shortages. In this paper, we analyze the multilayered network of sensors, actuators, communication, and analytics currently in use in PLF. We analyze the various aspects of sensing, communication, networking, and intelligence on the farm leveraging dairy farms as an example system. We also discuss the potential implications of advancements in communication, robotics, and artificial intelligence on the security and welfare of animals.

Graduate education is an important aspect of the life of most academic scientists and a serious responsibility because it comes with the obligation to help students achieve their career and life goals. It can also be very fulfilling for the graduate mentor in terms of personal satisfaction and advancement of the research program. Learning to be a good major professor is an active process that depends on developing a formal framework of education and modifying that framework for each student based on past experiences and experimentation, advice from colleagues, and the individual personality of the student. Perhaps most important is for the graduate mentor to buy into the success and well-being of the student. Among the characteristics that a major professor could seek to instill in his or her students are critical and independent thinking, self-confidence, a thick skin, teamwork, laboratory skills and understanding, and the ability for hard work. Work to make science joyful by celebrating accomplishments, creating a fun environment in the lab, and stressing the societal value of science as compared to personal rewards or ambition.Want advice on how to mentor graduate students? This paper, like all papers on graduate education written by Casida Award recipients, has some food for thought.Science is dependent on education of the next generation through the formal and informal instruction that new scientists experience in graduate school. This paper focuses on some approaches to be considered by the graduate mentor as he or she guides his students into the world of science.

Static quantification measures of chemical components are commonly used to make certain assumptions about forage or feed nutritive value and quality. In order for modern nutrient requirement models to estimate intake and digestibility more accurately, kinetic measures of ruminal fiber degradation are necessary. Compared to in vivo experiments, in vitro (IV) and in situ (IS) experimental techniques are relatively simple and inexpensive methods to determine the extent and rate of ruminal fiber degradation. This paper summarizes limitations of these techniques and statistical analyses of the resulting data, highlights key updates to these techniques in the last 30 yr, and presents opportunities for further improvements to these techniques regarding ruminal fiber degradation. The principle biological component of these techniques, ruminal fluid, is still highly variable because it is influenced by ruminally fistulated animal diet type and timing of feeding, and in the case of the IV technique by collection and transport procedures. Commercialization has contributed to the standardization, mechanization, and automation of the IV true digestibility technique, for example, the well-known DaisyIIIncubator. There has been limited commercialization of supplies for the IS technique and several review papers focused on standardization in the last 30 yr; however, the IS experimental technique is not standardized and there remains variation within and among laboratories. Regardless of improved precision resulting from enhancements of these techniques, the accuracy and precision of determining the indigestible fraction are fundamental to modeling digestion kinetics and the use of these estimates in more complex dynamic nutritional modeling. Opportunities for focused research and development are additional commercialization and standardization, methods to improve the precision and accuracy of indigestible fiber fraction, data science applications, and statistical analyses of results, especially for IS data. In situ data is typically fitted to one of a few first-order kinetic models, and parameters are estimated without determining if the selected model has the best fit. Animal experimentation will be fundamental to the future of ruminant nutrition and IV and IS techniques will remain vital to bring together nutritive value with forage quality. It is feasible and important to focus efforts on improving the precision and accuracy of IV and IS results.The in vitro and in situ techniques are as important to ruminant nutrition as they were 30 yr ago. There have been improvements in methodology and mathematical modeling of results, but there is still tremendous opportunity to improve the precision and application of in vitro and in situ techniques.In vitro and in situ techniques are important to studying ruminant nutrition because these procedures go beyond measures of components of a feedstuff in a laboratory by fermenting a sample in ruminal fluid. The in situ procedure was first described regarding ruminant nutrition in 1938 and in vitro in 1966 and both techniques have been refined over time to improve the reliability of results. This review focused on the state of knowledge 30 yr ago and significant discoveries that have impacted these techniques in the last 30 yr and shared a vision for future opportunities to refine these methods further. Commercialization of equipment and supplies has resulted in increased standardization of these methods; however, efforts should be made to continue to improve the standardization, and reliability of the results, of these procedures. Statistical analyses and data science applications are opportunities to expand these techniques to modern nutritional models and/or forecasting animal performance. The amount and kinetics of ruminal degradation estimate that in vitro and in situ techniques provide continue to be crucial to advance the efficiency and sustainability of ruminant animal production.

Poultry are sensitive to red objects, such as comb and blood on the body surface, likely inducing injurious pecking in flocks. Light is an important factor that affects the pecking behavior of poultry. A wooden box was built to investigate the effects of Light Emitting Diode (LED) light color (warm white and cold white) and intensity (5 and 50 lux) of background light on the discrimination of red objects in broilers. A piece of red photographic paper (Paper 1) was used to simulate a red object and paired with another piece of paper (Paper 2 to 8) with a different color. Bigger number of the paired paper indicated greater color difference. The experiment consisted of three phases: adaptation, training, and test. In the adaptation phase, birds were selected for the adaptation to reduce the stress from the box. In the training phase, birds were trained to discriminate and peck at Paper 1 when paired with Paper 8 under one type of background light. Twenty-three birds were tested when the paired paper was changed from Paper 7 to 2. Each pair of paper included 12 trials for every bird, and response time to peck and proportion of choices of Paper 1 in the last 10 trials were collected. The results showed that broilers tested under 5 lux light had longer response times than broilers tested under 50 lux light (P< 0.05). When Paper 1 was paired with paper 7, broilers tested under warm white light had lower proportion of choices of Paper 1 than those tested under cold white light (P< 0.05). Color difference had a significant effect on response time of broilers (P< 0.05). Moreover, the proportion of choices of Paper 1 decreased to 50% (chance-level performance) when color of the paired paper was gradually similar to Paper 1. Conclusively, rearing broilers in warm white rather than cold white light with appropriate light intensity should be recommended to reduce damaging pecking behavior in broiler production.Light color and intensity will affect the discrimination of red objects in broiler.Poultry are sensitive to red objects, such as comb and blood on the body surface, likely inducing injurious pecking in flocks. We built a wooden box to investigate the effects of light color (reddish and bluish) and intensity (5 and 50 lux) of background light on the discrimination of red objects in broilers. A piece of red photographic paper (Paper 1) was used and paired with another piece of paper (Paper 2 to 8) with a different color. Every bird was trained to discriminate and peck at Paper 1 when paired with Paper 8 under one type of background light. Then, Paper 8 was changed from Paper 7 to 2. Response time to peck and proportion of choices of Paper 1 were collected. We found that broilers under 5 lux light had longer response times than under 50 lux light. Broilers under reddish light had lower proportion of choices than under bluish light. Moreover, color difference had a significant effect on the response time and the proportion of choices. Conclusively, rearing broilers under reddish rather than bluish light with appropriate intensity should be recommended to reduce damaging pecking behavior in broiler production.

In early lactation, susceptibility to disease is greatest, impacting cow health and productivity and leading to economic losses. Mastitis is the most economically costly disease to the dairy industry and is most frequent at this time. The objective of this paper is to discuss the energetic fuels used by leukocytes in the metabolic response during mastitis that may reveal potential mechanisms linking mastitis with the development of subsequent metabolic diseases for dairy cows during lactation. Glucose and glutamine are the primary fuels used by leukocytes and are essential substrates for optimal leukocyte function. Yet because these substrates are in high demand to support milk synthesis during early lactation, their supply to leukocytes may be compromised and may partly contribute to immunosuppression observed at this time. Production-related metabolic diseases during early lactation, such as ketosis and hepatic lipidosis, can also adversely affect health and productivity. Risk of subsequent disease for cows during mastitis has not been fully elucidated. Regardless of stage of lactation and physiological state, increases in circulating NEFA and glucose and decreases in ketones during an intramammary inflammation in dairy cows have been reported. In addition, previous work indicates that hepatic metabolism may be impaired during inflammation. These results indicate a potential link between mastitis and the risk of subsequent metabolic disease for dairy cows during lactation. This paper will discuss the complex relationships between metabolism and immune function and how these immunometabolic interactions relate mastitis with increased risk of subsequent disease during early lactation.

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.

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.

This article provides a synopsis of the collection of papers provided by participants of the NE1727 Multistate project. Five reviews and thirteen primary research articles are included that provide contributions the group has made to understanding the role of the corpus luteum in reproduction, describing how the ovary influences fertility, delineating mechanisms controlling oocyte quality and early embryo development, and exploring new reproductive management schemes.This article provides a synopsis of the collection of papers provided by participants of the NE1727 Multistate project. Five reviews and thirteen primary research articles are included.

Our objectives were to compare the antifungal activity of 5 lignosulfonates, and 2 chitosans against fungi isolated from spoiled hay, and assess the effects of an optimized lignosulfonate, chitosan, and propionic acid (PRP) on high-moisture alfalfa hay. In experiment 1, we determined the minimum inhibitory concentration and minimum fungicidal concentration of 4 sodium lignosulfonates, 1 magnesium lignosulfonate, 2 chitosans, and PRP (positive control) against Aspergillus amoenus, Mucor circinelloides, Penicillium solitum, and Debaromyces hanseniiat pH 4 and 6. Among sodium lignosulfonates, the one from Sappi Ltd. (NaSP) was the most antifungal at pH 4. However, chitosans had the strongest fungicidal activity with the exception of M. circinelloidesat both pH 4 and 6. PRP had more antifungal effects than NaSP and was only better than chitosans for M. circinelloides. In experiment 2, we evaluated the effects of 3 additives (ADV): optimized NaSP (NaSP-O, UMaine), naïve chitosan (ChNv, Sigma–Aldrich), and PRP on high-moisture alfalfa hay. The experimental design was a randomized complete block design replicated 5 times. Treatment design was the factorial combination of 3 ADV× 5 doses (0, 0.25, 0.5, 1, and 2% w/w fresh basis). Additives were added to 35 g of sterile alfalfa hay (71.5 ± 0.23% DM), inoculated with a mixture of previously isolated spoilage fungi (5.8 log cfu/fresh g), and aerobically incubated in vitro for 23 d (25°C). After incubation, DM losses were reduced by doses as low as 0.25% for both NaSP-O and PRP (x¯=1.61) vs. untreated hay (24.0%), partially due to the decrease of mold and yeast counts as their doses increased. Also, hay NH3-N was lower in NaSP-O and PRP, with doses as low as 0.25%, relative to untreated hay (x¯=1.13 vs. 7.80% of N, respectively). Both NaSP-O and PRP increased digestible DM recovery (x¯=69.7) and total volatile fatty acids (x¯=94.3), with doses as low as 0.25%, compared with untreated hay (52.7% and 83.8 mM, respectively). However, ChNv did not decrease mold nor yeast counts (x¯=6.59 and x¯=6.16 log cfu/fresh g, respectively) and did not prevent DM losses relative to untreated hay. Overall, when using an alfalfa hay substrate in vitro, NaSP-O was able to prevent fungal spoilage to a similar extent to PRP. Thus, further studies are warranted to develop NaSP-O as a hay preservative under field conditions.In our first experiment, we assessed the antifungal activity of two major types of byproducts, one known as lignosulfonates (5 types), which are generated by paper mills, and another known as chitosans (2 types), which are generated from shellfish. These were tested against four fungi isolated from spoiled hay. We observed that acidic conditions are not necessary for chitosans but are crucial to activate the antifungal properties of lignosulfonates. Also, we found that sodium lignosulfonate from Sappi Ltd. was the most antifungal relative to other sodium lignosulfonates from other manufacturers. Chitosans had stronger fungicidal activity than propionic acid or lignosulfonates against all but one mold tested. In our second experiment, we compared the best treatments from experiment 1 against propionic acid using alfalfa hay as a substrate to grow the same fungi tested in experiment 1. None of the doses of chitosan prevented spoilage on high moisture hay, showing results similar to untreated hay. In contrast, an optimized sodium lignosulfonate and propionic acid prevented fungal spoilage of alfalfa hay with doses as low as 0.25%.An optimized paper mill byproduct prevented fungal spoilage of high moisture alfalfa hay to the same extent as propionic acid under aerobic conditions in vitro.

A renewed interest in data analytics and decision support systems in developing automated computer systems is facilitating the emergence of hybrid intelligent systems by combining artificial intelligence (AI) algorithms with classical modeling paradigms such as mechanistic modeling (HIMM) and agent-based models (iABM). Data analytics have evolved remarkably, and the scientific community may not yet fully grasp the power and limitations of some tools. Existing statistical assumptions might need to be re-assessed to provide a more thorough competitive advantage in animal production systems towards sustainability. This paper discussed the evolution of data analytics from a competitive advantage perspective within academia and illustrated the combination of different advanced technological systems in developing HIMM. The progress of analytical tools was divided into three stages: collect and respond, predict and prescribe, and smart learning and policy making, depending on the level of their sophistication (simple to complicated analysis). The collect and respond stage is responsible for ensuring the data is correct and free of influential data points, and it represents the data and information phases for which data are cataloged and organized. The predict and prescribe stage results in gained knowledge from the data and comprises most predictive modeling paradigms, and optimization and risk assessment tools are used to prescribe future decision-making opportunities. The third stage aims to apply the information obtained in the previous stages to foment knowledge and use it for rational decisions. This stage represents the pinnacle of acquired knowledge that leads to wisdom, and AI technology is intrinsic. Although still incipient, HIMM and iABM form the forthcoming stage of competitive advantage. HIMM may not increase our ability to understand the underlying mechanisms controlling the outcomes of a system, but it may increase the predictive ability of existing models by helping the analyst explain more of the data variation. The scientific community still has some issues to be resolved, including the lack of transparency and reporting of AI that might limit code reproducibility. It might be prudent for the scientific community to avoid the shiny object syndrome (i.e., AI) and look beyond the current knowledge to understand the mechanisms that might improve productivity and efficiency to lead agriculture towards sustainable and responsible achievements.Data analytics have evolved remarkably. This paper discussed the evolution of data analytics from a competitive advantage perspective within academia and illustrated the combination of different advanced technological systems in developing hybrid intelligent mechanistic models (HIMM). Data analytics tools are divided into 3 stages. The first stage (collect and respond) ensures that data are correct and free of influential data points, and it represents the data and information phases for which data are cataloged and organized. The second stage (predict and prescribe) results in gained knowledge from the data and comprises most predictive modeling paradigms, and optimization and risk assessment tools are used to prescribe future decision-making opportunities. The third stage (smart learning and policy making) aims to apply the information obtained in the previous stages to foment knowledge and use it for rational decisions. Although still incipient, HIMM form the forthcoming stage of competitive advantage. HIMM may not increase our ability to understand the underlying mechanisms controlling the outcomes of a system, but it may increase the predictive ability of existing models by helping the analyst explain more of the data variation. The scientific community needs to resolve the lack of transparency and reporting of artificial intelligence for code reproducibility.The emergence of hybrid intelligent systems by combining artificial intelligence (AI) algorithms with classical modeling paradigms such as mechanistic modeling and agent-based models might facilitate the predictive ability of existing models. The scientific community still needs to resolve the lack of transparency and reporting of AI for code reproducibility.

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.

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.

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.

Genetic selection for traits that have a direct impact on profitability, such as productive longevity (PL), which blends cow longevity with regular reproductive performance, is fundamental for the economic success of beef cow-calf operations. The purpose of this study was to develop a data screening strategy and a statistical model to predict genetic merit for PL in a multibreed beef cattle population. Pedigree (n= 1,352,765) and phenotype (n= 978,382) information were provided by Leachman Cattle of Colorado, and genotypes (n= 26,342) were provided by the Zoetis commercial genotyping laboratory. A repeatability model (REP) including the systematic effects of age at first calving, year-season of progeny birth, pedigree-based retained heterosis, and parity number, as well as the random effects of the additive genetic, permanent environment, contemporary group, and residual were fitted to adjust PL. In addition, a random regression model (RRM) was fitted to investigate PL considering the same effects, with the difference that random effects were regressed on parity. Estimated breeding values (EBV) were obtained by single-step GBLUP (ssGBLUP) and transformed to predict differences in the number of calves through linear regression. Predictive performance was assessed in a group of 7,268 cows born in 2010. Heritability estimates for PL were relatively low, with values of 0.109 for REP and a decreasing trend for RRM with values ranging from 0.16 to 0.04. Repeatability for PL was of moderate magnitude, with values of 0.415 for REP and from 0.29 to 0.57 for RRM. Heritability estimates suggest that most of the phenotypic variation was accounted for by environmental factors, but long-term genetic selection could still be effective. REP was more efficient than RRM, showing the lower number of iterations and time to reach convergence with comparable solutions to RRM. Validation results showed that correlations between EBV and phenotypes (observed/precorrected) increased over the years ranging from 0.04 to 0.92. Repeatability values and the validation approach suggested that using a cow’s first record (second parity success or failure) is a reasonably good indicator of posterior performance for PL. Therefore, the inclusion of PL in a multibreed genetic evaluation program and incorporation into selection indexes with existing economic traits can enable more profitable selection and breeding decisions in beef cattle herds.This paper presents a concept called productive longevity (PL) that blends cow longevity with reproductive performance measured by the number of calves regularly produced in the herd. Identifying animals with high genetic merit for PL is crucial to inform selection and breeding decisions, assisting beef cattle producers to improve profitability.Productive longevity (PL) is a concept that blends cow longevity with reproductive performance quantified by the number of calves regularly produced over the defined lifespan. Specifically, PL was defined as the number of calves consecutively and regularly produced from the second to the eighth parity at 9 yr of age, assuming the first calf was produced at roughly 2 yr of age. Genetic selection for PL can improve producers’ profit by identifying animals with higher genetic merit to produce more calves regularly, and consequently reduce replacement rate and related costs. The purpose of this study was to develop a data screening strategy and a statistical model to predict genetic merit for PL in a multibreed beef cattle population. Selecting bulls with high genetic merit for PL should yield on average 1.2 additional progeny per daughter compared to bulls with low genetic merit. Moreover, when assessing the predictive performance of PL, it was observed that having approximately the first three calving dates contributing to PL are good indicators of cows’ posterior performance. Such results highlighted the benefits and importance of PL for beef cow-calf producers to inform selection decisions, especially when incorporated into economic selection indexes to maximize profitability.

The transition period is a critical metabolic phase for dairy ruminants, especially those with high production levels. In spite of this, little is still known about dairy water buffalo. The aim of this study was to evaluate the effect of a commercial feed additive based on diatomaceous earth and hydrolyzed yeasts on health status, milk quality, and immune response of buffalo cows during the transition period. Eighty healthy Water buffaloes (Bubalus bubalis) of Italian Mediterranean breed were included in the trial. They were subdivided into two groups: one group received the additive (n= 40) while the control group (n= 40) received a placebo. The trial lasted 120 d, from 60 d before calving to 60 d in milk. Blood samples were collected from each buffalo at −60 (60 d from the expected calving), −30, 0 (calving), +15, +30, and +60 d (respectively, i.e., 15, 30, and 60 d in milking). The biochemical as well as the oxidative profile, and the antioxidant power and enzymatic activity were evaluated in the samples obtained. Moreover, acute phase proteins, reactive proteins, and interleukin plasma levels were determined. Peripheral blood mononuclear cells (PBMCs) and monocytes were isolated and viability, reactive oxygen species (ROS), and reactive nitrogen species were measured on PBMC and monocytes. The introduction of additives enhanced the total antioxidant capacity and enzyme activity, while no differences were observed in oxidation products throughout the trial. Additionally, it significantly reduced the synthesis of ROS in polymorphonuclear cells, supporting a potential positive response in animals experiencing inflammation. The impact of oxidation on the products was not evident. Despite higher enzyme levels in plasma, this did not necessarily correspond to significantly increased enzymatic activity but rather indicated a higher potential. From these results, it was evident that the transition period in buffaloes differs notably from what reported in the literature for cows, probably due to the absence of common postpartum production diseases in dairy cows and lower metabolic challenges linked to lower milk production in buffaloes. Few parameters exhibited notable changes during the transition period in buffaloes, notably certain antioxidant enzymes, PBMC viability, PBMC ROS production, and Hp levels.The observed heightened biological responsiveness in buffaloes, despite the absence of significant stress, implies the potential of diatomaceous earth and fungal cell walls to modulate physiological responses. These findings suggest avenues for further research on the nuanced impacts of these additives, offering insights into their role in promoting animal well-being during critical peripartum periods.The findings of this paper on the use of diatomaceous earth and yeast products during the transition period in buffaloes reveal that their inclusion does not significantly affect milk production, both qualitatively and quantitatively, or the overall health status of the animals. However, intriguingly, results pertaining to oxidative status and peripheral blood cells, stimulated ex vivo, indicate that even in the absence of pronounced stress during the peripartum period, the animals exhibit increased potential antioxidant response. These insights suggest a potential for enhancing physiological responses in transition period buffaloes, opening avenues for further research on the nuanced impacts of these additives and their implications for animal well-being.

Fall-calving primiparous beef females [body weight (BW): 451 ± 28 (SD) kg; body condition score (BCS): 5.4 ± 0.7] were individually-fed 100% (control; CON; n= 13) or 70% (nutrient restricted; NR; n= 13) of estimated metabolizable energy and metabolizable protein requirements from day 160 of gestation to calving. Post-calving, all dams were individually-fed tall fescue hay supplemented to meet estimated nutrient requirements for maintenance, growth, and lactation in Calan gates until day 149 of lactation, which limited calves to milk only. From day 150 of lactation until weaning at day 243, dams and calves were group-fed in drylots. Dam BW and metabolic status were determined every 21 d, and BCS and backfat (BF) were determined every 42 d of lactation until weaning. Pre-weaning calf BW, size, and metabolic status were determined every 21 d. Data were analyzed with nutritional plane, calving date, and calf sex (when P < 0.25) as fixed effects. Circulating metabolites included day and nutritional plane × day as repeated measures. We previously reported that post-calving, NR dams were 64 kg and 2.0 BCS less than CON, but calf BW and size at birth were not affected. During the first 147 d of lactation, NR dams gained more (P < 0.01) BW than CON and increased (P < 0.01) BCS, while CON decreased (P ≤ 0.01) BCS and BF. Previously, NR dams had lower (P< 0.01) circulating triglycerides on day 1 of lactation, tended to have lower (P= 0.08) triglycerides on day 21, and had lower (P≤ 0.04) non-esterified fatty acids (NEFA) on days 21 and 243 than CON. Maternal glucose and urea N were not affected (P ≥ 0.73). At weaning, NR dams weighed 17 kg less (P = 0.15), were 0.67 BCS lower (P < 0.01), and tended to have less (P = 0.06) BF. Calves born to NR dams weighed less (P = 0.02) than CON by day 42 of age and were 13% smaller (P < 0.01) at weaning. Calf girth measures diverged (P≤ 0.05) by day 21 of age, and skeletal size measures were less (P≤ 0.08) for calves born to NR dams at most timepoints after day 63 of age. Calves born to NR dams tended to have lower (P = 0.09) circulating urea N pre-weaning than CON, but glucose, triglycerides, and NEFA were not affected (P ≥ 0.16). In summary, first-parity beef females that were nutrient restricted during late gestation experienced compensatory growth and gained body condition during lactation but were still thinner at weaning. Nutrient restriction reduced pre-weaning calf growth, likely due to decreased milk production.First-parity beef females that were nutrient restricted during late gestation and then fed to meet estimated nutrient requirements during lactation experienced compensatory growth but were still in poorer body condition at weaning than controls. Despite a similar birth weight, calves born to nutrient restricted dams diverged in body weight by day 42 of age and weighed less at weaning.Nutrient requirements increase substantially during late gestation in the beef female; however, poor forage nutrient availability can result in undernutrition. For heifers, the added nutrient requirements needed to continue growing during their first pregnancy and lactation pose an even greater challenge. It is plausible that lingering effects of late gestational nutrient restriction may exist for the dam and calf pre-weaning. We report that first-parity beef females that were nutrient restricted during late gestation and then fed to meet estimated nutrient requirements during lactation recovered quickly metabolically and experienced compensatory growth, but still had less body condition at weaning than controls. Late gestational nutrient restriction did not affect calf size at birth but resulted in calf body weight and size measures diverging early in life. Ultimately, nutrient restriction resulted in a 13% decrease in weaning weight, which was likely due to decreased milk production (in a companion paper). Despite this, metabolic status of calves born to nutrient restricted dams was not greatly altered. In summary, first-parity beef females that were nutrient restricted during late gestation prioritized partitioning nutrients to maternal growth and energy reserves over milk production during lactation, but dams were thinner at weaning, and pre-weaning calf growth was slowed.

A mechanistic, dynamic model was developed to calculate body composition in growing lambs by calculating heat production (HP) internally from energy transactions within the body. The model has a fat pool (f) and three protein pools: visceral (v), nonvisceral (m), and wool (w). Heat production is calculated as the sum of fasting heat production, heat of product formation (HrE), and heat associated with feeding (HAF). Fasting heat production is represented as a function of visceral and nonvisceral protein mass. Heat associated with feeding (HAF) is calculated as ((1 − km) x MEI), where kmis partial efficiency of ME use for maintenance, and MEI = metabolizable energy intake) applies at all levels above and below maintenance. The value of kmderived from data where lambs were fed above maintenance was 0.7. Protein change (dp/dt) is the sum of change in the m, v, and wpools, and change in fat is equal to net energy available for gain minus dp/dt. Heat associated with a change in body composition (HrE) is calculated from the change in protein and fat with estimated partial efficiencies of energy use of 0.4 and 0.7 for protein and fat, respectively. The model allows for individuals to gain protein while losing fat or vice versa.When evaluated with independent data, the model performed better than the current Australian feeding standards (Freer et al., 2007) for predicting protein gain in the empty body but did not perform as well as for gain of fat and fleece-free empty body weight. Models performed similarly for predicting clean wool growth. By explicit representation of the major energy using processes in the body, and through simplification of the way body composition is computed in growing animals, the model is more transparent than current feeding systems while achieving similar performance. An advantage of this approach is that the model has the potential for wider applicability across different growth trajectories and can explicitly account for the effects of systematic changes on energy transactions, such as the effects of selective breeding, growth manipulation, or environmental changes.This paper presents a revised dynamic, mechanistic model of heat production and body composition in sheep where heat production is calculated internally as the sum of fasting heat production, heat associated with feeding, and heat from changes in bodily protein and fat pools. The resulting model reflects the variation in heat production that arises both from feed, the animal, and the intersection of these two sources, and is a simpler and more flexible way to predict energy requirements and heat production in growing ruminants than traditional feeding systems.Based on prior work by Oltjen et al. (2006), a revised dynamic, mechanistic model was developed to improve the prediction of the composition of protein and fat in the body of growing ruminants. The revised model calculates heat production (HP) internally as a function of fasting HP, heat associated with feeding, and HP from changes in fat and protein within the body. Heat associated with product formation is calculated from changes in body protein and fat, with separate efficiencies for each, while heat associated with feeding is a constant proportion of metabolizable energy intake and applies at all levels of feeding above and below maintenance. When evaluated against novel data, the revised model performed similarly to current Australian feeding standards (Freer et al., 2007) Unlike the Freer model, the revised model captures variation in HP arising from feed as well as gain of protein and fat. The revised model explicitly represents protein in the body as two pools with markedly different rates of energy expenditure, improving representation of the underlying biology compared to current feeding systems. This provides a more flexible way to predict energy requirements and body composition in growing animals while achieving similar performance to current feeding systems.

The microbiome has been linked to animal health and productivity, and thus, modulating animal microbiomes is becoming of increasing interest. Antimicrobial growth promoters (AGP) were once a common technology used to modulate the microbiome, but regulation and consumer pressure have decreased AGP use in food animals. One alternative to antimicrobial growth promoters are phytotherapeutics, compounds derived from plants. Capsaicin is a compound from the Capsicumgenus, which includes chili peppers. Capsaicin has antimicrobial properties and could be used to manipulate the gastrointestinal microbiome of cattle. Both the rumen and fecal microbiomes are essential to cattle health and production, and modulation of either microbiome can affect both cattle health and productivity. We hypothesized that the addition of rumen-protected capsaicin to the diet of cattle would alter the composition of the fecal microbiome, but not the rumen microbiome. To determine the impact of rumen-protected capsaicin in cattle, four Holstein and four Angus steers were fed rumen-protected Capsicumoleoresin at 0 (Control), 5, 10, or 15 mg kg−1diet dry matter. Cattle were fed in treatment groups in a 4 × 4 Latin Square design with a 21-d adaptation phase and a 7-d sample collection phase. Rumen samples were collected on day 22 at 0-, 2-, 6-, 12-, and 18-h post-feeding, and fecal swabs were collected on the last day of sample collection, day 28, within 1 h of feeding. Sequencing data of the 16s rRNA gene was analyzed using the dada2 pipeline and taxa were assigned using the SILVA database. No differences were observed in alpha diversity among fecal or rumen samples for either breed (P> 0.08) and no difference between groups was detected for either breed in rumen samples or for Angus steers in fecal samples (P> 0.42). There was a difference in beta diversity between treatments in fecal samples of Holstein steers (P< 0.01), however, a pairwise comparison of the treatment groups suggests no difference between treatments after adjusting for multiple comparisons. Therefore, we were unable to observe substantial overall variation in the rumen or fecal microbiomes of steers due to increasing concentrations of rumen-protected capsaicin. We do, however, see a trend toward increased concentrations of capsaicin influencing the fecal microbiome structure of Holstein steers despite this lack of significance.This paper presents a study of the microbiome of Angus and Holstein steers fed rumen-protected Capsicumoleoresin. Capsaicin does not appear to affect the rumen microbiome of either breed but might affect the fecal microbiome of Holstein steers.The microbiome is the collection of microbes present in an animal’s body and has been discovered to be directly connected to animal health and productivity. In production animals, such as feedlot cattle, the microbiome can be modulated by antimicrobials to promote growth, but increasing consumer pressure to reduce antimicrobial use has producers seeking alternatives. Capsaicin is a phytotherapeutic derived from chili peppers that can be used to modulate the microbiome due to its antimicrobial properties. Eight steers were fed rumen-protected Capsicumoleoresin to determine its effect on average daily gain. In addition, rumen and fecal samples were collected for microbiome testing. No differences were detected in the rumen microbiomes between cattle fed capsaicin (treatment) or those that received no capsaicin (control). While no overall effect was observed on the fecal microbiome of cattle fed different doses of capsaicin or control, we did observe changes in fecal beta diversity due to capsaicin treatment in Holstein steers fed greater doses. The fecal microbiome structure of Holsteins fed greater dosages of capsaicin differed from those fed control or low doses, as observed by the presence of two distinct clusters. This observation suggests an impact of greater doses of capsaicin treatment on microbiome structure.

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.

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.

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.

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.

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.

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 rpcis 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 rpcestimates in pigs. The rpchas 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 , 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 rpcestimates from 27 studies were considered, published between 1964 and 2017. The average rpcestimate 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 rpcwas around 0.6. Genotype by environment interactions appeared to have a smaller contribution to rpcthan genotype by genotype interactions. More research regarding the impact of the different components on the rpccan help to improve breeding programs. Future studies are advised to report characteristics of the herd environments in detail, to report estimated h2and additive genetic variances for purebreds and crossbreds, to report the estimated rpcwith standard errors or confidence intervals, to estimate separate rpcfor different pure lines, and to genotype the animals under study.

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.

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.

Carbohydrate is one of the major components in dairy cow’s diet. Dietary carbohydrate can be divided into structural and non-structural fractions, within which neutral detergent fiber (NDF) and starch are the major components of the structural and non-structural carbohydrates. Meta-analysis was conducted to investigate the effect of dietary NDF and starch content on milk yield and composition in dairy cows in this study. 567 research papers retrieved from the Web of Science, Science Direct, PubMED and NCBI. The keywords include different combinations of dairy cows, milk production, milk composition, carbohydrate and forage to concentrate ratio. Through literature retrieval and data screening, 27 papers were collected including 119 experimental treatments. Data was analyzed using the MIXED model of SAS. Correlationship and meta regression analysis were performed comparing dietary NDF, starch and NDF:starch ratio on milk production and composition of milk component. Compared with dietary NDF and starch content, NDF:starch ratio was found to be highly correlated with DMI (rP2=-0.730, P2=0.664, P2=-0.788, PP=0.019), 1.36 kg (P=0.003), 0.06 kg (PPP=0.002) and 0.07 (P=0.007) unit, respectively. There was no significant meta regression relevance between dietary NDF:starch ratio and milk lactose content (P=0.893), milk fat yield (P=0.421). The meta-analysis results indicate that NDF:starch ratio could be used as a useful indicator for evaluating the dietary carbohydrates when formulating rations for dairy cattle.Key Words:

This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () 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−1were rapidly calculated with the Henderson's rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix–vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of A22with 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, is no longer a limiting factor in the computations.

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.

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.

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 Succinivibrionaceaeand Methanomassiliicoccaleswere 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 Methanomassiliicoccalesarchaeal 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.