Search

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., (© Crown copyright 2022.)

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science.)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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 Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Several ruminant animals rely almost exclusively on the complex polysaccharide matrix from the plant cell wall (CW) as their primary energy source via volatile fatty acids produced through ruminal and some hindgut fermentation processes. The CW contains different types and proportions of polysaccharides, proteins, phenolic compounds, and minerals in their macromolecular structure that influence the rate and extent of fiber digestion and selective retention of particulate matter due to its physical characteristics (buoyancy and comminuting) in the reticulorumen. The biosynthetic formation of the CW dictates possible manipulation mechanisms (targeted plant and microbes selection) and processing methods (physical, chemical, microbial, and enzymatic treatments and the use of genetically engineered bacteria) to increase its digestibility, leading to better utilization of the CW by the ruminant animal and hopefully lower the contribution of ruminants' greenhouse gas emissions. Early studies on lignin biosynthesis have led to more advanced studies focusing on replacing traditional monolignols with homopolymers that are easier to deconstruct or degrade. Concurrently, laboratory methods must be developed, evaluated, and modified to accurately reflect the digestibility and nutritive value of CW brought about by modern manipulation mechanisms or processing methods. However, the laboratory methods must also be reliable, precise, feasible, trivial, easy to implement, and cost-effective, but at the same time environmentally friendly and aware. For instance, although the acid detergent lignin has been demonstrated to behave uniformly as a nutritional entity, its chemical determination and association with carbohydrates still lack consensus. Spectroscopy (near-infrared and Raman) and in vitro gas production techniques have been adopted to assess plant chemical composition and nutritive value, but an incomplete understanding of the impacts caused by disrupting the CW for sample processing still exists. Different variations of multicompartmental and time- and age-dependent mathematical models have been proposed to determine the ruminal rates of degradation and passage of fiber. However, low-quality and incomplete data due to inconsistent marker results used to determine passage rates and transit time of fiber in the gastrointestinal tract have hindered advancements and adoptions of the next generation of computer models to understand ruminal fiber degradation., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science.)

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Advancements in precision livestock technology have resulted in an unprecedented amount of data being collected on individual animals. Throughout the data analysis chain, many bottlenecks occur, including processing raw sensor data, integrating multiple streams of information, incorporating data into animal growth and nutrition models, developing decision support tools for producers, and training animal science students as data scientists. To realize the promise of precision livestock management technologies, open-source tools and tutorials must be developed to reduce these bottlenecks, which are a direct result of the tremendous time and effort required to create data pipelines from scratch. Open-source programming languages (e.g., R or Python) can provide users with tools to automate many data processing steps for cleaning, aggregating, and integrating data. However, the steps from data collection to training artificial intelligence models and integrating predictions into mathematical models can be tedious for those new to statistical programming, with few examples pertaining to animal science. To address this issue, we outline how open-source code can help overcome many of the bottlenecks that occur in the era of big data and precision livestock technology, with an emphasis on how routine use and publication of open-source code can help facilitate training the next generation of animal scientists. In addition, two case studies are presented with publicly available data and code to demonstrate how open-source tutorials can be utilized to streamline data processing, train machine learning models, integrate with animal nutrition models, and facilitate learning. The National Animal Nutrition Program focuses on providing research-based data on animal performance and feeding strategies. Open-source data and code repositories with examples specific to animal science can help create a reinforcing mechanism aimed at advancing animal science research., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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 hansenii at 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. circinelloides at 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., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Beef cows with excess androstenedione (A4; High A4) in follicular fluid (FF) and secreted by the ovarian cortex have been reported from the University of Nebraska-Lincoln physiology herd displaying characteristics reminiscent of polycystic ovary syndrome (PCOS). Thus, we hypothesized that naturally occurring High A4 cows were present in other dairy and beef herds. Fourteen Jordan (Amman, Jordon) dairy heifers and 16 U.S. Meat Animal Research Center beef heifers were classified by FF (High A4: A4 > 40 ng/mL and Control: A4 < 20 ng/mL) and/or cortex culture media (High A4 > 1 ng/mL/d or Control < 1 ng/mL/d). High A4 dairy heifers (n = 6) had greater A4 concentrations (7.6-fold) in FF and (98-fold) greater in ovarian cortex culture media with greater numbers of primordial and fewer later-stage follicles than Controls (n = 8) even after 7 d of culture. Also, the ovarian cortex had greater staining for Picro Sirius red in High A4 dairy heifers compared with Controls indicating increased fibrosis. Thecal cells from High A4 dairy heifers had greater STAR, LHCGR, CYP17A, CD68, and PECAM mRNA expression with increased mRNA abundance of CYP17A1 and CD68 in the ovarian cortex cultures compared with Control dairy heifers. Similarly, cortex culture media from High A4 beef heifers (n = 10) had increased A4 (290-fold; P ≤ 0.001), testosterone (1,427-fold; P ≤ 0.001), and progesterone (9-fold; P ≤ 0.01) compared with Control heifers with increased primordial follicles and decreased later-stage follicles even after 7 d of culture, indicating abnormal follicular development. High A4 ovarian cortex cultures from beef heifers also had increased fibrosis markers and greater expression of PECAM (P = 0.01) with a tendency for increased vascular endothelial cadherin compared with Controls (n = 6). These two trials support our hypothesis that naturally occurring androgen excess cows are present in other dairy and beef herds. The ability to identify these females that have excess A4 ovarian microenvironments may allow for their use in understanding factors causing abnormal follicle development linked to androgen excess and inflammation., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Titanium dioxide has been used as a marker for determining diet digestibility indirectly, but some authors have expressed difficulty in measuring TiO2 concentrations in fecal material. We developed an accurate and precise method to determine TiO2 concentrations in equine feces. The method includes dry-ashing samples, digestion with (NH4)2SO4 in concentrated sulfuric acid, followed by the addition of H2O2 to produce a yellow to orange color that can be read spectrophotometrically. Accuracy was tested by spike recovery, and precision was tested by examining the coefficient of variation (CV) between duplicates of 449 individual samples. The method described here was compared with a previously published method by examining CV between duplicates of samples analyzed using both methods and comparing them using a paired t-test. Titanium dioxide spike recovery averaged 106%, and the CV between duplicates averaged 4.0%, with 79% of sample pairs having a CV of <5%. When compared with a previously published method, the method described here had a lower CV between duplicates (P < 0.0001). The method described here provides an accurate and precise quantitative analytical procedure for TiO2 in equine fecal samples., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

We recently reported factors leading to different severity of ruminal pH drop in primiparous cows fed the same diet during transition and early lactation. The present study evaluates the effects of those severities on performance and several blood and balance parameters in the same 24 primiparous cows from 3 wk before calving until wk 10 in lactation. Dietary concentrate was increased for all cows from 32 before calving to 60% (DM basis) over the first wk in lactation, resulting in a diet with 40% non-fiber carbohydrates (NFC), and 14.4% physically effective fiber (peNDF>8). Ruminal pH was monitored with indwelling systems in all cows during the study; then several indices of duration and magnitude of pH change were used as indicators of the severity of ruminal pH drop. Accordingly, as reported in the companion paper, the cows were classified as either higher pH drop (HIGH; n = 9), moderate (MOD; n = 9) and lower (LOW; n = 6) severity of pH drop. In the present report, body weight, body condition score, and back fat thickness decreased but rumen mucosa thickness increased after calving in all cows (P < 0.05). Post-partum daily energy intake in HIGH was greater (P < 0.05) than the other categories. In addition, there were 5.6 kg extra of energy-corrected milk in HIGH (P < 0.05). Independent of severity of pH drop, blood glucose, non-esterified fatty acids, beta-hydroxybutyrate, bilirubin and cortisol were higher, whereas triglycerides, total protein, globulin, albumin, urea nitrogen, cholesterol, calcium and phosphorous were lower at calving (P < 0.05). Blood cholesterol was greater in HIGH than the other pH categories (P < 0.05); insulin and reproductive variables were not affected by severity of pH drop. Overall, primiparous cows fed the same diet showed different severity of ruminal pH drop, but the individual variation in ruminal pH depression was not related to a negative impact on milk yield, energy balance or blood metabolites during the study. This indicates that the impact of low ruminal pH on the animals may depend not only on severity of the pH drop (average of 403 min/d of pH<5.8 over 13 wk) but also on diet characteristics. Specifically, drops of ruminal pH can be tolerated by primiparous cows during the first 10 wk in milk consuming a diet containing 40% NFC and 14.4% peNDF>8. It would be worth studying the severity of ruminal pH drop in a larger sample size during the entire lactation and if the severity is sustained on the next lactation., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal 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 DaisyII Incubator. 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 Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Mycotoxicosis are a common problem in livestock, where a group of six major mycotoxins represents a high risk for animal health and production profits. Mycotoxin binders (MTB) can reduce the mycotoxin burden in the gastrointestinal tract of the animal. Mycotoxin binders are classified in inorganic, as clays and activated carbon (AC), and organic, as yeast cell wall (YCW) and micro-ionized fibers. The adsorption of mycotoxins into MTB is due to: 1) chemical interactions where the cation exchange capacity involves different types of bounds like ion-dipole, Van der Walls forces, or hydrogen bonds; and 2) to physical characteristics of MTB like pore size, or mycotoxin structure and shape. The adsorption capacity of MTB is determined using different in vitro tests that mimic the gastrointestinal tract of the animals. A literature search was conducted to identify in vitro research where the efficacy of adsorption of MTB was determined. The search was based on 8 MTB [AC, bentonite, clinoptilolite, hydrated sodium calcium aluminosilicate (HSCAS), montmorillonite (MMT), sepiolite, YCW and zeolite] and 6 mycotoxins [aflatoxin (AF), deoxynivalenol (DON), fumonisin (FUM), ochratoxin (OTA), T-2 toxin and zearalenone (ZEA)]. Sixty-eight papers with 1842 data were selected and analyzed with the PROC MIXED of SAS. The response variable was the percentage mycotoxins adsorption by MTB, and the model included the fixed effects of MTB, mycotoxins, incubation media, pH and their interactions, and the random effect of the study. Differences were considered significant when P < 0.05 and with tendency when 0.05 < P < 0.10. The mycotoxins adsorption capacity was 83% ± 1.0 for AC, 76% ± 3.1 for MMT, 62% ± 1.0 for bentonite, 55% ± 1.9 for HSCAS, 52% ± 9.1 for sepiolite, 52% ± 4.3 for clinoptilolite and 44% ± 0.4 for YCW. For mycotoxins, the adsorption of AF was 76% ± 0.6, for FUM was 50% ± 1.8, for OTA was 42% ± 1.0, for ZEA was 48% ± 1.1, for DON was 35% ± 1.6, and for T-2 was 27% ± 2.8. The pH affected the adsorption capacity of YCW with higher adsorption at low pH, and the adsorption of OTA and ZEA, where OTA adsorption tended to be lower at intermediate pH, and adsorption of ZEA tended to be higher at the two-steps pH. The potential adsorption of some essential nutrients, including amino acids and vitamins, should also be considered. Results should be used as a guide in the selection of the appropriate mycotoxin binder based on the predominant mycotoxin in feeds., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

This paper presents the application of machine learning algorithms to identify pigs' behaviors from data collected using the wireless sensor nodes mounted on pigs. The sensor node attached to a pig's back senses the acceleration and angular velocity in three axes, and the sensed data are transmitted to a host computer wirelessly. Two video cameras, one attached to the ceiling of the pigpen and the other one to a fence, provided ground truth for data annotations. The data were collected from pigs for 131 h over 2 mo. As the typical behavior period depends on the behavior type, we segmented the acceleration data with different window sizes (WS) and step sizes (SS), and tested how the classification performance of different activities varied with different WS and SS. After exploring the possible combinations, we selected the optimum WS and SS. To compare performance, we used five machine learning algorithms, specifically support vector machine, k-nearest neighbors, decision trees, naive Bayes, and random forest (RF). Among the five algorithms, RF achieved the highest F1 score for four major behaviors consisting of 92.36% in total. The F1 scores of the algorithm were 0.98 for "eating," 0.99 for "lying," 0.93 for "walking," and 0.91 for "standing" behaviors. The optimal WS was 7 s for "eating" and "lying," and 3 s for "walking" and "standing." The proposed work demonstrates that, based on the length of behavior, the adaptive window and step sizes increase the classification performance., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Skeletal muscle hypertrophy is a culmination of catabolic and anabolic processes that are interwoven into major metabolic pathways, and as such modulation of skeletal muscle metabolism may have implications on animal growth efficiency. Muscle is composed of a heterogeneous population of muscle fibers that can be classified by metabolism (oxidative or glycolytic) and contractile speed (slow or fast). Although slow fibers (type I) rely heavily on oxidative metabolism, presumably to fuel long or continuous bouts of work, fast fibers (type IIa, IIx, and IIb) vary in their metabolic capability and can range from having a high oxidative capacity to a high glycolytic capacity. The plasticity of muscle permits continuous adaptations to changing intrinsic and extrinsic stimuli that can shift the classification of muscle fibers, which has implications on fiber size, nutrient utilization, and protein turnover rate. The purpose of this paper is to summarize the major metabolic pathways in skeletal muscle and the associated regulatory pathways., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In the dairy cow, negative energy balance affects milk yield and composition as well as animal health. Studying the effects of negative energy balance on dairy cow milk production is thus essential. Feed restriction (FR) experiments attempting to reproduce negative energy balance by reducing the quantity or quality of the diet were conducted in order to better describe the animal physiology changes. The study of FR is also of interest since with climate change issues, cows may be increasingly faced with periods of drought leading to a shortage of forages. The aim of this article is to review the effects of FR during lactation in dairy cows to obtain a better understanding of metabolism changes and how it affects mammary gland activity and milk production and composition. A total of 41 papers studying FR in lactating cows were used to investigate physiological changes induced by these protocols. FR protocols affect the entire animal metabolism as indicated by changes in blood metabolites such as a decrease in glucose concentration and an increase in non-esterified fatty acid or β-hydroxybutyrate concentrations; hormonal regulations such as a decrease in insulin and insulin-like growth factor I or an increase in growth hormone concentrations. These variations indicated a mobilization of body reserve in most studies. FR also affects mammary gland activity through changes in gene expression and could affect mammary cell turnover through cell apoptosis, cell proliferation, and exfoliation of mammary epithelial cells into milk. Because of modifications of the mammary gland and general metabolism, FR decreases milk production and can affect milk composition with decreased lactose and protein concentrations and increased fat concentration. These effects, however, can vary widely depending on the type of restriction, its duration and intensity, or the stage of lactation in which it takes place. Finally, to avoid yield loss and metabolic disorders, it is important to identify reliable biomarkers to monitor energy balance., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Blood sample collection from the caudal vena cava at the site of uterine-ovarian drainage provides a more exact evaluation of the concentration and pattern of secretion of uterine or ovarian secreted products for studies of reproductive processes in cyclic and pregnant cattle compared with samples collected from general circulation. This paper describes a thorough and updated procedure for cannulating the coccygeal vein into the caudal vena cava for the collection of serial blood samples at or near the site of uterine-ovarian drainage. Concentrations of progesterone were quantified in cows of different reproductive tract sizes with an active corpus luteum to assess the distance for proper catheter placement compared with circulating concentrations collected from the jugular vein. This procedure has a low risk for side effects, can be used effectively in pregnant animals with no major consequence to the viability of the pregnancy, and provides means for frequent collections up to 12 d., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Precision feeding (PF) with the daily mixing of 2 diets with different lysine content (high (H) or low (L)) was previously reported for growing pigs to reduce protein intake and N excretion compared with a conventional feeding (CF) based on a single diet (C). Using a simulation approach based on farm data, the objective of the present paper was to describe and evaluate a decision support system for the PF of gestating sows allowing the daily distribution of a tailored ration to each sow. Two datasets, 1 of 2,511 gestations (farm A) and 1 of 2,528 gestations (farm B), reporting sows' characteristics at insemination and objectives at farrowing were used as inputs for a Python model. This model, mainly based on InraPorc, calculates the nutrient requirements of each sow over gestation and simulates the impact of PF in comparison to CF. Simulated diets L, H, and C contained 3.0, 6.5, and 4.8 g/kg of standardized ileal digestible lysine (SID Lys) and 2.0, 3.3, and 2.5 g/kg of standardized total tract digestible phosphorus (STTD-P), respectively. The influence of farm, parity, gestation week, and their interactions, on calculated SID Lys and STTD-P requirements was analyzed applying a mixed model. The calculated SID Lys and STTD-P requirements increased markedly in the last third of gestation (P < 0.01) and were higher for primiparous than for multiparous sows, unless after week 14 for STTD-P requirement. The calculated SID AA and mineral requirements were lower for farm B than farm A (respectively, 2.94 vs. 3.08 g/kg for SID Lys and 1.30 vs. 1.35 g/kg for STTD-P, P < 0.01). On average, feed L represented 86% and 92% of the feed projected to be delivered by the PF strategy in farms A and B, respectively. Compared to CF, average calculated dietary SID Lys content was lowered by 27% and 32% with PF, for farms A and B, respectively, while average calculated dietary phosphorus content was lowered by 13% and 16%. The simulated proportions of sows in excess and deficient in SID Lys were reduced with PF. Compared to CF, the PF strategy allowed for a 3.6% reduction in simulated feed cost per sow during gestation, and reduced nitrogen and phosphorus intake (by 11.0% and 13.8%, respectively) and excretion (by 16.7% and 15.4%, respectively). To conclude, these simulations indicate that PF of gestating sow appears to be relevant to meet the amino acid requirement while reducing feed cost, and supplies and excretion of nitrogen and phosphorus., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Traditional feed composition tables have been a useful tool in the field of animal nutrition throughout the last 70 yr. The objective of this paper is to discuss the challenges and opportunities associated with creating large feed ingredient composition tables. This manuscript will focus on three topics discussed during the National Animal Nutrition Program (NANP) Symposium in ruminant and nonruminant nutrition carried out at the American Society of Animal Science Annual Meeting in Austin, TX, on July 11, 2019, namely: 1) Using large datasets in feed composition tables and the importance of standard deviation in nutrient composition as well as different methods to obtain accurate standard deviation values, 2) Discussing the importance of fiber in animal nutrition and the evaluation of different methods to estimate fiber content of feeds, and 3) Description of novel feed sources, such as insects, algae, and single-cell protein, and challenges associated with the inclusion of such feeds in feed composition tables. Development of feed composition tables presents important challenges. For instance, large datasets provided by different sources tend to have errors and misclassifications. In addition, data are in different file formats, data structures, and feed classifications. Managing such large databases requires computers with high processing power and software that are also able to run automated procedures to consolidate files, to screen out outlying observations, and to detect misclassified records. Complex algorithms are necessary to identify misclassified samples and outliers aimed to obtain accurate nutrient composition values. Fiber is an important nutrient for both monogastrics and ruminants. Currently, there are several methods available to estimate the fiber content of feeds. However, many of them do not estimate fiber accurately. Total dietary fiber should be used as the standard method to estimate fiber concentrations in feeds. Finally, novel feed sources are a viable option to replace traditional feed sources from a nutritional perspective, but the large variation in nutrient composition among batches makes it difficult to provide reliable nutrient information to be tabulated. Further communication and cooperation among different stakeholders in the animal industry are required to produce reliable data on the nutrient composition to be published in feed composition tables., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Society of Animal Science.)

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., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science.)

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., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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.

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 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.

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 () is, however, lower than unity for many traits. When is low, the use of CB performance in selection is required to achieve sizable genetic progress. The objectives of this paper were to describe the different components and importance of , and to review existing literature that report estimates in pigs. The has 3 components: 1) genotype by genotype interactions, 2) genotype by environment interactions, and 3) differences in trait measurements. We theoretically showed that direct selection for CB performance reduces the response to selection in purebreds for.

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.

This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (). 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 () including genotyped animals and their ancestors. The elements of were 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 with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When the equations in ssGBLUP are solved with the PCG algorithm, 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.

Puberty onset is a developmental process influenced by genetic determinants, environment, and nutrition. Mutations and regulatory gene networks constitute the molecular basis for the genetic determinants of puberty onset. The emerging knowledge of these genetic determinants presents opportunities for innovation in the breeding of early pubertal cattle. This paper presents new data on hypothalamic gene expression related to puberty in (Brahman) in age- and weight-matched heifers. Six postpubertal heifers were compared with 6 prepubertal heifers using whole-genome RNA sequencing methodology for quantification of global gene expression in the hypothalamus. Five transcription factors (TF) with potential regulatory roles in the hypothalamus were identified in this experiment: , , , , and . These TF genes were significantly differentially expressed in the hypothalamus of postpubertal versus prepubertal heifers and were also identified as significant according to the applied regulatory impact factor metric ( < 0.05). Two of these 5 TF, and , were zinc fingers, belonging to a gene family previously reported to have a central regulatory role in mammalian puberty. The gene belongs to the family of homologues of Drosophila sine oculis () genes implicated in transcriptional regulation of gonadotrope gene expression. Tumor-related genes such as and are known to affect basic cellular processes that are relevant in both cancer and developmental processes. Mutations in were associated with puberty in humans. Mutations in these TF, together with other genetic determinants previously discovered, could be used in genomic selection to predict the genetic merit of cattle (i.e., the likelihood of the offspring presenting earlier than average puberty for Brahman). Knowledge of key mutations involved in genetic traits is an advantage for genomic prediction because it can increase its accuracy.

The objectives of this study were to determine the effect of 1) immunological castration (Improvest, a gonadotropin releasing factor analog-diphtheria toxoid conjugate) management strategy (age at slaughter and time of slaughter after second dose) and 2) sex on lipid oxidation and sensory characteristics of bacon stored under simulated food service conditions. For Objective 1, immunological castration management strategies included 24-wk-old immunologically castrated (IC) barrows 4, 6, 8, or 10 wk after the second Improvest dose (ASD); 26-wk-old IC barrows 6 wk ASD; and 28-wk-old IC barrows 8 wk ASD ( = 63). Objective 2 ( = 97) included IC barrows, physically castrated (PC) barrows, and gilts slaughtered at 24, 26, and 28 wks of age. Bellies from 2 slaughter dates were manufactured into bacon under commercial conditions. Bacon slices were laid out on parchment paper, packaged in oxygen-permeable poly-vinyl-lined boxes, and frozen (-33°C) for 1, 4, 8, or 12 wk to simulate food service conditions. At the end of each storage period, bacon was evaluated for lipid oxidation, moisture and lipid content, and sensory characteristics. Data from both objectives were analyzed using the MIXED procedure in SAS with belly as the experimental unit. For both objectives, as storage time increased, lipid oxidation of bacon increased ( < 0.01), regardless of management strategy or sex. Also, there was no sex or management strategy × week of frozen storage interaction for any traits evaluated ( ≥ 0.25). For Objective 1, lipid content of bacon from IC barrows increased as time of slaughter ASD increased ( < 0.05), regardless of age at slaughter. Additionally, there were no differences in sensory attributes of bacon across management strategies. For the evaluation of sex effects in Objective 2, lipid oxidation was greater ( < 0.05) in IC barrows compared with PC barrows but was not different than gilts ( > 0.05). After 12 wk of frozen storage, lipid oxidation values for IC barrows, PC barrows, and gilts were still below 0.5 mg malondialdehyde/kg of meat, the threshold at which trained panelists may deem a food to be rancid. In conclusion, bacon shelf life characteristics were not altered by the immunological castration management strategy and bacon from IC barrows was similar to bacon from gilts. Therefore, bacon from IC barrows would result in shelf life and sensory quality similar to PC barrows and gilts.

This paper will argue that understanding animal welfare and the individual vulnerability to stress-related disease requires a fundamental understanding of functional individual variation as it occurs in nature as well as the underlying neurobiology and neuroendocrinology. Ecological studies in feral populations of mice, fish, and birds start to recognize the functional significance of phenotypes that individually differ in their behavioral and neuroendocrine response to environmental challenge. Recent studies indicate that the individual variation within a species may buffer the species for strong fluctuations in the natural habitat. Similarly, evolutionary ancient behavioral trait characteristics have now been identified in a range of domestic farm animals including cattle, pigs, and horses. Individual variation in behavior can be summarized in a 3-dimensional model with coping style, emotionality, and sociality as independent dimensions. These dimensions can be considered trait characteristics that are stable over time and across situations within the individual. This conceptual model has several consequences. First, the coping style dimension is strongly associated with differential stress vulnerability. Social stress studies show that proactive individuals are resilient under stable environmental conditions but vulnerable when outcome expectancies are violated. Reactive individuals are, in fact, rather flexible and seem to adapt more easily to a changing environment. A second consequence relates to genetics and breeding. Genetic selection for one trait usually implies selection for other traits as well. It is discussed that a more balanced breeding program that takes into account biologically functional temperamental traits will lead to more robust domestic farm animals. Finally, the relationship between temperamental traits, animal production, fitness, and welfare is discussed.

Polycystic ovary syndrome (PCOS) is a common condition affecting women of reproductive age. This disorder is characterized by hyperandrogenism and anovulation and is frequently associated with comorbidities such as infertility, metabolic syndrome, type 2 diabetes, and cardiovascular risk factors. Although the causes of PCOS are unknown, this review focuses on the most accepted theory involving insulin action but will also elaborate on a novel concept: the role of lipotoxicity in the development of androgen overproduction, in addition to its known role in insulin resistance. This review will also shed a spotlight on 2 drugs that target lipotoxicity and are, therefore, known or promising for the treatment of PCOS manifestations: peroxisome proliferator-activated receptor γ and angiotensin II type 2 receptor agonists. This paper, therefore, emphasizes the need to further explore the pathophysiology of PCOS and particularly the role of lipotoxicity. Indeed, this new mechanism deserves attention to develop therapeutic approaches that will directly target the root of this condition and not only bandage its associated consequences.

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.

Glucocorticoids (corticosterone in birds and rodents and cortisol in all other mammals) are glucoregulatory hormones that are synthesized in response to a range of stimuli including stress and are regularly measured in the assessment of animal welfare. Glucocorticoids have many normal or non-stress-related functions, and glucocorticoid synthesis can increase in response to pleasure, excitement, and arousal as well as fear, anxiety, and pain. Often, when assessing animal welfare, little consideration is given to normal non-stress-related glucocorticoid functions or the complex mechanisms that regulate the effects of glucocorticoids on physiology. In addition, it is rarely acknowledged that increased glucocorticoid synthesis can indicate positive welfare states or that a stress response can increase fitness and improve the welfare of an animal. In this paper, we review how and when glucocorticoid synthesis increases, the actions mediated through type I and type II glucocorticoid receptors, the importance of corticosteroid-binding globulin, the role of 11 β-hydroxysteroid dehydrogenase, and the key aspects of neurophysiology relevant to activating the hypothalamo-pituitary-adrenal axis. This is discussed in the context of animal welfare assessment, particularly under the biological functioning and affective states frameworks. We contend that extending the assessment of animal welfare to key brain regions afferent to the hypothalamus and incorporating the aspects of glucocorticoid physiology that affect change in target tissue will advance animal welfare science and inspire more comprehensive assessment of the welfare of animals.

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.

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

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.

The selection of animals for improved performance affects the profitability of pig fattening and has environmental consequences. The goal of this paper was to examine how changes in genetic and market parameters impact the biophysical (feeding patterns, timing of slaughter, nitrogen excretion) and economic (return per pig space unit) results describing pig fattening in a Finnish farm. The analysis can be viewed as focusing on terminal line breeding goals. An integrated model using recursive stochastic dynamic programming and a biological pig growth model was used to estimate biophysical results and economic values. Combining these models allowed us to provide more accurate estimates for the value of genetic improvement and, thus, provide better feedback to animal breeding programs than the traditional approach, which is based on fixed management patterns. Besides the benchmark scenario, the results were simulated for 5 other scenarios. In each scenario, genotype was improved regarding daily growth potential, carcass lean meat content, or the parameters of the Gompertz growth curve (maturing rate [], adult weight of protein [α], and adult weight of lipid mass []). The change in each parameter was equal to approximately 1 SD genetic improvement (ceteris paribus). Increasing , , daily growth potential, or carcass lean meat content increased the return on pig space unit by €12.60, €7.60, €4.10, or €2.90 per year, respectively, whereas an increase in decreased the return by €3.10. The genetic improvement in and resulted in the highest decrease in nitrogen excretion calculated in total or per kilogram of carcass gain but only under the optimal feeding pattern. Simulated changes in the Gompertz growth function parameters imply greater changes in ADG and lean meat content than changes in scenarios focusing on improving ADG and lean meat content directly. The economic value of genetic improvements as well as the quantity of nitrogen excreted during the fattening period largely depends on feeding. Improved genotypes can require changes in pig management pattern. Estimating the influence of the genotype on the nitrogen excretion without considering changes in the management pattern can result in flawed conclusions. To improve overall economic performance and to decrease the environmental footprint of fattening pig production, the pig producer can adjust the herd management pattern according to the pigs' genetics.

The objective of this paper was to estimate the genetic parameters of certain internal diseases (multifocal necrosis in the liver [MNL], bovine abdominal fat necrosis [BFN], and inflammation of the large intestine [ILI]) and to investigate relationships between the internal diseases and carcass traits (carcass weight [CW], rib eye area [REA], rib thickness [RT], subcutaneous fat thickness, and beef marbling score [BMS]). Records of the internal diseases and the carcass traits of 5,788 Japanese Black cattle were used for this study. The data for all the diseases were recorded as binary data as to whether the disease was observed (1) or not (0). Genetic parameters were estimated using linear and threshold animal models. The prevalence rates of MNL, BFN, and ILI were 16.1, 23.0, and 6.8%, respectively, and heritability estimates ± posterior SD were 0.18 ± 0.06, 0.28 ± 0.06, and 0.18 ± 0.06, respectively. The genetic correlations of MNL with BFN and ILI were -0.23 (P = 0.19) and -0.49 (P < 0.05), respectively. In contrast, the correlation between BFN and ILI was positive and high (0.96; < 0.05). The phenotypic correlations of the internal diseases with the carcass traits were weak and not different from 0 except for those of BFN and ILI with CW, REA, and RT, which were all negative (P < 0.05) and ranged from -0.21 (ILI and CW) to -0.05 (BFN and RT). The genetic correlations of MNL with the carcass traits were all weak and not significant. In contrast, the genetic correlations of BFN and ILI with CW, REA, and RT were all negative and significant (P < 0.05) between BFN and CW (-0.36) and between BFN and RT (-0.33). However, the genetic correlations for BFN and ILI with BMS were all positive at 0.21 P( = 0.16) and 0.39 (P < 0.05), respectively. The estimated heritability of the internal diseases suggests that they can be genetically improved. Genetic relationships of BFN and ILI with meat quantity traits were favorable for beef cattle breeding, because lower disease liabilities were associated with improved CW, REA, and RT for BFN and ILI. However, rather strong and unfavorable genetic relationships were found between BFN or ILI and BMS, which is related to meat quality. This suggests that selection for animals with superior BMS would increasingly lead to animals with higher liability to the internal diseases.