bluuebunny/crossref_metadata_2025 huggingface dataset:
126 Prevalence of Right Sided Heart Failure in Angus Influenced and Beef on Dairy Fattening Feedlot Cattle
Authors: Kukor Isabella , Mark Enns R , Holt Timothy N , Cleveland Matthew , Holland Ben P , Word Alyssa B , Ellis Guy , Theurer Miles , Speidel Scott E
Published: November 2023

Bovine Congestive Heart Failure (BCHF) exists in feedlot cattle at low to moderate elevations, but previous studies focused solely on beef cattle. The economic advantage of breeding beef sires to dairy dams results in an increase in the number of beef on dairy (DXB) crossed cattle in the feedlot system, though not much is known on the comparative prevalence of BCHF in DXB compared with beef cattle breeds. The inclusion of a new breeding program for dairy producers leads to another potential source of variability (i.e., dairy influenced genetics) for heart disease research in the feedlot sector. Heart failure in the feedlot is typically known as Feedlot Heart Disease (FHD) and has been reported to result in unfavorable carcass and growth performance damaging quality and efficiency throughout the lifetime of cattle. The unknown prevalence of heart failure in DXB cattle may be a problem for producers since little is known about which breeds are at greater risk. The severity of FHD for both beef and DXB cattle was determined by individually grading hearts at harvest using a 1 to 5 grading system corresponding to the stage of heart remodeling with a score of 1 (normal heart) and greater scores indicating increased remodeling. The objective of this study was to examine the prevalence of heart remodeling in both an Angus influenced group of beef cattle and a sample of DXB cattle. Heart scores were collected on an Angus influenced group raised in the panhandle of Texas (1,103m) and totaled 1,634 animals (Grade 1: 347; Grade 2: 848; Grade 3: 371; Grade 4: 68; Grade 5: 0). Heart scores were collected on a DXB group raised in western Kansas (846 m) and totaled 2,328 animals (Grade 1: 913; Grade 2: 1,162; Grade 3: 225; Grade 4: 27; Grade 5: 1). Proportions of moderate to severe heart remodeling were 0.27 and 0.11 for Angus influenced and DXB, respectively. In conclusion, preliminary research on heart remodeling in both samples indicates a greater proportion of cattle experiencing heart remodeling in beef breeds versus the DXB cattle, although further investigation is needed to make conclusions about the consistency of heart remodeling and breed effects.
137 Longitudinal Heritability of Ocular Microbiota in Preweaned Beef Cattle
Authors: Lakamp Drew , Neujahr Alison , Hille Matthew , Loy J Dustin , Fernando Samodha C , Spangler Matthew L
Published: November 2023

The bovine ocular microbiome is of interest because of its potential role in ocular disease, such as infectious bovine keratoconjunctivitis. The ocular bacterial community of 223 pre-weaned beef calves from a single cohort were sampled four times; day 0, day 21, day 41, and day 139 (mean calf ages 65, 86, 99, and 204 days, respectively). The bacterial community was phenotyped using the V4 region of the 16S rRNA gene and were grouped into amplicon sequence variants (ASV) which were taxonomically classified at the phylum and family level. Heritability was estimated for the log transformed relative abundance of each phylum, family, and ASV at each time point using ASReml v. 4.2. The model included calf age at time of sampling, calf sex, pinkeye vaccination treatment group, breed fractions, and retained heterosis as fixed effects. The random effect of animal had a (co)variance structure given by an H matrix comprised of 3,025 animals of which 1,207 were genotyped including all of the sampled animals. Heritability estimates ranged from 0.16 to 0.95 at the ASV level, 0.18 to 0.91 at the family level, and 0.18 to 0.81 at the phylum level. Standard errors were approximately 0.20 on average. The number of features (ASV, family, or phylum) with estimates greater than their standard error changed across time points (Table 1). Overall, time point 1 had the largest number of features with estimates greater than their standard error, followed by time point 4. Time point 3 had a greater number ASV with estimates greater than their standard error than time point 2; however, time point 2 had greater numbers of families and phyla with estimates greater than their standard errors. This trend likely stems from the fact that time points 1 and 4 had the greatest diversity of features while time point 2 had the least diversity. Average Chao1 index diversity estimates of ASV were 144.6, 28.1, 64.7, and 194.6, for time points 1 through 4, respectively. We hypothesize this is due to the perturbation of the ocular microbiome at sampling and the fact that overall diversity in the microbiome recovered by the final sampling, although, species composition differed compared with time point 1. While no ASV, family, or phylum had a heritability estimate greater than its standard error across all time points, certain features had heritabilities greater than their standard errors at multiple time points. The phylum Armatimonadota had heritability estimates greater than their standard error at time points 1, 2, and 4 (0.52, 0.18, and 0.37, respectively). Additionally, an ASV of the Streptococcus genus had estimates of 0.26, 0.45, and 0.26 at time points 1, 2, and 3, respectively. These estimates demonstrate that certain features of the ocular microbiome are influenced by host genetics.
144 Using Digital Twin Simulations to Optimize Genetic Selection in an Admixed Beef Herd
Authors: Engle Bailey N , Moser Gerhard , Villiers Kira , Suarez Mathias , Grey Eliza , Hayes Ben J , Kelly Matthew
Published: November 2023

Polled cattle, or those that are naturally hornless, are highly advantageous from both an economic and animal welfare perspective. While there are a number of different mutations that result in a polled phenotype, a duplication on bovine chromosome 1, referred to as the Celtic mutation, is the most common within beef cattle. Tropically adapted beef cattle typically have some degree of indicine influence, which are all naturally horned breeds. Polled genes must therefore be introgressed into tropically adapted cattle from naturally polled breeds, such as Angus, carrier of the Celtic mutation. However, Angus are not as well suited for hot, humid climates and introgression cannot happen at the expense of productivity or adaptation. Therefore, strategies must be developed to balance the introgression of poll alleles into a population with continued genetic improvement for economically important traits. This study utilized a privately owned, commercial population of admixed cattle to address this challenge. These cattle (n = ~3,000; single year cohort) were previously genotyped and SNP effects were estimated for heifer pregnancy as part of the internal genetic evaluation of the private enterprise. Management and selection of male and female replacements were modelled using actual production parameters in a digital twin framework. Four different breeding systems, multi-sire natural service, artificial insemination, and invitro fertilization collecting either five or 10 oocytes, were modelled and compared with account for differences in selection intensity. Selection index weighting of poll vs heifer pregnancy was modelled in eight different combinations. Genetic crosses were then simulated using the real genotypes in the genomicSimulation package in R for 5, 10, and 30 years of crosses to evaluate long and short-term consequences. We found that this approach of combining digital twin modelling with simulated crosses can be used to identify the selection strategy that should be applied to optimally balance selection for poll and economically important traits, such as fertility. This modelling approach provides a framework that may be customized to fit specific production parameters or genetic populations representing individual operations. More generally, this particular study used cattle and management practices representative of the northern Australian beef industry, and may be used as a reference for producers in that production system of strategies for increasing the rate of polled animals in their herds. Furthermore, this approach may be applied to the selection for or against other single gene traits, such as many genetic disorders. The USDA is an equal opportunity provider and employer.
143 Validation Strategies of Sensor Data in the Context of Heat Stress Detection
Authors: Lemal Pauline , Tran Marie-Nguyet , Schroyen Martine , Gengler Nicolas
Published: November 2023

Evaluating genetically heat stress (HS) of cattle is still a major challenge, also because of the need to have access to HS sensitive phenotypes relating their reaction to temperature and humidity as described by the temperature-humidity index (THI). A solution could be to add the information of data from sensors. However, activity (ACT) and rumination (RUM) sensors reflect no “production” [i.e., milk yields (MY), milk composition, but also intake or growth] related aspects which is suboptimal. Therefore, the objectives of this study were thus to assess the possible usefulness for adding sensors data for HS genetic evaluation based on a dairy situation. But the context described can be easily enlarged and include beef cattle and pigs. Strategy was illustrated using daily SenseHub collar records that were obtained from October 2019 to July 2022 in six dairy herds in Wallonia in Southern Belgium. A total of 453 Walloon Holstein cows were followed during this period for activity time. Meteorological data and a HS sensitive “production” trait (here MY) were also obtained from 2015 to 2022 for 1,740 cows from the same herds. In total 32,154 MY, 130,867 ACT and 130,848 RUM records were available. The thresholds at which the different traits start to be affected by HS were estimated at a THI of 63, 64 and 66, respectively, for MY, ACT and RUM. A three-trait random regression reaction norm model was fitted with these different thresholds. Estimated heritability values were 0.19 ± 0.03, 0.14 ± 0.06 and 0.21 ± 0.05 respectively for MY, ACT and RUM using AIREML. Ratios of variance associated to THI and constant genetic effect were 0.002, 0.02 and 0.04 indicating a stronger reaction by RUM but also ACT than MY to THI. However, it is uncertain if the reactions of these sensor traits are reflected in the production traits. Estimated genetic correlations of regressions on THI for ACT and RUM showed values of 0.51 ± 0.70 and 0.08 ± 0.54 with MY and 0.77 ± 0.69 between ACT and RUM. The first eigenvector of the correlation matrix explained 65% of the total variability and standardized coefficients of this eigenvector were 0.42, 0.69 and 0.59 for MY, ACT and RUM. A total of 50% of the variance associated with the regression on THI for MY could be explained by the regressions on THI for ACT and RUM. Adding the constant genetic effects for ACT and RUM only marginally increased to 51% the ability to explain the effect of THI on MY. Based on these results, reactions of different indicator traits, here sensor data, may reflect a different reaction than production and therefore economically important traits. Additional research, also beyond dairy cattle, should continue to address this topic that is very important for the validation of sensor data detecting HS.
141 Exploring Alternative Approaches for the National Argentine Angus Cattle Genomic Evaluation Program
Authors: Curutchet Agustin R , Fragomeni Breno O , Guitou Horacio , Monti Aldo
Published: November 2023

Beef cattle production is a relevant component of the Argentine economy. The Argentine Angus is the most common beef cattle breed, and this population has a large economic and cultural importance for the country. In 2019, a national genomic selection program was implemented in the Argentine Angus population. The objective of this study was to explore alternative approaches for the genomic selection program in Argentinean Angus so that it will better serve the stakeholders. For this study, a total of 9,944 phenotypes for weaning weight from Argentine Angus born in 2018 were used. All animals had pedigree information for at least 3 generations. Genomic information on 1,078 individuals for 150k SNPs was used. The genotyped individuals were connected to the population by pedigree. Weaning weight EPDs were calculated under an animal model considering contemporary group as fixed effects and additive direct and maternal genetic effects as random. Variance components were estimated using AIREMLF90, and EPDs were calculated using BLUPF90. The enhanced EPDs were the genomic estimated breeding values obtained by single-step GBLUP, by including the genomic information into the original model. Validation of the EPDs and enhanced EPDs were made by cross-validation, where a random sample of 70 genotyped individuals with more than 20 progeny was the validation population. The reference breeding values were the EPDs of the validation population with complete data. In a second analysis, the phenotype of the progeny of the validation population was removed. The correlation between the reduced and complete EPDs was the accuracy for this study, according to the LR method. The accuracy obtained by this method was overestimated due to potential relationships between animals in the training and validation population. However, the enhanced EPDs were 4% more accurate than the pedigree EPDs. The next steps of this research are exploring alternative approaches of the validation methodology, which will be accomplished by implementing a forward-in-time cross-validation. Such validation will be performed once more data is included in the model. Additionally, other models will be tested to identify the ideal combination of effects that will yield higher accuracies. Details regarding the construction of the genomic relationship matrix will be tested with the overarching goal of improving convergence without affecting accuracy. Moreover, a weighted single-step GBLUP approach will be tested to verify if prediction accuracy can improve even further. Finally, indirect prediction models will be tested so that the Argentine Angus Association can implement interim enhanced EPDs for young animals. In conclusion, including genomic data into this dataset yielded greater accuracy and such values are expected to improve even further after optimization.
151 Depth Video Data-Enabled Predictions of Longitudinal Dairy Cow Body Weight Using Thresholding and Mask R-Cnn Algorithms
Authors: Bi Ye , Campos Leticia , Wang Jin , Yu Haipeng , Hanigan Mark , Morota Gota
Published: November 2023

Cow body weight monitoring is critical in farm management because it is associated with the growth, nutritional status, and health of dairy cows. Regularly collecting body weight records often requires human labor or expensive equipment. Recently, a low-cost 3D depth sensor camera-enabled computer vision analysis has been proposed to address the body weight prediction challenges. Cow body weight is a repeated trait; however, past body weight prediction research only used data collected at a single time point. Furthermore, the utility of deep learning on body weight predictions remains unanswered. Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data and compare the performance of thresholding and Mask R-CNN deep learning approaches. We set up an Intel Realsense D435 camera at the Virginia Tech Kentland farm to collect top-view videos for 10 Holstein and 2 Jersey cows for 28 days, twice per day. Real body weight records were also collected by a walk-over-weighing system simultaneously. A total of 40,405 depth images and depth map files were obtained. We explored three approaches to segment the cow body from the background, including single thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived biometric measurements, length, width, height, and volume, were estimated from segmented images and fitted in ordinary least squares. Two cross-validation designs, forecasting and leave-several-cow-out, were used to evaluate prediction performance. The correlation between image-derived biometric measurements and body weight ranged from 0.84 to 0.95. On average, the Mask-RCNN approach resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.96 and 3.54% in the forecasting cross-validation, respectively. The predictive performance of the single and adaptive thresholding approaches was similar. The Mask-RCNN approach was also the best in the leave-several-cow-out cross-validation, followed by adaptive and single thresholding. The prediction coefficients of determination of Mask-RCNN for leave-one-cow-out, leave-two-cow-out, leave-three-cow-out, leave-four-cow-out were 0.05, 0.69, 0.85, and 0.90, respectively, while mean absolute percentage errors were 4.51%, 4.51%, and 4.59%, 4.70% respectively. Our results suggest predicting cow body weight from depth video data using deep learning is feasible.
54 Large-Scale Crispr Functional Characterization of Genomic Elements in Rainbow Trout (Oncorhynchus mykiss)
Authors: Phelps Michael
Published: November 2023

The wealth of genetic information that is now available for many agricultural animal species has opened vast potential to apply this new knowledge to improve agricultural production and sustainability. The field, however, is now faced with the daunting task of trying to understand what is arguably the most complex coded system known to mankind. In biomedical model systems, extensive efforts over decades have used single candidate gene approaches to uncover the function of many protein-coding genes and non-coding RNAs, but how this information translates to agricultural animals and their production traits is often unknown. Not only is repeating this vast effort for every agricultural species not practical, the current pace of genomic discovery is woefully inadequate for investigating animal genomes and therefore a radically new approach is needed. Using rainbow trout (RBT, Oncorhynchus mykiss) as a novel agricultural animal experimental system we have initiated a large-scale functional genomics screen that aims to characterize the function of over 1,000 genomic elements in a large animal model. The initial screen will focus on understanding how different genomic elements regulate growth and environmental resilience in these fish by combining multiplex CRISPR genome editing technology and phenotypic screening approaches to identify novel allele combinations from families of edited fish. The research is a paradigm shift in our approach to characterizing gene function, moving away from candidate-based approaches to a more genomics view of gene function. This work will shed light on the complex genetic mechanisms underlying two important production traits in fish while helping to advance our understanding of large animal functional genomics.
68 Integrating Enviromics, Genomics, and Machine Learning for Precision Breeding of Resilient Beef Cattle
Authors: Rosa Guilherme J M , Lourenco Daniela , Rowan Troy N , Brito Luiz F F , Gondro Cedric , Huang Jingyi , de Souza Simone Valle
Published: November 2023

Animal breeding is one of the main pillars of livestock production. Statistics, computer science, and genomics have transformed productivity in the industry. Another round of breakthroughs is expected to come from harnessing the power of big data and machine learning analytics to address the complex interaction between animal genetics and the environment. Developing methods that enable precise selection decisions for animals in diverse production environments is expected to result in significant productivity gains and a reduction in welfare issues, while also mitigating the environmental impact of suboptimal allocation of resources to ill-adapted animals. Due to climate uncertainty, an understanding of these genetic and environmental interactions will be critical for decision-making. Our multi-institutional group initiated a project to generate a comprehensive enviromics data lake to develop and apply novel methods for breeding more adapted and resilient beef cattle for varying environments. Using GIS technology and integrating various sources of environmental information from publicly available databases and satellite imaging, detailed descriptions of soil, climate, forage, and weather conditions will be created for thousands of U.S. farms, representing millions of cattle, with phenotypic and genotypic data. Farms will be comprehensively described in terms of their facilities and management practices through surveys. Machine learning and artificial intelligence techniques will then be used to predict future animal performance for precision livestock management. The proposed models will also be biologically validated by measuring direct indicators of animal resilience. Results will be communicated to beef cattle producers and industry groups to foster the implementation of environment-aware approaches into routine genetic evaluations. In this talk, we will discuss this ongoing project in detail and present some preliminary results.
69 The Bigger Picture of Phenomics: Whole-System Support for Systems Research, Models and Databases
Authors: McNamara John P
Published: November 2023

Phenomics started with Mendel’s elegantly simple empirical studies. Soon after, Fisher discovered purines were rich in nuclei of all cells. A century later, we have the technology to compare the actual base pair sequence of DNA and RNA with the outward expressions of phenotypes. We have sire evaluation based on genomic and phenomic measure. But, being human, it is quite common to get immersed in exciting technology and tend to forget why we developed the technology in the first place. We only refer to ensuring a stable efficient food supply of plants and animals in the reports that we expect decision makers to read and give us more money. It is, however, truly wonderful that we now have the techniques to study the questions of the ages. But the future of phenomics is built on the foundations of the past. In 1974, A. E. Freeman and others convened a meeting under the mechanism of the newly formed Regional Research Project 2 on Defining the Inefficiency of Animal Production. From that workshop came the first modern multistate genetic study in dairy cattle with the objective to compare the phenotypes (milk component production, energy and nitrogen metabolism, hormones) of dairy animals that were born from bulls of High (+795 kg PTAM) and Low (167 kg PTAM) bulls. Please remember this study was designed in the mid 1970s and the first data collected in the early 1980s, decades before efficient methods of DNA and RNA composition were available. Studies conducted at Washington State University demonstrated marked genetic and environmental differences in adipose tissues of dairy animals. Enzymes involved with catabolic reactions were highly associated with High PTAM bulls while enzymes associated with anabolic reactions in adipose tissue were more closely related to increased energy intake regardless of genetic background. With mRNA chips, we were able, after almost 40 years, to directly relate variation in mRNA expression in the adipose tissue with the genetic background of the animals. This was the first such discovery in the field, and has led to more defined and expansive phenomics studies. In the year 2023, the use of genomics, phenomics, databases, large scale, cooperative studies and team science are specific recommendations, strategies and goals of the USDA, NIFA, The National Academies, and Land-Grant Universities. The AgBioData consortium is one example of international teams working together to expand the use of genetic and genomic databases, to prepare undergraduate and graduate level educational materials on FAIR databases and to encourage and support sustainable, robustly funded public and private partnerships in the use of integrated studies and large databases, including phenomics, with the sole purpose of expanding a robust, resilient, safe and plentiful food supply.
72 The AG2pi Vision for Resources in Agricultural Genomics and Phenomics: How Asas Can Contribute
Authors: Tuggle Christopher K , Scott Nicole , Clarke Jennifer , Murdoch Brenda M , Dekkers Jack C M , Ertl David , Lyons Eric , Lawrence-Dill Carolyn J , Schnable Patrick S
Published: November 2023

To meet the challenges of an increasing global population and multiple environmental stressors on agricultural production, it is essential to better understand how genotype (G) and environment (E) influence phenotype for traits of economic importance in agriculture. To accomplish this requires interdisciplinary teams consisting of researchers from crop and livestock sciences, genetics, genomics, computational and data sciences, and engineering. Towards this end, in 2020, Congress established the Agricultural Genome to Phenome Initiative (AG2PI). An important objective of this presentation is to encourage strong participation of animal science researchers and industry in ongoing AG2PI activities. The purpose of the initial AG2PI project, awarded to Iowa State University and the Universities of Arizona, Idaho and Nebraska, was to assemble and prepare a community to conduct AG2P research across the plant and animal kingdoms. The AG2PI project aims to: a) identify research gaps and opportunities, b) foster community solutions to these challenges, and c) rapidly disseminate findings for community use. Training workshops, field days, conferences, community surveys, and seed grants have all contributed to these goals. Opinions and ideas from the crop and livestock genetics communities are key to advancing AG2P science and related work. AG2PI has used online community surveys and in-person conferences to gather these and to facilitate transdisciplinary communications. In September 2022, AG2PI held a conference titled “Thinking Big: Visualizing the Future of AG2PI”, in which attendees participated in focused small group discussions to identify which agricultural G2P-related topics are most critical for future R&D funding and which may be the most difficult to achieve. These discussions are summarized in a concept paper for the community, shared with USDA-NIFA, and submitted for publication. A follow-up conference was held June 14-15, 2023, in Kansas City, where people working in both the animal and plant genetics research communities and industry stakeholders attended. Outcomes from this conference will be summarized in this presentation, and requests for animal scientists to provide further input will be made. The future of agricultural genomics will be shaped by both the participants in AG2PI and the solutions developed by the AG2PI community. Funding for this work comes from USDA NIFA awards 2022-70412-38454, 2021-70412-35233, and 2020-70412-32615.
99 Noncoding Rnas Alter Our Interpretations of Genome to Phenome
Authors: Hagen Darren E , Goldkamp Anna K
Published: November 2023

Gene transcription and protein translation are core components in the process of gene expression. Previous research on the regulation of gene expression largely focuses on events prior to translation, including epigenetic regulation, transcription, and RNA processing. However, translation acts as an additional layer of regulation that plays an important role in gene expression and function. Highly expressed genes are thought to be codon-biased to support efficient translation, in which the encoded codons correspond to highly abundant tRNAs. Further, synonymous SNPs were once considered to be silent due to the degeneracy of the genetic code. However, synonymous SNPs may disturb protein abundance and function through alterations in translational efficiency and suboptimal pairing to lowly abundant tRNAs. Our previous study identified variation in tRNA expression within bovine liver and muscle tissue, suggesting tissue-specific modulation of translation. In addition, advances in sequencing technologies have recently permitted the study of the translatome, which refers to the entire population of mRNA associated with ribosomes for protein synthesis and can be investigated through ribosome profiling. Here, we applied Quantitative Mature tRNA sequencing (QuantM-tRNA-seq) and ribosome profiling to investigate the relationship between tRNA expression and translational stalling events. Moreover, we have identified translationally regulated genes underlying tissue-specific biological processes and found that many upregulated and downregulated genes coincided with high and low translational efficiency respectively. We have also successfully defined stalling sites that depict the regulatory information encoded within the coding sequence of transcripts, which could control translation rate and facilitate proper protein folding. This work offers an atlas of distinctive stalling sites across bovine tissues, which provides an opportunity to predict codon optimality and understand tissue-specific mechanisms of regulating protein synthesis.
154 Antibody and Cell-Mediated Responses Associated with Bvdv and Interferon-Gamma Expression in Reciprocal Angus-Brahman First Generation Crosses
Authors: Chapman MacKenzie G , Falkenberg Shollie M , Dassanayake Rohana P , Herring Andy D
Published: November 2023

The influence of sex and reciprocal cross type were evaluated in Brahman-sired (B × A) and Angus-sired (A × B) F1 cattle following modified-live viral vaccination regarding virus neutralization titers (VNT) and cell-mediated immune (CMI) responses following in vitro exposure to Bovine Viral Diarrhea Virus (BVDV) Type 1 (BVDV1) and Type 2 (BVDV2) genotypes. VNT against BVDV were based on virus neutralization assay, and CMI responses were measured using the PrimeFlow mRNA assay for determination of the frequency of BVDV and interferon-gamma (IFN-γ) positive cells. Mixed model procedures utilized individual calf ID as a random effect with fixed effects of F1 cross type, calf sex, evaluation date, and potential 2-way interactions; models for frequency of BVDV and IFN-γ positive cells, stimulation conditions (BVDV1, BVDV2, mitogen, none) and potential two-way and three-way interactions of fixed effects. Relationships of immune responses with calf birth weight and weaning weight and steer carcass traits were also investigated. This study found several CMI traits differed due to the type of cross and calf sex; however, no differences in VNT were attributed to these effects. Cells from B × A animals consistently ranked approximately 20% greater for BVDV positive cells (BVDV+) than those from A × B animals pre-MLV vaccination; however, post-MLV vaccination, cells from A × B animals ranked 20% greater (P < 0.05) for CD2+, CD4+, and CD8+ cells. Post- MLV steers consistently had 30 to 35% greater (P < 0.05) frequencies of BVDV+ cells following viral exposure; steers had greater frequencies (P < 0.05) of CD4+ and CD335+ cells expressing IFN-γ and ranked greater for CD2+ and CD8+ cells when compared with heifers in both crosses. Only vaccination timing affected BVDV VNT. No significant correlations involving VNT and CMI responses were observed among A × B animals when stimulated with BVDV1, but among B × A animals, positive correlations were observed with IFN-γ in CD4+ (r = 0.406, P = 0.049) and CD335+ (r = 0.472, P = 0.020). When stimulated with BVDV2, positive correlations of 0.33 to 0.39 (P < 0.05) were observed between BVDV and IFN-γ frequency within cell type; these were not significant when stimulated with BVDV1, except for CD335+ cells (r = 0.41, P = 0.003). No correlations for VNT and CMI responses were observed when stimulated with BVDV2. No major correlations between immune responses and weight or carcass traits were observed, and immune response category for BVDV or IFN-γ frequency did not influence birth weight or weaning weight. Differences in CMI responses involving reciprocal cross types and sex observed in this project may relate to widely reported reciprocal cross differences in birth weight in Bos indicus - Bos taurus crossbred cattle and warrant additional study.
162 Evaluating the Potential of a Thermal Imaging System to Identify Subclinical Mastitis in Dairy Cattle
Authors: Shrestha Bhuwan , Piñeiro Juan M , Paudyal Sushil
Published: November 2023

The objective was to evaluate the potential for the use of a thermal imaging system for the identification of subclinical mastitis in dairy cows. Multiparous Holstein dairy cows (n = 131) within 150 days in milk (DIM; range 65 to 155) were enrolled in the study at a commercial dairy farm in central Texas. Milk samples (10 mL) were collected from each quarter separately and evaluated for somatic cell counts (SCC; n*1000), fat (%), protein (%), lactose (%), SNF (%), BHBA (mM/L), acetone (mM/L), and milk urea nitrogen (MUN; mg/dL). A minimum of four thermal images per cow representing all four quarters were captured using a handheld infrared camera (Fluke TiS 75+) from approximately one-meter distance from the cow. All images were taken inside the milking parlor and after the morning milking during February 2023 to minimize the impact of daily ambient temperatures. Skin temperatures from each quarter (maximum, minimum, average) were extracted using Fluke Connect software. A total of 524 quarter-level milk samples and corresponding udder images were obtained, and 10 images were discarded from analyses due to the lack of appropriate resolution and focus. To prevent collinearity, a correlation matrix was developed using PROC CORR in SAS and variables with significant correlations were removed from the model. Subclinical mastitis was defined at the quarter level when SCC > 250,000. Statistical analyses were performed using mixed linear regression models (PROC MIXED) with cow and DIM as random effects in the model, and logistic regression model (PROC LOGISTIC) was fitted to evaluate the probability of the disease based on temperature and milk variables. Maximum and average udder temperatures did not demonstrate a significant relationship with subclinical mastitis status whereas minimum udder temperature was greater in sick quarters (29.6 ± 0.52 vs 27.5 ± 0.45; P = 0.003). Increased minimum udder temperature was also associated with BHBA (P < 0.0001), acetone (P < 0.0004), and MUN (P < 0.0007) concentrations in milk. Cows with subclinical mastitis had decreased BHBA (0.14 ± 0.037 vs 0.28 ± 0.031; P = 0.0036) and acetone (0.017 ± 0.003 vs 0.044 ± 0.003; P < 0.001). Odds of subclinical mastitis were associated with MUN values in milk (OR 0.94; CI 0.89 - 0.98) but not with any udder surface temperature variables. We conclude that an increase in the minimum temperature of the udder could be used as an indicator of subclinical mastitis but studies with larger datasets and using advanced image analysis techniques are warranted to validate these results.
176 Relationships Among Early Life Weight and Growth Among Halfblood Bos Indicus Male Calves and Associations with Carcass Weight
Authors: Lake Steven Gunari , Riley David G , Herring Andy D
Published: November 2023

Nellore-Angus steers (n = 572) born in 2009-2013, 2015, and 2019 and Brahman-Angus reciprocal steers (n = 73) born in 2019 and 2020 were evaluated for calf and carcass weights (HCW). Calves were born and reared at the McGregor Research Center and included F2, F3, F4, and F5 Nellore-Angus and F1 Brahman-Angus. Calves were weighed at 4 approximate times: within 48 h of birth (BWT), 50 d age (spring-working, SWWT, not available for 2009-2010), 180 d age (pre-weaning, PRWWT), and approximately 210 d age (weaning, WWT). Animals born in the same year were managed similarly, and cow herds were managed together in most cases except during annual natural service, multi-sire breeding seasons. Animals born in the same year were managed as contemporaries post-weaning and for feedlot finishing. Mixed model analyses of variance for calf traits included genetic type (nested within calf birth year), calf birth year, age of dam category, and covariates of Julian birth date (BWT, SWWT) or calf age (PRWWT and WWT) as fixed effects. In each year at least 2 genetic types were present; however, because of imbalance across years, only genetic types produced in the same years were compared following significant F-tests. For these analyses cow age category did not affect calf BWT or SWWT, but showed influence for PRWWT (P = 0.006) and WWT (P < 0.001); cow age category did not affect calf ADG. Differences in calf weights (P < 0.001) and calf ADG (P < 0.001) due to the genetic type were driven by reciprocal differences between Brahman-Angus crosses and potential maternal hybrid vigor advantages for F2 Nellore-Angus calves from F1 dams vs. other genetic types. Brahman-sired F1 (B × A) males were 12.7 to 14.1 kg heavier for BWT (P < 0.001) and 14.9 to 19.2 kg heavier for SWWT (P < 0.001) than Angus-sired F1 males (A × B), but persistence to weaning varied across years. It appears in these data that some genetic types may be more sensitive to individual year effects, but it is uncertain why. Correlations of calf weight traits with HCW were inconsistent across genetic types. No calf weight was associated with HCW in A × B crosses, and only PRWWT in B × A related to HCW (r = 0.44, P = 0.007). Among Nellore-Angus crosses, BWT showed small to moderate correlation to HCW (0.23 < r < 0.46); however, among the F4 and F5 calves, later weights (SSWT, PRWWT and WWT) had large correlations with HCW (0.50 < r < 0.79) whereas these correlations ranged from 0.24 to 0.37 in F2 and F3 steers. It appears animals of similar Bos indicus percentage may have quite different calf growth patterns related to HCW.
20 Methane Accounting and Its Importance to the Beef industry’s Implied Contribution to Climate Warming
Authors: Beck Matthew R , Thompson Logan
Published: November 2023

At 27% of U.S. methane (CH4) emissions (3% of U.S. total emissions), enteric CH4 emissions represent the largest source of U.S. CH4 emissions, surpassing natural gas systems according to the latest report from the Environmental Protection Agency (EPA). Total manure CH4 emissions represent 1% of total U.S. emissions. Enteric and manure CH4 emissions from the beef industry represent 2.1% and 0.03%, respectively, of U.S. total emissions. These contributions of CH4 emissions to total U.S. greenhouse gas emissions are determined by relating CH4 emissions to a carbon dioxide (CO2) equivalent basis (CO2-e) by multiplying the amount of CH4 emissions by its global warming potential (EPA uses 25 for CH4 on a 100-y basis; GWP100). The GWP100 method was adopted following the Kyoto Protocol in 1997, and ever since this time its appropriateness for short-lived climate forcers (SLCF; e.g., CH4 has a 12-y atmospheric lifespan) has been debated. This debate exists because GWP100 is a misnomer, as it is known to have no relationship with the contribution of a gas to warming. Since CH4 is a SLCF, when emission rates are decreasing there is a decreasing concentration of CH4 in the atmosphere. To account for SLCF, an alternative accounting methodology, termed GWP-star (GWP*), has been proposed to convert SLCF to a CO2-warming equivalence (CO2-we). The CO2-we values from GWP* methodology has been demonstrated to closely relate to warming contributions compared with the CO2-e value from the GWP100 method. Applying the GWP* metric to enteric and manure CH4 emissions from the beef industry reduces the implied contribution to climate warming of these emission sources by 92% and 62%, on average from 2010-2020, respectively, compared with the GWP100 methodology. There are currently promising mitigation technologies emerging for mitigating enteric CH4 emission. For example 3-nitrooxypropanol may reduce enteric CH4 by 30% and Asparagopsis seaweed may reduce it by 80%. We explored the effects of these technologies on CO2-we if they had 100% adoption by the beef industry and if these mitigation potentials were consistent across all production systems (admittedly a big assumption). Both technologies would result in negative CO2-we for 20-y, after which a new baseline would be achieved and the CH4 emissions would begin contributing to warming again (i.e., accumulating CH4 in the atmosphere). During the 20-y of net-cooling, a 30% and 80% reduction in enteric CH4 would result in -143 (3% of U.S. total CO2 emissions) and -461 (10% of U.S. total CO2 emissions) million metric tons of CO2-we/y, respectively. Ultimately, adopting GWP* not only provides estimates that actually relate to contributions to climate warming, therefore providing a more appropriate metric than what is currently used, but also provides a means for the beef industry to leverage mitigation strategies to be a solution for climate change.
91 Grazing management–Tools for Improving the Efficiency of Grazing Beef Production
Authors: Thompson Logan , Maciel Isabella , Beck Matthew R , Rowntree Jason E
Published: November 2023

The beef sector is facing mounting challenges related to its perceived impact on climate change and other negative ecological externalities that can occur from the rearing of beef cattle. This would include greenhouse gas emissions (GHG), soil degradation, eutrophication of water ways and a decrease in biodiversity, to name a few. Within the industry, the largest contributor of GHG comes from the grazing sector, predominantly in the form of enteric methane (CH4) produced during the reticulo-rumen fermentation process. This presents a unique challenge, as the grazing sector is one where animal scientists have low precision with respect to experimental design and data collection, and where producers assert a lower level of dietary control relative to confined operations. While enteric CH4 inhibitors are getting closer to regulatory approval, their ability to reduce emissions in grazing systems is still relatively unknown due to the “pulse” supplementation strategy being unstudied to date. Therefore, management decisions that improve the quality of the forage base are the most practical strategies to reduce enteric CH4 production. This would include strategies such as adaptive grazing management, inclusion of high-quality forages, and improved forage diversity as potential tools to reduce emissions, dependent on geographic location. Furthermore, how the human dimension influences soil-plant-animal interrelationships may lead to beneficial ecological outcomes such as carbon sequestration and soil health. Therefore, potential “win-win” outcomes may be achieved. However, climatic gradients, and more specifically moisture, can influence or limit outcomes and thus interpreting research should be done on a geographical and climatic specific basis. The objective of this presentation is to outline and examine how management decisions can influence the efficiency and environmental impact of U.S. beef production, and to identify potential limitations and knowledge gaps in need of future research.
190 The Nutrigenomic Effects of Dietary Choline and L-Carnitine on the Muscle and Liver of Lean and Overweight Cats
Authors: Fletcher Jess , Grapentine Sophie , Rankovic Alexandra , Brisson Brigitte , Sinclair Melissa , Shoveller Anna Kate K , Altom Eric K , Bakovic Marica , Verbrugghe Adronie
Published: November 2023

The objective of this study was to investigate the effects of dietary choline and L-carnitine on the lipotropic gene expression in the muscle and liver of overweight and lean cats, when fed for body weight (BW)maintenance. Overweight [n = 6, body fat percent (BF%): 33.2 ± 1.38] and lean (n = 6, BF%:18.4 ± 0.93), 1-2 year old, male, neutered cats received choline (378 mg/kg BW0.67), L-carnitine (200 mg/kg BW) and no supplementation (control) in a 3x3 Latin square design for 6 weeks, with a 2-week washout between treatments periods. Each treatment was offered with a commercial extruded diet; diet amount was provided to maintain current BW. Cats were anesthetized and quadriceps vastus lateralis (30-50 mg) and liver (30-50 mg) biopsies were harvested after each treatment period using an open muscle or laparoscopic biopsy technique respectively. Tissue RNA was isolated with TRIzol and sequenced using directional PolyA RNA-seq. The expressed genes were aligned using the reference genome, Felis_catus_9.0, Ensembl v.107. Differential gene expression was analyzed for 29,928 genes with SEQUIN, using edgeR and TMM normalization. Pathway analysis was performed using EnrichR databases, BioPlanet 2019 and KEGG 2021. Using a two-group comparison, global analysis revealed 92 and 759 differentially expressed genes in the liver and muscle, respectively of overweight cats compared with lean (P < 0.05). Affected pathways included fat digestion and absorption, including peroxisome proliferator-activated receptor (PPAR) signaling in the liver, and immunity related pathways in muscle (P < 0.05). Upon further analysis using a two-group comparison between overweight and lean cats for each treatment, 11 and 692 significant genes were found in choline treated liver samples and L-carnitine treated muscle samples, respectively (P < 0.05). Similar to the global analysis, genes related to fat metabolism, including PPAR signaling, were significant in the choline treated liver samples, and immunity related genes were significant in the L-carnitine treated muscle samples (P < 0.05). Based on preliminary data, supplemental dietary choline may be beneficial for overweight cats as it may increase fat metabolism in the liver, mitigating risks associated with feline hepatic lipidosis (FHL). Supplemental dietary L-carnitine may also be beneficial due to its effects on immunity related pathways, including pathogen defense. To investigate the full effects of choline and L-carnitine on gene expression, further studies are warranted including older, chronically obese cats, as well as confirmed cases of FHL. Acknowledgements: Natural Sciences and Engineering Council of Canada, Elmira Pet Products and Balchem Corp.