bluuebunny/crossref_metadata_2025 huggingface dataset:
391 The impact of trace mineral source on systemic immunological status associated with early embryonic loss in lactating dairy cattle
Authors: Leach Mary A , Tucker Hannah L M , Tucker Heather A , Seymour William , Eckelkamp Elizabeth , Shepherd Elizabeth , Mathew Daniel , McLean Kyle J
Published: September 2024

Milk production has doubled from 1970 to 2009 but has been associated with a decline in fertility. Around 25% of failed pregnancies in dairy cattle are due to embryonic loss. Mineral bioavailability may affect embryonic retention, so we hypothesized that Zn, Cu, and Mn sources will influence systemic immunological status, which affects early embryonic retention. Holstein cows (n = 58) were randomly assigned to one of two daily oral drench trace mineral (Zn, Cu, and Mn) treatments 30 d before breeding (d -30): 1) Inorganic (ITM) or 2) Metal Methionine Hydroxy Analog Chelate (MMHAC). Before treatments began, cows were assigned to a semen type, beef vs. Holstein, equally, across treatments. All cows underwent a double Ovsynch protocol and were artificially inseminated on d 0. Humoral immunological status was assessed in serum samples taken on d 0 and d 7. Immune activity was quantified via a D2Dx systemic immunity score, which assesses samples for the presence of IgG, IgM, cytokines, and complement proteins. Ultrasonography was utilized for pregnancy confirmation on d 35, classifying cows as pregnant (P) or open. Progesterone analysis from d 21 plasma was used to categorize open cows as either non-pregnant (NP) or early embryonic loss (EEL). Statistical analyses in R Studio were completed, in two models based on pregnancy (P vs EEL and NP vs EEL), to determine the effects of mineral treatment, pregnancy status, semen type, and all interactions on humoral immune status. In the P vs EEL model, there were no effects (P > 0.30) of semen type, mineral treatment, pregnancy status, or any interactions on systemic immunity score on the day of breeding (d 0) or d 7. On d 0, cows provided MMHAC had a systemic immunity score of 0.073 ± 0.008 but were not different (P > 0.34) from ITM cows (0.063 ± 0.006). This trend continued on d 7 with systemic immunity scores of MMHAC cows not differing (P > 0.32) from ITM cows (0.055 ± 0.004 and 0.061 ± 0.003; respectively). There was a tendency (P = 0.10) for the three-way interaction of semen type x pregnancy x treatment to influence the immunity score change from d 0 to d 7. Pregnant cows on ITM treatment bred with beef semen had a positive change in immunity score (0.020 ± 0.0085). Whereas ITM cows bred with Holstein semen that underwent EEL had a negative change (-0.015 ± 0.0085); all other means were intermediate. In the NP vs EEL model, there were no effects (P > 0.14) of semen type, treatment, pregnancy status, or any interactions on systemic immunity score on d 0, d 7, or the change between d 0 and d 7. In summary, trace mineral bioavailability tended to affect systemic immunological status in P vs EEL cows but not in NP vs EEL cows, which may allow for the elucidation of immunological status differences during pregnancy establishment.
386 Pregnancy supportive metabolites are enhanced in Brahman cows on day 15 of the estrous cycle
Authors: Smith Damon , Poliakiwski Brette , Vilches Mario , Muntari Musah , Singleton Sarah A , Polanco Odile , Melo Gabriela , Mercadante Vitor R G , Moorey Sarah , Ealy Alan , Biase Fernando , Lamb Cliff C , Pohler Ky G
Published: September 2024

Bos indicus-influenced cattle are more resilient to pregnancy loss while receiving a restricted nutrient diet compared with Bos taurus cattle, but the mechanism behind this resilience is still unknown. To undercover this mechanism, the present study was designed with Angus (Bos taurus), Brangus (Bos indicus-influenced), and Brahman (Bos indicus) cows randomly assigned (d -30) to either a 100% maintenance diet (n = 22), or a 70% maintenance diet (n = 23) for 45 d. All cows were submitted to a 5-d CO-Synch + CIDR synchronization protocol on d -7 with ovulation induction representing d 0. A cervically navigated foley catheter was placed in the uterine horn ipsilateral to the corpus luteum on d 15 of the estrous cycle. Approximately 50 mL of phosphate buffered saline was infused into the uterus then recovered for uterine flush analysis. Recovered uterine fluid was subjected to metabolomic analysis by ultra-high performance liquid chromatography - high resolution mass spectrometry. A significant breed by metabolite interaction occurred (P < 0.001). A treatment by metabolite interaction was also observed (P = 0.015) in which maintenance and restricted cows clustered within their respective treatment when plotted in an orthogonal PCA graph. Of the 101 metabolites analyzed, 7 metabolites (aspartate, glutamate, hydroxyphenylacetate, taurine, uric acid, uridine, and xanthine) differed (P < 0.05) among breeds. Brahman cows had the greatest value of all 7 metabolites (P < 0.05) except hydroxyphenylacetate (P > 0.10). Angus and Brangus metabolomic values were similar for aspartate, taurine, and uridine (P > 0.10) but differed for glutamate, hydroxyphenylacetate, uric acid, and xanthine (P < 0.05). Two metabolites that were greatest in Brahman cows (aspartate and glutamate) contribute significantly to the Kreb Cycle, indicating that Brahman cows have the ability to maintain proper amounts of substrates for energy production compared with Angus cows. Both glutamate and aspartate also aid in the removal of toxic ammonium as they are substrates for a-ketoglutarate and oxaloacetate, respectively, which both consume ammonium in their respective reactions. Furthermore, uric acid (which has a precursor of xanthine) was also greatest in Brahman cows and is a potent antioxidant with strong capabilities of scattering reactive oxygen species. Finally, aspartate also aids in the detoxification of ammonium within the urea cycle. Taken together, these four metabolites (aspartate, glutamate, uric acid, and xanthine) are working together but separately with the overall goal to provide more functional energy, while removing unnecessary toxins which may be two mechanisms in how Bos indicus-influenced cows are better able to sustain optimal reproductive efficiency than Angus cows.
389 Impact of a Controlled Internal Drug Release device on circulating and local concentrations of progesterone
Authors: Perry George A , Carson Chloey P , Quail Lacey K , Blaske Sarah , Ketchum Jaclyn N
Published: September 2024

Controlled Internal Drug Release (CIDR) devices have become an important component of reproductive management programs in many beef herds. When a CIDR is inserted into a cow, circulating concentrations of progesterone mimic a functional corpus luteum. Some data, however, suggest that concentration of progesterone following insertion of a CIDR might have negative consequences on fertility in Bos indicus animals. Therefore, the objective of this study was to determine the impact of a CIDR device on circulating and uterine tissue concentrations of progesterone. A blood sample and a uterine biopsy were collected from non-suckled postpartum cycling Brahman cows (n = 10; Cycling) during the midluteal stage. A blood sample and a uterine biopsy were also collected from non-suckled postpartum cycling Brahman cows (n = 9; CIDR) after a CIDR had been in place for 5 d. To collect uterine biopsies from CIDR cows, the uterine biopsy tool was placed inside a plastic covering, both were passed into the cervix of the cow before the tool was extended through the covering and into the uterus. After the biopsy was collected, the tool was carefully removed to prevent sample contamination. Blood samples were centrifuged, and serum was collected to determine concentrations of progesterone. Uterine biopsies were snap frozen until extraction. Biopsies were weighted, thawed, and homogenized in assay buffer. Samples were then extracted with methyl tert-butyl ether and resuspended in assay buffer. Serum concentrations are reported as ng/mL and uterine concentrations are reported as ng/g. Statistical analysis was performed using the GLM procedure in SAS with treatment (CIDR vs Cycling), tissue type (uterine vs serum) and their interaction included in the model. There was a significant effect of treatment (P = 0.0062), tissue (P = 0.0063) and a treatment by tissue interaction (P = 0.0068). Animals with a CIDR had greater concentrations of progesterone than Cycling animals (274.77 vs 4.37 ng, respectively). Uterine samples had greater concentrations of progesterone than serum samples (274.40 vs 4.74 ng, respectively). These increased concentrations of progesterone were mainly from CIDR uterine samples (543.24 ng/g) which had greater (P < 0.001) concentrations of progesterone compared with all other samples, which did not differ from each other (P > 0.98; CIDR Serum 6.29 ng/mL, Cycling Serum 3.2 ng/mL, and Cycling Uterus 5.54 ng/g). In summary, CIDRs increased circulating concentrations of progesterone sufficient to keep animals from exhibiting estrus, and were not different from cows in the midluteal stage of their cycle. Local concentrations in uterine tissue; however, were significantly greater when a CIDR was in place compared with uterine tissue collected during the midluteal phase. The implications of these significantly greater concentrations of progesterone in uterine tissue warrant further investigation.
461 Machine learning and AI to improve genetic prediction in beef cattle: Potential uses and misuses
Authors: Spangler Matthew L
Published: September 2024

The current objective is to discuss if and where machine learning (ML) and artificial intelligence (AI) fit in the genetic prediction toolbox. Critical to the discussion is recognizing that, by definition, models currently employed for routine genetic prediction are part of the ML family, albeit linear as opposed to deep, and that ML fits under the more general umbrella of AI. Henderson’s Mixed Model Equations (MME) have served the animal breeding and genetic community well for several decades and have proven useful at predicting the additive genetic merit of animals. Moreover, extensions of the MME have clearly demonstrated the ability to fit non-additive genetic effects such as dominance and epistasis. The literature contains multiple examples of comparisons of genetic prediction between ML and mixed linear models, with a general consensus that ML does not outperform the MME when the trait(s) analyzed are largely additive in nature. With this in mind, the question of where ML fits is appropriate to ask. Given not all data are equally informative due to potential heterogeneity in the data generation process it is reasonable to employ ML to categorize data based on quality. Examples of using deep learning exist as a means of avoiding rules-based approaches to data editing and to categorize and weight data based on quality prior to inclusion in routine genetic evaluations. Additionally, the phenotypes used in genetic evaluations themselves could be predicted by ML. This might be the case in the use of animal tracking to garner data on social behavior, animal health and welfare, or metrics of libido and fertility; parasite loads predicted from camera data; or animal body weight gain or feed intake predicted from partial measurements. In all cases, the size of the raw data and lack of biological interpretation associated with it could necessitate the use of a predicted value. However, the error structure associated with the predicted values and the potential for compounding errors in genetic prediction need to be considered. Given the phenotype of an animal arises due to genetic and environmental effects and the complex interactions among and between these two broad sources of variation, the use of ML could be envisioned to predict phenotypic outcomes to aid in management and marketing decisions. To exploit this possibility, and outperform mixed linear models, the environmental data used would need to be sufficiently granular and high dimensional. There also exists the potential to employ ML to perform feature selection, as in the case of selecting variants from whole genome sequencing where feature subsets may be informed by multiple layers of external information. The use cases for AI also include decision support to help practitioners make use of enumerable genetic predictions given their unique objectives and circumstances.
463 Cyber-Physical Systems with robots and AI for precision dairy farming
Authors: Kaur Upinder
Published: September 2024

The emergence of precision livestock farming (PLF) offers a transformative lens through which to prioritize animal welfare. At the heart of this transformation lies the rumen, a complex microbial ecosystem within dairy cows responsible for converting feed into nutrients and impacting cow health, productivity, and environmental footprint. Despite its pivotal role, our understanding of the inner workings of the rumen remains limited due to a lack of real-time monitoring tools. My research leverages AI and robotics in PLF to address this gap, focusing on developing a closed-loop system for continuous, in-vivo rumen monitoring. This system employs cyber-physical systems (CPS) embedded with modular sensors and adaptive algorithms. These sensor modules, powered by AI-based data tracking, autonomously monitor rumen temperature, humidity, pressure, and methane concentration, enabling early detection of imbalances and prompt interventions to maintain optimal rumen health. A key innovation is the self-powered in-vivo robot capable of traversing the rumen’s stratified layers. Guided by AI-powered localization, this robot unlocks unparalleled access to new data, aiding in identifying specific regions associated with conditions like subacute ruminal acidosis (SARA) and methane production. This non-invasive approach eliminates the need for traditional cannulation, minimizing stress on the animal and opening new avenues for animal scientists. The impact of this research extends beyond individual cow health. By optimizing rumen function, we can improve feed efficiency, reduce methane emissions, and promote overall sustainability in dairy farming. Additionally, real-time data insights pave the way for precision nutrition, tailored interventions, and improved disease prevention, directly enhancing animal welfare. This novel approach to rumen monitoring represents a significant step towards realizing the full potential of PLF. By integrating AI and robotics, we can usher in a future where animal welfare and environmental sustainability go hand-in-hand, ultimately contributing to a more responsible and ethical dairy industry.
451 Establishing producer research sites for the development of beef and bison climate-smart agriculture
Authors: Zuidema Dalen , Cammack Kristi M , Blair Amanda , Menendez Hector M , Brennan Jameson R , Graham Christopher , Short Rachel A
Published: September 2024

Greenhouse gas (GHG) emissions and livestock production are topics that are increasingly intertwined. While livestock production is frequently presented as harmful to the environment, this perception fails to consider the carbon sequestration that takes place in livestock grazing systems and the role that grazing ruminants have in maintaining healthy grassland ecosystems. Grazing lands, which are often unsuited to row crop agriculture, are estimated to account for 25% of the global soil sequestration potential for soil carbon storage. Grazing livestock producers are often overlooked in the GHG reduction conversation, despite over 70% of beef GHG emissions being attributed to the cow-calf sector, which is primarily grazing. Furthermore, there is a relative lack of data surrounding the implementation of climate-smart agriculture practices on grazing operations and a lack of cost-effective methods to measure carbon/GHG sinks and sources on these landscapes. The potential climate benefits are considerable, with nearly 940 million acres of rangelands in the U.S. supporting forage-based livestock production. South Dakota State University recently received funding to support a large-scale climate-smart project focused on grazing beef and bison. For this project, we are utilizing the Cottonwood Field Station that has over 80 yr of historical grazing data, along with six grazing beef and bison ranching operations in the Northern Great Plains. The work on these ranches will focus on measuring and monitoring the impacts of climate-smart NRCS land practices, including cover crops, improved pasture and range seedings, prescribed grazing, and prescribed burning. A significant focus will be extensive, annual soil sampling across these operations to continuously monitor changes in soil organic carbon, bulk density, texture, pH, and soil microbiome community profiles. Each operation will have GreenFeed methane pasture units deployed for 5 yr to monitor changes in beef cattle and bison methane emissions in response to climate-smart practice adoption. We are also assessing biodiversity pre and post climate-smart practice adoption, as biodiversity improvements may be one of the most overlooked and yet most important responses to climate-smart land practice implementation. Over the duration of this project, a large-scale, comprehensive data collection will be achieved and used to refine GHG and carbon sequestration estimates associated with range livestock systems. Data resulting from this project will be used to inform research protocols and prediction models, fill knowledge gaps, and shape science-based discussions and marketing strategies for climate-smart agricultural commodities.
68 Wildfire smoke simulations and the effects of smoke exposure on cattle acute phase protein response
Authors: Rezende Aline , Ferri Erica , Ramalho Ana Laura , dos Santos Carlos Eduardo , Ferreira Matheus , Rezamand Pedram , Skibiel Amy , Wollstein Katherine , Cruickshank Jenifer , Ranches Juliana
Published: September 2024

The incidence of and acreage burned by wildfires are globally increasing, with predictions of more devastating fires in the future. The impacts of wildfire smoke exposure on have been of research interest over the years, although the scope of the literature is limited. Evaluating the effects of wildfire smoke exposure is challenging because of the unpredictability of fires, limiting the ability of researchers to evaluate this phenomenon. For this study, a wildfire smoke simulation was created to evaluate the effects of smoke exposure on beef cattle health, performance, and behavior. Heifers [n = 8 Angus x Hereford; body weight (BW) = 532 kg; 8 mo of age) were acclimated to individual pens in a completely closed barn for 7 d (d -7 to -1). Heifers were maintained in the barn for 36 d and exposed to wildfire smoke simulation for 7 consecutive days (d 0 to 7) while inside the barn. Heifers were individually fed alfalfa hay ad libitum, and feed intake was recorded daily. Health scores were collected daily from each heifer by 4 trained technicians, and blood samples were collected weekly for evaluation of acute phase proteins, cytokines, hormones, and hematology. Heifer behavior was recorded continuously using 4 cameras for the length of the study. Air quality was monitored daily every 1 min, using 2 air quality monitors. Preliminary data were analyzed using MIXED procedure of SAS. Heifers were considered the experiment unit serving as its own control, where variables were analyzed over time. Significance was set at P ≤ 0.05. Air quality, assessed with PM2.5, changed over time (P < 0.0001), reaching a daily average of 159 µg/m3 during the smoke simulation period. Although the air quality daily average was above the EPA standard (35 µg/m3), the smoke dissipated during the day, leading to wide variation in the 24 h-cycle (7.60 to 433 µg/m3; P < 0.0001), which might not truly represent air quality conditions during wildfires. Regardless, heifer plasma ceruloplasmin concentration increased over time (P = 0.05), reaching a peak during smoke exposure (29 mg/mL), which was maintained (P = 0.39; 27 mg/mL) for 1 wk post-smoke exposure before returning to nadir (23 mg/mL). An increase in acute phase proteins stimulated by smoke exposure has been observed in cattle naturally exposed to wildfires. Therefore, exposure to wildlife smoke might elicit an inflammatory response, potentially leading to reduced cattle performance, which could be accompanied by changes in behavior. This research was supported by the intramural research program of the USDA-NIFA, Award #2023-68008-39173.
66 Australian decision support tools for prediction of ruminant performance: Current tools and potential applications
Authors: Dougherty James C H , Liedloff Adam C , Watt Lucy J , Keogh Thomas , Thomas Dean T , Oddy V Hutton
Published: September 2024

Australia is one of the largest beef and lamb producers in the world, with a wide diversity of grazing systems ranging from intensive, integrated crop-livestock systems in the temperate south to the extensive, low-input beef systems of the hot, arid rangelands of north and central Australia. To help understand and predict animal performance across these diverse production systems, Australian researchers have built decision support tools (DSTs) specifically for these grazing conditions. Some examples of DSTs designed for Australian ruminant production systems include the GRAZPLAN suite of software models, the Crop and Livestock Enterprise Model (CLEM), DairyMod, BreedCow, and the Drought and Supplementary Feeding Calculator (DAFSC), and many of these include a biophysical model based on the Australian ruminant feeding standards. Widely used for a range of industry and research applications, these tools are key to helping the livestock industry improve the environmental and economic sustainability of livestock production in the face of a changing climate. Historically DSTs have been used by producers to predict animal performance for a given feedbase, allowing for prediction of short-term (≤ 30 d) performance and optimizing supplemental feeding. Over time, the use of DSTs has shifted and expanded, and they are increasingly being used in tactical forecasting to help explore the impacts of different management strategies in response to variation in seasonal feed, climate, and rain forecasts. Because these tools can represent a wide range of temperate and tropical pasture species and animal genotypes, they can be used to explore the effects of using different breeds and species to respond to climate change, or to explore the effects of genetic selection within herds. Tools that use a systems approach to modelling, such as GRAZPLAN, CLEM, and Breedcow, can also be used to help producers navigate sustainability credentialling, compare the effects of management strategies on environmental sustainability and economic sustainability, as well as to explore the broader impacts of on-farm management decisions in terms of sustainability and efficiency at the regional or food system level. While these tools were developed for Australian production systems, many of these have international parallels, such as beef and sheep grazing systems in the semi-arid rangeland of the western USA and central Queensland and New South Wales; sheep grazing in the Mediterranean rangelands of California and the southern coast of Western Australia; and the temperate pasture-based diary regions of Tasmania, New Zealand, and Ireland. Because of these similarities, the ability of these models to flexibly represent diverse production systems gives them strong potential for use by industry and research at a broader scale by international audiences.
182 Converting spring-born heifers into a summer-calving herd increases subsequent cow longevity and productivity
Authors: Mulliniks Travis , Goodnight Tim , Musgrave Jacki A , McCarthy Kacie L
Published: September 2024

Developing replacement heifers is one of the most expensive and complex management decisions for a cow-calf producer, which also has long-term implications for profitability. The objective of this study was to determine the impact of converting spring-born heifers into a summer-calving herd on growth, reproductive performance, longevity, and productivity compared with early or late-born May heifers. Over a 3 yr period, Red Angus/Simmental crossbred heifers (n = 273) were utilized to determine the impact of converting March-born heifers (Convert; n = 90) to a May-calving herd compared with May-born heifers on reproductive performance, body weight (BW), and calf performance from a yearling to 5 yr of age. May-born heifers were retrospectively grouped into 2 different groups: heifers born in the first 21 d of calving as a heifer calf (Early; n = 123) or heifers born after the 21 d of calving as a heifer calf (Late; n = 60). Heifers were exposed to bulls for a 45-d breeding season with a bull-to-heifer ratio of 1:20. Heifers were synchronized with a single injection of prostaglandin F2α (5-mL i.m.; Lutalyse, Zoetis, Parsippany, NJ) 5 d after bulls were introduced in the pasture for breeding. Data were analyzed as a randomized design using the MIXED procedure (SAS Inst. Inc., Cary, NC, USA). All binomial data were analyzed using PROC GLIMMIX. Heifer served as the experimental unit with treatment and year developed set as fixed effects. Heifer BW at December, May, July, and pregnancy check (Oct) were greater (P < 0.01) for Convert heifers with no difference between Early and Late born heifers. Heifer BCS at pregnancy check was not different (P = 0.13) among heifer treatment groups. However, pregnancy rate tended (P = 0.08) to increase for Convert heifers with no difference between Early and Late born May heifers. After 5 yr of age, retention rate was increased (P = 0.04) in Convert cows compared with Early and Late May-born cows with no difference (P = 0.84) between the two May-born heifer groups. Total kilograms of calf weaned was greater (P = 0.04) in Convert cows than their counterparts with no differences (P = 0.94) between Early and Late May-born cows. This study implies that selecting replacement heifers from an earlier calving season for replacements in a later calving season increases the longevity and long-term productivity of the cowherd than selecting heifers within the same calving season. This may be even more important in resource-poor breeding environments that create reproductive challenges for heifers and rebreeding young cows.
457 Prediction of methane emissions using rumination time and milk mid-infrared spectral data via artificial neural networks
Authors: Lopes Lucas S F , Shadpour Saeed , Miglior Filippo , Tulpan Dan , Schenkel Flávio S , Baes Christine F
Published: September 2024

Cattle methane emissions (ME) account for approximately 6% of global anthropogenic greenhouse gas emissions. Given the challenges in measuring ME directly from individual animals, there is a need for the development of novel indirect methods. Rumination time (RT) and milk mid-infrared spectral data (MIR) show promise for the indirect assessment of ME in dairy cows. Both traits have been used as indicators of reproduction, production, and gas emission traits. Methodologies combining the use of MIR and machine learning algorithms such as artificial neural networks (ANN) for the prediction of ME have been successful; however, the inclusion of RT has not been assessed. This study aimed to evaluate the impact of RT on milk MIR-based models using ANN for the prediction of ME. One-week averages for RT, ME, and MIR from first-lactation Canadian Holstein cows (n = 412) were calculated. Six data sets were evaluated using a multilayer perceptron ANN. All sets included age at calving, season of calving and days in milk as model factors, but varied in using milk MIR data points (1,060 or 235) and including or not including RT. The ANN architecture consisted of one input layer, one hidden layer with one or more neurons, and one output layer. Results showed that sets using both RT and milk MIR data achieved correlations from 0.5 to 0.6 between predicted and observed ME. Notably, the inclusion of RT did not improve the performance of the models. Predictions may be improved through the use of larger data sets, the use of daily records, and inclusion of data across herds and lactations. Optimizing parameters of the ANN could also improve predictions. Further research is needed to fully assess the potential of RT as a predictor of ME in dairy cows.
450 Understanding communication barriers: Demographic variables and language needs in the interaction between English-speaking agriculture professionals and Spanish-speaking animal caretakers
Authors: Aguiriano Allen Jimena Martinez , Pietrosemoli Silvana , Schmidt Marcelo , Garcia Arlene , Salazar Leonor , Awosile Babafela
Published: September 2024

This study focused on assessing the language needs of English-speaking animal professionals in their interactions with the Hispanic/Spanish-speaking animal caretakers. A survey was administered to a target audience of non-Spanish speaking and bilingual animal professionals to identify communication gaps while interacting with Hispanic caretakers. The data were analyzed with descriptive and inferential statistics, including ordinal regression analyses, to examine the impact of demographic variables on respondents’ answer choices. Results showed that English-speaking professionals struggled with written and oral communication, which differed compared with bi-lingual professionals (P < 0.05). Additionally, female professionals showed differences in their responses to statements regarding the aspects of the Spanish language deemed necessary for interacting with Hispanic animal caretakers, as well as the topics likely to be addressed when agriculture professionals communicate with animal caretakers (P < 0.05). Hispanic professionals demonstrated a significant inclination towards strong disagreement with respect to the major issues encountered when interacting with Hispanic animal caretakers, particularly in understanding written information provided by animal caretakers in Spanish. These findings indicate that there are communication gaps that need to be addressed to improve communication with on-farm Hispanic animal caretakers and consequently contribute to enhancing animal health, welfare, and production.
122 Impact of an active-dry yeast (Saccharomyces cerevisiae CNCM I-1077) delivered via free-choice mineral on growth parameters of beef cow-calf pairs grazing native rangelands
Authors: Adams Paige R , Jolly-Breithaupt Melissa L , Villot Clothilde , Spare Mark , Karges Kip
Published: September 2024

Throughout the grazing season, cows are exposed to forages of varying nutritive quality that must be efficiently utilized for reproductive and lactation performance for ranch profitability. Season, environment, and forage varieties can impact forage quality and animal performance. Supplementing active-dry yeast improves fiber digestibility; however, supplementing active-dry yeast to grazing, lactating beef cows has not been evaluated extensively. Therefore, the study objective was to evaluate the effects of feeding an active-dry yeast to range beef cows via free-choice mineral 30 d prior to calving through weaning on calf growth performance and cow body condition score (BCS). Multiparous red Angus cows (n = 436; 6.5 ± 1.9 yr) grazed open native rangeland near Medicine Lodge, Kansas from February to August 2023. At trial initiation, cows were sorted by initial BCS onto one of two mineral treatments: 1) CON; no yeast (n = 213 cows) or 2) SC 1077; Saccharomyces cerevisiae CNCM I-1077 (1 x 1010 cfu/g; n = 223 cows). Mineral consumption was targeted at 113.4 g/d with fresh mineral added to feeders when depleted every 2 to 3 d. Mineral consumption and yeast viability was reported monthly. Individual calf birth weights were recorded on a 25% randomized sub-set group for each treatment due to management practice feasibility with treatment means extrapolated across the entire herd for calf gain calculations. At time of weaning, individual calf body weights (BW) and cow BCS were collected. The data were fitted using a linear model in R including treatment group, calf sex and cow age, wherein the difference between dietary groups was assessed through ANOVA. Statistical significance was considered at P ≤ 0.05. Neither treatment × cow age nor sex × treatment interactions were observed for all measured parameters (P ≥ 0.26). Mineral consumption (83.0 ± 9.1 g/d) was not different between the two treatments for the length of the trial (P = 0.55). Measured calf birth weights (31.9 ± 0.30 kg) were not different for the sub-set group (P = 0.44). Weaning weight, ADG, and BW gain were greater for SC 1077 calves compared with control calves for both the sub-set and entire herd (P ≤ 0.01). The SC 1077 mineral treatment improved cow BCS at time of weaning compared with controls for both sub-set groups and entire herd (P ≤ 0.02). Pregnancy rates were not different between treatments for the entire herd (P = 0.99) and the sub-set group (P = 0.32); however, a 6.8% numerical increase in pregnancy rate was observed for SC 1077. To conclude, providing SC 1077 via free-choice mineral improved cow BCS and calf growth parameters which could be attributed to improved cow rumen function and fiber digestibility.
130 Meta-analysis of the impact of Quebracho and Chestnut tannin extract supplementation on the growth performance in growing and finishing beef cattle
Authors: Lee Mingyung , Manella Marcelo Q , Desrues Olivier , Tedeschi Luis O
Published: September 2024

Integrating tannin extracts (TE) into the diet of growing and finishing beef cattle has shown promise in optimizing protein utilization while potentially mitigating methane emissions. Despite these potential benefits, there is a notable gap in a comprehensive analysis of the influence of TE on growth rate. This study addressed this gap by collecting data on the supplementation of a blend of condensed (from Schinopsis spp.) and hydrolyzable (from Castanea spp.) tannins, which has about 70% total tannins, and its impact on critical metrics, such as average daily gain (ADG, kg/d), dry matter intake (DMI, kg/d), and feed efficiency ratio (FER, g/kg). Our objective was to conduct meta-regression analyses to understand better the nuanced effects of condensed tannin supplementation on the overall performance of beef cattle during the critical stages of growth and finishing. The dataset was constructed by collecting published studies that reported in vivo experimental data on growing and finishing beef cattle supplemented with different levels of TE. It contained 132 observations from 35 publications. The meta-analysis was conducted by linear regression with a mixed-effects model fitted by the restricted maximum likelihood method using the nlme package of R software (ver. 4.3.1). Studies were assumed as random variables, and their interaction with the intercept and slope were included in the regression. The number of animals per treatment was used as a study-specific weight. Studies accounted for 32%, 35%, or 27% of the total variance when regressing ADG, DMI, or FER, respectively, on TE (% DM). After adjusting for random effects, the ADG increased (P < 0.001) with increasing levels of dietary TE (P < 0.001): 1.344 (± 0.0071) + 0.240 (±0.0298) × TE (n = 99, r2 = 0.40). It was observed that as the dietary TE level increased, its impact on the ADG tended to decrease from 0.33 kgּ d-1ּ % TE-1 when fed up to 0.5% TE to 0.24 kgּ d-1ּ % TE-1 when fed up to 1.5% TE, suggesting a possible nonlinear relationship between these two variables. Similarly, DMI decreased slightly (P < 0.001) at 0.61 kg/d/% TE, but with a lower precision (n = 115, r2 = 0.14). Consequently, FER increased (P < 0.001) at 17 kgּ d-1ּ % TE-1 (n = 93, r2 = 0.34). Initial findings suggest that incorporating TE into the diet of growing and finishing cattle, up to a concentration of 0.5% DM, positively influences the growth rate and enhances FER while minimally affecting DMI. Subsequent research endeavors should delve into a more intricate examination of optimal TE inclusion levels and explore additional performance indicators for a comprehensive understanding of its impact.
126 The inclusion of Aspergillus oryzae prebiotic counteracts the decline on neutral detergent fiber digestibility associated with increased intake level, on corn silage and sorghum silage-based diets
Authors: Podversich Federico , Maderal Araceli B , de Vargas Juan J , Fernandez-Marenchino Ignacio , Tarnonsky Federico , Cuervo Wilmer , Gomez Camila , Schulmeister Tessa M M , Bargo Fernando , Ipharraguerre Ignacio R , DiLorenzo Nicolas
Published: September 2024

The inclusion of Aspergillus oryzae prebiotic (AOP; Amaferm, Biozyme Inc., St Joseph, MO) in ruminant diets has been proven to stimulate cellulolytic ruminal microorganisms, although, its positive effect on nutrient digestibility seems to be diet dependent. Our objective was to evaluate how AOP influences the apparent total tract digestibility of dry matter (DM) and neutral detergent fiber (NDF) at varying levels of intake, for diets with different forage sources and inclusion levels. Data were compiled from 3 experiments containing 4 diets for backgrounding cattle, assessing the use of AOP as a feed additive. The studies were Exp. 1): corn silage-based diet (CS; n = 24 steers), Exp. 2-A): sorghum silage-based diet (SS; n = 20 heifers), Exp. 2-B): citrus pulp and cotton gin byproducts-based diet (BP; n = 20 heifers), and Exp. 3): bermudagrass hay-based diet (BH; n = 24 steers). Separate ANCOVA analyses by diet were conducted using SAS, including in the model the effect of treatment (TRT; AOP vs. No AOP) and the interaction between TRT and level of intake (dry matter intake; DMI). Level of intake was calculated as percentage of body weight (DMI/BW). Least square mean differences between treatments were conducted at different levels of intake at which both treatments were represented. For apparent total tract digestibility of DM and NDF (NDFD), the TRT × DMI/BW interaction was significant (P ≤ 0.05) in the case of the CS and SS diets, but not in the BP and BH diets (P ≥ 0.10). For both silage diets, NDF and DM digestibility decreased as the level of intake increased. Consequently, the positive effect of AOP on NDFD was greater at increased levels of intake (3.3 and 3.5% of the BW for CS and SS, respectively), resulting in 9.8% improvement on NDFD for the CS diet (P = 0.04, 37.9 vs. 41.6% for No AOP vs. AOP, respectively) and 25% improvement in the SS diet (P = 0.01, 42.5 vs. 53.2% for No AOP vs. AOP, respectively). At a moderate intake (2.5% of the BW), AOP improved NDFD by 6.0 and 14.1% for the CS and SS diets, respectively (P ≤ 0.05). While at reduced intake (1.8 and 1.7% of BW, for CS and SS), NDFD did not differ (P ≥ 0.10) between treatments for either CS (47.9%) or SS (56.5%) diets. In conclusion, the inclusion of AOP in silage-based diets attenuates the decline on apparent total tract digestibility of NDF associated with increased feed intake, helping to maintain digestibility closer to its maximum potential. Whereas, at reduced levels of intake, it is more unlikely to observe a positive effect of AOP on fiber digestibility.
133 Effect of feeding two different feed additives (Prime Force and Optipartum C+) on finishing cattle performance and carcass characteristics
Authors: Lorences Sofia Suarez , McDermott Rebecca L , Benton Josh R , Lemaster Willard , Bondurant Robert G , MacDonald James C , Erickson Galen E
Published: September 2024

A feedlot study was conducted to evaluate the effect of two feed additives, Prime Force (Furst-McNess Company, Rockford, IL) and Optipartum C+ (AB Vista, Plantation, FL), on finishing cattle performance and carcass characteristics. In this experiment, steers [n = 320; 390 ± 9.5 kg of body weight (BW)] were used and four treatments were evaluated in a total of 32 pens, with 10 steers per pen and 80 steers per treatment. Treatments were arranged in a 2 × 2 factorial and included a control diet with no additives; a diet containing Prime Force targeted at 68 g/steer daily; a diet containing Optipartum C+, targeted at 20 g/steer daily; and a diet containing a combination of both products (68 g/steer Prime Force and 20 g/steer Optipartum C+). Cattle were fed for 151 d, but treatments were imposed the last 67 d before slaughter (i.e., fed control diet to all cattle until d 84). Performance is for the total 151-d feeding period. No interaction between feed additives was observed for performance or carcass traits (P ≥ 0.41), therefore main effects are presented. Feeding Prime Force increased final BW (P < 0.04) when compared with cattle not fed Prime Force. A tendency was observed for greater adjusted final BW (P = 0.06) and ADG (P = 0.08) for cattle fed Prime Force compared with cattle fed no Prime Force. Cattle fed Prime Force had numerically greater intake (P = 0.11) than cattle fed no Prime Force, therefore gain to feed (G:F) was not impacted (P = 0.54). Ribeye area and fat depth were not different (P ≥ 0.17) for cattle fed Prime Force versus cattle that had no Prime Force added to the diet, but marbling was numerically improved (P = 0.11) due to Prime Force. Carcass-adjusted final BW (P = 0.11) was numerically greater for cattle fed Optipartum C+ and ADG tended (P = 0.09) to be greater for cattle fed Optipartum C+ compared with cattle fed no Optipartum C+. Dry matter intake was not different (P = 0.54) for cattle fed Optipartum C+ versus cattle with no Optipartum C+ in the diet, therefore G:F was numerically improved (P = 0.13) by the increase in ADG for cattle fed Optipartum C+. Ribeye area, fat, and marbling were not different (P ≥ 0.30) between cattle fed Optipartum C+ and those that did not receive Optipartum C+ in the diet. Incidence of liver abscesses was not affected by feeding either additive (P = 0.40). Based on results from this study, adding Prime Force to a finishing diet tended to improve BW and ADG compared with cattle not fed Prime Force. Feeding Optipartum C+ tended to increase ADG and improve G:F due to equal DMI when compared with diets with no Optipartum C+ added.
123 Effects of a Saccharomyces cerevisiae based direct-fed microbial on performance, health, and rumen bacterial community of newly weaned beef steers during a 56-d receiving period
Authors: Treon Emily , Sidney Taylor S , Taiwo Godstime A , Idowu Modoluwamu , Leal Yarahy , Ologunagba Deborah , Ogunade Ibukun M
Published: September 2024

We examined the effects of a blend of Saccharomyces cerevisiae, multiple live probiotic bacteria and their fermentation products on performance, health, and the ruminal bacterial community of newly weaned beef steers during a 56-d receiving period. Newly weaned Angus crossbred steers [n = 40; 221 ± 25.6 kg body weight (BW); 180 ± 17 d of age] were stratified by BW into four pens (10 steers per pen) such that each pen had a similar average BW at the beginning of the experiment. The pens were randomly assigned to receive a corn silage basal diet (CON; n = 20) or the basal diet supplemented with 9 g·steer⁻¹·d⁻¹of PRO feed additive (PRO; n = 20). The PRO additive is a blend of Saccharomyces cerevisiae and the fermentation products of Enterococcus faecium, Bacillus licheniformis, Bacillus subtilis, Lactobacillus animalis, Propionibacterium freudenreichii. The DMI and water consumed were monitored using the GrowSafe intake nodes and custom flow meters, respectively. Body weights were recorded weekly to calculate average daily gain (ADG). Before morning feeding, 10 mL of blood was taken from each steer on d 0 to 7, and thereafter weekly for analyses of immune cells, plasma glucose and NEFAs. On d 56, rumen fluid samples (200 mL each) were collected from all the steers for microbiome analysis. Over the 56-d receiving period, the supplemental PRO had no effects on DMI, water intake, or ADG. However, compared with CON, beef steers fed supplemental PRO tended to have greater ADG (P = 0.08) and BW (P = 0.07) during the first 14 d of the study. There was a treatment × day interaction (P ≤ 0.05) for WBC, neutrophils and monocytes over the 56 d such that beef steers fed supplemental PRO had decreased blood concentrations on certain days during the first 7 d after weaning, indicating reduced inflammation or stress response. The results of the rumen microbiome analysis revealed that the relative abundance of complex fiber degrading or obligate proton-reducing bacterial genera such as Bacteroides, Ruminococcus gauvreauii group, Desulfovibrio, Syntrophococcus, and Acetitomaculum were greater (P ≤ 0.05) in beef steers fed supplemental PRO compared with CON. This study demonstrated that dietary supplementation of PRO improved the growth performance, reduced stress or inflammatory response during the initial days after weaning and altered the ruminal bacterial community towards increased relative abundance of bacterial genera associated with improved rumen function.
187 Exploring deep shotgun sequencing to understand the rumen microbial function in Angus bulls with divergent RFI EPD
Authors: Sidney Taylor S , Taiwo Godstime A , Treon Emily , Ogunade Ibukun M
Published: September 2024

We investigated the rumen microbiome of Angus bulls selected for Residual Feed Intake-Expected Progeny Difference (RFI-EPD) utilizing deep shotgun metagenomic sequencing. Negative RFI-EPD bulls (NegRFI: n = 10; RFI-EPD = -0.3883 kg/d) and Positive RFI-EPD bulls (PosRFI: n = 10; RFI-EPD = 0.2935 kg/d) were selected from a group of 59 Angus bulls [average body weight (BW) = 427.81 ± 18.77 kg] fed a high-forage total mixed ration after a 59-d testing period. At the end of the 59-d period, rumen fluid samples were collected for bacterial DNA extraction and subsequent shotgun metagenomic sequencing. Results of the metagenome analysis revealed greater gene richness in NegRFI bulls compared with PosRFI. Analysis of similarity revealed a significant difference (P = 0.05) in the rumen microbial community between the NegRFI and PosRFI. Linear Discriminant Analysis effect size (Lefse) was used to identify the differentially abundant taxa. The Lefse results showed that class Fibrobacteria (LDA = 5.1) and genus Fibrobacter (LDA = 4.8) were the most differentially enriched markers in NegRFI bulls, compared with PosRFI bulls. Relative abundance of the carbohydrate-active enzymes was also compared using lefse analysis. The results showed greater relative abundance of glycoside hydrolases and carbohydrate-binding modules such as GH5, CBM86, CBM35, GH43, and CBM6 (LDA > 3.0) in NegRFI bulls whereas GH13 and GT2 were greater in PosRFI. The distinct metabolic and microbial profiles observed in NegRFI, compared with PosRFI bulls, characterized by greater gene richness and specific taxa such as Fibrobacter, and variations in carbohydrate-active enzymes, underscore the potential genetic and functional differences in their rumen microbiome. These findings contribute to a deeper understanding of the interplay between host genetics, microbiota, and feed efficiency in Angus bulls, opening avenues for targeted interventions and advancements in livestock management practices.