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The objectives of this study were to examine the relationship between residual feed intake (RFI) and DM and nutrient digestibility, in vitro methane production, and volatile fatty acid (VFA) concentrations in growing beef cattle. Residual feed intake was measured in growing Santa Gertrudis steers (Study 1; n = 57; initial BW = 291.1 ± 33.8 kg) and Brangus heifers (Study 2; n = 468; initial BW = 271.4 ± 26.1 kg) fed a high-roughage-based diet (ME = 2.1 Mcal/kg DM) for 70 d in a Calan-gate feeding barn. Animals were ranked by RFI based on performance and feed intake measured from day 0 to 70 (Study 1) or day 56 (Study 2) of the trial, and 20 animals with the lowest and highest RFI were identified for subsequent collections of fecal and feed refusal samples for DM and nutrient digestibility analysis. In Study 2, rumen fluid and feces were collected for in vitro methane-producing activity (MPA) and VFA analysis in trials 2, 3, and 4. Residual feed intake classification did not affect BW or BW gain (P > 0.05), but low-RFI steers and heifers both consumed 19% less (P < 0.01) DMI compared with high-RFI animals. Steers with low RFI tended (P < 0.1) to have higher DM digestibility (DMD) compared with high-RFI steers (70.3 vs. 66.5 ± 1.6% DM). Heifers with low RFI had 4% higher DMD (76.3 vs. 73.3 ± 1.0% DM) and 4 to 5% higher (P < 0.01) CP, NDF, and ADF digestibility compared with heifers with high RFI. Low-RFI heifers emitted 14% less (P < 0.01) methane (% GE intake; GEI) calculated according to Blaxter and Clapperton (1965) as modified by Wilkerson et al. (1995) , and tended (P = 0.09) to have a higher rumen acetate:propionate ratio than heifers with high RFI (GEI = 5.58 vs. 6.51 ± 0.08%; A:P ratio = 5.02 vs. 4.82 ± 0.14%). Stepwise regression analysis revealed that apparent nutrient digestibilities (DMD and NDF digestibility) for Study 1 and Study 2 accounted for an additional 8 and 6%, respectively, of the variation in intake unaccounted for by ADG and mid-test BW0.75. When DMD, NDF digestibility, and total ruminal VFA were added to the base model for Study 2, trials 2, 3, and 4, the R 2 increased from 0.33 to 0.47, explaining an additional 15% of the variation in DMI unrelated to growth and body size. On the basis of the results of these studies, differences in observed phenotypic RFI in growing beef animals may be a result of inter-animal variation in apparent nutrient digestibility and ruminal VFA concentrations. [ABSTRACT FROM AUTHOR]

Interrelationships between retained energy (RE) and retained protein (RP) that are essential in determining the efficiency of use of feeds and the assessment of energy and protein requirements of growing cattle were analyzed. Two concerns were identified. The first concern was the conundrum of a satisfactory correlation between observed and predicted RE (r = 0.93) or between observed and predicted RP when using predicted RE to estimate RP (r = 0.939), but a much lower correlation between observed and predicted RP when using observed RE to estimate RP (r = 0.679). The higher correlation when using predicted vs. observed RE is a concern because it indicates an interdependency between predicted RP and predicted RE that is needed to predict RP with a higher precision. These internal offsetting errors create an apparent overall adequacy of nutrition modeling that is elusive, thus potentially destabilizing the predictability of nutrition models when submodels are changed independently. In part, the unsatisfactory prediction of RP from observed RE might be related to the fact that body fat has a caloric value that is 1.65 times greater than body protein and the body deposition of fat increases exponentially as an animal matures, whereas body deposition of protein tends to plateau. Thus, body fat is more influential than body protein in determining RE, and inaccuracies in measuring body protein will be reflected in the RP comparison but suppressed in the RE calculation. The second concern is related to the disconnection when predicting partial efficiency of use of metabolizable energy for growth (kG) using the proportion of RE deposited as protein—carcass approach—vs. using the concentration of metabolizable energy of the diet—diet approach. The culprit of this disconnection might be related to how energy losses that are associated with supporting energy-expending processes (HiEv) are allocated between these approaches. When computing kG, the diet approach likely assigns the HiEv to the RE pool, whereas the carcass approach ignores the HiEV, assigning it to the overall heat production that is used to support the tissue metabolism. Opportunities exist for improving the California Net Energy System regarding the relationships of RE and RP in computing the requirements for energy and protein by growing cattle, but procedural changes might be needed such as increased accuracy in the determination of body composition and better partitioning of energy. [ABSTRACT FROM AUTHOR]

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

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

Precision Livestock Farming (PLF) is a technology-driven approach comprising sensors, cameras, global positioning system tracking, and data analytics to collect real-time data on animal behavior, health, welfare, and performance that enables feedyard operations to make informed decisions and take proactive measures to ensure optimized management and the well-being of their livestock and production efficiency. PLF systems leverage advanced analytical tools and techniques, such as artificial intelligence (AI), including machine learning and deep learning, by integrating data provided by various sensors and software with related datasets and standard AI models to obtain unique data-driven decisions on a range of management practices. The most imminent benefits of PLF include reduced environmental impact and increased profitability. Current PLF tools for climatesmart feedyard operations include 1) feed management systems that use data from feed intake and animal behavior to optimize feed efficiency and minimize waste, which has the potential to decrease the amount of greenhouse gas (GHG) emissions linked to livestock feeding and feed production; 2) environmental monitoring systems to collect local temperature, humidity, and air quality to optimize the pen environment, which can reduce stress on animals, reduce diseases, such as the incidence of respiratory and foot infections, and improve their overall health; 3) feeding systems to use data from individual animals to provide individualized feed and nutrient programs, thus, helping to improve and select for feed efficiency and reduce the amount of feed wasted, assisting with mitigating GHG emissions associated with livestock feeding and feed production; 4) individual animal monitoring systems to access behavior, health and well-being of individual animals that, through early disease symptoms detection, can prevent and reduce recovery time, and reduce the amount of medication applied to animals, reducing GHG gas emissions associated with pharmaceutical products; and 5) waste management systems to help reduce GHG emissions associated with manure production by optimizing manure management and reducing the amount of waste, which can also help reduce unpleasant odors and improve the overall environment conditions in and around feedyards. Waste management systems could also measure the soil and water contamination with fugitive nutrients, feed additives, implants, antibiotics, and other pharmaceuticals administrated to animals to maintain health standards. The adoption of PLF tools in feedyard operations aims to create a sustainable and efficient livestock industry by reducing waste, optimizing resources, and improving animal welfare through advanced technologies and analytical tools. Additionally, PLF tools enable feedyards to optimize feeding and breeding programs, minimize waste and environmental pollution, and improve the quality and safety of animal products. In today's fast-paced industry, PLF tools are essential for feedyard operations to monitor, analyze, and make informed decisions about the well-being and production efficiency of their livestock, ultimately contributing to a more sustainable and efficient livestock industry. [ABSTRACT FROM AUTHOR]

Management strategies implemented in cowcalf operations are essential to capitalize on heavier feeder calves, but field data collection can restrict their practical application. A computer program to optimize weaning weight, profitability, and environmental sustainability of grazing cow-calf operations was developed by combining an existing mechanistic nutrition model with pasture biomass estimated using satellite images. Data from the Texas A&M University McGregor Research Center was used as a testbed for the computer model. Pasture forage data from 2016 to 2022 was collected via satellite imagery, and the predicted pasture biomass was adjusted based on thirdparty algorithms (Sigfarm Intelligence LLC). Nutrient requirements were estimated using the Ruminant Nutrition System (RNS), and a constant pasture nutritive value was obtained. The potential dry matter intake (DMI) was estimated from the initial herd size (input). Actual average weaning weight was used to predict cow milk yield and total metabolizable energy requirements, which was used to estimate the required DMI (DMR). The actual forage allowance was correlated to DMR, and we simulated the forage allowance to support increased DMI and, consequently, increased milk yield to achieve a targeted average weaning weight. Based on Intergovernmental Panel on Climate Change (IPCC) methodology, environmental impact was evaluated from greenhouse gas emission estimates. Our simulations demonstrated that increasing the target weaning weight of a smaller herd with fewer mature cows (757 cows) would produce heavier calves, resulting in a 6% decrease in calf gain per pasture area. Nevertheless, the net income increased in scenarios with heavier weaned calves. The enhanced productivity with heavier weaned calves and fewer cows resulted in a lower carbon emission per kg of produced weaned calves. Based on our simulation, a herd size between 757 and 812 mature cows yielding calves weaned between 280 and 300 kg would be an optimal management strategy for the studied operation. This computer model provides the fundamental framework for a decision-support tool for producers to optimize their cow-calf operations while producing ideal weaned calves for feedlot operations. [ABSTRACT FROM AUTHOR]

Ruminal acidosis is a common metabolic disorder that affects feedlot and dairy cattle and occurs when the supply of organic acids from fermentation exceeds its absorption, accumulating acid content in the rumen. Virginiamycin (VM) has inhibitory effects on primary liver abscess-causing bacteria. In feedlot animals, dietary VM has been shown to reduce rumen acidosis and improve feed conversion actively. This study evaluated ruminal pH over 150 days on feed (DOF) of growing and finishing beef cattle and investigated the relationships between ruminal pH and growth and development outcomes and the effectiveness of the continuous or intermittent provision of VM in 120 Angus-crossbred steers. Steers received VM (240 mg/d) as follows: no VM (T000); VM in the last 50 d (T001); VM for the last 100 d (T011); VM in the first 50 d (T100); VM in the first 100 d (T110); and VM for 150 d (T111). Animals were fed a grower-type diet (metabolizable energy [ME]: 2.45 Mcal/kg; crude protein [CP]: 12.2 % dry matter basis [DM]) for 50 d before the adaptation of a finisher-type diet (ME: 2.60 Mcal/kg; CP: 10.6% DM) over a 17-d period. The finishertype diet was fed for the remaining 84 days. On d -1 and d 82 each animal received an indwelling pH bolus to record ruminal pH at 10-min intervals. Acidosis bout event was categorized as if the pH stayed below 5.6 for 3 hours. Data were analyzed using a random coefficient statistical model, binary data was analyzed using the logit link function and correlations were analyzed using PROC CORR in SAS (Table 1). Duration under pH 5.8 and 5.6 tended to have a negative correlation with gain to feed (G/F; P = 0.080), and % of the time that pH remained below 5.6 when under 5.8 was negatively correlated with G/F (P = 0.007) and tended to have a negative correlation with average daily gain (ADG; P = 0.081). Similarly, acidosis bouts event and duration of bout event (DURB) tended (P = 0.074) to have a negative correlation with G/F, while ADG was negatively correlated (P = 0.035). The number of acidosis bout events and probability of acidosis bout events to occur increase linearly (R2 = 0.89, P < 0.001, and R2 = 0.77; P < 0.001, respectively) as DOF progressed. The probability of an acidosis bout event decreased by 46% for the T111 treatment compared with T000 (35.43% vs. 18.82%; P = 0.049). Acidosis bout event seems to affect the growth and development of beef steers negatively, and it appears to be more critical as DOF progress. Administration of VM at 240 mg/d throughout the whole feeding period seems to reduce the incidence of acidosis bout events. [ABSTRACT FROM AUTHOR]

Modeling the individual animal response to heat stress (HS) conditions is challenging because of the complex interactions that characterize the system behavior. In explaining the resulting animal behavior, the dynamicity, nonlinearity, and delays in the HS response are often unaccounted for or misinterpreted. The system dynamics (SD) methodology, a mathematical modeling approach based on feedback loop structures, allows for modeling and understanding the behavior of complex systems over time. By applying SD methodology, this study developed a preliminary conceptual model to capture the cow response and observed milk yield (MY) under HS. The data on the temperature-humidity index (THI) and MY used for model development were collected from a dairy cattle farm in August 2021. The parameters related to the HS response of 20 selected cows were used for calibration and parameterization of the model. To minimize the effect of the lactation stage on milk production and model results, 20 cows were selected for days in milk (DIM) to be between 70 and 220 d. After the parameter calibration using MY data, it was found that the historical data pattern of 13 out of 20 cows followed the expected behavioral pattern generated by the model. In contrast, the behavior of the remaining seven cows did not align with that generated by the model. Therefore, based on their patterns, the cows were identified as fitting or non-fitting the model's structure. The structure of the model captured the effect of HS on fitting cows with high accuracy (mean absolute percentage error, MAPE < 5%; R2 > 0.6; concordance correlation coefficient, CCC > 0.6). At the same time, the behavior of the non-fitting cows could not be explained by the defined parameter space. We believe they either had heat-resistant behavior or experienced different biological delays than average. Based on the obtained results, the evaluation of parameter values should be done only for the fitting cows, as the work aimed to develop a model to understand the HS response. The behaviors generated by the model can help farmers and decision-makers distinguish heat-sensitive from heat-tolerant cows and quantify the animal response in terms of MY so that mitigation strategies can be implemented. [ABSTRACT FROM AUTHOR]

The profitability of beef feedlot operations depends on accurately predicting the energy and nutrient requirements of the animal to optimize animal growth and carcass quality to meet market specifications. The composition of the gain (i.e., fat and protein) dictates the growth of the animal, and it is highly correlated with the body composition of the mature animal, assuming that the composition of body gain is constant for animals at the same maturity degree. Once the mature weight for a given body composition (i.e., fat) is known, the maturity degree can be estimated, and the equivalent body weight (EqBW) can be used to calculate energy requirements for growth. Several standard feeding systems and nutrition models use the adjusted final body weight (AFBW) at a known fat composition to estimate EqBW. The AFBW is the body weight (BW) at, usually, 28% empty body fat (EBF). Thus, AFBW can be estimated if a given empty BW (EBW) and composition are known, and an EBW-to-EBF factor is used to calculate AFBW from the current BW. Several nutrition models, including the Cattle Value Discovery System, have adopted the 14.26 kg of EBW for each % of EBF. Therefore, this study aimed to determine the amount of body mass deposited per change in body fat content of different breeds commonly used in Australia. We randomly allocated 30 Angus, 30 Brahman, and 29 Charolais into six slaughter groups (6 animals per breed per slaughter group) fed for up to 200 days. Internal organs and carcasses were dissected into physically separable fat, lean, and bones. When we regressed EBW on EBF, the overall regression yielded a slope of 11.2 ± 0.69 kg/%EBF [n = 89, r² = 0.75, and root of the mean square error (RMSE) = 55.7 kg], likely not different from the 14.26 kg/% EBF. However, slopes were different among breeds (P < 0.001), i.e., there was an interaction between breeds and EBF when the breed was added to the ordinary least-square mean regression as a classificatory variable. The slopes were 16.5 ± 0.84 kg/% for Charolais (n = 29, r² = 0.933, RMSE = 33.2 kg), 13.2 ± 0.73 kg/% for Angus (n = 30, r² = 0.921, RMSE = 34.4 kg), and 10.1 ± 1.06 kg/% for Brahman (n = 30, r² = 0.765, RMSE = 38.3 kg). When we regressed the amount of EBF on EBW, we obtained 0.40, 0.58, and 0.52 kg EBF/kg EBW for Charolais, Angus and Brahman, respectively. This confirms that Charolais deposited less EBF per unit of gain compared with Angus and Brahman. Given the similarity between the 14.26 kg/% to the overall value of 11.2 kg/%, we recommend using the 14.26 EBW-to-EBF factor to estimate AFBW when the genetic group is unknown. [ABSTRACT FROM AUTHOR]

Precision livestock farming (PLF) is an emerging field that uses technology to optimize livestock production and management. It involves using sensors, cameras, and other data collection devices to gather information on animal behavior, health, and welfare. A digital twin is a virtual replica of a physical system that can be used for simulation, testing, and optimization. This tutorial will provide an overview of the procedure for creating a PLF digital twin with cameras and sensors. We will discuss the benefits of PLF, the hardware and software components needed, and the steps involved in creating a digital twin. PLF digital twins have a range of potential applications and benefits, including improved livestock health and welfare, increased productivity, and efficiency, and reduced environmental impact. At the end of the tutorial, we will host a roundtable discussion of the potential applications and benefits of PLF digital twins. [ABSTRACT FROM AUTHOR]

The study aimed to investigate the effects of two feeding times and two sources of energy supplementation on pH, short-chain fatty acids (SCFA), and in vitro gas production (IVGP) of beef cattle reared in Urochloa brizantha cv. Marandu pastures. We used eight rumen-cannulated ½ Aberdeen Angus x ½ Nellore young bulls (280±7 kg) distributed in a double 4x4 Latin square design. Treatments consisted of supplementation at 0.3% of body weight with corn or citrus pulp, supplied at 0900 or 1700. Ruminal fluid pH was measured with a digital pH analyzer and SCFA was analyzed on a Shimadzu HPLC. The IVGP was determined using a transducer and a data logger for 48 h after incubation of samples combining forage and supplement in real intake proportion (88% forage, 12% supplement). Considering the rapid fermentation of sugars, animals supplemented with pectin maintained higher pH (P = 0.021) due to changes in bacterial populations to digest the pectic substances (Table 1). Lactate production after starch digestion caused lower pH. Despite the differences in evaluated energy substrates, there was no effect on the primary SCFA concentration (P > 0.05). Citrus pulp provided greater formic (P = 0.005) and valeric (P = 0.042) acids concentrations compared with corn, but there was no influence of formic acid in lactic acid production (Table 1). The greater IVGP by citrus pulp (P = 0.0140; Figure 1) may be related to its greater carbohydrate fraction A, soluble with a rapid rate of rumen degradation, and also because its ruminal fermentation results in greater acetic acid production, which provides more ruminal H2 and CO2, favoring the CH4 production. The citrus pulp can be used as an alternative source to corn because it might prevent acidosis, does not affect the main SCFA production, and is a non-human edible feed. [ABSTRACT FROM AUTHOR]

The proper quantification of forage allowance for ruminants in grass-based production systems has always been challenging. At the field scale, pasture management based on ground-level measurements, such as clipping or plate meter, is labor intensive and hampers the assessment of spatial and temporal variability. On the lookout for a solution, this study aimed to estimate marandu palisade grass forage mass based on satellite images using two machine learning (ML) algorithms, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). The experimental area comprised Marandu palisade grass pasture (Brachiaria brizantha 'Marandu'), summing 33 paddocks (42 ha), receiving or not N fertilization. Pastures were managed under continuous stocking to maintain grazing height fixed at 25 cm during the growing season, using the put-and-take methodology with young beef bulls. Field dataset collection (total forage mass and morphological composition) and satellite images were assessed from Dec/2015 to Mar/2019 during the growing or wet season. The satellite images (Landsat-8 and Sentinel-2) were downloaded from US Geological Survey (USGS, http://earthexplorer. usgs.gov). Six spectral bands (Bd) and five vegetation indices (VI, Table 1) were used as input variables to MLR and ANN models. Datasets were randomly divided into a training set (80%) and a testing set (20%). Analyses were run in Python 3 (version 3.7). ML models are generally data-driven and require a large amount of data for better performance, and the best accuracy was achieved by using all Bd and VIs as input variables for both models (Table 1). In general, ANN produced better estimates than MLR models. Bd+VI better predicted leaf mass than total forage mass. Our results show that remotely sensed observations, based on satellite images, are a promising and effective tool for tropical grassland monitoring and management under continuous stocking rates. São Paulo Research Foundation (FAPESP) (grant ≠15/16631-5; 17/18750-7; 20/14367-7). [ABSTRACT FROM AUTHOR]

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

This research evaluated the effects of supplementing extruded dried distillers' grains (DDG) cubes to grazing steers on subsequent finishing performance and carcass characteristics. Steers (n = 140) grazed tall fescue (Festuca arundinacea)/bermudagrass (Cynodon dactylon) pastures from 14 April to 17 September 2020 with supplementation treatments (n = 3 pastures/treatment) that included: 1) Fertilized Control (FC), no supplementation on N fertilized pastures (112 kg N/ha); 2) Fertilized Supplement (FS), supplemented DDG cubes at 2.9 kg/d prorated for 3-d/wk feeding on N fertilized pastures; and 3) Supplement (S), supplemented DDG cubes at 0.75% of BW/d prorated for 5-d/wk feeding on unfertilized pastures. All animals were followed through the finishing phase in a commercial feedyard to investigate carryover effects on feedlot performance in 2 pens comingled across supplemental treatment. Carcass characteristics were evaluated via ultrasonography on d0 and d84 of finishing, and measurements were obtained at harvest. Feed DMI were segregated to individual animals according to Guiroy et al. (2001) and Tedeschi and Fox (2020b). Supplemented animals were heavier (P < 0.01) at feedlot entry than FC, but harvest BW did not differ (P = 0.23). However, S and FS required 37 fewer days on feed (P = 0.01) than FC. Supplementation on pasture reduced total feed required (P = 0.02) and feed costs (P = 0.01) relative to FC. Gains were greater (P = 0.02) for FC and FS than S from d0 to d84, but did not differ thereafter (P = 0.15). At harvest, FC had the greatest dressing percent (P < 0.01) and lower yielding carcasses than FS (P = 0.01), but did not differ (P = 0.11) from S. Overall, extruded DDG cube supplementation during grazing did not negatively affect subsequent feedlot performance or carcass characteristics, but reduced the total feed required for finishing. [ABSTRACT FROM AUTHOR]

Intelligent societies are built through education. Wisdom gained through scientific knowledge and discourse is necessary for spreading reliable information and building consensus among citizens of the six continents on earth. Internationalization empowers the fostering and discovery of novel techniques and resources that could solve contemporary issues assailing humankind's prosperity, including sustainability, environmental pollution, and hunger. A community might have found solutions to problems other communities face; however, our failures of globalization postpone their resolution. Animal science is not immune to these failures. Livestock production could incorporate know-how from other communities, borrowing from experiences of student and faculty exchange and study abroad programs. The tremendous evolutionary aspects of ruminants' digestive system allow them to thrive in diverse and sometimes inhospitable ecosystems and convert human-inedible resources into high-quality animal products for human consumption. Humankind relies on ruminants for energy and protein consumption, wool, and draft power. This symbiotic relationship has led to extreme production systems due to economy-of-scale factors and the availability of resources in some countries. But, other countries have relied on rustic, low-intrusive production systems that are more amenable to the environment and more conducive to sustainability. Therefore, breaking the boundaries across nations through international partnership programs will lead to sharing scientific knowledge and enhancing management programs essential to the long-term continuation of this human-ruminant symbiotic relationship within responsible animal welfare programs. Besides improving the communication among societies and spreading scientific advancements, such internationalization programs will shape students, i.e., the workforce of the future, into citizens of the world with accurate and helpful information about the language, culture, problems, and needs of other civilizations and how to enrich our likelihood and theirs. As a result, leaders of the many different nations worldwide can, once again, expand the frontiers of scientific knowledge in seeking sustainable, responsible, and affordable animal agriculture activities. [ABSTRACT FROM AUTHOR]

Several wild and domesticated ruminants rely almost exclusively on the complex carbohydrate matrix from the plant cell wall (PCW) as their primary energy source via volatile fatty acids produced through ruminal and some hindgut fermentation processes. The PCW comprises different proportions and types of polysaccharides, pectic substances, polyphenols (lignin, tannins), and amino-N compounds (amino acids, peptides, proteins) that impact its digestibility and nutritive value. Thus, understanding the biosynthetic formation of the PCW will shed some light on possible manipulation mechanisms (e.g., genetic selection, management, and fertilization) and processing methods (e.g., physical, chemical, or enzymatic treatments) to increase its digestibility, leading to better utilization of the PCW by the ruminant animal. Laboratory techniques must be developed, evaluated, and modified to accurately and reliably reflect the digestibility and nutritive value of PCW brought about by modern manipulation mechanisms or processing methods. The chemical determination of lignin and its association with carbohydrates still lack consensus, although acid detergent lignin has been demonstrated to behave uniformly as a nutritional entity. Spectroscopy (near-infrared and Raman) and in vitro gas production techniques have been adopted to assess plant chemical composition and nutritive value, but an incomplete understanding of the impacts caused by disrupting the PCW for sample processing still exists. The limitations of these laboratory techniques have partially led to their association with mathematical models to improve the explanation of fermentation dynamics. Different variations of multicompartmental and time- and age-dependent mathematical models based on gamma distribution have been proposed to determine the ruminal rates of degradation and passage of fiber. However, low-quality data due to inconsistent marker results used to determine passage rates and transit time of fiber in the gastrointestinal tract have hindered advancements and adoptions of the next generation of computer models to understand fiber degradation in the rumen. The association of mechanistic modeling and artificial intelligence will perhaps improve PCW nutritive value predictions. [ABSTRACT FROM AUTHOR]

Animal scientists have become more enthusiastic about developing machine learning (ML) models to improve the predictability of variables of interest. However, adequate data is limited either because ways to collect the data are scarce or because the process is expensive, time- or labor-consuming, or it simply takes too long to be collected. In addition to the usual hurdles in developing ML models, e.g., appropriate technique, the number of layers, and their activation, enough high-quality data is an essential requirement for ML that is often neglected. Should the data come from longitudinal or cross-sectional experiments of how many subjects? When enough high-quality, reliable data is limiting, one alternative is to create synthetic datasets that reflect the correlation among input and output variables of interest. The probability distribution for each variable needs to be well defined, and the correlation among variables must be taken into account. The normal distribution may not always be a reasonable assumption for all variables; thus, variable-specific distributions must be used with their appropriate parameters. An adequate range (min and max) must be provided for each variable to represent it. Shortcomings might occur when nonlinear relationships occur between variables. The synthetic dataset will also fail to provide good predictability when the new inputs do not have similar correlations, as did the variables used to build the synthetic dataset. It is common to standardize or normalize each variable during ML development. Each variable is normalized based on its minimum and maximum values. A common mistake during the ML development process is that training and evaluation subsets are normalized independently when both should be normalized using the range of the complete dataset; otherwise, normalization becomes dependent on the subset, and the ML weights will differ from one epoch to another. This might weaken the ML predictability because the correct range for back-transforming the output to its original value is unknown. [ABSTRACT FROM AUTHOR]

The effects of a nutritional packet offered to beef yearling-steers during the feedlot-finishing phase on feeding behavior were evaluated. Crossbred-Angus steers (n = 30; initial BW = 542 ± 8 kg) were assigned to pens (15 steers/pen; animal = experimental unit) in a randomized complete block design. Steers were offered a steam-flaked corn-based finishing diet ad libitum, and treatments as follows: 1) control and 2) 30 g/steer-daily (DM-basis) of a nutritional packet (live yeast [Saccharomyces cerevisiae; 8.7 Log CFU/g], vitamin C [5.4 g/kg], vitamin B1 [13.33 g/kg], NaCl [80 g/kg], and KCl [80 g/kg]). Ground corn was used as a carrier and included at 1% of diet DM. Orts were subtracted from the dietary DM offered to calculate DM intake. Feeding behavior (meal frequency, meal size, bunk visit duration, and meal criterion) were continuously recorded using the Smartfeed system (individual animals). Three periods during the last 60 days before cattle harvest were assessed: 1) d7-11; 2) d28 -32; and 3) d49-53. Periods were used to compute meal criteria for each animal using the Meal Criterion Calculation (v. 1.9) software. Data were analyzed using the GLIMMIX procedure of SAS. No treatment × period interactions were observed (P ≥ 0.32) for meal frequency, duration (min/meal), and intake (kg/meal). Steers offered the nutritional packet had greater overall meal duration during periods 1 and 3 (75 and 77 min/d; P < 0.01), but did not differ (68 min/d; P = 0.09) for period 2 (Control AVR = 55 min/d). Steers offered the nutritional packet had greater (P ≤ 0.01) meal frequency (20 vs. 16 meals/d), while individual meal duration (3.85 min/meal) and meal intake (0.46 kg/meal) were not affected by treatments (P > 0.86). Improved meal frequency and overall time spent eating, without affecting intake, may induce a more desirable daily distribution of nutrients. [ABSTRACT FROM AUTHOR]

Virginiamycin (VM) is an antibiotic that possesses antimicrobial properties due to its ability to block protein synthase in Gram-positive bacteria. It reduces lactic acidosis and the incidence of liver abscesses. Animal growth and development is brought about because of improved ruminal health, and animal health and welfare. This study evaluated the continuous or intermittent provision of VM during the growing and finishing phases on 120 Angus-crossbred steers (291 ± 28 kg) in 20 pens equipped with a Calan gate feed system from which animals received VM (240 mg/d) as follow: no VM (T000); VM in the last 50 d (T001); VM for the last 100 d (T011); VM in the first 50 d (T100); VM in the first 100 d (T110); and VM for 150 d (T111). Data were analyzed using a random coefficients model with the pen as a random effect and animals within treatment as the subject. The T011 tended (0.075 ≥ P ≥ 0.052) to have a greater carcass, final shrunk body weight compared with T110. Empty body fat (EBF; %) tended (0.080 = P = 0.050) to be less for animals that consumed VM regardless of the period or length of feeding. Dry matter intake (DMI) was lesser for T111 compared with T000, and greater for T011 compared with T110 (P = 0.028), which resulted in greater average daily gain (ADG) with no difference in feed efficiency (P = 0.015 and 0.225, respectively). ADG-adjusted metabolizable energy (aME) content increased by 3.08% for T111 compared with T000. This study indicated that daily supplementation of 240 mg VM during the whole feeding phase increased feed efficiency and paME, whereas when withdrawn during the end of the finishing phase (T110) can impair DMI and ADG. [ABSTRACT FROM AUTHOR]

Nutritional grouping (NG) strategies have been proposed mainly in dairy cattle to improve economic efficiency (30-60$/cow per year) of dairy farmers. In dairy cattle, NG is made according to net energy (NE) requirements estimated from some animal variables (i.e., milk yield and dairy merit). However, NG has not been evaluated for beef cattle, likely because data are not readily available and existing protocols have been fixed for decades by the beef industry, such as allotting by body weight (BW): growing diet for 180 to 400-kg animals, and finishing diet for 400 to 550-kg animals. Therefore, we investigated an unsupervised machine learning (ML) technique in R to sort animals at the arrival according to their DM intake (DMI), NE and metabolizable protein (MP) requirements, estimated through INRA from BW and average daily gain. We utilized data from 77 growing Friesian steers allotted conventionally by BW (139±21.6 kg) in 4 pens, weighed every 3 weeks (n=9 weighings) during 190 days, and fed a high-energy diet (1.59 Mcal NE/kg DM and 89 g MP/kg DM). Results identified that estimated performances were significantly (P< 0.002) different between 3 groups made from ML and between 4 pens made from BW; however, ML groups presented a minor within-group variability of NE and MP requirements (-25%CV) than pens. We also regressed energy and protein intake minus their respective requirements by the duration of the experiment observing a better fitting (R2= 0.93 vs 0.80) when allotting animals by groups than by pens, which implicates better feed efficiency if diets were formulated according to requirements established by ML groups. We demonstrated that ML techniques could help the decision-making of beef farmers to better sort animals according to their requirements and not only by BW. The next step is to formulate rations according to animal requirements to improve efficiency. [ABSTRACT FROM AUTHOR]

In the beef cattle system, liver abscesses are the most common liver abnormality and are the leading cause for condemnations at slaughter. Virginiamycin (VM) has been used to prevent sub-acute ruminal acidosis (SARA) and subsequently control liver abscess incidence. This study observed the interrelationship between VM inclusion (240 mg/hd/d), ruminal pH dynamics, and hepatic blood metabolites over a 150-d feeding trial. Housed in a Calan gate system, 120 Angus crossbred steers (291 ± 28 kg) were randomly assigned to 1 of 6 dietary treatments: no VM (T000); VM for the last 50 d (T001); VM for the last 100 d (T011); VM for the first 50 d (T100); VM for the first 100 d (T110); and VM for 150 d (T111). Steers were orally administered 2 indwelling smaXtec rumen pH and temperature recording boluses on d -4 and d 84. On d -7, 28, 56, 84, 112 and 140, blood samples were collected via jugular venipuncture and all plasma samples were analyzed for concentrations of albumin, alkaline phosphatase, direct bilirubin, total bilirubin, gamma-glutamyl transferase, and total protein using an automated blood analyzer (Carysta High Volume Chemistry Analyzer; Zoetis). Haptoglobin (HPT) was measured following a colorimetric method based on peroxidase activity. Data were analyzed using a random coefficients model with the pen as a random effect and animals within treatment as the subject. Results indicated that VM did not convalesce hepatic function (P > 0.05), but HPT had increased concentrations on d 84 for VM excluded treatments (42.75 vs. 93.25 mg/L). Comparatively, T111 tended to have less time under pH 5.8 (2.50 h/d) when compared with T001, T100, and T000 (5.27; 4.94; and 4.23 h/d, respectively; P = 0.107). Therefore, VM should be fed from the growing phase to slaughter to capture the full potential of the product on animal health status. [ABSTRACT FROM AUTHOR]