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Our objective was to determine the inter- relationship between rumen health dynamics and animal growth and development characteristics of growing and finishing steers receiving virginiamycin (VM). A companion article was published previously (Rivera et al., 2024).

Our objective was to observe the effects of the interrelationship among virginiamycin (VM) inclusion (240 mg/d), ruminal pH dynamics, and hepatic plasma metabolites on rumen and animal health during a 150-d feeding trial.

Our objective was to evaluate the effects of pre-finishing plane of nutrition of stocker steers on subsequent feedlot performance and carcass characteristics over 2 years.

Leveraging computer vision (CV) methods to study pen-fed cattle feeding behavior presents several advantages, including monitoring animal health and identifying feed efficient animals. This research employed a region-based convolutional neural network (RCNN) combined with the common objects in context (COCO) dataset for automatic livestock recognition using CV techniques in experimental feedlot pens. Thirty Angus-influenced steers were allocated in one pen with four automated feed intake systems (AFIS; Vytelle SENSE). CV data were recorded during daylight hours using a webcam (Microsoft LifeCam Cinema) with a resolution of 1280x720 pixels at ten frames per second connected to a video surveillance camera software (Contaware, Switzerland). The CV dataset obtained by the cameras, Figure 1, was benchmarked with feeding behavior data obtained from observed annotations (OA) and AFIS. The CV model utilized was a Mask-RCNN with pre-trained weights on the COCO dataset. The Mask RCNN algorithm can identify and locate multiple objects, objects of different scales, and overlapping objects within an image. The COCO dataset is a large and diverse set of annotated images that can be used for training and evaluating object detection models. The fully trained Mask-RCNN model takes each video frame as input and outputs the pixel segmentation of each object detected. For the CV analyses, each animal bounding box was associated with a score threshold, and each pixel within was assigned a probability threshold; both ranging from 0 to 100. The higher the score and probability thresholds are, the fewer the boxes and pixels will be marked as the animal boxing shape, resulting in less feeding detection from the CV system. In contrast, a low score and probability thresholds produce many predictions, including false detections, which might be misleading. Therefore, a dataset analysis was conducted to optimize the best threshold combination. We evaluated the accuracy and precision of the CV model compared with OA and AFIS by analyzing a dataset section of feeding events of one feed bunk using a 0.5 score and 0.5 probability thresholds. We obtained the values for the true positive (TP), false positive (FP), true negative (TN), and false negative (FN) to calculate the accuracy = [TP+TN]/[TP+TN+FP+FN], and precision = [TP]/[TP+FP], Table 1. As a result, the CV model achieved 95.39% and 99.82% accuracy and 93.60% and 99.90% precision when compared with OA and AFIS, respectively. Our findings showcase the promising capabilities of employing the Mask-RCNN algorithm and COCO datasets for detecting beef cattle feeding behavior in feedlot pens. This approach holds significant value for the cattle industry by enabling precise monitoring and analysis of individual animal behavior within feedlot pens, facilitating early detection of behavioral abnormalities.

Bovine respiratory disease (BRD) is a complex multifactorial disease that results in more than $1 billion in economic losses for the United States beef cattle industry, accounting for approximately 30% of the total antimicrobial use (AMU) in food animal production systems. The prevalence and production losses due to BRD vary significantly between different production systems, and is influenced by the host, pathogen, and environmental risk factors. These multiple risk factors together have a cumulative effect and will increase the prevalence of the disease if no preventive strategies are implemented in the system. Moreover, the increased AMU to treat BRD results in antimicrobial resistance, further complicating the issue. A linear, event-based solution to address such a complex problem will fail to consider the feedback loops and time delays in the system and may cause unintended consequences that might make the problem even more complex. Hence, we propose a more comprehensive system dynamic model to simulate an integrated beef production system with respect to BRD and predict the dynamic behavior of the system under different preventive strategies currently available to the beef industry. Our conceptual model accounts for host risk factors (auxiliary variables in the model) like age, nutritional status, daily weight gain, prior exposure to pathogens, and genetics. The environmental risk factors such as air quality, transportation, temperature humidity index, overcrowding, and source of animals were also included in the model. The various preventative strategies to reduce the incidence of BRD, including vaccination, biosecurity, adequate colostrum, and nutrition, were also added to the model. The key outcome variables considered were net profitability and AMU. The model simulates multiple scenarios by leveraging the auxiliary variables and can provide information about the system’s net profitability and total AMU. The model explains the interconnected effect of various risk factors associated with BRD in multiple complex scenarios and helps understand the combined effect of adopting various preventive strategies and host-environmental factors in controlling BRD. The initial result from our dynamic model suggests that using a combination of preventive measures and avoiding the selected host-environmental risk factors can minimize AMU while improving profit. The long-term goal of our modeling effort is to enable the beef system stakeholders to make informed decisions and strategies to mitigate the impact of BRD, thereby contributing to improved profitability. Through a systems-thinking approach, our model can contribute to addressing the challenges in BRD management currently faced by the US-based integrated beef production systems.

Sub-acute rumen acidosis (SARA) is a ruminal digestive disorder with a prevalence of 24 to 30% in United States-based feedlots and is responsible for a lost economic opportunity of approximately $1 billion annually. Our objective for this study was to develop a decision tool which feedlot decision-makers could use to make routine managemental interventions that can mitigate the prevalence of SARA. The continuous reduction in rumen pH (pH < 5.6) is the primary causative factor that triggers its onset. However, this prolonged lower rumen pH results from the interaction between multiple other etiological factors. We developed a random forest-based decision tree prediction tool which used seventeen plausible etiological factors, which we broadly classified under four categories, namely 1) animal factors, 2) nutritional factors, 3) environmental factors, and 4) management factors (Table 1), to predict the probability of SARA in a typical US feedlot. A synthetic dataset of 100 observations was created based on a survey conducted among subject matter experts, including ruminant nutritionists, epidemiologists, and decision modelers to chart out the alleged relationship between the above seventeen etiological factors and the probability of a beef cattle population contracting SARA. The random forest model (R, 2021) was used to train as well as test the boot-strapped (80:20 split) synthetic dataset. Ability of random forest to limit overfitting without substantially increasing error due to bias contributed to the high positive predictive value (84%) of the model. Our results indicated that beef yearling cattle with mild temperament, with a rumen pH maintained above 5.6, while being fed three or more times a day, for more than 150 days on concentrate feed, with a stocking density greater than 7 m2, with excellent preconditioning, fed either sorghum or oats which are tempered with a physically effective fiber of 15% and above, under ambient temperature and humidity, along with the feeding of prebiotics, probiotics and ionophores had the least probability to contract SARA. In conclusion, our model can be used by feedlot decision modelers to test different permutations and combinations of these seventeen etiological factors customized to their respective operations to predict the probability of SARA in their herd. Future studies will improve the accuracy of the model by training it on more robust epidemiological datasets sourced from beef herds located in multiple geographic regions.

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 climate-smart 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.

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 finisher-type 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 T111treatment 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.

Virginiamycin (VM) possesses antimicrobial properties due to its blocking of protein synthase in Gram-positive bacteria, allowing it to reduce lactic acidosis and the incidence of liver abscesses in ruminants. 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 and degradation, accumulating acid content in the rumen. The objective of this study was to evaluate the effects of three different doses of VM administration on in vivo and in vitro ruminal digestion kinetics of beef steers consuming a high-grain diet [metabolizable energy (ME): 2.99 Mcal/kg; Crude Protein (CP): 15.2 % dry matter basis (DM)]. Nine ruminally cannulated British-crossbred steers (596 ± 49 kg) were assigned to this experiment. Animals were housed in three pens (n = 3/pen) equipped with a Calan gate feed system and water trough. Pens were enrolled in a 3×3 Latin square design containing three periods of 16 d, and a 5-d washout interval between periods. Dietary treatments consisted of VM administration at 0 mg/d (VM0), 180 mg/d (VM180), and 240 mg/d (VM240). During d 15 and 16 of each period, about 600 mL of rumen fluid was collected before (0 h) and at 4, 8, 12, and 16 h relative to the morning feed (0730 h) pH and redox potential (Eh) measurements were taken immediately after collection using a portable pH and redox meter, and subsamples were taken for volatile fatty acids (VFA), and NH3-N analyses. During the 4-h post-morning feed rumen collection, rumen inoculum was utilized to perform in vitro gas production (IVGP) measurements. All statistical procedures were performed using SAS software where steer was considered the experimental unit, and period and square were included as random. Acetate, propionate, and total VFA did not differ among treatments (P≥ 0.50), whereas butyrate increased linearly (P= 0.033) as the VM dose increased. Acetate:propionate ratio did not differ among treatments (P= 0.273). Lactate concentration decreased linearly (P= 0.027) as the VM dose increased; likewise, pH increased linearly (P= 0.019) as the VM dose increased. Branched-chain VFA and NH3-N concentrations increased linearly (P≤ 0.056) as the VM dose increased. The total and rate of gas production were similar among treatments (P≥ 0.161). However, second-pool gas production increased linearly as VM inclusion increased (P= 0.023). The in vitro neutral detergent fiber digestibility did not differ among treatments (P= 0.984). The provision of VM altered the rumen dynamics in a dose-dependent manner. Animals consuming high-grain diets will likely promote rumen health through a more stable pH and fermentation profile.

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.

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.

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.

Grasslands cover approximately 40.5% of the surface of the earth and 80% of agriculturally productive land. After forests, grasslands are the primary carbon sink source and the most used feed source for livestock production. Regularly monitoring grasslands assures efficient management and sustainability of pasture-based production systems. Conventional ground-based methods to monitor grassland production and management rely on field measurements, which are time-consuming and usually restricted to small-scale assessment. Using satellite information allows for large-scale monitoring of grasslands and capturing the spatial variability of the land surface with high temporal resolution. Various methods for grassland monitoring based on satellite data can be applied, such as classifications, correlations/regression analyses, and time series analyses. Depending on the purpose of the application, these methods are sometimes combined to derive grassland management and production information. The ability of satellite-based data to quantify vegetation characteristics depends on the type of sensor and instrumentation features, such as spectral, radiometric, spatial, and temporal resolution, polarization, and angularity. The models to estimate grassland biomass based on remote sensing have been chiefly focused on optical systems. The spectral reflectance of raw bands and vegetation indices were used as proxies to investigate spatial and temporal patterns of grassland production. Optical (multispectral or hyperspectral) sensors are passive and require sunlight, so they depend highly on the weather (cloud) and light conditions. Thus, there has been increasing interest in active sensors, such as Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LIDAR) sensors, which are not constrained due to clouds but are more complex. Using satellite data in combination with field measurements has commonly yielded regression models (e.g., linear, power, logarithmic, multiple linear) for estimating grassland biomass or biophysical characteristics (e.g., chlorophyll, leaf area index) of different types of grasslands. The exponential evolution of digital computers has pushed forward machine learning-based regression methods to estimate biomass. Random forest, support vector machines, and artificial neural networks are the most used algorithms. The possibility of accurate mapping and monitoring of biomass and nutritional attributes of grasslands based on satellite provides essential insights into the decision support system for pasture management. A better understanding of the nitrogen status of pastures, forage biomass, and its nutritive value is instrumental in livestock and forage management. Timely prediction of these variables can help improve decision-making by grazing land managers on, for instance, the adjustment of stocking rate or adequate supplementation to match the needs of animals toward more sustainable production. Future use of satellite-based grazing models in tandem with ruminant nutrition models will enable to development of decision-support tools to assist with many aspects of livestock production in diverse environmental conditions and accounting for temporal variability. (FAPESP #2020/14367-7)

This study evaluated the effects of live yeast (LY; Saccharomyces cerevisiae) on rumen parameters and in situ DM digestibility (DMD) and NDF digestibility (NDFD) during 3 consecutive feeding phases: grower (GRW) for 27 d, transition (TRANS) for 14 d, and finisher (FIN) for 14 d.

This study evaluated the effects of supplementing dry live yeast (LY; Saccharomyces cerevisiae) on in vitro gas production (IVGP) fermentation dynamics, pH, and CH4 concentration at 48 h, and in situ rumen parameters and digestibility of DM (DMD) and NDF (NDFD) of growing cattle during 3 feeding phases: grower (GRW) for 17 d (38% steamed-flaked corn; SFC), transition (TRANS) for 15 d (55.5% SFC: 1.2 Mcal/kg NEg), and finisher (FIN) for 13 d (73% SFC: 1.23 Mcal/kg NEg). Twenty British-crossbred, ruminally cannulated steers (183 kg ± 44 kg) 6 mo of age were blocked by weight into 5 pens containing Calan gate feeders and received a control (CON) diet (17.2% CP, 35.8% NDF, 86.7% DM) without LY on days -12 to 0. After that, animals were randomly assigned to treatments (TRT), 5 animals per TRT: CON or LY at inclusion rates of 5 g/d (LY1), 10 g/d (LY2), or 15 g/d (LY3) top dressed every morning at 0800 for 45 d. The DMD and NDFD were assessed during 7 separate collection days using in situ nylon bags containing 5 g of GRW, TRANS, or FIN diets, incubated at 1200 for 48 h. Protozoa counts (PC) were determined during 5 collection periods. Data were analyzed as a repeated measure within a randomized complete block design, assuming a random effect of the pen. For GRW, TRT altered the total gas production of the nonfiber carbohydrate (NFC; P = 0.045) and the fractional rate of degradation (kd) of the fiber carbohydrate (FC) pool (P = 0.001) in a cubic pattern (P ≤ 0.05): LY2 had the most gas production and fastest kd. TRT also influenced DMD (P = 0.035) and NDFD (P = 0.012) with LY2 providing the greatest digestibility. For TRANS, TRT tended to affect the NFC kd (P = 0.078) and influenced pH (P = 0.04) and DMD (P < 0.001) in which LY2 yielded the fastest kd, highest pH, and greatest DMD. For FIN, there was an effect of TRT on total gas production (P < 0.001) and kd (P = 0.004) of the NFC pool, FC kd (P = 0.012), in vitro CH4 concentration (P < 0.001), PC (P < 0.001), DMD (P = 0.039), and NDFD (P = 0.008). LY1 had the highest PC and provided the greatest DMD and NDFD. LY2 had the fastest kd of both the NFC and FC pools and had the least CH4 concentration. LY3 had the greatest NFC gas production. No specific dose-response pattern was observed, but 10 g/d provided the most beneficial result for all diets. We concluded that supplementation with LY affected IVGP as well as ruminal parameters and digestibilities.

Reliable assessments of indigestible dietary components are required when using internal markers to estimate diet digestibility and determine the potentially digestible portion of the fiber. The lack of a standardized methodology and understanding of how antinutritional factors influence indigestible residues can result in erroneous estimates with inconsistent variation across trials and among studies. Previous studies have detailed suitable bag porosity and sample size (SS) with incubation length (IL) varying from 96 to 504 h, with many assuming that 288-h IL yields truly indigestible components. Recent studies have primarily investigated the variation that exists among feedstuffs, but most have failed to account for possible effects of secondary compounds. Using 2 similar concentrate diets, one of which contained supplemental condensed tannins (CT), we investigated the effect of bag type (BT; 10 and 25 μm), SS (20 and 40 mg/cm2), and IL (288 and 576 h) on in situ indigestible DM (iDM) and neutral detergent fiber (iNDF) residues of feed and feces, and resultant DM and NDF digestibilities. There were no 3-way interactions (P > 0.05), but 2-way interactions were present for iDM and iNDF residues with BT × SS influencing the control (no CT) ration (P < 0.01), SS × IL impacting feed containing CT (P < 0.01), and BT × IL affecting both feedstuffs (P ≤ 0.01). For the control diet, only BT × SS affected DM and NDF digestibilities. Whereas the CT diet did not demonstrate any significant interactions for digestibilities. Values of iDM were largely influenced by contamination that varied greatly based on intrinsic factors associated with the bag and incubation duration. The presence of CT influenced iDM and iNDF to varying degrees due to possible trapping of CT-substrate complexes. For the control diet, the use of 25-μm bags resulted in lower fecal recoveries relative to the 10 μm (P < 0.01). However, there appears to be a dynamic relationship among BT, SS, and IL within respective diets and sample types that can affect indigestible components and resultant digestibility estimates. Based on simulations from these data, the sample size required to attain 90% power when utilizing 2 incubation animals exceeds the triplicate and quadruplicate replications commonly utilized. Further emphasizing the necessity for a more complete understanding of incubation dynamics to design biologically and statistically valid investigations.

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 R2 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.

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.

Maintaining optimal body condition is an important concern for horse owners and managers as it can affect reproductive efficiency, athletic ability, and overall health of the horse; however, information regarding dietary requirements to maintain or alter BCS in the horse is limited. A recently developed model had high accuracy in predicting the energy required to alter BCS in the horse. However, the model was restricted to sedentary mares, while many horses are subject to physical work. The objective of this study was to expand the scope of that model to include exercising horses by incorporating previously published estimates of exercise energy expenditure and then testing the expanded model. Stock type horses (n = 24) were grouped by initial BCS (3.0 to 6.5) and assigned to treatments of light (L), heavy (H), or no-exercise control (C). Horses were fed according to the model recommendations to increase (I) or decrease (D) two BCS within 60 d. Thus, six treatments were obtained: HD, HI, LD, LI, CD, CI. Mean DE intake Mcal/d for each group was HD = 19.3 ± 0.90, HI = 29 ± 0.84, LD = 13.2 ± 0.54, LI = 23.1 ± 1.39, CD = 12.1 ± 0.79, and CI = 21.9 ± 0.94. BCSs were evaluated by three independent appraisers, days 0 and 60 values were used to calculate the average BCS change for HD = -0.88 ± 0.24, HI = 1.13 ± 0.24, LD = -1.5 ± 0.29, LI = 0.88 ± 0.38, CD = -1.38 ± 0.13, and CI = 1.35 ± 0.14. Statistical comparison of final observed and model predicted values revealed acceptable precision when predicting BCS and BW respectively in control horses (r2 = 0.91, 0.98) but less precision when predicting body fat (BF) (r2 = 0.51). Model precision for BCS, BW, and BF respectively in lightly (r2 = 0.29, 0.85, 0.57) and heavily (r2 = 0.04, 0.84, 0.13) exercised horses was low. Model accuracy was acceptable across all treatments when predicting BW (Cb = 0.97, 0.96, 0.98). However, accuracy varied when predicting BCS (Cb = 0.82, 0.89, 0.41) and BF (Cb = 0.80, 0.55, 0.87) for the control, light, and heavy exercise groups, respectively. These results indicate that the revised model is acceptable for sedentary horses but the predictability of the model was insensitive to the exercising horse, therefore the exercise energy expenditure formulas incorporated into the model require revision. Packaging this model in a format that facilitates industry application could lead to more efficient feeding practices of sedentary horses, generating health, and economic benefit. Further investigation into energy expenditure of exercising horses could yield a model with broader applications.

The use of active dried yeast (ADY) in the diets of feedlot steers may improve feed efficiency, growth performance, and reduce days on feed. Strategic timing of ADY inclusion in the diet may increase feed conversion or aid in the dietary transition from growing to finishing diets. One hundred twenty steers, blocked by weight, were fed four diets for 164 d: grower (70 d), first transition diet (7 d), second transition diet for (7 d), and finisher (80 d) in a GrowSafe System. Four treatment sequences of ADY inclusion were evaluated in a Balaam's design where steers were fed a control diet before and after the grower phase (CC), control before and ADY after the grower phase (CY), ADY before and control after the grower phase (YC), and ADY before and after the grower phase (YY). A random coefficients model was used to evaluate the following variables of interest: feeding performance and growth traits, including biometric measurements and carcass ultrasound measurements, and carcass characteristics. Treatment was a fixed effect and block was a random effect. Treatment did not affect feeding performance or behavior (P ≥ 0.14). The rate of change of biometric measurements were not different (P ≥ 0.16) across treatment groups except for rib girth circumference, which was greater for the YY and CY groups intermediate for the CC group and least for the YC group (0.828 and 0.809 vs. 0.751 vs. 0.666 cm/d, respectively; P < 0.01). Faster growth rates of rib girth circumference resulted in larger final measurements for steers that were finished on ADY (P < 0.01). Ultrasound measurements (backfat, LM area, intra-muscular fat, and rump fat) were not different across treatments (P ≥ 0.15). However, there was a tendency for the YC group to have a slower rate of back fat deposition than other treatment groups (P = 0.09). Steers' final shrunk BWs did not differ (P = 0.61), but shrink percentage was greater for CC than for YY groups (3.7% vs. 2.7%, respectively; P = 0.05). Carcass characteristics were not different across treatments (P ≥ 0.20). Crude fat, CP, ash and moisture analyses of the 9th to 11th rib section were not different across treatments, and there was no difference in adjusted final shrunk BW (P ≥ 0.45). Feeding the ADY strain used in this study to growing and finishing feedlot steers increased rib girth circumference development rate and reduced shrink loss without affecting feeding behavior, feeding performance, or carcass characteristics.

Management strategies implemented in cow-calf 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 third-party 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.

The judicious use of commercial products in livestock operations can be part of a sustainable and environmentally friendly production scenario. This study was designed to gather published data of virginiamycin (VM) used in feedlot conditions of the United States and analyze its effectiveness and optimum dosage in reducing the liver abscess incidence (LAI). The dataset contained 26 studies that evaluated more than 7,156 animals of diverse breeds fed in several regions in the United States under different management. Statistical analyses included contingency tables to assess the nonparametric independence of the LAI, meta regression analysis to remove study effects and to evaluate LAI and animal performance, broken-line analysis to determine thresholds of VM dosage on LAI, and residual-based shading mosaic plots to illustrate the contingency analysis. There were 1,391 of 5,430 animals with LAI scores 1, 2, or 3 (LAI1–3) and 651 of 4,690 animals with LAI A+ (score 3). Our analyses suggested that there was a significant dependency (χ2P-value < 0.001) and significant asymmetry (McNemar’s test P-value < 0.001) between LAI and VM treatment for both LAI1–3and LAI A+. For the LAI1–3group, only 22.5% of the treated animals had liver abscesses compared with 31.7% of the control animals. The metaregression analysis indicated that LAI1–3was linearly reduced (P< 0.001) by about 0.42% per mg/kg of DM of VM. The lower 95% confidence interval of the intercept for LAI1–3and LAI A+ obtained with a generalized nonlinear mixed regression was 18.7 and 20.3 mg/kg of DM, respectively. The broken-line regression analysis identified 2 thresholds for LAI (23.9 and 12.3 mg/kg of DM) at which the reduction in total LAI1–3and LAI A+, respectively, would decrease faster as VM dosage increases (from 2.14% to 6% and from 1.91% to 4.33% per mg of VM per kg of DM, respectively). Additionally, our analyses indicated that after accounting for the study effects, VM significantly increased ADG at 2.08 g BW/d per mg/kg DM compared with 0.92 g BW/d per mg/kg DM for monensin (P< 0.001), suggesting that VM was about 2.3 times more effective in increasing ADG for the same dosage and feeding period length. All analyses yielded consistent results that led us to conclude that VM is effective in reducing LAI when fed between approximately 12 and 24 mg/kg of DM, and the maximum reduction might occur at approximately 24 mg/kg of DM or higher.

Season of forage growth and supplementation have the potential to affect digestion and animal performance. The objectives were to evaluate the ruminal digestion kinetics of ‘Tifton 85’ bermudagrass (T85) [Cynodon dactylon(L.) Pers. × C. nlemfuënsisVanderyst] as affected by seasonality and rate of supplemental dried distillers’ grains with solubles (DDGS). Samples were harvested in June, August, and October 2014. Six ruminally‐fistulated steers were allocated to three pens. Pens (experimental unit) were randomly assigned one of three rates of DDGS: 0, 2.5, or 10 g kg–1body weight (BW) as fed. Duplicate samples of each seasonality were inserted into the rumen of each animal en masseand removed sequentially after 2, 4, 8, 12, 24, 72, or 96 h. Degradation of dry matter (DM) decreased with increasing seasonality (P≤ 0.01) and DDGS (P≤ 0.04). The indigestible fraction (U) of DM from T85 was least (P< 0.05) for June (190 g kg–1), followed by August (337 g kg–1), and greatest for October (407 g kg–1). The Ufrom T85 DM was not different (P= 0.47) based on rate of DDGS (312 g kg–1). There was an interaction of seasonality and DDGS for T85 NDF (P= 0.01) and ADF disappearance (P< 0.01). Increasing DDGS improved degradation of June but not October seasonality. Harvests from later in the season may have altered the cell wall structural profile, and increases in DDGS supplementation might have created an inhospitable rumen environment for fiber‐degrading bacteria.

Advancements in precision livestock technology have resulted in an unprecedented amount of data being collected on individual animals. Throughout the data analysis chain, many bottlenecks occur, including processing raw sensor data, integrating multiple streams of information, incorporating data into animal growth and nutrition models, developing decision support tools for producers, and training animal science students as data scientists. To realize the promise of precision livestock management technologies, open-source tools and tutorials must be developed to reduce these bottlenecks, which are a direct result of the tremendous time and effort required to create data pipelines from scratch. Open-source programming languages (e.g., R or Python) can provide users with tools to automate many data processing steps for cleaning, aggregating, and integrating data. However, the steps from data collection to training artificial intelligence models and integrating predictions into mathematical models can be tedious for those new to statistical programming, with few examples pertaining to animal science. To address this issue, we outline how open-source code can help overcome many of the bottlenecks that occur in the era of big data and precision livestock technology, with an emphasis on how routine use and publication of open-source code can help facilitate training the next generation of animal scientists. In addition, two case studies are presented with publicly available data and code to demonstrate how open-source tutorials can be utilized to streamline data processing, train machine learning models, integrate with animal nutrition models, and facilitate learning. The National Animal Nutrition Program focuses on providing research-based data on animal performance and feeding strategies. Open-source data and code repositories with examples specific to animal science can help create a reinforcing mechanism aimed at advancing animal science research.In the era of big data and artificial learning, developing open-source examples specific to animal science is essential to train the next-generation workforce to realize the potential of precision livestock management technology fully.Livestock production is undergoing a new revolution of incorporating advanced technology to inform animal management. As more and more technologies come to market, new challenges arise with developing a workforce trained to handle big datasets generated from these technologies and turning datasets into insight for livestock producers. This can be especially challenging as multiple data streams ranging from climate and weather information to real-time metrics on animal performance need to be efficiently processed and incorporated into animal production models. Open-source code is one possible solution to these challenges because it is designed to be made publicly available so any user can view, alter, and improve upon existing code. This paper aims to highlight how open-source code can help address many of the challenges of precision livestock technology, including efficient data processing, data integration, development of decision tools, and training of future animal scientists. In addition, the need for open-source tutorials and datasets specific to animal science are included to help facilitate greater adoption of open science.

Over the last three decades, agent-based modeling/model (ABM) has been one of the most powerful and valuable simulation-based decision modeling techniques used to study the complex dynamic interactions between animals and their environment. ABM is a relatively new modeling technique in the animal research arena, with immense potential for routine decision-making in livestock systems. We describe ABM’s fundamental characteristics for developing intelligent modeling systems, exemplify its use for livestock production, and describe commonly used software for designing and developing ABM. After that, we discuss several aspects of the developmental mechanics of an ABM, including (1) how livestock researchers can conceptualize and design a model, (2) the main components of an ABM, (3) different statistical methods of analyzing the outputs, and (4) verification, validation, and replication of an ABM. Then, we perform an overall analysis of the utilities of ABM in different subsystems of the livestock systems ranging from epidemiological prediction to nutritional management to livestock market dynamics. Finally, we discuss the concept of hybrid intelligent models (i.e., merging real-time data streams with intelligent ABM), which have applications in artificial intelligence-based decision-making for precision livestock farming. ABM captures individual agents’ characteristics, interactions, and the emergent properties that arise from these interactions; thus, animal scientists can benefit from ABM in multiple ways, including understanding system-level outcomes, analyzing agent behaviors, exploring different scenarios, and evaluating policy interventions. Several platforms for building ABM exist (e.g., NetLogo, Repast J, and AnyLogic), but they have unique features making one more suitable for solving specific problems. The strengths of ABM can be combined with other modeling approaches, including artificial intelligence, allowing researchers to advance our understanding further and contribute to sustainable livestock management practices. There are many ways to develop and apply mathematical models in livestock production that might assist with sustainable development. However, users must be experienced when choosing the appropriate modeling technique and computer platform (i.e., modeling development tool) that will facilitate the adoption of mathematical models by certifying that the model is field-ready and versatile enough for untrained users.Agent-based modeling for developing intelligent, sustainable livestock systems.Agent-based modeling (ABM) is a well-known simulation technique that decision-makers of livestock systems can use to develop holistic, long-term, and well-informed decisions. This modeling technique facilitates the investigation of complex systems of different individuals, given its capability to simulate individual agents, their specific characteristics, and their inherent capacity to memorize individuals’ past behaviors. Livestock systems are complex systems involving multiple stakeholders with collaborative and sometimes competing interests; thus, ABM might aid in achieving sustainability goals of interest to livestock systems. The modeling processes involved in developing a generic ABM and its utilities are described, so that livestock researchers can build multiple models customized for their research needs. We discuss numerous software platforms that livestock systems modelers can utilize towards this goal. A brief overview of the state-of-the-art ABM developed by different domain experts researching livestock systems was done so that decision modelers working in the field can use those models to conceptualize and design their models for their specific research needs. We also made a case for hybridizing the ABM with real-time data streaming technology to support precision livestock sensor initiatives to enhance the utility of agent-based models for real-time decision-making.

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

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

This article provides a science-based, data-driven perspective on the relevance of the beef herd in the U.S. to our society and greenhouse gas (GHG) contribution to climate change. Cattle operations are subject to criticism for their environmental burden, often based on incomplete information disseminated about their social, economic, nutritional, and ecological benefits and detriments. The 2019 data published by the U.S. Environmental Protection Agency reported that U.S. beef cattle emitted 22.6% of the total agricultural emissions, representing about 2.2% of the total anthropogenic emissions of CO2equivalent (CO2e). Simulations from a computer model developed to address global energy and climate challenges, set to use extreme improvements in livestock and crop production systems, indicated a potential reduction in global CO2e emissions of 4.6% but without significant enhancement in the temperature change by 2030. There are many natural and anthropogenic sources of CH4emissions. Contrary to the increased contribution of peatlands and water reservoirs to atmospheric CO2e, the steady decrease in the U.S. cattle population is estimated to have reduced its methane (CH4) emissions by about 30% from 1975 to 2021. This CH4emission deacceleration of 2.46 Mt CO2e/yr2might be even more significant than reported. Many opportunities exist to mitigate CH4emissions of beef production, leading to a realistic prospect of a 5% to 15% reduction in the short term after considering the overlapping impacts of combined strategies. Reduction strategies include feeding synthetic chemicals that inactivate the methyl-coenzyme M reductase (the enzyme that catalyzes the last step of methanogenesis in the rumen), red seaweed or algae extracts, ionophore antibiotics, phytochemicals (e.g., condensed tannins and essential oils), and other nutritional manipulations. The proposed net-zero concept might not solve the global warming problem because it will only balance future anthropogenic GHG emissions with anthropogenic removals, leaving global warming on a standby state. Recommendations for consuming red meat products should consider human nutrition, health, and disease and remain independent of controversial evidence of causational relationships with perceived negative environmental impacts of beef production that are not based on scientific data.Articles from the popular press and specialized journals often use incomplete, misinterpreted, or inaccurate information on beef production’s impact on climate change, especially methane emissions. Such misinformation is often erroneously merged with nutritional recommendations for consuming red meat related to human diseases.This article aims to provide data-driven information about the relevance of the U.S. beef cattle herd to our society and its greenhouse gas (GHG) contribution to climate change. The Environmental Protection Agency reported that U.S. beef cattle emitted 22.6% of the total agricultural emissions, representing about 2.2% of the total anthropogenic emissions of carbon dioxide equivalent (CO2e). Although the GHG contribution of the U.S. beef cattle production is small, there are many opportunities to reduce enteric methane emissions from beef cattle, with realistic estimates of a 5% to 15% reduction. However, net-zero emissions will be challenging to achieve for beef production. Considering the relatively minor contribution of beef cattle production to GHG emissions, other sources with a greater contribution to GHG emissions should be a much higher priority for mitigation as they would have a more substantial impact on slowing global warming. Recommendations by health professionals for consuming red meat products should consider human nutrition, health, and disease and remain independent of perceived negative environmental impacts of beef production that are not based on scientific data.

Several ruminant animals rely almost exclusively on the complex polysaccharide matrix from the plant cell wall (CW) as their primary energy source via volatile fatty acids produced through ruminal and some hindgut fermentation processes. The CW contains different types and proportions of polysaccharides, proteins, phenolic compounds, and minerals in their macromolecular structure that influence the rate and extent of fiber digestion and selective retention of particulate matter due to its physical characteristics (buoyancy and comminuting) in the reticulorumen. The biosynthetic formation of the CW dictates possible manipulation mechanisms (targeted plant and microbes selection) and processing methods (physical, chemical, microbial, and enzymatic treatments and the use of genetically engineered bacteria) to increase its digestibility, leading to better utilization of the CW by the ruminant animal and hopefully lower the contribution of ruminants’ greenhouse gas emissions. Early studies on lignin biosynthesis have led to more advanced studies focusing on replacing traditional monolignols with homopolymers that are easier to deconstruct or degrade. Concurrently, laboratory methods must be developed, evaluated, and modified to accurately reflect the digestibility and nutritive value of CW brought about by modern manipulation mechanisms or processing methods. However, the laboratory methods must also be reliable, precise, feasible, trivial, easy to implement, and cost-effective, but at the same time environmentally friendly and aware. For instance, although the acid detergent lignin has been demonstrated to behave uniformly as a nutritional entity, its chemical determination and association with carbohydrates still lack consensus. Spectroscopy (near-infrared and Raman) and in vitro gas production techniques have been adopted to assess plant chemical composition and nutritive value, but an incomplete understanding of the impacts caused by disrupting the CW for sample processing still exists. Different variations of multicompartmental and time- and age-dependent mathematical models have been proposed to determine the ruminal rates of degradation and passage of fiber. However, low-quality and incomplete data due to inconsistent marker results used to determine passage rates and transit time of fiber in the gastrointestinal tract have hindered advancements and adoptions of the next generation of computer models to understand ruminal fiber degradation.In the last 30 years, the scientific community has gained a much better understanding of plant cell wall biosynthesis and how to manipulate it to favor its utilization by ruminant animals. Furthermore, we have developed many innovative and advanced mechanisms to improve the prediction of the cell wall’s digestibility.The underlying principles of forage cell wall utilization by ruminants have been known for over 50 years, but a significant amount of knowledge of the structure and synthesis of critical components of the plant cell wall, mechanisms and methods to alter its digestibility, and assessment techniques to quantify its components as well as their fermentability has been accumulated in the last 30 years. Such knowledge has even allowed us to make recommendations about the importance of fiber in the diet to improve animal performance and welfare. For instance, some industries (especially the paper mill and biofuels) have attained significant advancements toward modifying plant lignin (a critical component of the cell wall that reduces fermentability) and lignin-degrading microorganisms that could assist the animal nutrition community in increasing the digestibility of forage cell wall without further pretreatment. There are many techniques and technologies available to increase cell wall digestibility and, consequently, animal productivity. However, each has potential and limitations, and when used alone, it may not yield the best outcome. From a ruminant nutrition perspective, combining such techniques and technologies with the next generation of mathematical models seems more likely to yield significant improvements in forage cell wall digestibility.

The objective of this trial was to determine the influence of live yeast supplementation (LY), environmental condition (ENV), and their interaction (TRT) on energy partitioning, nitrogen metabolism, and ruminal fermentation dynamics of steers receiving a grower-type diet. The effects of LY and ENV were investigated using a 2 × 2 crossover design that spanned five periods. Eight Angus-crossbred steers were randomly split into pairs and housed in four outdoor pens outfitted with an individualized feeding system. Animals were limit-fed a grower diet (DIET) at 1.2% shrunk body weight (SBW) with no live yeast supplementation (NOY) or a grower diet top-dressed with 10 g LY/d for 14 d (1.2 × 1012CFU/d). On days 13 and 14, animals were subjected to one of two ENV conditions, thermoneutral (TN; 18.4 ± 1.1 °C, 57.6 ± 2.8% relative humidity [RH]) or heat stress (HS; 33.8 ± 0.6 °C, 55.7 ± 2.7% RH), in two side-by-side, single-stall open-circuit, indirect respiration calorimetry chambers. Data were analyzed using a random coefficients model. Carryover effects were examined and removed from the model if not significant. Gross (GE), digestible, metabolizable, heat, and retained energies were not influenced by DIET, ENV, or TRT (P≥ 0.202). Gaseous energy, as a percentage of GE, tended to increase during HS (P= 0.097). The only carryover effect in the study was for oxygen consumption (P= 0.031), which could be attributed to the tendency of NOY (P= 0.068) to have greater oxygen consumption. DIET, ENV, or TRT (P≥ 0.154) had no effects on total animal methane or carbon dioxide emissions. Similarly, DIET, ENV, or TRT (P≥ 0.157) did not affect ruminal pH, redox, protozoa enumeration, ruminal ammonia concentrations, and acetate-to-propionate ratio. Propionate concentrations were the greatest in animals in TN conditions receiving LY (P= 0.034) compared to the other TRT. This effect is mirrored by TN-LY tending to have greater acetate concentrations (P= 0.076) and total VFA concentrations (P= 0.065). Butyrate concentrations tended to be greater for animals fed LY (P= 0.09). There was a tendency for LY to have elevated numbers of Fusobacterium necrophorum(P= 0.053). Although this study lacked effects of LY on energy partitioning, nitrogen metabolism, and some ruminal parameters during HS, further research should be completed to understand if LY is a plausible mitigation technique to enhance beef animals’ performance in tropical and sub-tropical regions of the world.This study investigates the efficacy of live yeast supplementation as a mitigation technique for heat stress in beef cattle fed grower-type diets, and its effects on energy partitioning, nitrogen metabolism, and ruminal fermentation dynamics. Additionally, this study utilizes an in vitro model to simulate the performance of rumen inoculum after heat stress or live yeast supplementation.About 70% of global beef production is located in tropical and sub-tropical regions. With elevated temperatures and significant humidity, these regions impose heat stress on beef animals. Heat stress is the main antagonist to ruminant production as it decreases dry matter intake and digestion and increases energy expenditure due to the animal’s need for thermoregulation. Supplementation of live yeast products has proven efficacious at improving ruminal fermentation dynamics. This study sets out to determine if live yeast supplementation to animals in heat stress conditions can positively affect energy partitioning, nitrogen metabolism, and ruminal parameters. Additionally, this study models the ruminal performance after exposure to heat stress or live yeast supplementation. This study identified several interesting in vitro dynamics of previously stressed- or supplemented rumen fluid. Although there were a lack of effects for live yeast supplementation on energy partitioning, nitrogen metabolism, and some ruminal parameters during heat stress, further research should be completed in order to understand if live yeast supplementation is a plausible mitigation technique to enhance the performance of beef animals reared in tropical and sub-tropical regions of the world.

Rumen acidosis is a common metabolic disorder occurring when organic acid production exceeds clearance capacity, reducing ruminal pH. Acidosis occurrence has been directly correlated to the ratio of concentrate to forage in the diet. However, the rates of substrate fermentation and acid absorption vary at different locations in the rumen. The objective of this study was to determine the pH in different locations of the rumen using 16 rumenally- cannulated steers (309 ± 43 kg) receiving quebracho extract (QT; Schinopsis balansae) within a grower-type diet [25:75 forage-to-concentrate, dry matter (DM) % basis]. Animals were randomly assigned to one of four dietary treatments (n = 4): QT at 0, 1, 2, and 3% of DM (QT0, QT1, QT2, and QT3). Animals were adapted to the basal diet (QT0) for 12-d before being introduced to predetermined treatments for four weeks, with feed provided twice daily to allow ad libitum intake. Weekly measurements of ruminal fluid pH and redox potential (Eh) were taken four h post-feeding using a portable pH and redox meter probe in four locations of the rumen (cranial sac, ventral sac, dorsal sac, and reticulum). Data were analyzed using a random coefficients model with the pen as a random effect and week as repeated measures. The DM intake was included as a covariate. There was no interaction among diet, location, and week (P ≥ 0.925) on pH. Overall, ruminal pH was lower for QT0 and QT1 compared to QT3 (P < 0.001). Ruminal pH in the cranial sac and reticulum was greater than in the dorsal sac (5.98, 6.03, and 5.87, respectively; P = 0.001). Redox potential was lower for QT0 in week 1 than all other treatments (P = 0.042). This study indicated that pH differs among locations of the rumen regardless of QT supplementation level and days on feed.

We aimed to assess whether predicting the metabolizable energy (ME) to digestible energy (DE) ratio (MDR), rather than a prediction of ME with DE, is feasible and to develop a model equation to predict MDR in beef cattle. For this, we constructed a literature database based on published data. A meta-analysis was conducted with 306 means from 69 studies containing both dietary DE and ME concentrations measured by calorimetry to test whether the exclusion of the y-intercept is adequate in the linear relationship between DE and ME. A random coefficient model with study as the random variable was used to develop equations to predict MDR in growing and finishing beef cattle. The developed equations were evaluated with other published equations. The no-intercept linear equation represented the relationship between DE and ME more appropriately than the equation with a y-intercept. Within our growing and finishing cattle data, the animal’s physiological stage was not a significant variable affecting MDR after accounting for the study effect (P = 0.213). The mean (± SE) of MDR was 0.849 (± 0.0063). Two linear equations with the dry matter intake and content of several dietary nutrients were developed to predict MDR. When using these equations, the observed ME was predicted with high precision (R2 = 0.92). The model accuracy was also high, as shown by the high concordance correlation coefficient (> 0.95) and small root mean square error of prediction (RMSEP), less than 5% of the observed mean. Moreover, a significant portion of the RMSEP was due to random bias (> 93%), without mean or slope bias (P > 0.05). We concluded that dietary ME in beef cattle could be accurately estimated from dietary DE and its conversion factor, MDR, using the two equations developed in this study.

The objectives of this multipart study were 1) to assess the efficacy of sampling methods of methane concentration ([CH4]) of headspace gas produced during in vitro gas production (IVGP) fermentation, 2) to verify whether headspace [CH4] sampled from an exetainer has the same [CH4] as the headspace of IVGP bottles, 3) to measure relative humidity (RH) within an IVGP bottle, and 4) to compare [CH4] on a dry-gas (DG) basis when accounting for water vapor pressure (Pw). The original IVGP protocol recommends placing bottles on ice (0 °C) for 30 min to stop fermentation (ICE). A laboratory protocol recommends placing the bottles in the refrigerator (4 to 6 °C) to slow fermentation for 48 h and subsequently allowing the bottles to return to ambient temperature before sampling (FRIDGE). This study evaluated the previous methods against a direct sampling of the headspace gas after incubation (DIRECT). Rumen inoculum from four rumen-cannulated beef steers was combined and homogenized before incubating the fermentable substrate of ground alfalfa hay. After 48 h of IVGP incubation, each bottle was randomly assigned to a treatment protocol. The pressure (P), volume (V), and temperature (T) of headspace gas in each bottle were recorded. Headspace gas was then thoroughly mixed, and 12 mL gas was removed into an evacuated exetainer for [CH4] sampling via gas chromatography (EXET; Objective 1). Eight bottles from ICE and FRIDGE were randomly selected to follow EXET, whereas the remaining bottles had [CH4] directly measured from their headspace (BOTT; Objective 2). Five diets of differing feed composition and nutrient densities were used with a blank to test the RH of the IVGP slurry (Objective 3). Using RH, [CH4] was transformed to a DG basis to account for Pw(Objective 4). Statistical analysis was completed using a random coefficients model. There were no differences between EXET and BOTT (P= 0.28). The RH of the IVGP slurry was 100% (P = 1.00), confirming that IVGP gas is saturated with water vapor. The P, V, and Tdiffered among treatments (P< 0.01). The [CH4] of DIRECT, ICE, and FRIDGE were different (P< 0.01). Dry-gas P, V, and [CH4] differed among treatments (P< 0.01). As the methods differ in their assessment of [CH4], there is no clear recommendation. Instead, to present a more accurate [CH4], P, V, and Tshould be measured when sampling headspace gas and equations presented should be used to remove volume inflation due to water vapor and present [CH4] on a DG basis.This study shows that postincubation treatment of in vitro gas production fermentation bottles can cause discrepancies in the quantification of methane in the headspace gas. To reduce those discrepancies, this study suggests a system of equations to remove water vapor from the headspace gas to present methane estimates on a dry-gas basis.Greenhouse gas emissions (GHG) from ruminant production equate to 81% of total global livestock supply chain emissions, with 51% originating from beef cattle production. Traditional in vivo estimation methods of methane (CH4), a highly scrutinized greenhouse gas, are timely and costly. In vitro gas production (IVGP) methods can accurately describe CH4emission patterns from the rumen but tend to overestimate quantities. Additionally, in vivo estimation methods present CH4on a dry-gas basis, whereas in vitro do not. In vitro methods utilize a gas chromatography machine to estimate CH4. Laboratory constraints can impose deviations to a strict IVGP protocol. This multi-objective study evaluates three treatment methods of IVGP bottles to understand whether discrepancies exist in CH4estimation when deviating from the published protocol. To estimate CH4from IVGP more accurately and provide a more comparable number to in vivo methods, this study also evaluates environmental conditions within an IVGP bottle to formulate a system of equations to calculate CH4on a dry-gas basis. This study found that the treatment method of the IVGP bottle had an impact on CH4estimation, and the developed equations should be utilized to produce more comparable estimates.

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.Interest and investment in precision technologies are growing within the livestock sector. Though these technologies offer many promises of increased efficiency and reduced inputs, there is a need to assess the opportunities and challenges associated with precision technology implementation in livestock production systems. In this review, precision livestock measurement and management tools are explained in the context of a logical and iterative five-step process that highlights the need for systems computer modeling to realize anticipated benefits from these technologies and avoid unintended consequences. This review includes key case studies to highlight past challenges and current opportunities within operations that house animals in a central area or building with sufficient infrastructure (confined livestock production systems) and other operation settings that utilize large grasslands that contain far less infrastructure (extensive livestock production systems). The key to precision livestock management success is training the next generation of animal scientists in computer modeling, precision technologies, computer programming, and data science while still being grounded in traditional animal science principles.Precision technology is rapidly being implemented in confined and extensive livestock production systems. Understanding what has been done is critical to successful short- and long-term gains in multifaceted and complex animal–plant–soil–water systems, requiring effective integration of precision system models.

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams (“big data”) and the exponential increase in computing power have allowed the appearance of “new” modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine “old” and “new” modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.Modeling in the animal sciences has received a boost by large-scale adoption of sensor technology, increased computing power, and the further development of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL) models. Together with open-source programming languages, extensive modeling libraries, and heavy marketing, modeling reached a larger audience via AI. However, like most technological innovations, AI overpromised. By adopting an almost singular model-centric view to solving business needs, models failed to integrate with existing business processes. Models, especially AI, need data and both need humans. Together, they need room to learn and fail and by offering them as the end-solution to a problem, they are unable to act as sparring partners for all relevant stakeholders. In this article, we highlight fundamental model limitations exemplified via AI, and we offer solutions toward a more sustainable adoption of AI as a catalyst for modeling. This means sharing data and code and placing a more realistic view on models. Universities and industry both play a fundamental role in offering technological prowess and business experience to the future modeler. People, not technology, are the key to a more successful adoption of models.The hype of artificial intelligence is at an end, revealing to a larger audience the inherent dependencies and limitations of modeling as a human process. Technology is good, but data and humans are essential in enabling a more sustainable role for models in the animal sciences ecosystem.

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

This study determined the energy requirement for maintenance of purebred Nellore cattle and its crossbreds using data from a comparative slaughter trial in which animals were raised under the same plane of nutrition from birth through slaughter and born from a single commercial Nellore cowherd. A total of 79 castrated steers (361 ± 54 kg initial body weight [BW]) were used in a completely randomized design by age (22 mo ± 23 d of age) with four genetic groups (GG): Nellore (NL), ½ Angus × ½ Nellore (AN), ½ Canchim × ½ Nellore (CN), and ½ Simmental × ½ Nellore (SN). The experimental design provided ranges in metabolizable energy (ME) intake (MEI), BW, and average daily gain needed to develop regression equations to predict net energy for maintenance (NEm) requirements. Four steers of each GG were slaughtered to determine the initial body composition. The remaining 63 steers were assigned to different nutritional treatments (NT) by GG; ad libitum or limit-fed treatments (receiving 70% of the daily feed of the ad libitum treatment of the same GG). Full BW was recorded at birth, weaning, 12, 18, and 22 mo. In the feedlot, steers were fed for 101 d a diet containing (DM basis) 60% corn silage and 40% concentrate. No difference in age at weaning (P= 0.534) and slaughter (P= 0.179 and P= 0.896, for GG and NT, respectively) were observed. AN steers were heavier at weaning weight, yearling weight and had higher empty BW (EBW; P= 0.007, P= 0.014, and P< 0.001, respectively) in comparison to NL, CN, and SN. There were no interactions (P> 0.05) between GG and NT for any variable evaluated. When fed ad libitum, AN steers had higher daily MEI (Mcal/d; P< 0.001) in comparison to NL, CN, and SN. On a constant age basis, differences were observed on body composition (P< 0.05) between GG. The slope (P= 0.600) and intercept (P= 0.702) of the regression of log heat production on MEI were similar among GG. Evaluating at the same age and the same frame size, there were no differences in NEm requirement between Nellore and AN (P= 0.528), CN (P= 0.671), and SN (P= 0.706). The combined data indicated a NEm requirement of 86.8 kcal/d/kg0.75EBW and a ME required for maintenance requirement had a common value of 137.53 kcal/d/kg0.75EBW. The efficiency of energy utilization for maintenance and the efficiency of energy utilization for growth values were similar among GG (P> 0.05 and P> 0.05, respectively) and were on average 63.2% and 26.0%, respectively. However, although not statistically different, the NEm values from NL showed a decrease in NEm of 5.76% compared with AN steers.Although several studies have shown that the maintenance energy expenditures vary with breeds, there has been no available data comparing the energy requirements of different genetic groups of beef cattle determined during the finishing phase when raised under the same plane of nutrition from birth through slaughter born from a single cowherd. This study evaluated the influence of purebred Nellore and its crosses with Simmental, Angus, and Canchim slaughtered at the same age and body composition on their net energy requirement for maintenance (NEm). Animals were reared in tropical conditions, receiving only free-choice minerals from birth through the beginning of the feedlot phase, potentially altering the intake, carcass composition, mature weight, and consequently, affecting the energy requirement for maintenance during the finishing period. The pooled data analysis for Nellore and its crosses resulted in common NEm requirement of 86.9 kcal/d/kg0.75of empty body weight (EBW). However, although not statistically different, the NEm values from Nellore (NL) and Angus × Nellore (AN) were 85.5 and 90.8 kcal/d/kg0.75EBW, respectively, showing a decrease in NEm of 5.76% for NL in comparison with AN steers.The knowledge and understanding of the maintenance energy requirements among beef cattle breeds is key to the success of diet formulation in feedlot cattle, aiming for optimal production and sustainable profit. Studies evaluating the energy requirement of purebred Nellore cattle and its crossbreds reared in the same experimental conditions from birth to slaughter will give valuable information for nutritionists and consultants working in the beef production system under tropical conditions.

Our objectives were to 1) investigate the difference in chemical composition and disappearance kinetics between loose dried distillers’ grains (DDG) and extruded DDG cubes and 2) evaluate the effects of supplementation rate of extruded DDG cubes on voluntary dry matter intake (DMI), rate and extent of digestibility, and blood parameters of growing beef heifers offered ad libitum bermudagrass (Cynodon dactylon) hay. To characterize the changes in chemical composition during the extrusion process, loose and extruded DDG were evaluated via near-infrared reflectance spectroscopy, and dry matter (DM) disappearance kinetics were evaluated via time point in situ incubations. Extruded DDG cubes had greater (P≤ 0.01) contents of fat, neutral detergent insoluble crude protein, and total digestible nutrients, but lower (P≤ 0.01) neutral and acid detergent fiber than loose DDG. Additionally, the DM of extruded DDG cubes was more immediately soluble (P< 0.01), had greater (P< 0.01) effective degradability and lag time, and tended (P= 0.07) to have a greater disappearance rate than loose DDG. In the 29-d supplementation rate study, 23 Charolais-cross heifers were randomly assigned to one of four supplemental treatments: 1) control, no supplement; 2) low, 0.90 kg DDG cubes per d; 3) intermediate, 1.81 kg DDG cubes per d; or 4) high, 3.62 kg DDG cubes per d. Titanium dioxide was used as an external marker to estimate fecal output and particulate passage rate (Kp). Blood was collected from each animal to determine supplementation effects on blood metabolites. Indigestible neutral detergent fiber was used as an internal marker to assess the rate and extent of hay and diet DM digestibility (DMD). Increasing supplementation rate increased Kpand total diet DMI linearly (P< 0.01), yet linearly decreased (P< 0.01) hay DMI. Hay DMD decreased quadratically (P< 0.01), while total diet DMD increased linearly (P< 0.01) with increased DDG cube inclusion. Supplemented heifers had greater (P= 0.07) blood urea nitrogen concentrations than control animals 4 h post-supplementation. Intermediate and high rates of supplementation resulted in lower (P< 0.01) serum nonesterified fatty acid concentrations post-supplementation than control heifers. Concentrations of serum glucose and lactate were greatest (P≤ 0.06) 8 h post-supplementation. Our results suggest that extruded DDG cubes may be an adequate supplement for cattle consuming moderate-quality forage, and further research is warranted.Growing cattle are oftentimes provided supplemental concentrate as a source of protein and energy in order to meet performance goals when consuming low-quality forages. The effects of supplemental concentrate on forage intake vary, which may be related to the quality of forage and the characteristics of the supplement being evaluated. Dried distillers’ grains (DDG) are a by-product of ethanol production and have become a common supplement for growing cattle due to the increased energy and rumen undegradable protein content. A stable DDG cube made via a novel extrusion process may be advantageous for pasture supplementation due to the reduced risk of loss of product from wind and soil mixing that is common with loose DDG. The effects of supplementation rate of traditional concentrate sources on forage intake are abundant, but research regarding extruded DDG cubes is almost nonexistent. Thus, our objective was to evaluate extruded DDG cube supplementation rate (0, 0.90, 1.81, or 3.62 kg DDG cubes per d) for growing cattle on voluntary intake and digestibility of moderate-quality forage. Although increasing supplementation rate reduced forage intake and digestibility, total diet intake and digestibility were increased. Our results suggested that extruded DDG cubes have potential as a supplement for cattle consuming moderate-quality forage.A novel extrusion process has allowed for the production of a stable dried distillers’ grains cube that is a potential supplement for growing cattle consuming moderate-quality forages.

The objective of this study was to assess the accuracy of equations used to estimate ultrasound carcass composition for prediction of empty body gain (EBG). The dataset consisted of 8 trials with Angus and Brangus bulls or Brangus heifers (N = 897) fed moderate energy diets for 70 d. Initial and final ultrasound carcass measurements were obtained by trained technicians. Initial and final empty body fat percentage (EBF) was computed using 5 equations: 1) carcass trait equation from Guiroy et al. (2001; 10.2527/2001.7981983x), 2) carcass trait equation 1 from Tedeschi et al. (2004; 10.1016/S0308-521X(03)00070-2), 3) carcass trait equation 6 from Baker et al. (2006; 10.2527/jas.2006-006), 4) ultrasound trait equation 8 from Baker et al. (2006; 10.2527/jas.2006-006), and 5) pEBF = 27.1902 + 47.6268 × uBF – 0.5687 × uLMA – 0.1178 × uBF × pHCW + 0.0014 × uLMA × pHCW; R2= 0.64, RMSE = 2.46% developed from the training dataset of Baker et al. (2006). Empty body gain was predicted using NASEM (2016) equations. Eq. 5 had slightly improved R2(0.64 vs. 0.62) and RMSE (2.46 vs. 2.49%) compared with Eq. 4 in the training dataset. When comparing observed and predicted EBG, Eq. 4 had intercept and linear coefficients nearest to 0 and 1, respectively, the greatest CCC (0.799), and mean bias closest to 0 (0.025 kg/d) compared with the other equations. ADG, ultrasound traits, and sex were significant (P< 0.05) variables accounting for variation in differences between observed and predicted EBG, suggesting that these equations are somewhat lacking in their ability to capture the relationships between ultrasound carcass composition traits and retained energy. ADG was less important and gain in LMA was more important in explaining differences between observed and predicted EBG for Eq. 5 than the other equations. These results indicate that use of live animal ultrasound measurements can predict retained energy with reasonably accuracy.

Reliable predictions of metabolizable energy (ME) from digestible energy (DE) are necessary to prescribe nutrient requirements of beef cattle accurately. A previously developed database that included 87 treatment means from 23 respiration calorimetry studies has been updated to evaluate the efficiency of converting DE to ME by adding 47 treatment means from 11 additional studies. Diets were fed to growing-finishing cattle under individual feeding conditions. A citation-adjusted linear regression equation was developed where dietary ME concentration (Mcal/kg of dry matter [DM]) was the dependent variable and dietary DE concentration (Mcal/kg) was the independent variable: ME = 1.0001 × DE – 0.3926; r2= 0.99, root mean square prediction error [RMSPE] = 0.04, and P< 0.01 for the intercept and slope. The slope did not differ from unity (95% CI = 0.936 to 1.065); therefore, the intercept (95% CI = −0.567 to −0.218) defines the value of ME predicted from DE. For practical use, we recommend ME = DE – 0.39. Based on the relationship between DE and ME, we calculated the citation-adjusted loss of methane, which yielded a value of 0.2433 Mcal/kg of dry matter intake (DMI; SE = 0.0134). This value was also adjusted for the effects of DMI above maintenance, yielding a citation-adjusted relationship: CH4, Mcal/kg = 0.3344 – 0.05639 × multiple of maintenance; r2= 0.536, RMSPE = 0.0245, and P< 0.01 for the intercept and slope. Both the 0.2433 value and the result of the intake-adjusted equation can be multiplied by DMI to yield an estimate of methane production. These two approaches were evaluated using a second, independent database comprising 129 data points from 29 published studies. Four equations in the literature that used DMI or intake energy to predict methane production also were evaluated with the second database. The mean bias was substantially greater for the two new equations, but slope bias was substantially less than noted for the other DMI-based equations. Our results suggest that ME for growing and finishing cattle can be predicted from DE across a wide range of diets, cattle types, and intake levels by simply subtracting a constant from DE. Mean bias associated with our two new methane emission equations suggests that further research is needed to determine whether coefficients to predict methane from DMI could be developed for specific diet types, levels of DMI relative to body weight, or other variables that affect the emission of methane.

The energy requirements, feed intake, and performance of grazing animals vary daily due to changes in weather conditions, forage nutritive values, and plant and animal maturity throughout the grazing season. Hence, realistic simulations of daily animal performance can be made only by the models that can address these changes. Given the dearth of simple, user-friendly models of this kind, especially for pastures, we developed a daily gain model for large-frame stockers grazing bermudagrass sCynodon dactylon(L.) Pers.], a widely used warm-season perennial grass in the southern United States. For model development, we first assembled some of the classic works in forage-beef modeling in the last 50 yr into the National Research Council (NRC) weight gain model. Then, we tested it using the average daily gain (ADG) data obtained from several locations in the southern United States. The evaluation results showed that the performance of the NRC model was poor as it consistently underpredicted ADG throughout the grazing season. To improve the predictive accuracy of the NRC model to make it perform under bermudagrass grazing conditions, we made an adjustment to the model by adding the daily departures of the modeled values from the data trendline. Subsequently, we tested the revised model against an independent set of ADG data obtained from eight research locations in the region involving about 4,800 animals, using 30 yr (1991–2020) of daily weather data. The values of the various measures of fit used, namely the Willmott index of 0.92, the modeling efficiency of 0.75, the R2of 0.76, the root mean square error of 0.13 kg d−1, and the prediction error relative to the mean observed data of 24%, demonstrated that the revised model mimicked the pattern of observed ADG data satisfactorily. Unlike the original model, the revised model predicted more closely the ADG value throughout the grazing season. The revised model may be useful to accurately reflect the impacts of daily weather conditions, forage nutritive values, seasonality, and plant and animal maturity on animal performance.

Plant secondary metabolites (PSM) and yeast supplemented in growing cattle have been reported as improving dry matter digestibility (DMD) and reducing enteric methane emissions. This study aimed to evaluate the effect of combined supplementation with condensed tannin (CT; Schinopsis balansae) extract and active dry yeast (ADY; Saccharomyces cerevisiae) on fermentation dynamics, utilizing in vitro gas production (IVGP) technique. A 2 × 2 Latin square design was used to study fermentation patterns of four dietary treatments (CON- no CT and no ADY, ADY alone, CT alone or combined CT + ADY). Animals received daily CT at 1% DM and 10 g of ADY, respectively. On d 0, 7, 14, 21, 28, and 35 rumen inoculum was collected from 23 fistulated steers (284.3 ± 4.1 kg) four hours post-feeding. Samples were incubated under anaerobic conditions at 39oC for 48 h with 200 mg of a grower diet (14.8% CP, 40.6% NDF, 88.5% DM). Gas parameters were analyzed using a mixed linear statistical model. There was a day effect for total gas production (TGP; P < 0.001), non-fiber carbohydrate degradation (P = 0.031) and fractional degradation assuming an asymptote model (P = 0.015). Both asymptote and non-fiber fractional degradation rate estimates had an interaction between Day × TRT (P = 0.001 and 0.0104, respectively). Data were analyzed using polynomial contrasts showed a difference in non-fiber fractional rate of fermentation for CON × CT and CT × ADY (P = 0.052 and 0.054, respectively). This was also true if an asymptote model was assumed (P = 0.0 34 and 0.034, respectively). We concluded that combined supplementation of CT and ADY exhibited similar IVGP trends over time, this may be because animals only received a grower diet at 1.5% shrunk BW. Future studies should investigate the impact of combined supplementation on varying levels of concentrate diets.

The objective of this trial was to determine the influence of live yeast supplementation (LY) and temperature exposure (TEMP) on the ruminal fermentation characteristics of steers receiving a grower diet. The effects of LY and TEMP were investigated using a 2 x 2 crossover design that spanned five periods. Eight Angus crossbred steers (365 ± 32 kg) were randomly split into pairs and housed in four outdoor pens outfitted with an individualized feeding system. Animals were limit fed a grower diet (DIET) at 1.2% SBW with no live yeast supplementation (NOY) or a grower diet top-dressed with 10 g live yeast/d for 14 days (1.2 × 1012CFU/d). On days 13 and 14, animals were subjected to one of two TEMP conditions, thermoneutral (TN; 18.4 ± 1.1°C, 57.6 ± 2.8 % RH) or heat stress (HS; 33.8 ± 0.6°C, 55.7 ± 2.7 % RH), in two side-by-side, single-stall open-circuit, indirect respiration calorimetry chambers. Data were analyzed using a random coefficients model. Carryover effects were examined and removed from the model if not significant (P > 0.05). There was no effect of DIET, TEMP, or DIET×TEMP (P > 0.05) on ruminal pH, redox, ciliated protozoa count, acetate, butyrate, total VFA, and ruminal ammonia concentrations. Similarly, the acetate to propionate ratio was not influenced by DIET, TEMP, or DIET × TEMP (P ≥ 0.190). Propionate concentration was the greatest in animals in TN conditions receiving LY (P = 0.008). Compared to HS+NOY, HS+LY (P = 0.003) and TN+LY (P = 0.043) had greater ruminal enumerations of Fusobacterium necrophorum. This suggests LY (P = 0.010) provided a favorable environment for F. necrophorum during heat stress. Live yeast supplementation did not improve overall ruminal fermentation during heat stress. Additional research is required to better understand the dynamic relationship between live yeast and temperature exposure.

The objective of this study was to describe two chromatography equipment and their methods (EM) and to evaluate their adequacy in estimating ruminal volatile fatty acid concentrations (VFA). Adequacy was assessed through precision and accuracy using three standard mixtures of known acetate, propionate, and butyrate concentrations. The standard mixtures were prepared for VFA analysis using high-performance liquid chromatography (HPLC) or gas chromatography (GC). Each mixture was injected ten times into each EM. The comparison was assessed with rumen fluid samples from four cannulated steers offered three diets at 2% BW. Diet A simulated a forage-based diet offered to cattle during the winter. Diet B simulated a grower-type diet offered to weaned calves. Diet C simulated a finisher-type diet offered to finishing cattle. Rumen fluid was collected three hours after the morning feeding for seven days for each diet and strained through 8 μm porosity fiberglass wool. Two 2-mL aliquots were stored at -20°C for HPLC analysis, while two 8-mL aliquots were diluted with 2 mL of 25% meta-phosphoric acid and stored at -20°C for GC analysis. Chromatograms without a flat baseline were removed from the analysis. For the adequacy evaluation, HPLC (R2= 0.997; Cb = 0.874) was more precise and accurate at estimating total VFA than GC (R2= 0.447; Cb = 0.763). When compared with the standards, HPLC estimated less (P < 0.001) total VFA (98.8 ± 10.3 mM) than GC (110.5 ± 17.4 mM). Concentrations for acetate, propionate, and butyrate in rumen fluid samples were estimated for each EM and analyzed using a random coefficients model. Similarly, estimates for acetate, propionate, and butyrate were less for HPLC than GC (P ≤ 0.002). VFA estimation differs depending on EM chosen. Further research should identify the source of difference in VFA estimation from each EM.

Rumen acidosis is a common metabolic disorder occurring when organic acid production exceeds clearance capacity, reducing ruminal pH. The occurrence of acidosis has been directly correlated to the ratio of concentrate to forage in the diet. However, rates of substrate fermentation and acid absorption vary at different locations in the reticulo-rumen. The objective of this study was to determine the pH and redox potential (Eh) in different locations of the reticulo-rumen using 16 ruminally cannulated steers (309 ± 43 kg) receiving different supplementation levels of quebracho extract (QT; Schinopsis balansae) within a grower type diet (CP: 13.4%; total digestible nutrients [TDN]: 70.4%; and ME: 2.55 Mcal/kg, dry matter [DM] basis). Animals were randomly assigned to one of four dietary treatments: QT at 0%, 1%, 2%, and 3% of DM (QT0, QT1, QT2, and QT3, respectively), containing about 0%, 0.7%, 1.4%, and 2.1% of condensed tannins (CT), DM basis, respectively. Animals were adapted to the basal diet for 12 d before being introduced to predetermined treatments for 4 weeks (wk), with diets provided twice daily to allow ad libitum intake. Weekly measurements of ruminal fluid pH and Eh were taken 4 h post-feeding using a portable pH meter with two probes (pH and redox) in four locations of the reticulo-rumen (reticulum, cranial sac, dorsal sac, and ventral sac). Data were analyzed using a random coefficients model with the pen as a random effect and wk as repeated measures, with DM intake included as a covariate. There was no interaction among treatments, location, and wk (P≥ 0.882) on reticulo-ruminal pH. Overall, ruminal pH was lower for QT0 and QT1 compared to QT3 (P< 0.001). The pH in the reticulum was greater than those of the ventral and dorsal sacs (6.05 vs. 5.94, 5.89, respectively; P≤ 0.001) but similar to cranial sac (6.00). Reticular pH was positively correlated with the ruminal locations (≥0.78; P< 0.001). The linear equation to estimate ruminal mean pH using reticulum pH had an intercept and slope different from zero (P≤ 0.04), but CT (% DM) was not different from zero (P= 0.15), root mean square error of 0.15, and R2of 0.778: 0.723 (±0.36) + 0.857 (±0.059) × reticulum pH + 0.033 (±0.023) × CT. The Eh was lower for QT0 in week 1 than all other treatments (P< 0.001). We concluded that reticulo-ruminal pH differs among locations in the rumen regardless of QT supplementation level and days on feed, with reticular pH being the highest.

Nutrition models are important in predicting animal growth; however, little research has focused on nursing calf performance submodels. This project’s objective was to determine the accuracy and precision of two equations to compute nursing calves’ feed intake. Data were collected on 394 nursing calves from 4 sets of cows (years 1953, 1959, 1964, 1974) of various breeds in which monthly milk yield and butterfat content, individual calf feed intake, and birth and weaning weights were measured during their first three lactations. Cows were milked at 14-d intervals to determine milk yield. The calf feed intake equations used to predict observed feed intake were Equation 9.1 (TED06; Tedeschi et al., 2006, In “Nutrient Digestion and Utilization in Farm Animals: Modeling Approaches”) and Equation 25 (TED09; Tedeschi and Fox, 2009, J. Anim. Sci. 87:3380). Peak milk was estimated from lactation yield using the NASEM (2016) milk yield equation. The average (SD) peak milk, calf ME intake (MEI) over a 240-day preweaning period, and weaning weight were 10.84 (5.64) kg/d, 1,286 (328.71) Mcal of ME, and 280.93 (46.70) kg, respectively. When compared to the observed calf feed intake, TED06 and TED09 had Pearson correlation coefficients of 0.19 and 0.59, respectively. The MEI mean biases were -355.3 and 190.7 Mcal of for TED06 and TED09, respectively, indicating a 27.6% over prediction and 14.8% under prediction, respectively. The RMSE and R2from linear regression of observed on predicted values of calf MEI were 308.7 Mcal and 0.0365 for TED06, and 253.2 Mcal and 0.3514 for TED09, respectively. In conclusion, neither equation adequately predicted calf feed intake, but the TED09 equation was more accurate and precise than the TED06 equation. Further research is needed to enhance our understanding of factors affecting feed intake of nursing calves to develop better prediction equations.

There is no clear method to measure biological efficiency in grazing beef cows. The objective of this study was to evaluate a nutrition model to estimate biological efficiency in mature cows. Data from dams (n = 160) and their 2ndand 3rdprogeny were collected from 1953 through 1980. Individual feed intake was measured at 28-d intervals for lifetime of dams and during 240-d lactation for progeny. Body weight of progeny were measured at birth and weaning, and dams at parturition and weaning each production cycle. Milk yield of dams was measured at 14-d intervals by hand milking. Metabolizable energy required (MER) and predicted milk energy yield (MEY) of each cow was computed using the CVDS beef cow model for each parity. Biological efficiency was computed as the ratio of cow ME intake (MEI) to calf weaning weight (WW) based on observed (MEI/WW) and predicted (MER/WW) values. Pearson correlation coefficients were computed using corr.test function in R software. Average (SD) cow weight, calf weaning weight, cow MEI, and observed MEY were 507 (81) and 548 (88) kg, 287 (49) and 294 (44) kg, 9406 (2695) and 9721 (2686) Mcal, and 1009 (538) and 1051 (521) Mcal, for progeny 2 and 3, respectively. Cow MEI and MER (0.87 and 0.85), and observed and predicted MEY (0.51 and 0.51) were positively correlated for progeny 2 and 3, respectively. The CVDS model under predicted cow MEI [mean bias = 1685 (1718) and 1658 (1702) Mcal] and MEY [mean bias = 82 (465) and 129 (450) Mcal] for progeny 2 and 3, respectively. Observed and predicted progeny feed intake were not correlated. Observed and predicted biological efficiency were positively correlated (0.63 and 0.61) for progeny 2 and 3, respectively. In conclusion, nutrition models can reasonably predict biological efficiency, but further refinement of the relationship between calf feed intake and milk yield could improve prediction.