Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content.

Grasslands cover approximately 24% of the Earth's surface and are the main feed source for cattle and other ruminants. Sustainable and efficient grazing systems require regular monitoring of the quantity and nutritive value of pastures. This study demonstrates the potential of estimating pasture leaf forage mass (FM), crude protein (CP) and fiber content of tropical pastures using Sentinel-2 satellite images and machine learning algorithms. Field datasets and satellite images were assessed from an experimental area of Marandu palisade grass (Urochloa brizantha sny. Brachiaria brizantha) pastures, with or without nitrogen fertilization, and managed under continuous stocking during the pasture growing season from 2016 to 2020. Models based on support vector regression (SVR) and random forest (RF) machine-learning algorithms were developed using meteorological data, spectral reflectance, and vegetation indices (VI) as input features. In general, SVR slightly outperformed the RF models. The best predictive models to estimate FM were those with VI combined with meteorological data. For CP and fiber content, the best predictions were achieved using a combination of spectral bands and meteorological data, resulting in R2 of 0.66 and 0.57, and RMSPE of 0.03 and 0.04 g/g dry matter. Our results have promising potential to improve precision feeding technologies and decision support tools for efficient grazing management.

The influence of virginiamycin on digestion and ruminal parameters under feedlot conditions.

This experiment aimed to assess the impact of virginiamycin on in vitro gas production dynamics, rumen kinetics, and nutrient digestibility in beef steers fed a grain-based diet. Nine ruminally cannulated British-crossbred steers (596 ±â€…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 troughs. 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 virginiamycin (VM) administration at 0 (VM0), 180 (VM180), or 240 mg/d (VM240). During days 15 and 16 of each period, about 600 mL of rumen fluid and urine samples were collected before (0 h), and at 4, 8, 12, and 16 h after the morning feed (0730 hours), rumen inoculum was used to take pH and redox potential measurements immediately after collection using a portable pH and redox meter, and subsamples were taken for volatile fatty acids (VFA) and NH3-N analyses, and urine samples were composited daily and analyzed for creatinine and purine derivatives (PD) content to estimate microbial crude protein flow. During the 4-h post-morning feed rumen collection, rumen inoculum was utilized to perform in vitro gas production measurements. Fecal samples were collected on day 16 of each period to estimate nutrient digestibility using acid detergent insoluble ash as an internal marker. Animals were considered the experimental unit for the statistical analyses, and periods and squares were included as random variables. The total and rate of gas production were similar among treatments (P ≥ 0.17). The second-pool (i.e., fiber) gas production increased linearly as VM inclusion increased (P = 0.01), with VM240 being greater compared to VM180 and VM0 (7.84, 6.94, and 6.89 mL, respectively). Ruminal pH linearly increased as VM increased, with VM240 being greater than VM0 and VM180 intermediate (5.90, 5.82, and 5.86, respectively; P = 0.03). The VFA concentrations did not differ (P ≥ 0.13), but the acetate-to-propionate ratio was the highest in VM240 (P = 0.005). Branched-chain VFA increased (P ≤ 0.03) while lactate concentrations decreased (P = 0.005) linearly with VM. The ruminal NH3-N concentration was the lowest in the VM0 (P = 0.006). The estimated absorbed PD, purine derivative to creatinine index, and microbial N flow increased linearly with VM (P ≤ 0.07). The provision of VM influenced rumen dynamics in a dose-dependent manner.

ASAS-NANP symposium: mathematical modeling in animal nutrition: agent-based modeling for livestock systems: the mechanics of development and application.

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 (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.

ASAS-NANP symposium: mathematical modeling in animal nutrition-Making sense of big data and machine learning: how open-source code can advance training of animal scientists.

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.

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.

ASAS-NANP symposium: Mathematical Modeling in Animal Nutrition: The power of identifiability analysis for dynamic modeling in animal science:a practitioner approach.

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.

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.

Forages and pastures symposium: an update on in vitro and in situ experimental techniques for approximation of ruminal fiber degradation.

Static quantification measures of chemical components are commonly used to make certain assumptions about forage or feed nutritive value and quality. In order for modern nutrient requirement models to estimate intake and digestibility more accurately, kinetic measures of ruminal fiber degradation are necessary. Compared to in vivo experiments, in vitro (IV) and in situ (IS) experimental techniques are relatively simple and inexpensive methods to determine the extent and rate of ruminal fiber degradation. This paper summarizes limitations of these techniques and statistical analyses of the resulting data, highlights key updates to these techniques in the last 30 yr, and presents opportunities for further improvements to these techniques regarding ruminal fiber degradation. The principle biological component of these techniques, ruminal fluid, is still highly variable because it is influenced by ruminally fistulated animal diet type and timing of feeding, and in the case of the IV technique by collection and transport procedures. Commercialization has contributed to the standardization, mechanization, and automation of the IV true digestibility technique, for example, the well-known DaisyII Incubator. There has been limited commercialization of supplies for the IS technique and several review papers focused on standardization in the last 30 yr; however, the IS experimental technique is not standardized and there remains variation within and among laboratories. Regardless of improved precision resulting from enhancements of these techniques, the accuracy and precision of determining the indigestible fraction are fundamental to modeling digestion kinetics and the use of these estimates in more complex dynamic nutritional modeling. Opportunities for focused research and development are additional commercialization and standardization, methods to improve the precision and accuracy of indigestible fiber fraction, data science applications, and statistical analyses of results, especially for IS data. In situ data is typically fitted to one of a few first-order kinetic models, and parameters are estimated without determining if the selected model has the best fit. Animal experimentation will be fundamental to the future of ruminant nutrition and IV and IS techniques will remain vital to bring together nutritive value with forage quality. It is feasible and important to focus efforts on improving the precision and accuracy of IV and IS results.

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.

Galyean appreciation club review: a holistic perspective of the societal relevance of beef production and its impacts on climate change.

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 CO2 equivalent (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 CH4 emissions. 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 CH4 emission deacceleration of 2.46 Mt CO2e/yr2 might be even more significant than reported. Many opportunities exist to mitigate CH4 emissions 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.

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.

Forages and Pastures Symposium: revisiting mechanisms, methods, and models for altering forage cell wall utilization for ruminants.

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

The 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 Impact of Fetal Programming in Ewe Nutrition with Chromium Propionate or Calcium Salts of Palm Oil on the Meat Quality and Bone of the Progeny.

This study aimed to evaluate the inclusion of chromium propionate or calcium salts of palm oil in ewes' diet during the final third of gestation and lactation on progeny performance, carcass characteristics, non-carcass components, and bone density. Forty-three ewe, Santa Inês and Dorper breed, three ± one-year-old, and body weight 57 ± 10 kg were used. The experimental design was in casual blocks in three treatments, CTL treatment (n = 15) with starch from corn; CR (n = 15) diet CTL plus chromium propionate; PF (n = 13) diet CTL plus calcium salts of palm oil. After weaning, 23 male lambs from these ewes were confined in individual stalls, with the same diet for 60 days, slaughtered. The data were analyzed using the SAS program, PROC GLM, and compared the means using Tukey's test at 5% probability. The maternal diet did not alter the dry matter intake, feeding efficiency, and average daily weight gain. Therefore, weights (weaning and slaughter) and carcass yield were higher for CR and PF groups than for CTL (P < 0.05). The treatment did not influence the loin eye area and fat thickness (P > 0.05). The spleen and the respiratory tract were smaller for PF and larger for CTL (P < 0.05). Leg weight was higher for CR. The perimeter and depth of the shank for the CR and PF lambs were higher, indicating an effect of maternal nutrition in this commercial cut. The CR group had a smaller epiphysis measurement and femur length than the CTL group. We concluded that the fetal programming effect in ewes fed with Cr propionate and Ca salts of palm oil benefited the progeny by increasing their body weight, better carcass yield, and a higher proportion of prime cuts.

Effect of live yeast supplementation on energy partitioning and ruminal fermentation characteristics of steers fed a grower-type diet in heat-stress conditions.

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 × 1012 CFU/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.

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.

Technical note: Evaluation of sampling methods for methane concentration from in vitro fermentation.

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 T differed 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 T should 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.

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 CH4 emission patterns from the rumen but tend to overestimate quantities. Additionally, in vivo estimation methods present CH4 on 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 CH4 estimation when deviating from the published protocol. To estimate CH4 from 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 CH4 on a dry-gas basis. This study found that the treatment method of the IVGP bottle had an impact on CH4 estimation, and the developed equations should be utilized to produce more comparable estimates.

Quantification of methane emitted by ruminants: a review of methods.

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH4), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH4 emissions. This review documents existing methods and methodologies to measure and estimate CH4 emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH4 have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH4 for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH4 from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH4 emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH4 emissions from manure depends on the type of storage, animal housing, CH4 concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH4 emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH4 in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice.

There is a need to accurately and precisely quantify greenhouse gas (GHG) emissions, specifically methane (CH4), to ensure correct reporting of GHG inventories and, perhaps more importantly, determine how to best mitigate CH4 emissions. The objective of this study was to review existing methods and methodologies to quantify and estimate CH4 emissions from ruminants. Historically, most techniques were developed for specific purposes that may limit their widespread use on commercial farms and for inventory purposes and typically required frequent calibration and equipment maintenance. Whole animal and head respiration chambers, spot sampling techniques, and tracer gas methods can be used to measure enteric CH4 from individual animals, but each technique has its own inherent limitations. The measurement of CH4 emissions from manure depends on the type of storage, animal housing, CH4 concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the most complex variable creating many uncertainties. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH4 emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources. Top-down approaches estimate the amount of CH4 in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point.

Nutrient content and in vitro degradation study of some unconventional feed resources of Bangladesh.

This study aimed to evaluate six unconventional feed resources of Bangladesh, including water hyacinth (Eichhornia crassipes), banana leaves (Musa paradisiaca), roadside grass (Stenotaphrum secundatum), bamboo leaves (Bambusa vulgaris Scrad), Seaweed (Hypnea sp.) and sugarcane bagasse (Saccharum griffithii). Evaluations were based on dry matter (DM), crude protein (CP), crude fiber (CF), neutral detergent fiber (NDF), acid detergent fiber (ADF), ether extract (EE), ash content, DM and OM digestibilities and fractional rate of degradation. Two conventional feeds, i.e., rice bran and german grass, were used as the positive control. Samples (400 mg) were incubated with rumen liquor in an in vitro fermentation chamber at 0, 6, 12, 24, 48, 72, and 96 h for the degradation kinetic studies. The CP contents of 10.13, 10.63, 10.21, and 8.49 % were found in seaweed, banana leaf, water hyacinth, and bamboo leaf, respectively. The NDF values ranged between 16.5 and 75.6% and ADF varied from 9.7 to 58.8% in this study. The highest value of NDF (75.6%) and ADF (58.8%) were found in sugar cane bagasse and the lowest value of NDF (16.5%) and ADF (9.7%) were as observed in seaweed. However, higher DM degradation (33.5-42.8%) was found in seaweed during the incubation periods of 24-96 h. A significant (P < 0.05) increased of OM degradation (44.9%) compared to other feed resources was also observed in seaweed at 96 h of in vitro incubation. Water hyacinth, banana leaves, german grass, and sugarcane bagasse had greater DM digestibility (32.9-36.3%) compared to roadside grass, bamboo leaves, and rice bran (24.8-29.1%). The higher total OM digestibility of seaweed found (>44.9%) can be associated with the presence of large quantities of fraction b (>39.2 %), resulting in moderate amounts of undegradable fraction (U) (57.2 %). This study provides a comparative estimate of ruminal DM and OM degradation characteristics for seaweed and some other unconventional feed resources, which might be helpful for their inclusion in the diet according to the ruminally undegraded to degraded DM and OM intake ratio.

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production.

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.

The energy requirement for maintenance of Nellore crossbreds in tropical conditions during the finishing period.

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/kg0.75 EBW and a ME required for maintenance requirement had a common value of 137.53 kcal/d/kg0.75 EBW. 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/kg0.75 of 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/kg0.75 EBW, respectively, showing a decrease in NEm of 5.76% for NL in comparison with AN steers.

ASAS-NANP symposium: mathematical modeling in animal nutrition: the progression of data analytics and artificial intelligence in support of sustainable development in animal science.

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.

ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences.

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.

Effects of supplementation rate of an extruded dried distillers' grains cube fed to growing heifers on voluntary intake and digestibility of bermudagrass hay.

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 Kp and 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 mathematical nutrition model adequately predicts beef and dairy cow intake and biological efficiency.

The beef cow-calf sector accounts for 70% of feed consumed and greenhouse gases emitted for the beef industry, but there is no straightforward method to measure biological efficiency in grazing conditions. The objective of this study was to evaluate a mathematical nutrition model to estimate the feed intake and biological efficiency of mature beef cows. Data from dams (N = 160) and their second and third progeny (312 pairs) were collected from 1953 through 1980. Individual feed intake was measured at 28-d intervals year-round for dams and during 240-d lactation for progeny. Body weights of progeny were measured at 28-d intervals from birth to weaning, and of dams at parturition and weaning each production cycle. The milk yield of dams was measured at 14-d intervals. Dam metabolizable energy intake (DMEI) and milk energy yield (MEL) of each cow were predicted using the Cattle Value Discovery System beef cow (CVDSbc) model for each parity. Biological efficiency (Mcal/kg) was computed as the ratio of observed or predicted DMEI to observed calf weaning weight (PWW). Pearson correlation coefficients were computed using corr.test function and model evaluation was performed using the epiR function in R software. Average (SD) dam weight, PWW, DMEI, and observed MEL were 527 (86) kg, 291 (47) kg, 9584 (2701) Mcal/production cycle, and 1029 (529) Mcal, respectively. Observed and predicted DMEI (r = 0.93 and 0.91), and observed and predicted MEL (r = 0.58 and 0.59) were positively correlated for progeny 2 and 3, respectively. The CVDSbc model under-predicted DMEI (mean bias [MB] = 1,120 ± 76 Mcal, 11.7% of observed value) and MEL (MB = 30 ± 25 Mcal, 2.9% of observed value). Observed and predicted progeny feed intake were not correlated (r = 0.01, P-value = 0.79). Observed and predicted biological efficiency were positively correlated (r = 0.80 and 0.80, P-value ≤ 0.05) for parity 2 and 3, respectively, and the CVDSbc model under-predicted biological efficiency by 11% (MB = 3.59 ± 0.25 Mcal/kg). The CVDSbc provides reasonable predictions of feed intake and biological efficiency of mature beef cows, but further refinement of the relationship between calf feed intake and milk yield is recommended to improve predictions. Mathematical nutrition models can assist in the discovery of the biological efficiency of mature beef cows.