Galyean Appreciation Club review: knowledge gaps in the nutrition of grazing beef cattle

Abstract

Feeding the increasing world population is a grand challenge for animal agriculture. Ruminants in general, and beef cattle specifically, fill a unique role in addressing this challenge as they convert large quantities of forage-based feed resources, which are inedible by humans, into high-quality, nutrient food products for human consumption. In North American beef cattle production systems, grazed forage represents a large portion of whole-herd dietary intake. In fact, within the United States, approximately 57% of the feed energy required to produce 1 kg of beef carcass weight is derived from grazing systems, and another 19% of feed energy is provided by harvested forage sources. Nutritional management of grazing beef herds is a critical component of efficient and effective production systems. Understanding nutrient supply and demand fosters more precise approaches to strategic supplementation practices. Limited source data from grazing beef cattle constrain modeling approaches of nutrient supply and demand and consequently limit our ability to nutritionally manage grazing beef cattle. Reviewing the available data exposes numerous knowledge gaps in the nutrition of grazing beef cattle. Needs include more robust source data collection and data collection technologies in the specific areas of intake, macro and micronutrient supply and demand, and nutrient demands associated with environmental extremes and physical activity. Additionally, more accurate and precise mathematical models are needed in the area of intake, energy, protein, and micronutrient supply and demand. In addition, coupling investigations of grazing livestock nutrition with behavior, climate, land use, socioeconomic, and sustainability needs will foster a more holistic understanding of the importance of accurately assessing and managing grazing cattle nutrition. Extensive opportunities exist to improve grazing livestock nutrition and consequently enhance sustainable capture of grazed forage nutrients and improved beef cattle production from grazing lands; however, limited funding streams to support these research efforts limit progress.

To more effectively understand and manage grazing beef cattle nutrition, more robust source data are needed, and more precise and accurate mathematical models should be developed. An increased understanding of grazing livestock nutrition will enhance the sustainable management of beef cattle nutrition and, thereby, increase production efficiencies from grazing lands.

Introduction

The United Nations (2024) median world population projections indicate that we will exceed 10 billion people by 2065, and the demand for human food from livestock enterprises is expected to increase accordingly (Tedeschi and Beauchemin, 2023). Animal food products represent nutrient-dense foods that provide not only protein but other essential micronutrients that can be difficult to source from plant-based products (Leroy et al., 2022; Vieux et al., 2022). Approximately 40% of the global value of agricultural outputs arises from livestock production, providing livelihood support to a large portion of the human population (FAO, 2009). Clearly, meeting the growing world’s human food demands for animal-sourced foods in sustainable ways is a grand challenge for animal agriculture. Ruminant animals are unique among food animalsbecause of their ability to turn feed resources that are inedible by humans (forages, crop residues) into nutrient-dense foods for human consumption (Lardy and Caton, 2012; Reynolds et al., 2015).

In North American beef cattle production systems, grazed forage represents a large portion of the dietary intake, which is even more pronounced in cow-calf and stocker operations (NASEM, 2016). Understanding the nutrient demands of grazing beef cattle presents unique challenges for researchers and producers alike, particularly in terms of the complexities of nutrient intake and digestion in grazing systems compared to confined feeding scenarios (NASEM, 2016). Effectively addressing these challenges is a key component of efficient and sustainable beef cattle production systems.

Traditional approaches to managing grazing cattle nutrition rely on research-based data, empirical knowledge, and generalized guidelines, which often fail to account for the intricate interactions between animals, their diets, and the variable environments they inhabit. The need for more robust source data in critical areas of grazing nutrition of beef cattle is readily apparent from a detailed literature review and will be summarized herein. Traditional approaches to managing grazing beef cattle nutrition lead to significant knowledge gaps in our understanding. Mathematical models based on data can yield additional insights into filling these knowledge gaps by providing a framework to simulate complex biological processes and integrate diverse data streams (Tedeschi and Fox, 2020). Nutritional models can help researchers explore how nutrient requirements change in response to variations in grazing conditions, forage quality, and animal characteristics.

The focus of this review is to briefly summarize the literature and identify knowledge gaps in the grazing nutrition of beef cattle. The approach focuses on critical aspects of grazing livestock nutrition, including intake, nutrient supply and demand, and predictive modeling needs. Our anticipated outcomes are that information contained in this review will guide scientists and livestock specialists and inform future efforts directed towards assessing nutrient supply and demand for sustainable beef cattle production.

Water and Feed Consumption

Water intake

Water supply is critical for efficient and sustainable grazing beef cattle production systems. Primary sources of water intake for grazing livestock include drinking water, water associated with dietary ingredients, and, in the winter, consumed snow and ice (Petersen et al., 2015; NASEM, 2016). In addition to consumed water, small, but likely not significant, amounts of metabolic water contribute to the overall water pool (NRC, 1981). Water requirements are affected by pregnancy, lactation, rate and composition of gain, feed intake, distance traveled (work), physiological stress, and environmental factors, including water availability and animal access, ambient temperature, exposure to wind and sun, and length and intensity of climate extremes (NASEM, 2016). The literature has limited data regarding grazing beef cattle water consumption and supply and demand, which is partly because of the varying sources (ponds, wells, streams, forage-associated water, and snow) contributing to water intake. Nonetheless, water is critical to efficient and sustainable grazing-based beef cattle production systems, and limiting water intake will also limit Dry matter intake (DMI) (Bond et al., 1976; NASEM, 2016). Consequently, water intake is related to nutrient intake and thereby nutrient use efficiency. Available data indicate that water access, as measured in confinement by linear trough space availability (Mader et al., 1997) impacts overall water intake by cattle and is an important management consideration during heat stress. Water sources can also affect production outcomes, as steers grazing pastures have performed better with water provided via a trough compared with pond water sources (Bica et al., 2021); however, water quality may have differed between pond and trough sources. Additional research investigating water supply and demands of grazing livestock cattle should lead to improved management practices and, consequently, enhanced livestock production efficiencies from grazing lands.

Water quality is predominantly driven by odor and taste (organoleptic properties), total dissolved solids and other physiochemical properties, presence of toxic substances, excessive mineral content, and bacterial contamination (NRC, 2001; Petersen et al., 2015; NASEM, 2016). Petersen et al. (2015) concluded, from a 5-yr study in the Northern Great Plains, that water quality varied widely across sources and time, and that water mineral composition and projected intakes occasionally exceeded recommended tolerances. Limited data in the area of water quality represent a research opportunity that holds promise for improving the nutrition of grazing beef cattle. In addition to the need for research generating source data regarding water quantity and quality needs for grazing beef cattle, the NASEM (2016) stated: “Updated equations for predicting water intake by beef cattle in various physiological stages and production settings are needed.”

Predicting water intake

As indicated previously, water is a critical nutrient often overlooked in the nutrition and nutritional models for grazing cattle. Most mathematical models and predictive equations are based on environmental temperature, feed intake, animal BW and gain, and water salinity indices from data collected in the 1950s (NASEM, 2016). Ahlberg et al. (2018) reviewed current approaches to modeling water intake in beef cattle, emphasizing the need for models that account for the variability in water intake associated with environmental factors such as temperature, humidity, solar radiation, and wind speed. Their study, however, primarily focused on feedlot settings, suggesting that further research is needed to adapt these models to grazing systems, where water availability and quality can vary significantly. Gregorini et al. (2018) expanded on this by developing the MINDY model, which simulates the diurnal drinking and urination patterns of grazing cattle. Their findings highlight how variations in forage nutrient composition, particularly crude protein concentrations, influence water intake and urinary nitrogen excretion, which are critical factors in grazing systems.

As noted previously, the effect of water quality on nutrient utilization is another area requiring further investigation (NASEM, 2016). Olkowski (2009) discussed how water contaminants, such as heavy metals, nitrates, and other toxic substances, can adversely affect livestock health and performance. This includes indirect impacts on feed and water intake, which can subsequently influence nutrient absorption and overall metabolic function. These interactions underscore the need for more source data to foster the construction of modern decision-support tools that incorporate water quality measurements and their broader implications on feed intake, nutrient absorption, and metabolic functions.

Difficulties associated with collecting accurate data on water intake for individuals or even groups of cattle in grazing systems, where cattle frequently drink from natural sources like rivers, tanks, and ponds, present a significant challenge. Menendez and Tedeschi (2020) and Menendez et al. (2023) developed a mathematical model to address the limitations of current water footprint assessment methods, which are mainly static and fail to account for the dynamic variability in water availability and quality. These authors emphasized the need for modern decision-support tools that incorporate both short- and long-term environmental fluctuations, as well as cattle nutrient and growth dynamics. In cases where freshwater resources are becoming increasingly strained, accurately accounting for freshwater intake and requirements is essential for assessing the feasibility of livestock production in specific geographic regions. This level of precision is necessary to ensure that livestock systems remain viable, particularly in areas prone to drought or water scarcity.

Dry matter intake

DMI and efficiency (both digestive and metabolic) are primary drivers of beef cattle production (Grovum, 1987; NASEM, 2016). The overarching centrality of DMI to individual and whole-herd productive and nutrient use efficiencies can hardly be overstated. Nutritional prediction models depend on DMI as a foundational aspect of construction; consequently, the reliability of estimates of DMI greatly impacts all other aspects of nutritional and production management. Intake of grazed forages is essential for successful and sustainable beef cattle production programs, and research in this area presents difficulty challenges. The 8^th Revised edition of the Nutrients Requirements of Beef Cattle (NASEM, 2016) stated, “Additional effort should be devoted to predicting feed intake by nonpregnant, pregnant, and lactating beef cows.” The NASEM (2016) committee further stated that “The reliability of existing equations to predict feed intake by grazing ruminants is questionable, and further research is needed to develop equations that can be applied to beef cow-calf and stocker cattle operations based on grazed forages.”

Nonetheless, predicting feed intake in grazing ruminants requires an adequate database from which to build robust equations. Concerns arise because the existing equations were developed primarily from data generated with cattle managed in confinement where feed intake was measured directly (NRC, 1984, 1987, 1996; Anele et al., 2014; NASEM, 2016). Galyean and Gunter (2016) reviewed challenges associated with predicting feed intake in extensive grazing systems. These authors suggested that incorporation of production data (e.g., ADG, BW, and milk yield) increased the accuracy of regression models to predict feed intake. They also suggested that until adequate approaches were developed to more accurately predict intake of grazed forages, progress to improve predictions equations would likely be slow. This sentiment is also reflected in the work of Tedeschi et al. (2019c) and Smith et al. (2021). The beef cow consumes over 70% of the total energy required to produce beef (Holder et al., 2022) , and therefore, effective management strategies need to adequately consider dietary intake. Improvements are being made in intake predictions for confined beef cows (Gross et al., 2024a), but most beef cows spend most of their time on grazing lands. Current technologies and approaches to estimating grazed forage intake, however, are problematic and result in highly variable estimates (Galyean and Gunter, 2016; Tedeschi et al., 2019c; Smith et al., 2021). In addition, real-time intake and production data are rarely available in applied production settings, which further exacerbates the challenges of building precise and accurate intake prediction equations in grazing beef cattle. Existing data clearly point towards large knowledge gaps in estimating intake in grazing beef cattle, including: 1) the development of technologies and techniques that significantly improve our ability to generate intake measurements; 2) the generation of accurate and precise source data sets with a wide range of grazing beef cattle; and 3) the improvement of predictive models to use in management decisions and research arenas that will accurately reflect observable conditions and responses, including forage quality and intake regulation issues.

Predicting DMI

Limitations in developing accurate intake models for grazing cattle stem from the absence of reliable and accurate methods to measure feed intake in grazing animals. Coleman (2005) emphasized the difficulty of determining both intake and diet quality in grazing systems, noting that current methods are laborious, expensive, and often lack precision and accuracy. Charmley et al. (2023) also discussed that, despite decades of research, accurately predicting intake remains elusive, particularly in extensive grazing systems. These challenges are compounded by the diverse environmental and behavioral factors affecting intake, further complicating the development of reliable intake models (Hyer et al., 1991a). Most mathematical models and empirical equations developed to predict feed intake have focused on confined animals, with relatively few attempts made to accurately estimate intake by grazing animals (Tedeschi and Fox, 2020). Given the inherent complexities in predicting forage intake in grazing cattle, particularly the variability introduced by selective grazing, pasture structure, and environmental factors, empirical models (i.e., equations) currently in use often fail to capture the dynamic, spatial, and temporal scales at which cattle forage, resulting in prediction errors.

Sollenberger and Vanzant (2011) highlighted the importance of both forage nutritive value and quantity in determining individual animal performance. They demonstrated that while forage quantity drives the proportion of potential average daily gain achieved, nutritive value sets the upper limit for performance. This interaction between forage quality and quantity is critical for understanding intake, particularly in grazing systems where both can vary dramatically. Abiotic factors (e.g., topography, water availability) and biotic factors (e.g., forage quality and quantity) have important roles in determining the grazing patterns of large herbivores (Bailey et al., 1996; Bailey and Provenza, 2008). These authors discussed how animals match their grazing time to the availability of high-quality forage and proposed that cognitive mechanisms, such as spatial memory, influence foraging behavior, especially in environments with variable forage availability.

Minson and McDonald (1987) offered a practical approach by predicting forage intake based on animal production data such as liveweight and growth rate. Their model assumes that intake is driven primarily by the animals’ energy requirements and that the forage quality can be estimated indirectly. While their method has been validated with both temperate and tropical forages, it is most applicable in controlled or simpler grazing systems where energy intake closely correlates with growth. This approach is similar to calculating DMI requirements necessary to support production used in modern computer models (Tedeschi et al., 2004; NASEM, 2016; Tedeschi and Fox, 2020). Minson and McDonald (1987), however, acknowledged that more complex or diverse grazing environments might require additional considerations beyond energy-driven intake models.

Gregorini et al. (2017) emphasized the need for grazing models that account for the complex interplay between animal behavior, environmental conditions, and management practices. These authors highlighted that grazing decisions are influenced by behavioral patterns such as grazing time, mastication, and meal structure, as well as environmental factors like pasture availability and sward structure. They proposed a more holistic approach to modeling intake, suggesting that integrating these behavioral and environmental factors could improve intake predictions in diverse and complex grazing systems.

Hyer et al. (1991)  developed a mechanistic model to predict forage intake that incorporated ruminal fill and nutrient passage rates; however, the model was found to be insufficient for predicting forage intake when protein supplementation was involved, highlighting a critical area where intake models fall short. Similarly, Coleman (2005) noted that predicting potential intake based solely on forage characteristics can be problematic, as factors like forage availability and animal physiology also influence intake.

Baker et al. (1992) developed the FORAGE model, which simulates forage intake by considering mechanistic components of grazing behavior such as bite-size, biting rate, and grazing time. The model demonstrated that when forage availability is limited, intake is constrained by these factors, underscoring the importance of integrating sward structure and forage quality into intake models. This model also accounts for diet selection based on plant group preferences, illustrating the complexity of predicting intake in dynamic grazing environments.

Modern decision-support tools might address the limitations in intake prediction by developing more sophisticated models that account for various influencing factors, including microbial growth dynamics, nutrient composition, and environmental interactions (Hyer et al., 1991a). Incorporating behavioral and environmental measurements, such as grazing time, sward structure, and climatic conditions, into intake models could improve their accuracy in diverse grazing scenarios (Charmley et al., 2023). In addition, leveraging digital technologies to enhance data collection, particularly for monitoring bite mass, grazing behavior, and other factors influencing scale intake in extensive grazing systems could lead to more accurate intake predictions (Charmley et al., 2023).

Energy

Energy supply and demand

Approximately 57% of the feed energy required to produce 1 kg of carcass weight is derived from grazing systems in the U.S. beef cattle industry. Another 19% of feed energy is provided by harvested forage sources. Thus, 76 to 81% of all feed energy required to produce beef is generated through forage production, with feed grains and byproduct concentrate feeds supplying 19 to 24% (NASEM, 2016; Rotz et al., 2019). Even though forages represent a large portion of energy expenditure in beef production, progress in filling knowledge gaps remains slow which is primarily the result of the continued challenges associated with measuring feed intake, diet digestibility, and animal performance in grazing systems and in animals fed strict forage diets in confinement (Coleman 2005; Charmley et al., 2023).

In recent decades, the beef industry’s advances in productivity have come from a combination of new technology adoption (primarily in the postweaning phases) and aggressive selection for output traits such as weaning weight growth, yearling weight growth, milk production, and carcass weight (Capper, 2011; Kuehn and Thallman, 2016). The migration from selling fed cattle on a live BW basis to a carcass basis also has resulted in a steady increase in days on feed over time. Feeding cattle with high genetic capacity for growth for a longer period generally increases hot carcass weight, marbling, and profitability.

Although the above trends have led to increased beef production per cow, an unintended consequence has been a concomitant increase in mature cow BW and associated nutrient requirements. Nephawe et al. (2004) reported a genetic correlation of 0.81 between finished steer carcass weight and mature cow BW. According to the genetic trend in the popular Angus breed, mature cow BW has been increasing steadily for the last 50 yr. The change in mature cow BW in the commercial cow/calf sector is reflected in the change in average annual federally inspected cow carcass weights. From 1978 to 2023, average cow carcass weights increased by 75 kg (Economic Research Service, 2024), which is equivalent to an increase of 154 kg in live BW (Apple, 1999). This change in mature cow BW is expected to result in an increase annual forage requirement by 1,205 kg (Gross et al., 2024b) or 27%. Interestingly, several studies indicate that increased mature cow BW leads to decreased ranch profitability when calves are marketed at weaning (Scasta et al., 2015; Beck et al., 2016; Bir et al., 2018). These effects are largely a result of increased feed intake and, thereby, reduced carrying capacity on a given land base, resulting in fewer calves to market at a lower price per unit of BW. In all these studies, mature cow BW is used as a proxy for feed intake. It is largely unknown if increased mature BW and genetic capacity for growth are associated with changes in forage intake scaled to BW, maintenance energy requirements (scaled to BW), efficiency of metabolizable energy (ME) conversion to net energy, or reproductive efficiency. Possible unintended consequences of increased cow BW and associated greater intake for the cow/calf segment include more acres required per cow/calf unit, increased methane emissions per cow, more days throughout the production cycle in negative energy balance, or more purchased/harvested feeds required to avoid reproductive failure.

Maintenance energy accounts for about 73% of the average annual energy requirement, with 10% partitioned to pregnancy and 17% to milk production (Briggs et al., 2022). Numerous reports suggest a positive relationship between maintenance energy requirement and genetic capacity for milk yield, mature size, and growth (Ferrell and Jenkins, 1984, 1987; Solis et al., 1988; Laurenz et al., 1991). These studies, however, were structured to determine differences in maintenance requirements among breeds and breed crosses rather than within a breed. In contrast, Briggs et al. (2022) reported a negative relationship between milk yield and maintenance energy requirement in mature Angus cows. These authors suggested that cows with lower maintenance had greater net energy available to partition between milk energy production and body tissue maintenance or gain. Ferrell and Jenkins (1987) noted that variation in maintenance requirement is greater than variation in requirements for growth, gestation, or lactation, suggesting there is a substantial opportunity to select animals with lower maintenance energy requirements. In fact, Freetly et al. (2023) reported a moderate heritability (h² = 0.31) for ME for maintenance in mature beef cows of various breeds. With continued aggressive selection for growth and carcass weight, a better understanding of how these traits affect beef cow forage utilization efficiency and robustness is a critical knowledge gap.

residual feed intake (RFI) is 1 method commonly used to rank animals within a contemporary group for feed efficiency. Although studies are limited, lower maintenance energy requirements for efficient (low RFI) pigs (Barea et al., 2010) and growing cattle (Castro-Bulle et al., 2007; Lawrence et al., 2012; Fitzsimons et al., 2013; Menezes et al., 2020) have been reported. Andreini et al. (2020) measured 15% lower maintenance energy requirement in low-RFI compared with high-RFI steers and found that feed restriction decreases maintenance requirement more in low-RFI steers (32%) than in high-RFI steers (18%). Cows with lower net energy for maintenance requirements could retain more net energy for maternal tissue energy storage (body condition) or increased milk yield without consuming more feed. Several studies report greater partial efficiency of ME use for growth in low-RFI cattle (Nkrumah et al., 2006; Cantalapiedra-Hijar et al., 2018); however, reported effects of selection for RFI on female and male reproductive performance are inconsistent. Numerous studies found that selection for improved RFI results in leaner body composition with antagonistic effects on fertility (Shaffer et al., 2011; Fontoura et al., 2016; Rubens et al., 2018), but other experiments indicate no effect of selection for RFI on measures of reproductive efficiency (Blair et al., 2013; Rossi et al., 2022; Hall et al., 2024). Arguably, reproductive efficiency, forage utilization efficiency, and the interaction between these traits remain a major knowledge gap if the industry is to make progress in beef production efficiency and carbon footprint.

Caton and Olson (2016) reviewed the Beef Cattle Nutrient Requirement Model’s (BCNRM; NASEM, 2016) approach to determining energy requirements and the effects of grazing activity, season, temperature, and behavior on maintenance energy requirements. In the BCNRM, the heat of activity associated with obtaining feed is pooled with maintenance energy requirements (NASEM, 2016). Nonetheless, numerous researchers (Havstad and Malechek, 1982; Di Marco and Aello, 2001; Brosh et al., 2006; Kaufmann et al., 2011) have suggested that energy expenditure during grazing can be up to 50% greater than the predictions of existing models, which are based primarily on data from confined animals. In addition recent data with pregnant cows indicate the providing shade during hot enviroments (Silva et al., 2023) and the presence of mud during rainy seasons (Nickles et al., 2022) alters cow performance, likely through altered energetics, but not measured calf outcomes. Clearly, a better understanding of energy use associated with grazing activity is needed, considering the widely variable terrain, carrying capacity, and environmental conditions pervasive in beef cattle production systems in the U.S.

Predicting energy expenditure and supply

One of the most significant challenges in modeling grazing cattle nutrition is accurately predicting energy expenditure and supply. Tedeschi (2023) proposed an approach to estimate the energy expenditure of grazing animals by integrating concepts from existing mathematical models developed for confined cattle. The critical innovation is incorporating additional factors specific to grazing, such as physical activity, grazing, and ruminating, which are not typically accounted for in models for confined animals. The model builds on the British feeding system (Agricultural and Food Research Council, 1993) by adding the energy cost of physical activity and the heat increment of eating and ruminating into the total energy requirement. This method requires iterative optimization because it depends on ME intake. Another method revises the estimation of the efficiency of using ME for growth, factoring in the protein proportion in retained energy, the animal’s degree of maturity, and average daily gain. This adjustment, drawn from the Australian feeding system (CSIRO, 2007), leads to more accurate predictions of energy requirements. Instead of merging incompatible sub-models, Tedeschi (2023) suggested that this holistic approach improves predictions of energy expenditure for grazing animals and offers more precise insights into their energy needs for growth, physical activity, and eating. Further validation of this approach is required before widespread adoption.

The diversity and variability of forage resources in grazing systems further complicate energy supply modeling. Predicting energy availability from various forages is challenging because of multiple factors, including plant maturity, grazing selectivity, and environmental conditions (Van Soest, 1994; Tedeschi and Fox, 2020), underscoring the need for more sophisticated models that account for these complexities. While current precision of current prediction models range are highly variable, the use of remote sensing data, advanced statistical methods, and modern artificial intelligence techniques show potential for improving predictions of herbage mass and quality (Chen et al., 2021; Fernandes et al., 2024; Ogungbuyi et al., 2024). Significant discrepancies between predicted and observed energy balances in grazing cattle across different seasons, particularly in tropical and subtropical regions, are likely caused by seasonal variability in forage quality and availability. Thus, the seasonal fluctuations in energy requirements and supply present an additional modeling challenge. Given that tropical forages tend to mature quickly with temperature and rainfall, resulting in decreased digestibility and energy content (Van Soest, 1994), existing mathematical models, which are often calibrated for temperate climates, can overestimate forage quality in the tropics, underestimating the energy expenditure required for cattle to graze on lower-quality tropical forages, especially during the dry season (Charmley et al., 2023).

Although the calculation of retained energy and retained protein continues to require refinement, additional challenges are especially pertinent to grazing animals becuase of existing inconsistencies in the prediction of retained energy and retained protein (Tedeschi, 2019b). These inconsistencies highlight issues such as the interdependency of retained energy and retained protein, which affects prediction accuracy when sub-models are changed independently. In addition, revisions of existing mathematical models should incorporate more dynamic representations of seasonal effects on both animal physiology and forage characteristics. This can be achieved by developing more accurate methods for estimating energy expenditure during grazing, potentially incorporating wearable sensor technologies (Greenwood et al., 2016), integrating advanced forage quality prediction models that account for the spatial and temporal variability in grazing systems (Gregorini et al., 2017), and creating dynamic models capable of adjusting energy requirement and supply predictions based on seasonal changes and localized environmental conditions (Freer et al., 1997). Furthermore, (Tedeschi, 2019b) suggested that improving the accuracy of body composition determination, particularly the partitioning of energy between fat and protein deposition, is essential to resolve the offsetting errors that destabilize model predictability, thereby enhancing the reliability of future grazing models. Incorporating these considerations will result in more robust models capable of addressing the complexities inherent in grazing systems.

Protein

Protein supply and demand

Protein nutrition of beef cattle has been studied for decades (NRC 1984, 1996, 2000; NASEM, 2016) and centers around the supply, demand, and metabolism of protein and other nitrogenous compounds. Inadequate protein supply compromises the production and performance efficiencies of both the dam and the offspring. Major knowledge gaps in protein nutrition of grazing beef cattle center around both supply and demand issues. Protein supply can be challenging to determine in grazing situations becuase of seasonal changes in forage quantity and quality. As discussed previously in the intake section, DMI is a major driver of beef cattle performance, is difficult to determine in grazing settings, and is compromised by inadequate protein intake. Likewise, having a reliable estimate of protein intake, including crude protein, ruminal degradable and undegradable protein, microbial protein supply, and metabolizable protein (MP), is difficult in grazing settings.

Research needs in the area of protein nutrition of beef cattle identified by the Nutrient Requirements of Beef Cattle subcommittee (NASEM, 2016) include 1) equations that provide more precise predictions of microbial protein synthesis in the rumen; 2) the efficiency with which ruminally degradable protein is converted to microbial crude protein; 3) equations to accurately predict recycled nitrogen (N) across a wide range of diets; and 4) an improved undestandng of the efficiency of conversion of MP to net protein. In addition, little is known about the efficiency with which individual amino acids are used for protein deposition by beef cattle. These articulated research needs (NASEM, 2016) were inclusive of grazing and pen fed cattle and represented broad knowledge gaps. Effective predictive equations are predicated on the presence of robust source data, which are limited in many situations. For grazing beef cattle, data are even more sparse.

Predicting microbial protein syntheses (MPS) has been addressed in the ensuing timeframe (Tedeschi et al., 2017; Galyean and Tedeschi, 2023, 2024). Generally, data used by the aforementioned scientists included both pen- and grazing-based source data, so the application should be broadly applied, while not being specific to grazing beef cattle. Microbial protein synthesis accounts for from 40% to nearly 100% of the intestinal supply of MP, depending on circumstances. Estimates of MPS are essential for predicting MP supply in beef cattle. The approach used to estimate MPS by NASEM (2016) represented improvements over previous methods (NRC 1996, 2000); however, large variability with current approaches (Tedeschi et al., 2017), which compromises the effectiveness of estimating MP supply, particularly in the grazing beef cattle, and consequently many nutritionists and producers default to working on a crude protein basis. High variability in MPS could be decreased through better source data for model construction and improved mechanistic model approaches. Improvements in the prediction of MPS have recently been published (Galyean and Tedeschi, 2023, 2024); however, prediction models are only as good as the source data to construct and validate them. Knowledge gaps in this area represent real opportunities to improve protein use efficiency, nitrogen capture, and overall production efficiency by grazing ruminants.

Modeling protein metabolism

Modeling protein metabolism in grazing cattle presents unique challenges because of the variability in forage protein content and the complex interactions between diet and ruminal microbial growth. Current models often struggle to accurately predict MPS (Tedeschi et al., 2017), especially in grazing conditions where microbial crude protein production depends on fermentable ME, which fluctuates seasonally (CSIRO, 1990, 2007). Hristov et al. (2013a) underscored the need for improved models that account for the effects of grazing behavior and forage characteristics on ruminal microbial populations and their protein synthesis efficiency. In addition, the efficiency of MP utilization remains an area where current models fall short (Tedeschi et al., 2017; Tedeschi and Fox, 2020). Hristov et al. (2013b) reviewed various animal management strategies for mitigating non-CO₂ greenhouse gas emissions, including approaches to enhance feed efficiency. They emphasized the complex interactions between dietary factors, animal genetics, and environmental conditions that influence protein utilization, highlighting the need for more comprehensive models.

Accurately addressing protein quality in diverse forages is essential for improving nutrition modeling in grazing systems. A more detailed approach that goes beyond crude protein content, incorporating the degradability of protein fractions and their amino acid profiles, can more accurately represent the true protein value available to the animal. This is particularly important given the variability in forage quality and protein degradation rates, which directly affect MP supply. By incorporating these factors, nutrition models can better predict protein availability in grazing systems, leading to more precise nutritional management strategies (Poppi and McLennan, 1995; Lanzas et al., 2008). Critical steps still remain in advancing predictions of MPS under various grazing conditions, refining models of MP efficiency to account for a broader range of influencing factors, including greenhouse gas mitigation strategies, and incorporating detailed forage protein quality assessments into nutritional models.

Micronutrients

Micronutrients in grazing beef cattle

Vitamins:

Grazing beef cattle production systems present unique challenges in the area of micronutrients. Seasonal, geographic, and weather extremes can all impact access and availability of various micronutrients for beef cattle. Determining micronutrient requirements, dietary supply, and structuring supplementation programs to meet production goals are essential considerations for grazing cattle. In the context of this review, micronutrients refer to the known vitamins and minerals. For many of these, limited data are available for cattle, with even less data targeting grazing beef cattle. Grazing beef cattle production practices associated with micronutrient supplementation vary widely and range from providing no supplements at all, except perhaps salt, to providing vitamin and mineral supplements year around. As we consider micronutrients, our focus will be on new information since the last published (NASEM, 2016) nutrient requirements of beef cattle, existing knowledge gaps, and research needs.

In grazing beef cattle, lipid-soluble vitamins (A, D, E, and K) are usually not a concern with healthy animals consuming growing forages; however, data are limited. During the winter months or extended dormancy, liver stores of vitamin A can become depleted and result in deficiencies that can be protected against through the provision of additional vitamin A via multiple approaches (NASEM, 2016). Likewise, vitamin E supply for grazing cattle can become a concern during winter months or other climate extremes. Vitamin E needs for healthy, nonstressed grazing cattle are likely low and met via dietary components. Recommendations for newly received stressed calves were increased by NASEM (2016), but data are limited for other classes of beef cattle, and in particular grazing animals. The efficacy of additional vitamin E and potentially other antioxidants during times of stress (e.g., climate extremes, movement, breeding) on production outcomes in grazing cattle remains under investigation.

In grazing situations, exposure of cattle to adequate sunlight allows for conversion of vitamin D precursors in the skin, which can then be metabolized via the liver and kidney to active 1, 25 dihydroxycholecalciferol. Therefore, vitamin D has not traditionally been a concern in grazing beef cattle; however, limited data in this regard are available in the literature. Recent work with Nellore cows (Factor et al., 2024) reported that providing vitamin D as a component of a mixture of other vitamins (A, E, biotin, and beta-carotene) improved pregnancy outcomes and fetal growth. Others (Nelson et al, 2016; NASEM 2021; Peixoto de Souza, et al., 2022) reporting work with dairy cattle have indicated that vitamin D can improve reproduction, lactation, and overall health. Benefits of supplemental vitamin D in grazing beef cattle during lactation and/or breeding are not well defined and represent a knowledge gap.

Regarding water-soluble vitamins, it has been generally thought that requirements are met via ruminal microbial synthesis (NRC 1984, 1996). Nonetheless, even early work indicated that instances existed where water-soluble vitamin supply could result in relevant differences in beef cattle production (Smith et al., 1974). Little data are available regarding water-soluble vitamin supply and/or demand in grazing beef cattle, which represents a large knowledge gap given the metabolic importance of these molecules. The most recent nutrient requirements of beef cattle (NASEM, 2016) stated in the Research Needs chapter that “among the water-soluble vitamins, additional data on the effects of supplementation of biotin, folic acid, pyridoxine, and thiamine at physiological and pharmacological levels would provide important information for ruminant production and for subsequent committees working to establish requirements and recommendations.”

From a grazing beef cattle perspective, interest in biotin centers around its role as a cofactor in pyruvate carboxylase and propionyl-CoA-carboxylase within gluconeogenesis and with acetyl CoA-carboxylase and methylcrotonyl-CoA-carboxylase for fat synthesis (NASEM 2016, 2021), as well as its role in hoof health, which is possibly mediated through changes in keratinocyte differentiation and lipid synthesis (Queiroz et al., 2021). Most of the available work with biotin and cattle has a dairy focus; however, Campbell et al. (2000) reported that biotin improved hoof health in Canadian beef herds. Data in this area are lacking in grazing beef cattle, which creates a research need where new information could lead to strategic supplementation strategies to foster herd stayability and cow longevity.

Thiamin is synthesized by ruminal bacteria and is generally considered adequate unless diets contain elevated sulfur or there is the presence of disrupted ruminal fermentation or other antagonists (NASEM, 2016). In grazing situations, little research has been reported on thiamin but, situations with elevated sulfur consumption (water or diet) or compromised ruminal fermentation could theoretically cause issues with thiamin.

Folic acid, vitamin B6, choline, and vitamin B12 are involved in one-carbon metabolism along with several amino acids, minerals, and cofactors. The transfer of methyl groups is an essential metabolic process associated with amino acid metabolism, synthesis of purines, pyrimidines, and polyamines, and DNA methylation and demethylation processes associated epigenetic events, which are critical for fetal development (Sinclair et al., 2007; NASEM, 2016; Reynolds et al., 2017; Diniz et al., 2024; Crouse et al., 2024a, b). While a ruminal supply of these vitamins and cofactors is generally thought to be adequate, emerging data from multiple laboratories with beef heifer models of developmental programming (Caton et al., 2024)  fed predominantly forage-based diets indicate strategically supplementing specific vitamins, minerals, and amino acids and/or their combinations have relevant impacts on development, gene expression, and metabolomic profiles (Crouse et al, 2022b, c; Menezes et al., 2022, 2023; Syring et al., 2023, 2024; Crouse et al., 2024; Hurlbert et al., 2024a, b; Safain et al., 2024). While the aforementioned studies reflect proof of concept in confined forage-fed beef heifers, studies in grazing ruminants are absent in the literature and represent a knowledge gap.

Macrominerals:

The most recent Nutrient Requirements for Beef Cattle (NASEM, 2016), stated that “research to update the calcium and phosphorus requirements would be beneficial. In particular, the role of diet type (e.g., high-roughage vs. high-grain) and phosphorus concentration on excretion route of phosphorus needs to be further evaluated.” This knowledge gap remains and is even more pronounced in grazing beef cattle, as limited new data are available in the literature. Requirements for potassium in grazing beef cattle also are poorly defined (NASEM, 2016).

Salt has long been supplemented to grazing beef cattle (McDowell, 2003; Suttle, 2022) and little work has been published with grazing cattle since the NASEM (2016) publication. Salt is often used as an intake limiter in supplements provided to grazing beef cattle. Recent work (White et al., 2024) from Montana indicated that increasing levels of supplemental salt (up to 0.1% of BW) in supplements for beef cattle grazing dormant range forage increased water intake and ruminal liquid fill, while tending to decrease DMI and ruminal fill.

Little work has been conducted regarding magnesium needs of grazing beef cattle since the publication of the NASEM (2016). Grass tetany is a classic deficiency syndrome associated with low magnesium in grazing cattle, which producers and nutritionists actively manage during critical windows of susceptibility (NASEM, 2016).

Sulfur can present with both deficiencies and toxicities depending on various circumstances (Drewnoski et al., 2014; NASEM, 2016); The relationship between ingested sulfur and thiamin in regard to polioencephalomalacia has been discussed in multiple papers (Vasconcelos and Galyean, 2008; Neville et al., 2012; Amat et al., 2013; NASEM, 2016; Evans et al., 2024). Likewise, the role of sulfur in the diets and the formation of thiolmolybdates, which are interactions between sulfur and molybdenum and result in additional interactions with cooper in the rumen (Gould and Kendall, 2011) can decrease copper availability and alter systemic copper metabolism (NASEM, 2016). Sulfur can also interact with other trace elements, most notably selenium, with higher levels of sulfur potentially exacerbating low selenium supply (Abdel-Rahim et al. 1985) because of chemical similarities. Knowledge gaps in the area of interactions of sulfur with other trace elements create research opportunities that could lead to improved production outcomes.

Trace elements:

Trace mineral supplementation is common for grazing beef cattle, with Cu, Co, Mn, Se, Zn, and I (NASEM, 2016; Arthington and Ranches, 2021) being the most often supplied. Wide knowledge gaps exist regarding trace element nutrition in grazing beef cattle. Specifically, understanding of requirements, supply, interactions, precision delivery techniques, and implications for developing offspring and lactation efficiencies are limited in grazing cattle. A detailed discussion of trace element supplementation in grazing beef cattle is beyond the scope of this review, and readers are focused on several recent reviews for additional information. (Kegley et al., 2016; Greene, 2016; Stewart et al., 2016; Arthington and Ranches, 2021; Palomares, 2022; Van Saun 2023; Beck and Hall, 2023; Anas et al., 2023; Swecker 2023; Sagar and Van Saun 2023; Diniz et al., 2024; Weiss and Hansen, 2024).

Modeling micronutrient supply and demand

The complex nature of mineral nutrition in livestock (McDowell, 2003; Suttle, 2022) highlights the need for advanced computer models to predict mineral requirements and supply better. These mathematical models aim to formulate precise mineral nutrition plans, addressing both over-supplementation, which can lead to animal health risks, environmental contamination, and increased costs, and under-supplementation, which can hinder animal performance. Most existing models, such as those developed by the NASEM (2016) and other nutrient requirement systems (CSIRO, 2007; Valadares Filho et al., 2016), use a factorial approach to predict mineral requirements. This approach breaks down mineral needs into maintenance, growth, reproduction, and lactation categories. Although practical, the factorial method has significant limitations. For one, it tends to oversimplify the complex interactions between minerals and the environment, such as the antagonistic effects of certain minerals (e.g., molybdenum reducing copper absorption), which can lead to inaccurate predictions of mineral requirements in grazing systems (Arthington and Ranches, 2021). In addition, the factorial approach does not fully account for environmental variability, for example, changes in soil mineral content caused by pH, moisture levels, and other factors that significantly affect plant mineral content. This is especially problematic in grazing systems, where animals rely on forages that vary widely in mineral composition across different regions and seasons. This oversimplification means that current models often fail to capture the dynamic nature of mineral availability in forage-based systems (Suttle, 2022).

Alternative modeling approaches are being explored to overcome these limitations. Mathematical models must incorporate many factors, such as soil variability and plant genotype, which influence mineral content in forages. Dynamic models, for example, could integrate soil data, such as pH, mineral composition, and moisture levels, along with geospatial data on soil types across different regions. This would enable more accurate predictions of mineral uptake in plants and make models adaptable to various environmental conditions, but it would require integrating geospatial data on soil types across regions. Existing models in crop and livestock nutrition, such as DSSAT (Jones et al., 2003) or APSIM (Holzworth et al., 2014) can serve as a foundation for this type of integration, but these models would need enhancements to simulate the complex interactions in soil–plant–animal systems. Incorporating plant genotype data (e.g., differences between legumes and grasses or among cultivars) into models would allow for more refined predictions of forage mineral content; however, this requires a comprehensive database of mineral content for various forage species and their changes across different environments and seasons. This type of dynamic modeling would help predict mineral supply throughout the grazing season, considering plant maturity and seasonal environmental changes. Moreover, soil ingestion, an often-overlooked contributor to total mineral intake in grazing animals, could be modeled more accurately by including data on soil mineral content and estimates of soil ingestion rates under different grazing conditions. This approach would provide a more complete picture of mineral intake but poses its own challenges becuase of the lack of data on soil ingestion rates and the bioavailability of minerals from soil.

Thinking forward, while factorial models have provided a solid foundation for understanding mineral requirements, their limitations, especially in accounting for environmental variability and complex mineral interactions, indicate the need for more sophisticated models that could integrate real-time environmental data, dynamic soil–plant–animal interactions, and advanced methods to predict mineral intake and supply in grazing cattle systems. There is also a need for models that can better account for selective grazing behavior in mineral and vitamin intake predictions by incorporating animal behavior models into nutritional prediction tools. Nonetheless, developing such models requires a comprehensive, data-driven approach that fully captures the intricacies of the soil–plant–animal nexus, and addressing these challenges will require interdisciplinary research efforts and the development of more sophisticated, integrated modeling approaches. Such advancements will be crucial for optimizing nutrient management strategies and improving the efficiency and sustainability of grazing beef cattle production systems.

Modeling Nutrition of Grazing Animals

Comprehensive reviews of existing nutrient requirement models for beef cattle (Tedeschi et al., 2005; Tedeschi, 2019a) highlight their strengths and limitations. Tedeschi et al. (2019c), in particular, provided valuable insights into the use of models for assessing supplementation requirements in grazing ruminants. Widely used models, such as the NASEM (2016) beef cattle model, the Commonwealth Scientific and Industrial Research Organization (CSIRO, 2007), and the BR-Corte (Valadares Filho et al., 2016), have been instrumental in advancing our understanding of cattle nutrition. As Tedeschi et al. (2019c) also pointed out, however, many of the mathematical models were developed primarily using data from confined animals, which limits their direct applicability to grazing systems. As already noted, grazing animals face additional challenges, that are not fully captured by models built for confined conditions. Furthermore, Hristov et al. (2019) emphasized that most existing models are empirical rather than mechanistic, relying on observed data without necessarily accounting for the underlying biological processes (Bannink et al., 2016; White et al., 2016). This empirical nature, combined with the reliance on confinement-focused data, raises concerns about the ability of current models to accurately predict nutritional requirements and performance in more complex, variable grazing environments.

The importance of accurate nutritional models for grazing systems extends beyond just predicting nutrient requirements. As Appuhamy et al. (2016) demonstrated in their review of methane (CH₄) emission models, predicting feed intake is crucial for understanding and optimizing cattle nutrition and its environmental impacts. Their study, which compared various models across different geographic regions, highlights the complexity of accurately predicting feed intake and its downstream effects on cattle performance and the environmental footprint. Tedeschi and Beauchemin (2023) further emphasized the challenges associated with quantifying CH₄ emissions from grazing systems becuase of inherent methodological limitations, despite the many existing methods and approaches to measure CH₄ emissions by ruminants (Tedeschi et al., 2022).

Genetic variation in nutrient utilization is a critical but often-overlooked factor in current nutritional models for grazing livestock. Cantalapiedra-Hijar et al. (2018) reviewed the biological determinants of feed efficiency, emphasizing that metabolic and genetic factors cause significant variation between animals. Even when raised under similar conditions, individual cattle can exhibit widely differing nutrient utilization efficiencies, which current models fail to capture fully. Kenny et al. (2018) proposed that precision nutrition models integrating genetic, physiological, and environmental data can provide tailored nutritional recommendations for individual animals or groups. This precision is paramount in grazing systems, where both environmental conditions and genetic factors interact to influence performance.

To improve the accuracy of grazing system models, it is essential to include reliable source data on animal traits such as RFI, a key determinant of feed efficiency. Animals with lower RFI require less feed to maintain BW and productivity, making RFI a critical component of precision nutrition models (Kenny et al., 2018). Furthermore, the interaction between genetics and environmental conditions must be considered. Animals with different genetic profiles respond differently to stressors such as heat or drought. Incorporating genotype-environment interactions allows models to predict how animals will perform under varying environmental conditions, enabling more accurate herd management strategies (Cantalapiedra-Hijar et al., 2018). Incorporating genetic and physiological variation into predictive models is essential for optimizing grazing animal performance. By integrating genetic data with environmental conditions, models can provide more accurate predictions of individual and group responses to nutritional interventions.

Advancing nutritional models for grazing systems

As the complexity of cattle nutrition deepens, developing models that integrate nutritional, environmental, and animal-related factors has become increasingly important. Tedeschi and Fox (2020) proposed a systems approach to modeling ruminant nutrition, highlighting the need to understand the interactions between these components. The growing complexity of such interactions under grazing conditions demands interdisciplinary approaches and the use of advanced computational techniques.

Kebreab et al. (2019) stressed the importance of whole-farm models capable of holistically assessing the flow of nutrients, energy, water, and other environmental factors within a farm system. Their work focused on integrating data from various components of the dairy system to predict farm-level responses to stresses like climate change and resource availability. This systems approach aligns with the necessity of integrating complex nutritional interactions and environmental influences in grazing systems. Similarly, Kebreab et al. (2010) emphasized the need for models that account for the interaction between energy and protein in animal diets to improve nitrogen use efficiency and decrease environmental nitrogen losses. Their study demonstrated that incorporating energy intake as a covariate in models of nitrogen excretion enhances predictions, illustrating the importance of considering multiple factors simultaneously.

The development of integrated models that simultaneously consider multiple nutritional, environmental, and animal-related factors is critical for predicting nutrient flows and assessing environmental impacts in grazing systems. In particular, modern computational tools, such as machine learning and artificial intelligence, have the potential to model the complexity of these interactions and improve the accuracy of predictions (Kebreab et al., 2010, 2019). These tools can process large datasets and uncover patterns in nutrient dynamics that are difficult to capture using traditional empirical approaches.

Another significant challenge in advancing these models is the availability of diverse original-source datasets. Tedeschi et al. (2019c) reviewed data availability for nutritional modeling in grazing systems and highlighted substantial gaps in forage quality, pasture intake, and animal behavior data. Kebreab et al. (2019) similarly noted that big data and precision agriculture technologies offer a solution to these gaps, as modern dairy farms generate vast amounts of data that can be used to refine models. By integrating real-time data from farm sensors with whole-farm models, decision-making processes related to environmental sustainability and economic efficiency can be significantly enhanced.

To address the issue of limited datasets, advanced data collection technologies, such as remote sensing and data mining, and shared databases must be adopted to facilitate broader access to critical information (Kebreab et al., 2019; Tedeschi et al., 2019c, 2023). Improved coordination among research institutions and farms is essential to ensure a more holistic approach to data collection, which in turn will enhance the accuracy and applicability of nutritional models in diverse grazing environments.

Summary and Conclusions

Knowledge gaps in grazing beef cattle nutrition are abundant (Table 1). Addressing these knowledge gaps will require a concerted effort to generate more robust source data through improved technologies and enhanced development of mathematical models with broader and greater capabilities. By focusing on the areas and knowledge gaps outlined in this review, which included grazing beef cattle nutritional areas of intake, energy, protein, and micronutrient supply and demand, researchers can create more accurate and adaptable nutritional management approaches and mathematical models that incorporate dynamic environmental factors, precision nutrition, and genetic variation. Moreover, models need to be developed within the context of sustainable environmental concerns, socioeconomic impacts, and climate extremes. These types of advancements will not only enhance our understanding of grazing systems but will also provide practical tools for producers to optimize sustainability, reduce environmental impacts, and increase production efficiency in diverse grazing environments. These improvements will require interdisciplinary collaboration and the adoption of emerging technologies that are essential for future success. There is a critical need for more robust funding streams to address knowledge gaps in grazing beef cattle nutrition.

Table 1.

Knowledge gaps in grazing beef cattle nutrition

Item	Research needs in grazing beef cattle1
Water	• Improved estimates of water intake demand as influenced by physiology state, behavior, season, forage quality, climate extremes, activity, and water quality are needed. • New technologies to better estimate water intake • Enhanced approached to modeling water intake.
Dry mater intake (DMI)	• Better estimates of DMI are needed for all class of grazing beef cattle • Improved techniques and technologies for estimating DMI are needed • More detailed assessments of DMI as influenced by season, forage quality, climate extremes, cattle management, foraging behavior, supplementation programs, and alternative land uses are needed to improve modeling efforts. • More precise and accurate mathematical models are needed for estimating DMI of grazing beef cattle • Improvement of predictive models to use in management decision and research arenas that more accurately reflect observable conditions and responses. • More robust and modern decision-support tools are needed that couple predictive modeling, environmental changes, forage quality and other variables into decision processes.
Energy	• Filling knowledge gaps in energy nutrition of grazing beef cattle depends upon better estimates of intake, digestibility and animal performance. • Improved technologies for gathering source data are needed. • Information is limited regarding impacts of increased mature body weight and genetic capacity for growth on intake, efficiency of energy use for maintenance and growth (when scaled to body weigh; including methane emissions), and reproductive efficiency in grazing beef cattle • The relationships and interactions between forage utilization efficiency and reproductive efficiency remain a major knowledge gap. • A better understanding of energy use associated with grazing activity is needed. • More accurate and precise models to predict both energy supply and demand in grazing cattle are clearly needed.
Protein	• Reliable estimates of protein intake in a grazing setting are needed. • Improvements in estimating microbial protein synthesis, likely through improve technologies, should enhance modeling of protein nutrition. • Data are limited regarding nitrogen recycling in grazing beef cattle. • Information regarding protein supply and demand for pregnancy and lactation (particularly in first and second calf heifers), and potential impacts on developing offspring and cow longevity needed. • Improved source data and prediction models are needed to enhance protein use efficiency, nitrogen capture, and overall production efficiency by grazing beef cattle.
Micronutrients	• Limited recent data exist regarding supply and demand of micronutrients (vitamins and minerals) in grazing beef cattle and most practical applications are inferred from pen-based studies where intake is either known or controlled. • Additional knowledge regarding seasonal, geographical, and forage interrelationships regarding trace mineral supply are needed. • The role of ruminal bacterial supply of water-soluble vitamins during critical windows of fetal development, maternal stress, seasonal forage supply, and transitory intake suppression need to be better understood. • Precision nutrition and supplement technologies are needed for grazing beef cattle, particularly during time of stress or elevated nutrient demand. • Research is needed to update mineral supply and demand in grazing cattle. • Additional efforts towards understanding mineral interactions would improve our source data and inform mathematic models. • More accurate and precise models of micronutrient nutrition in grazing beef cattle are needed.

¹Selected research needs in grazing beef cattle nutrition. More details are provided within the text of the manuscript.

Glossary

Abbreviations

ADG

average daily gain

BW

body weight

CSIRO

Commonwealth Scientific and Industrial Research Organization

DMI

dry matter intake

FAO

Food and Agriculture Organization

MCP

microbial crude protein

ME

metabolizable energy

MP

metabolizable protein

MPS

microbial protein synthesis

NASEM

National Academies of Science Engineering and Medicine

NP

net protein

NRC

National Research Council

RDP

ruminal degradable protein

RFI

residual feed intake

Conflict of interest statement

The authors declare no financial conflict of interest with the content of the article.

Author contributions

Joel S. Caton (Conceptualization, Project administration, Writing—original draft, Writing—review & editing), David L. Lalman (Conceptualization, Writing—original draft, Writing—review & editing), and Luis O. Tedeschi (Conceptualization, Writing—original draft, Writing—review & editing)