Ammonia emissions from beef cattle feedyards: a review

Abstract

This review described the state of the science concerning the generation, measurement, and mitigation of ammonia (NH₃) emissions from beef cattle feedyards. NH₃ emissions primarily come from urinary urea in cattle manure. In the past, constant emission factors were used to inventory NH₃ emissions. Currently, NH₃ emission factors estimated by process-based mechanistic models reflecting various factors affecting NH₃ emissions in the feedyard environment are available. This review of current literature indicated the average NH₃ emissions from a beef cattle feedyard was approximately 119 g/head/day (range 24 to 318 g/head/day), and the average NH₃ flux was approximately 58 µg/m²/s (range 2 to 185 µg/m²/s). Although more realistic estimates of NH₃ emission flux from open-lot livestock facilities were being obtained using process-based models, there was still significant variation depending on the diet composition, manure management practices, and the feedyard environment, including both seasonal weather patterns and synoptic weather events. We note the need to improve inventories of NH₃ emissions into categories of crude protein percentage, manure management implemented, and feedyard environment. Some mitigation strategies can be effective, such as diet manipulation, growth-promoting technologies, and manure or pen-surface amendments. Of those, precision diet feeding to meet but not exceed protein requirements appeared to be the most practical way to reduce ammonia emissions over the animals’ feeding period; laboratory studies suggested that shorter-term reductions in emission flux may be possible with the other approaches, but they were far more speculative at this point as to both their efficacy and their cost of implementation.

1 Introduction

Over the past few decades, livestock and poultry farmers have scaled up farming operations to meet society’s demand for high-quality meats, milk, eggs, and by-products. The concentration of animals in feeding operations has played a large role in fulfilling the demand for animal protein with fewer animals and while using fewer land resources. Concentration of animals in close proximity during a portion of their production cycle also concentrates their nutrient emissions. More specifically, these undesirable potential implications of confined (or “concentrated”) animal feeding operations (CAFOs) are caused by gas and particulate matter (PM) emissions from various types of animal wastes, including manure (feces and urine), waste feed, bedding, and wastewater. Gaseous emissions from CAFOs include NH₃, greenhouse gases (carbon dioxide, CO₂; methane, CH₄; and nitrous oxide, N₂O), and other air pollutants such as volatile organic compounds (VOCs), many of which are odorous.

NH₃ emissions from CAFOs are a high-profile environmental quality concern because they can contribute to the eutrophication of surface waters, nitrate contamination of ground waters, soil acidity, secondary formation of fine PM, and impaired air quality (USEPA, 2004; Hribar, 2010; Brandani et al., 2023). Gaseous NH₃ in the atmosphere has been reported as a significant contribution to the formation of airborne fine particulate matter (PM2.5) through reactions with water vapor and other air pollutants, including oxidation products of sulfur dioxide (SO₂) or nitrogen oxides (NO and NO₂, or NOx) (Li et al., 2008; Wyer et al., 2022). Indeed, U.S. Environmental Protection Agency (USEPA) recently reduced the annual health-based National Ambient Air Quality Standard for PM2.5 from 12.0 µg/m³ to 9.0 µg/m³ (USEPA, 2024). Since NH₃ is a precursor gas that may be easier to mitigate than others among PM2.5’s other precursors (Meng et al., 2017; Gu et al., 2021; Wyer et al., 2022), if ambient PM2.5 standards are further reduced, ambient air-quality standards for NH₃ may be introduced. Furthermore, NH₃ may also contribute to climate change through N₂O formation as an intermediate byproduct of ammonium (NH₄⁺) oxidation in the microbial processes of nitrification and denitrification (USEPA, 2010). In addition, NH₃ emissions may contribute to nitrogen (N) deposition in neighboring ecosystems, which in turn may affect ecosystem function by promoting eutrophication, soil acidification, and disrupting biodiversity (Benedict et al., 2013; Thompson et al., 2015; Morris, 2016; Brandani et al., 2023).

As public awareness and concern over the potential adverse effects of NH₃ emissions on the environment and human health increased, governmental regulation of CAFOs led to a push and the adoption of sustainable management practices by livestock producers (Waldrip et al., 2015). Sound scientific evidence is needed to evaluate current and proposed regulations that go beyond encouraging practices regarding CAFOs, air quality, PM, and NH₃ emissions in particular. It is necessary to understand the emission mechanisms and processes influencing emissions, know the appropriate measurement methodologies and techniques and uncertainties associated with their use, evaluate current scientific literature for feedyard-based emissions data, survey the industry feedyard management practices, anticipate the impact of emerging regulatory trends and socioeconomics on emissions, and identify the most practical approaches to mitigation and potential barriers to their adoption.

In this review, we reported the state of the science concerning NH₃ emissions from beef cattle feedyards. The review is organized into five major areas: 1) pathway of NH₃ emissions through N metabolism in the ruminant animal, 2) dynamics of NH₃ emissions from pen surfaces, 3) methods quantifying NH₃ emissions in the feedyard, 4) the current level of NH₃ emissions in beef cattle feedyards, and 5) recommended management practices to mitigate NH₃ emissions from feedyards. Details of the literature search methodology, including search engines, terms used, inclusion and exclusion criteria, and the total number of materials reviewed, are provided in the Supplementary Material.

2 Pathway of ammonia emissions from ruminant animal

Ruminants, specifically pre-gastric fermenters, have a unique digestive system that evolved to digest and use forage resources that are less or not digestible in monogastric animals. The unique digestive organ called the reticulo-rumen is an anaerobic microbe fermenter with the remarkable ability to convert dietary protein into microbial protein. Ruminal microorganisms can use not only dietary protein but also non-protein-N, which does not contain amino acids (e.g., urea and NH₃) as N sources for microbial protein synthesis. In the reticulo-rumen, N sources are degraded by rumen microbes to peptides, amino acids, and eventually to NH₃ by the deamination of amino acids and then these compounds are used to synthesize microbial protein (Hristov and Jouany, 2005; NASEM, 2016). Microbial protein has a similar amino acid composition to the amino acid composition of tissue and milk protein, which makes it almost an ideal source of amino acids for the ruminant (NRC, 2001; Hristov et al., 2011). The metabolizable protein needs of the ruminant for maintenance and production are met primarily by microbial proteins that are washed out of the reticulo-rumen and feed proteins, which are not degraded by rumen microbes, with a small contribution from endogenous N, which originates from the animal’s own viscera rather than from dietary sources such as sloughed-off intestinal cells in the post-ruminal (small and large intestine) metabolism (NRC, 2001; NASEM, 2016). A portion of NH₃ produced in ruminal N metabolism is absorbed across the ruminal epithelium into the portal vein and converted mostly into urea by the urea cycle in the liver to avoid NH₃ toxicity (NASEM, 2016). Urea produced by the liver is partly excreted in the urine by the kidneys, with the remainder recycled back to the gastrointestinal tract (GIT) through either direct transfer from blood across the epithelial tissue or via saliva as a N source for protein synthesis (NASEM, 2016). The process in which NH₃ in the rumen is converted into urea by the liver and reused as a N source in GIT is called urea recycling (or N recycling), and it plays an important role in N preservation mechanism of the ruminant (NASEM, 2016). Undigestible and unabsorbed N sources in ruminal and post-ruminal metabolism are excreted as feces. The overall N metabolism pathway in the ruminant is shown in Figure 1.

Figure 1

The excretion of NH₃ is largely determined by the form of NH₃ in the ruminant’s metabolism. For example, if it is in a gas phase (NH₃), it is most likely to be excreted as eructation, exhaled gas, and flatus. However, if it is in an ionized (NH₄⁺) or solid form, it is more likely to be incorporated into the manure (feces and urine). Since the form of NH₃ produced in the digestion of the ruminant is determined by the NH₃/NH₄⁺ equilibrium state (Equation 1), it is important to understand the factors that affect the NH₃/NH₄⁺ equilibrium and the ruminal and post-ruminal environments. The equilibrium between NH₃ and NH₄⁺ is not a redox-dependent reaction, but a pH and temperature (T) dependent reaction in aqueous solutions (Equations 2, 3; Emerson et al., 1975) as illustrated in Figure 2. This is because there is no change in the oxidation states of N or hydrogen (H), which is the key feature of redox reactions.

Figure 2

Cofie et al. (2016), (b)Aguado et al. (2022).

The ruminal and post-ruminal environments depend on diet composition, management, and cattle’s health condition, but they are generally anaerobic, reductive (oxidation-reduction potential (Eh) of -250 to -450 mv; Van Soest, 1994),< 8 pH, and ~39°C (NASEM, 2016). Considering the general pH and T in the rumen and post-rumen, it is reasonably assumed that NH₃ exists in mostly the NH₄⁺ form. In addition, Mohiuddin and Khattar (2019) reported that the pKa of this reaction is about 9.15 and this reaction toward NH₄⁺ occurs almost instantaneously under biological conditions (pH 7.4 and 36.5°C). However, the aqueous solution in the rumen and post-rumen is more dynamic and complex due to anaerobic microbial interactions and reductive conditions, thus in addition to pH and T, the change in pressure, ionic strength, and salinity may affect the conversion of NH₃ form due to byproducts from microbial digestion and GIT metabolism.

Thus, based on the data investigated to date, we aimed to discuss specifically the form of NH₃ estimated to be emitted by each NH₃ emission pathway: 1) exhalation, 2) eructation, 3) flatus, and 4) the excreted N sources in manure, considering the unique N metabolism and the viscera environment of the ruminant. Environmental conditions and the predominant forms of NH₃ associated with each NH₃ emission pathway are described in the Supplementary Material. In summary, since pH and T are widely recognized as the primary factors influencing the chemical form of NH₃ under biological conditions, NH₄⁺ is the dominant form in the ruminant’s GIT. Consequently, most NH₃ emitted from ruminants is considered to originate from excreted N in manure (Figure 3).

Figure 3

3 Dynamics of ammonia emissions from pen surfaces

Excreted N in feces and urine is the main source of NH₃ emissions from the beef cattle feedyard. Fecal NH₃ is derived from undigested feed residues, microbial cells, endogenous secretions, sloughed cells from GIT (Waldrip et al., 2015), and urine NH₃ derived from urea, hippuric acid, and purine-based catabolism residues (Bristow et al., 1992).

Urea (CO(NH₂)₂) is not volatile, but once it comes in contact with the urease enzyme (urea amidohydrolase), which is ubiquitous in manure and soil (Waldrip et al., 2015), it is rapidly hydrolyzed to NH₃ and CO₂ (Bussink and Oenema, 1998). However, NH₃ from feces is generated through the slow process of organic N mineralization (Muck, 1982; Muck and Steenhuis, 1982; Waldrip et al., 2015). N compounds from feces are mineralized into NH₄⁺ by heterotrophic microorganisms in the manure (Horton et al., 2006; Zhang et al., 2007; Vavilin et al., 2008). Mineralized NH₄⁺ is slowly released as NH₃ by diffusive and convective mass transfer (Waldrip et al., 2015). The process for volatilizing NH₃ from cattle manure was summarized below (Figure 4, Equations 4-6; Brandani et al., 2023).

Figure 4

Most NH₃ emission from the beef feedyard originates from urine NH₃, particularly urinary urea (Bristow et al., 1992; Bussink and Oenema, 1998; Waldrip et al., 2013a). The urinary N could be volatilized from 4% to 71% (Bussink and Oenema, 1998; Waldrip et al., 2013a), while feces N volatilization is considerably less at 1% to 13% (Bussink and Oenema, 1998). Supporting this, Lee and Hristov (2010) observed that urinary N accounted for an average of 90% of the total emitted NH₃ during the first 10 d after excretion as cattle manure. In addition, several studies have reported that approximately 80% (range: 25 to 90%) of the urinary N is volatilized to NH₃ within the first 24 h after manure excretion (Stewart, 1970; James et al., 1999; Cole and Todd, 2009; Lee et al., 2009). Urea represents 50% to 90% or more of total urinary N (Bussink and Oenema, 1998; Reynal and Broderick, 2005; Vander Pol et al., 2008) and proportionally increases as dietary CP level and intake increase (Cole et al., 2005; Colmenero and Broderick, 2006; Todd et al., 2006; Cole and Todd, 2009; Waldrip et al., 2013a). Waldrip et al. (2013a) reported a moderate relationship (R² = 0.516) between dietary CP % and urinary N in finishing beef cattle.

The instantaneous magnitude and rate of NH₃ loss are the result of complex physical and chemical processes on feedyard surfaces (Harper, 2005; Freney and Simpson, 2013). They depend strongly on diet composition, manure properties, environmental factors (T, precipitation, humidity, and wind turbulence), manure properties, and management practices (Sommer et al., 1991; Ni, 1999; Brandani et al., 2023). In summary, NH₃ emission is mostly driven by four factors (Harper et al., 2010a): 1) total ammonia N (TAN) concentration of the manure, 2) T of the manure and pen surfaces, 3) pH of the manure and pen surfaces, and 4) the effectiveness of mass transfer and turbulent transport of the NH₃ away from the manure surface. The relationship between NH₃ volatilization and four key factors was summarized in the Supplementary Material. In conclusion, NH₃ volatilization increases with increasing TAN concentration, T, wind speed, and pH (Sawyer et al., 1978; Sommer et al., 1991; Arogo et al., 2006; Montes et al., 2009; Todd et al., 2011). Temperature and pH have been reported to be the most important factors influencing NH₃ volatilization (Arogo et al., 2006), as NH₃/NH₄⁺ are equilibrium-dependent (Figure 4). Supporting this, a significant correlation between the above parameters and NH₃ volatilization was reported in Redding et al. (2019) (Table 1).

4 Quantifying ammonia emissions

To accurately quantify NH₃ emissions in the feedyard, it is necessary to understand the characteristics of NH₃ emissions occurring in the feedlot environment. NH₃ is a colorless gas with a distinct, pungent smell. It occurs naturally and is normally found in trace amounts in the atmosphere (range: 1 to 25 ppb; Renard et al., 2004). Due to its high reactivity and the pervasiveness of the urease enzyme, the process of NH₃ formation and volatilization is almost instantaneous and begins immediately after manure is excreted (Hristov et al., 2011). Once emitted into the atmosphere, NH₃, where it is the dominant alkaline gas, reacts with atmospheric sulfuric and nitric acids forming ammonium sulfate, ammonium bisulfate, and ammonium nitrate, which precipitate in atmospheric water droplets as secondary fine particles (PM2.5) and are regulated by USEPA as a so-called “criteria” air pollutant (Renard et al., 2004; Brandani et al., 2023). NH₃ released into the atmosphere has a short lifespan in its gas phase of 2.5 to 36 h (Xie et al., 2024).

NH₃ emissions from open feedyards are generally lower than those encountered in closed or housed animal production systems (Todd et al., 2005, 2006; Hristov et al., 2011). This is because open feedyards are exposed to ambient air, allowing for dilution airflow that reduces NH₃ concentrations in the atmospheric boundary layer. Although ambient conditions are spatially and temporally variable, NH₃ emissions from beef feedyards are quickly dispersed by atmospheric turbulence (Waldrip et al., 2015). In other words, most agricultural open sources like a feedyards tend to be scattered both temporally and spatially, and most of the gaseous NH₃ emitted by feedyards may be shortly adsorbed to surrounding cropping and natural ecosystems by dry deposition (Harper et al., 2004; Harper, 2005), converted to fine particles, or mixed into the upper atmosphere. Additionally, manure in open feedyards is typically distributed over a larger area and may dry out more quickly due to exposure to sunlight and wind. Although more NH₃ may be released during the drying process, dried manure emits less NH₃ compared to the wetter manure often found in closed systems. In addition, open-lot feedyards often have lower stocking density compared to animal operations under the roof, which reduces the total amount of NH₃ being produced per unit of emitting area.

Quantifying gas emissions from open sources requires equipment that can measure low concentrations of NH₃ quickly, accurately, and robustly. Any measurement procedure that alters the natural ambient state (e.g., manure property and turbulence at the emitting surface) will introduce bias to measured NH₃ emission rates (Harper et al., 2010a). For NH₃, any measurement technology that interferes with the turbulent transport process away from the source (the rate-limiting process) can result in large errors. This is unlike CH₄, CO₂, or NO₂, which are less soluble and less affected by turbulent transport as mass-flow (biological) gases (Harper et al., 2010a). Therefore, measuring the NH₃ concentration emitted in the natural ambient state with the acquisition of weather data is better for the quantification of NH₃ emission in the feedyard compared to other approaches such as creating an artificial airflow inside a flux chamber.

Quantifying NH₃ emissions requires at least two components: 1) a method to measure the atmospheric concentration of NH₃ and 2) a method to measure weather data for converting the concentration into emission using a dispersion model or a method to directly measure the airflow rate (usually for lab scale) (Waldrip et al., 2015). It is important to note that concentration is only a percentage. 1 ppm of NH₃ just means that NH₃ is 0.0001% of the sampled air. It says nothing about the actual amount of NH₃ injected into the atmosphere. Therefore, it is necessary to approach the term of emissions (mass per time). If the total volumetric airflow (m³/s) and the NH₃ concentration (g/m³) from an emission source were measured, the two terms must be multiplied to obtain the emission rate in g/s. The main text of this review describes only the most widely used method currently applied in practice. However, the Supplementary Material provides an overview of five major approaches for measuring NH₃ concentrations (acid trap, chemiluminescence, electrochemical sensor, infrared analyzer, and tunable diode analyzer) and estimating NH₃ emissions (N mass balance, flow-through chamber, micrometeorological methods, air dispersion models, and satellite remote sensing). As each method has advantages, disadvantages, and applicability varies, readers are encouraged to consult all methods and select the one most appropriate for their specific research conditions and objectives.

4.1 Measurement of ammonia concentration

4.1.1 Open-path tunable diode laser absorption spectrometry

Open-path tunable diode laser absorption spectrometry (OP-TDLAS) is a technique designed to measure the path-averaged concentration of specific species within a gas mixture using laser-absorption spectrometry. The basic principle of OP-TDLAS involves passing the laser through the gas mixture, detecting the amount of light absorbed by NH₃ molecules at specific wavelengths according to the change in the degree of recovery rate from the detector, and then computing the NH₃ concentration based on the calibration between NH₃ concentration and the amount of light absorbed at certain wavelengths (Figure 5). Such a specific waveband (so-called narrow absorption line), specifically designed for NH₃, avoids mutual absorption interference of other gases such as CO₂, CH₄, and water vapor (Harper et al., 2010a). The amount of light absorbed by the NH₃ molecules is proportional to the concentration of NH₃ along the optical path. NH₃ gas molecules typically absorb light in the range of around 200 (Boreal Laser INC), 620~740, or 931~954 cm^-1 (Baldacchini et al., 1981; Hermanussen et al., 1986). To sum up, OP-TDLAS is designed to measure mean concentrations along an open path between the laser and the retroreflector and is a non-invasive technique.

Figure 5

The main advantage of using OP-TDLAS at beef cattle feedyards is quick, accurate, and robust NH₃ measurement in a feedyard environment where NH₃ rapidly volatilizes from relatively large emitting areas. As an example, the detailed specification of OP-TDLAS by Boreal Laser Inc (Edmonton, Canada) has 8–6500 or 40-15,000 ppm-m as a detectable NH₃ range and a ±2% uncertainty about reading accuracy. The response time required for measuring accurate NH₃ concentration is 1 s. Once factory calibration is completed, it has a longer calibration cycle than other sensors. If stored properly, the measurement will remain accurate for several years. In addition, the open path between the laser and retroreflector can typically be covered to 5~500 m in measurements, but this can be increased further depending on the performance of the reflector. In terms of disadvantages, OP-TDLAS is expensive and requires careful maintenance. It may require skilled operators for setup, calibration, and maintenance due to the complexity of the technology involved. It is susceptible to maintaining clear line of sight between the laser and retroreflector. Environmental conditions like dust and condensation can increase the opacity of the air along the optical path and degrade the quality of an instrument’s signal.

4.2 Estimation of ammonia emissions

4.2.1 Air dispersion models

Direct measurement of NH₃ emissions from open cattle feedyards is challenging due to their size, the spatial and temporal variable nature of emissions from open sources, and the labor, cost, and time consumption associated with measuring and maintaining instruments (Bonifacio et al., 2013). One may sidestep these problems by using an atmospheric dispersion model to deduce the emission indirectly (Flesch et al., 2004). Air dispersion models are mathematical tools used to characterize the atmospheric processes that disperse a pollutant emitted by a source and simulate the transport and dispersion of air pollutants in the atmosphere based on measured weather data and gas emissions (or concentration). These models help assess the NH₃ emission at various locations downwind from a source, providing valuable information for air quality management, environmental impact assessments, and regulatory compliance.

In the case of open beef feedyards, several air modeling systems could be applied such as 1) Gaussian model-based AERMOD (American Meteorological Society/Environmental Protection Agency Regulatory Model) system and 2) backward Lagrangian stochastic (bLS) model-based WindTrax system, but bLS model-based WindTrax is generally applicable for beef cattle feedyards.

4.2.1.1 Gaussian model-based AERMOD system

A Gaussian dispersion model describes the transport of pollutants from a point source as a steady-state plume whose horizontal and vertical spread are modeled as Gaussian distributions whose parameters are specified by ensembles of weather variables affecting boundary-layer turbulence. The AERMOD System is a steady-state, Gaussian plume model that incorporates air dispersion based on planetary boundary layer turbulence structure and scaling concepts, including treatment of both surface and elevated sources, and both simple and complex terrain (USEPA, available online: https://www.epa.gov/scram/air-quality-dispersion-modeling). It is the preferred regulatory dispersion model of USEPA and is a free system used for emission estimation of target gases across various industries. An advantage of such Gaussian model-based system is that the plume dispersion parameters are based on theory and inputs are well characterized by experimental data (Arogo et al., 2006). However, this Gaussian assumption is not valid for all variables associated with the atmosphere for all time, scales, and dynamics (Goodliff et al., 2020). Specifically, Harper et al. (2011) pointed out that it is hard to expect such universality of Gaussian distribution since the atmosphere in the feedyard does not adhere to Gaussian assumptions. In other words, the shortcomings and limitations of Gaussian model arise from the many simplifying assumptions implicit in the mathematical solutions of these models (such as conditions of steady, uniform flow and homogenous turbulence), and the assumption of vertical Gaussian concentration distribution which is often not realized in the boundary layer (Arogo et al., 2006).

4.2.1.2 bLS model-based WindTrax

Lagrangian stochastic models (LS) describe the trajectories of tracer particles in turbulence from a statistical perspective of random velocity fields. They are considered by some authors the most natural and accurate means of calculating atmospheric transport (Wilson and Sawford, 1996). Flesch et al. (1995) developed a “backwards” variant of this type of model, otherwise known as the bLS dispersion model. The bLS dispersion model calculates an ensemble of particle trajectories that are distinguished by each passing through an observation point (Flesch et al., 1995). In other words, particles are released at the receptor location and travel backward in time to the source location in bLS; by contrast, in forward or standard LS, particles are released at the source and travel to the receptor location (Li and Du, 2020). Specifically, the bLS model tracks the movement of individual air parcels or particles as they disperse in the atmosphere based on measured weather data and gas concentration. When used in conjunction with OP-TDLAS measurements of pollutant concentrations, thousands of model trajectories are calculated upwind of the OP-TDLAS path for the prevailing wind conditions (Harper et al., 2010a). The important information relating the concentration to the emissions is the set of trajectory intersections with the ground (touchdowns), and the needed concentration-emission rate (C-Q) relationship is determined by those touchdowns according to Equations 7, 8 (Flesch et al., 2004).

Where: Q: NH₃ emission rate (kg/m²/s) from the area source of known configuration. C: NH₃ concentration (mg/m³); Cb = the background NH₃ concentration (mg/m³). (C/Q)sim: a model prediction of the ratio of concentration to the emission. N: the total number of (computational) particles released from the source. W₀: the vertical velocity at touchdown within the source (m³/min per surface area; m²).

The advantages of the bLS dispersion model are its ability to accurately represent wind features near the ground, their role in gas transport, (Harper et al., 2011) and to be faster and more flexible in calculating turbulent dispersion from surface area sources than “forward” models (Flesch et al., 1995). Therefore, it is a particularly good choice for calculating the relationship between gas concentration and emission rate for ground-level sources and for concentration observations taken near the source (Harper et al., 2011). However, it assumes that the atmospheric surface layer is homogeneous, that flow is stationary and that the source strength is spatially uniform (Flesch and Wilson, 2005), assumptions that may be challenged by the complexity of some CAFOs (Hristov et al., 2011). To date, there is much previous research on the bLS being used to calculate gas emissions from feedyards (Harper et al., 2004; McGinn et al., 2007; Van Haarlem et al., 2008). Flesch et al. (2004) reported that bLS diagnoses the strength of a small ground-level source with small bias (within 2%), however, Harper et al. (2010a) and Harper et al. (2010b) compared the bLS accuracies to tracer gas studies, showing a nominal bLS accuracy of 100 ± 10%.

bLS model-based WindTrax is an easy-to-use graphical interface designed for the assessment of turbulent transport on the micro-meteorological scale and for simulating short-range atmospheric dispersion (for horizontal distances within about 1 km of the source) using bLS models (Thunder Beach Scientific, available online: http://www.thunderbeachscientific.com/). This program is free, and guidelines and introductions are provided so that users can use them correctly. Before running the program, users should carefully review the associated documentation for detailed guidance on model inputs, options, and best practices.

5 Ammonia emission factors from beef cattle feedyard

Although measurements of NH₃ emission have been improved, direct measurements at each feedyard are not feasible due to the time, cost, and labor required. In addition, NH₃ emissions in beef cattle feedyards vary greatly depending on the diet (e.g., CP%), environmental conditions (e.g., air T, wind speed, turbulence, and precipitation), and operation-specific management practices (e.g., stocking density, manure storage, feeding management, and manure handling). Thus, measurements taken at one point in time on one feedyard may not accurately capture seasonal and temporal fluxes of emissions that occur due to changes in weather, animal diet, or other management practices (Waldrip et al., 2015). Although the emissions cannot be represented in a single value due to variables in the operation-specific management practices and environment, the need for a standard representing NH₃ emissions in general from livestock operations for inventory purposes is being highlighted.

In the past, researchers focused on measuring emissions and comparing them to constant emission factors (EF), which are derived from the literature by selecting data from studies that measured emissions from operations that are assumed to represent production facilities for a specific livestock type and region. Although they are not a perfect standard, constant EF are often used by regulatory agencies and environmental advocacy groups to estimate the footprint of specific animal-production systems (Battye et al., 1994; USEPA, 2004; Eggleston et al., 2006). A review by Faulkner and Shaw (2008) identified a wide range of constant EF for NH₃ from beef cattle and proposed an annual NH₃ EF of 13.0 kg/animal for beef cattle feedyards, which is the same as that used by the USEPA for inventory purposes (USEPA, 2004). Despite frequent use in setting policy and inventory of emissions, constant EF have proven insufficient for quantifying gas fluxes from many systems, including feedyards (Todd et al., 2013; Waldrip et al., 2013b, 2014). This is because using a single EF and applying it universally cannot account for the previously discussed temporal and spatial differences in management practices and climatic conditions (NRC, 2002, 2003). Therefore, NRC (2003) identified the need for improved resolution in emissions reporting for animal agriculture and recommended a process-based modeling approach that includes mass balance constraints.

Process-based modeling uses mathematical models to simulate the many processes and interactions that occur within a system such as a beef cattle feedyard. Dynamic process-based models that quantify emissions based on classical principles of thermodynamics and kinetics potentially provide a cost-effective method of estimating emissions and evaluating how changing climate and management practices affect emissions from animal agriculture (Waldrip et al., 2015). Representative process-based modeling used to quantify NH₃ emissions from beef cattle feedyards includes IHF, modified mass difference approach, flux-gradient technique (ECV), and IDM.

We have summarized results reported to date for NH₃ flux (µg/m²/s) and the NH₃ EF (per capita emission rates; PCER, g/head/d) using several process-based models by season (Tables 2, 3). Open path laser was often used to measure the NH₃ concentration and bLS Inverse dispersion model was widely used to convert the measured NH₃ concentration into NH₃ emissions from the beef cattle feedyard. A wide range of NH₃ flux has been reported, from 2 to 185 µg/m²/s (average 58 µg/m²/s). It was generally found that NH₃ flux follows the following order: summer > autumn > spring > winter. Also, a wide range of NH₃ emissions have been reported ranging from 24 to 318 g/head/d, and average 119 g/head/d. The highest NH₃ emissions were observed in the autumn, but the variation was large in each season, thus, no significant differences were represented between seasons (p > 0.05). It was found that 9 to 116 (average 43) NH₃ kg/head/y are emitted annually.

Although it is agreed that more realistic NH₃ EF are being obtained using process-based models, there is still significant variation of estimated NH₃ EF depending on the diet, the model used, and the feedyard environment. This deviation is most likely caused by differences in geographical environment and management implemented in actual feedyards as well as inherent assumptions made in modeling. Therefore, when investigating EF in the future, a detailed description of the environment, diet, and management practices implemented by the feedyard is necessary to evaluate the impact of each category of manure management implemented and the feedyard environment and estimate accurate EF for each scenario. We suggest an investigation list of feedyard environments to aid in assessing NH₃ emissions in Table 4. The information on feedyard management and diet reported in the previous papers to date is insufficient to implement this categorization. Improved models and measurement equipment are still needed for estimating more accurate NH₃ EF measurements under the ever-changing feedyard environment in the future.

6 Management practices to mitigate ammonia emissions

The major best management practices (BMP) available for use to mitigate NH₃ emissions in open-lot livestock facilities have been listed and recommended by USDA-NRCS (Table 5; Brandani et al., 2023). BMP can be divided into management for pre- and post-excretion stages (Waldrip et al., 2015). The primary method of NH₃ mitigation in the pre-excretion stage is fundamentally precise diet feeding to meet beef cattle requirements, dietary manipulation to decrease N excretion (e.g., adjusting dietary CP concentration to meet animal’s need through phase feeding or oscillating), and supplementation to increase animal production (e.g., use of growth-promoting technologies). Methods for the post-excretion stage include applying the manure amendments to suppress hydrolysis of excreted urinary urea (e.g., use of urease inhibitors) or to capture (or absorb) the generated NH₄⁺ or NH₃ (e.g., use of biochar as absorbents).

The fundamental solution to mitigate NH₃ emission in beef cattle feedyards is to minimize N excretion in manure by optimizing pre-excretion stages management, but there is an intrinsic limitation in achieving this goal due to the inefficient utilization of feed N in the ruminant. The efficiency of N utilization in ruminants is typically low (around 25%) and highly variable (10% to 40%) compared with the higher efficiency of other production animals (Calsamiglia et al., 2010). The low efficiency implies that unutilized N is being released as manure into the environment. The excretion of manure with high N has the potential to increase NH₃ emissions into the environment. Any practice or condition that increases manure N content should generally be expected to increase NH₃ emissions. With typical finishing diets, approximately 10 to 20% of N intake is retained in animal tissues, 30 to 50% of fed N is excreted in the feces, and 40 to 70% of fed N is excreted in urine (Cole and Todd, 2009). Although it is most likely impossible to dramatically improve the inherently low efficiency of N utilization in cattle, the efficiency of N utilization can be improved through the understanding and modification of factors regulating the efficiency of N utilization in key processes, including N capture in the rumen, protein degradation, digestion and absorption in the GIT and AA utilization in peripheral tissues (Calsamiglia et al., 2010). In addition, proper processing of forages and feed, such as chopping and steam flaking, enhances digestibility, thereby improving feed efficiency. Therefore, the direction we should take to mitigate NH₃ emissions in feedyards is to maximize the use of feed N by optimizing the pre-excretion management while simultaneously minimizing the environmental impacts using post-excretion management. The summarized NH₃ mitigation practices are organized into the pre- and post-excretion stages below.

6.1 Ammonia mitigation practices in the pre-excretion stage

6.1.1 Precision feeding

The terminology of precision feeding was coined to suggest that livestock feeding can be fine-tuned to maintain or improve performance and better realize other benefits (Reddy and Krishna, 2009). In other words, the ingredients and chemical composition of the diet are modified over the growth stage of the animal so that the nutrient composition of the diet more closely meets the nutrient requirements of the animal and the excreted nutrients in manure are minimized (Brandani et al., 2023). The terminology of precision feeding has expanded its meaning to include mitigating the environmental impacts of animal production while maintaining or improving animal performance. The expansion of the term’s meaning is fundamentally due to the improvement of nutritional models based on the accumulated knowledge of livestock nutrient requirements and use by the animal, along with the development of feeding systems, which have made it possible to feed livestock closer to their requirements, thus reducing wastes (such as wasted feed ingredient, water, manure, and gas emissions) while maintaining or improving animal performance. Details regarding the development of nutritional models, as well as opportunities for improvement in current nutrition models, are presented in the Supplementary Material.

Ideally, a nutritionist can balance animal performance with subsequent effects on the environment using current nutrient models. However, the question remains whether precision feeding can be realistically applied to a commercial beef cattle feedyard (Waldrip et al., 2015). This is because there are several challenges to overcome for precision feeding to be practical. Factors that limit the practicality of precision feeding include (1) variability in animal nutrient requirements, (2) seasonal and climatic effects, (3) variability in the composition of feed ingredients, (4) logistics, and (5) variability in the estimation of DMI (Cole, 2003). Most of these limitations revolve around the risk of adversely affecting animal health or performance and feedyard benefits (Waldrip et al., 2015). For this reason, many nutritionists often incorporate safety margins in their dietary formulations and feeding recommendations to protect against such factors in order to ensure that the diet meets the nutrient requirements of the animal. A practical example of such safety margins is formulating the diet to contain more CP% than is expected to be required to meet the animal’s requirements. However, it is important to note that the decision to overfeed crude protein, as an example, may also be influenced by feed ingredient price and/or availability, such as when ingredient price encourages overfeeding N as a means of minimizing the cost of gain.

Although we acknowledge the practical limitations of implementing precision feeding, scientific advancements to date have led to the development of several effective strategies within precision feeding systems to mitigate NH₃ emissions. Representative examples include phase feeding and the use of growth-promoting technologies, which will be discussed in detail next. Dietary protein requirements decrease as cattle mature because of reduced protein deposition and simultaneous increase in fat deposition (Hristov et al., 2011; Waldrip et al., 2015). Phase feeding is a type of precision feeding where dietary protein concentrations are reduced late in the feeding period (Waldrip et al., 2015). Use of growth-promoting technologies are used in tandem with precision feeding strategies to maximize the efficiency and effectiveness of nutrient utilization by cattle, specifically for improving growth, feed efficiency, and production sustainability (Tedeschi et al., 2003). In conclusion, we believe that the factors limiting the practical use of precision feeding can ultimately be alleviated through the accumulation of knowledge and technology development from continued research, although the logistical hurdles of implementation at the feedyard-level remain to be overcome. Addressing these limitations and overcoming these hurdles to the adoption of precision feeding will help to maintain or improve animal performance while minimizing NH₃ emissions.

6.1.2 Manipulation of crude protein concentration and protein type

NH₃ emissions from beef cattle feedyards are sensitive to dietary CP concentrations (Cole et al., 2005; Todd et al., 2006, 2011). Approximately 25 to 50% of N intake is lost into the atmosphere as NH₃ when beef cattle are fed CP to meet their physiological and growth needs (Hristov et al., 2011; Todd et al., 2013). By reducing dietary CP content to match animal needs more closely, more urea recycling is stimulated, overall feed efficiency improves, and N losses are minimized (Galles, 2011). In previous studies, it was reported that NH₃ emission, as a consequence of volatilization of excreted N, could be reduced by a maximum of 67% when CP% in the feed was adjusted to around 10-11% from 13-16% (Table 6) in some situations. According to Menezes et al. (2016), the CP% of the diet for Nellore bulls was reduced from 14% to 10%, resulting in no significant difference in animal performance and carcass characteristics. However, it is important to note that reducing dietary CP levels below that required to meet the N needs of rumen fermentation and the AA needs of the animal would be expected to reduce growth performance and feed efficiency under most scenarios (Cole et al., 2005; Proctor, 2023). As a similar strategy to avoid these concerns, the method of phase feeding and oscillating dietary CP, which is the approach to more accurately apply CP according to the growth stage of livestock or environment, has been reported to mitigate N excretion and NH₃ emissions (Table 6).

In addition, manipulating the type of protein source in the diet can be helpful to mitigate N losses in manure. There are two types of protein: rumen degradable protein (RDP) and rumen undegradable protein (RUP). RDP is the protein broken down by the microbes in the rumen and used for microbial growth. RUP is the protein that escapes fermentation in the rumen and is digested in the small intestine. In beef cattle, 40 to 80% of non-retained N is excreted in the urine, and this quantity typically increases as dietary CP and RDP concentrations increase in the diet (NASEM, 2016). Therefore, N excretion can be reduced by increasing the proportion of RUP from the protein source required to satisfy the protein requirements (RDP+RUP) of cattle in the diet. However, it is important to ensure that RDP levels are high enough to satisfy the N requirement of the rumen microorganisms, as a deficiency would be expected to decrease the extent of fermentation and ultimately increase NH₃ emission intensity due to decreased feed efficiency. Increasing RUP level is also expected to increase N utilization efficiency by enhancing urea recycling to compensate for rumen microbial requirements due to RDP deficiency. Cole et al. (2005) reported that as RUP among the protein sources in the diet increased, the N excretion emitted from the urine decreased, and it affected the actual mitigation of NH₃ emissions. In addition, Batista et al. (2016) reported that corresponding with increased N intake, urinary N excretion was greater with supplementation, but supplementation did not affect fecal N excretion. Nevertheless, in response to RUP, fecal N excretion linearly increased, but urinary N excretion was not affected. In terms of NH₃ mitigation in ruminants, it is considered the reduced urinary N excretion will have a more positive effect on mitigating NH₃ emissions than the increased fecal N excretion by replacing RDP with RUP. However, there are a few things to consider with RUP. Batista et al. (2016), in their meta-analysis, reported that for diets with around 15% CP, a decrease in the efficiency of the incorporation of recycled N into ruminal microbial N was observed, with an efficiency of around 21%. This indicates that the efficiency of recycled N use is lower from RUP than the efficiency of consumed N use from RDP. Feeding RUP above requirements directly causes N excretion in manure. Also, to take advantage of efficient N recycling as RUP increases, the diet must be formulated to meet the energy requirements. This suggests that without an appropriate energy supply, enhanced N reuse from rumen microorganisms may not be obtained due to the lack of the carbon resources required for microbial protein production, leading only to increased N excretion rather than production of protein sources. In conclusion, decreasing CP concentration in the diet can potentially decrease NH₃ emissions, although it also decreases average daily gain, which can increase days on feed, the amount of manure deposited in the pens, and consequent increase in NH₃ emissions. Therefore, careful diet manipulation is needed to avoid unintended negative consequences for animal production and the environment.

6.1.3 Growth-promoting technologies

Growth-promoting technologies (implants and feed additives) are commonly used to reduce NH₃ emissions by less N excretion through increasing the efficiency of energy use for growth and by low cumulative NH₃ emissions from fewer days on feed required to reach finished weight. Although the specific mechanism for increasing productivity by growth-promoting technologies in beef cattle is different, growth-promoting technologies such as hormone implants and ß-adrenergic agonists increase nutrient use for protein synthesis and indirectly lead to decreased lipogenesis (Hutcheson et al., 1997; Nichols et al., 2002; Lean et al., 2014). It was reported that implants enhance both ADG and feed conversion, while implanted cattle often have less marbling and lower quality grades (Preston and Herschler, 1992; Selk, 1999; Ohnoutka et al., 2021). Also, monensin, which is generally included as a growth-promoting technology, is an ionophore antimicrobial that increases overall energy yield from feed and improves animal growth performance by increasing the ratio of propionate to acetate and decreasing the deamination of amino acids through preferentially inhibiting gram-positive bacteria in the rumen (Perry et al., 1976; Russell and Strobel, 1988; Tedeschi et al., 2003). Also, it prevents and controls Coccidiosis caused by Eimeria ssp in ruminants. An increase in protein synthesis with growth-promoting technologies would be expected to reduce N excretion. It has been reported that the use of conventional productivity-enhancing technologies (combination of implant, monensin, tylosin, ß-adrenergic agonists, and others), mitigated NH₃ emissions by 10~42%, but the effect of only implants mitigated 17% of NH₃ emissions (Stackhouse et al., 2012; Ross, 2021; Aboagye et al., 2022; Table 6). Additionally, the effect of only ß-adrenergic agonists reduced NH₃ emissions by 5 to 14% (Wendler et al., 2025; Table 6). The detailed mechanisms and effects of each growth-promoting technology are summarized in Brandani et al. (2023).

6.2 Ammonia mitigation practices in the post-excretion stage

6.2.1 Manure amendments

Manure amendment can be divided into chemical and physical amendments (Brandani et al., 2023). The NH₃ mitigation mechanism of chemical amendments is to add chemical compounds to manure to suppress the hydrolysis of excreted urinary urea or to create an environment with low pH, which is an unfavorable condition for NH₃ volatilization to occur. Representative examples of chemical amendments include urease inhibitors, N-(n-Butyl) thiophosphoric triamide, calcium chloride, humate, and aluminum sulfate. Physical amendments, such as biochar, carbon-rich material or biomass, bentonite, viscous plastic clay, and zeolite, microporous, crystalline aluminosilicate materials, act to adsorb NH₃ before being released into the atmosphere from the pen surface (Brandani et al., 2023).

Evaluation of manure amendment on the open feedyard surface to mitigate NH₃ emission has shown a wide range (19 to 98%) in mitigation effectiveness (Table 7). The urease inhibitor reduced NH₃ emissions by 26–66% on the manure surface in lab and pilot-scale studies but did not show significant mitigation at the field scale. However, nitrogen fertilizers coated with the urease inhibitors showed a significant mitigation of NH₃ emissions on grassland (67-79%). This suggests that further research is needed to determine the best application methods for urease inhibitors to achieve significant NH₃ reduction in feedyard manure. The effects of calcium chloride, humate, and aluminum sulfate, which lower the pH and inhibit urease decomposition, resulted in a mitigation rate of 20 to 71% (Shi et al., 2001; Spiehs and Woodbury, 2022) at the lab and pilot scale. As physical amendments, the lignite showed a mitigation rate of 66% (Chen et al., 2015) at the pilot scale. In addition, the mixture of biochar and bentonite showed a mitigation rate of 43%, and a 3% addition of zeolite reduced 10% of NH₃ emission (Szymula et al., 2021) at the lab scale. However, the low cost-efficiencies and the negligible financial benefit of NH₃ suppression made it challenging for manure amendment to be widely adopted in the feedyard. There is a growing need for technology that is economically advantageous and can be easily adapted to mitigate NH₃ emissions in the beef cattle feedyard effectively and at scale. In this respect, water application using sprinklers is one option exhibiting some promise (Lupis et al., 2012).

6.2.2 Water application

Water sprinklers are recognized to decrease dust emissions and have been adopted by some to mitigate heat stress for cattle, but they have not been used to mitigate NH₃ from the beef cattle feedyard (Brandani et al., 2023). The mechanism of water application to mitigate NH₃ comes from a dilution effect, which could relate to the simple leaching of aqueous NH₄⁺ away from the surface or absorption of volatilized NH₃. Hutchinson et al. (1982) suggested that precipitation events cause a dramatic increase in the size of the reservoir available for NH₃ to exist in solution-diluting the NH₄⁺ concentration and effectively decreasing the area of the air/water interface in the manure, the boundary at which volatilization occurs (40% NH₃ mitigation). However, a significant increase in NH₃ (160% NH₃ generation) was reported two days after precipitation, which leads us to question the temporal impact and whether water application is actually effective in mitigating NH₃ emissions in time scales relevant to feedyard management. Therefore, there is still a concern that it may cause more NH₃ volatilization in the long term by increasing the microbially mediated production of aqueous NH₃ within the water-filled pore space of the manure on the pen surface (Lee et al., 2023). As proof of this, a few studies have reported results contrary to the NH₃ mitigation with the water application (Table 7).

In summary, there is scientific agreement that the water application may mitigate NH₃ emissions (27~56%) under carefully controlled conditions and over short time scales. However, because of the lack of consensus on the use of water application, there is a concern that the NH₃ mitigation due to water sprinkling is temporary and generates more NH₃ during the evaporation process, especially when rapid evaporation of water occurs due to hot, windy weather. The impact of the water application on NH₃ emissions continues to be investigated and a clearer interpretation of this is expected to emerge in the future.

7 Discussion

The current major hurdle facing cattle feedyards in applying the above BMPs solely for NH₃ mitigation is whether the practically achievable benefits justify their costs. To be specific, feed composition is made close to the requirements of cattle with safety margins, and the pre-excretion technologies (e.g., growth-promoting technologies) are used to increase the nutrient-use efficiency of cattle, minimizing the nutrient excretion in most feedyards. According to Legesse et al. (2018), through such improvements in livestock management and in reproductive efficiency, NH₃ (kg) emitted per beef (kg) decreased 20% from 1981 to 2011. However, some studies have reported that the expansion of large-scale intensive livestock operations, such as CAFOs, has contributed to increasing total NH₃ emissions (Legesse et al., 2018; Schultz et al., 2019; Wyer et al., 2022). Therefore, to mitigate NH₃ emissions, higher-precision feeding and active use of pre- and post-excretion practices are necessary. However, overly strict implementation of precision feeding strategies may introduce unintended variability in livestock performance and increase operational costs due to reduced safety margins and the need to modify existing feedyard infrastructure. In addition, post-excretion BMPs constitute essentially unrecoverable expenses unless the BMP facilitates the production of a marketable product. Therefore, the benefits of the practices implemented to mitigate NH₃ emissions while bearing additional costs are an important factor in the feedyard’s decision to implement BMP.

High ambient NH₃ concentrations (average 42 ppm) have been reported to have a negative impact on the bovine lungs in respiration chamber-scale experiments, leading to increased total white cell and mononucleated cell counts (p< 0.05, Accioly et al., 2004). However, a knowledge gap exists on the effect of ambient NH₃ concentrations in the feedyard on animal productivity. Reported background concentrations of NH₃ at feedyards typically range from<1 to 2000 µg/m³ (0–3 ppm at 25 °C, 1 atm; Todd et al., 2005; Hristov et al., 2011). However, as highlighted by Hristov et al. (2011), the concentration of atmospheric NH₃ is highly variable in various forms (gas, particulate, and liquid) and depends on the presence of other compounds. Consequently, NH₃ concentrations are subject to considerable variability due to a combination of environmental and feedyard management factors, and under certain conditions, concentrations may reach levels that can potentially affect animal productivity. Additional research is needed to evaluate potential productivity improvement and its link to mitigating NH₃. Such studies could support the necessity of NH₃ mitigation efforts and offer practical benefits for livestock operations.

Based on the results currently reported, the following additional benefits can be considered for the use of BMP related to NH₃ mitigation. Precision feeding and diet manipulation aims to provide nutrient supply more precisely with the nutrient requirements, thus the benefits include economic returns through reduced excretion to the environment and improved efficiency of resource utilization by leading to decreased feed intake and thereby decreased enteric CH₄ emissions (Zuidhof, 2020; Galyean and Hales, 2023). Growth-promoting technologies increase the efficiency of energy and nutrient use, thereby increasing animal productivity and mitigating environmental effects while reducing the amount of time required to finish cattle. As an example, ionophores, one of the growth-promoting technologies, may decrease protein degradation in the rumen, increase feed protein utilization, and reduce N losses (Tedeschi et al., 2003). In addition, it can decrease feed intake (4%) without affecting animal performance, and mitigate 25% of enteric CH₄ emissions (Tedeschi et al., 2003).

In the case of manure amendment, it is not directly related to animal performance, but it is related to benefits for manure value (C:N ratio) and the mitigation of other gases (H₂S, GHGs, and VOCs). The C:N ratio in manure could vary greatly depending on diet, manure storage, manure management, and feedyard environments. It is generally reported that the C:N ratio of beef and dairy manure is 10 to 15:1 (Okopi et al., 2024). While close to the optimal C:N ratio (20 to 30:1) for net N mobilization through soil microorganisms (Hadas et al., 1992), manure C is insufficient in most cases. Manure amendments, which are a C source and particularly physical amendment, can improve C:N ratio and a MC (50-70%) for composting and land application. In addition, manure amendments have been reported to be effective in mitigating various gas emissions from cattle manure (Wheeler et al., 2011; Spiehs et al., 2019; Kaikiti et al., 2021; Chen et al., 2024). However, manure amendment could cause secondary air pollution from physical amendment (e.g., PM from biochar, Gelardi et al., 2019) or chemicals (e.g., CH₄ and H₂S from aluminum sulfate, Spiehs et al., 2019) added to prevent NH₃ gas volatilization. Since research on manure amendments has focused on target gas mitigation, there has been little research on the generation of by-products or gases after application (Maurer et al., 2016). It is important to be cautious when using the amendments to avoid secondary, perverse effects.

Lastly, the practice of water application was proposed as a method to reduce heat stress in terms of animal production, but it could potentially improve feed efficiency during the summer (Mader and Davis, 2004). Water application may be a cost-effective solution for industry PM control in some circumstances (Yonkofski et al., 2019), and it has been reported to have the mitigation effect of other gases (GHGs such as CH₄ and N₂O) as well (Parker et al., 2021). Precipitation, which is the natural way to apply water, was observed to mitigate the emission of CH₄ and N₂O below detection levels for several days after the precipitation event in the feedyard (Parker et al., 2021). In lab-scale experiments, increased N₂O emission has been observed after precipitation for several days (Parker et al., 2017, 2018), but this phenomenon has not been observed on the field scale (Parker et al., 2021). Further research is still needed because there are concerns about more gas volatilization during the drying process after water application and practical research is necessary into how water can be applied to feedyards as precipitation to achieve beneficial effects.

The direction we should take to mitigate NH₃ emissions in feedyards is to maximize N-use efficiency of beef cattle by optimizing the pre-excretion management while simultaneously minimizing the environmental impacts using post-excretion management. To encourage the adoption of a given management practice, more research is needed to quantify its benefits, to describe as fully as possible the conditions under which those benefits may be realized in practice and at scale, to develop new promising practices, and to reckon transparently with a practice’s perverse effects, if any.

8 Conclusion

NH₃ emitted from beef cattle feedyards is a high-profile environmental concern because of health hazards, its contribution to fine particulate formation, and contamination of air and surface waters. Mitigation of NH₃ emissions addresses social concerns, minimizes the risk of undesirable environmental events, and is important to the sustainability of the beef industry. In this review, we reported the state of the science concerning NH₃ emissions from beef cattle feedyards, methods for quantifying NH₃ emissions, NH₃ EF, and some management practices to mitigate NH₃. Ammonia emissions primarily come from urinary urea in cattle manure on feedyard surfaces. A significant portion of the N in the manure is converted to NH₄⁺ and is eventually volatilized to the atmosphere as NH₃. In the past, constant EFs were used to inventory NH₃ emissions. Currently, NH₃ EF estimated by process-based mechanistic models reflecting various factors affecting NH₃ emissions in the feedyard environment are available. As process-based mechanistic models, the backward Lagrangian stochastic model was widely used to convert NH₃ concentration measurements into emissions in the beef cattle feedyard. This review of current literature indicated the average NH₃ emissions from the cattle feedyard as 119 g/head/day (ranging from 24 to 318 g/head/day), and the average NH₃ flux rate as 58 µg/m²/s (ranging from 2 to 185 µg/m²/s). Although it is agreed that more realistic NH₃ EF are being obtained using process-based models, there is still significant variation of estimated NH₃ EF depending on the diet composition, the manure management, and the feedyard environment. We note the need to improve inventories of NH₃ emissions into categories of manure management implemented and feedyard environment. Some mitigation strategies can be effective, such as manipulating the diet to reduce N excretion, increasing animal performance with growth-promoting technologies, and using manure amendments. Of those, precision diet feeding to meet, but not exceed, protein requirements appears to be the most practical way to reduce N losses. However, careful diet manipulation and additional research are needed to avoid unintended negative consequences for animal production.

References

• 1

  AboagyeI. A.CordeiroM. R.McAllisterT. A.MayM. L.HannonS. J.BookerC. W.et al. (2022). Environmental performance of commercial beef production systems utilizing conventional productivity-enhancing technologies. Trans. Anim. Sci.6, txac074. doi: 10.1093/tas/txac074

  • CrossRef
  • Google Scholar
  • View reference in article

• 2

  AcciolyJ. M.TaylorE. G.CostaN. D.PethickD. W.WhiteC. L.PluskeJ. R.et al. (2004). Effect of atmospheric ammonia on bovine lung. Sci. Access1, 1–4. doi: 10.1071/SA0401001

  • CrossRef
  • Google Scholar
  • View reference in article

• 3

  AguadoD.Noriega-HeviaG.FerrerJ.SecoA.SerraltaJ. (2022). PLS-based soft-sensor to predict ammonium concentration evolution in hollow fibre membrane contactors for nitrogen recovery. J. Water Process Eng.47, 102735. doi: 10.1016/j.jwpe.2022.102735

  • CrossRef
  • Google Scholar
  • View reference in article

• 4

  ArchibequeS. L.FreetlyH. C.ColeN. A.FerrellC. L. (2007). The influence of oscillating dietary protein concentrations on finishing cattle. II. Nutrient retention and ammonia emissions. J. Anim. Sci.85, 1496–1503. doi: 10.2527/jas.2006-208

  • CrossRef
  • Google Scholar
  • View reference in article

• 5

  ArogoJ.WestermanP. W.HeberA. J.RobargeW. P.ClassenJ. J. (2006). “Ammonia emissions from animal feeding operations,” in 2006 ASABE Annual International Meeting. 1 (St. Joseph, Michigan: American Society of Agricultural and Biological Engineers). doi: 10.13031/2013.20247

  • CrossRef
  • Google Scholar
  • View reference in article

• 6

  BaekB. H.ToddR.ColeN. A.KozielJ. A. (2006). Ammonia and hydrogen sulphide flux and dry deposition velocity estimates using vertical gradient method at a commercial beef cattle feedlot. Int. J. Global Environ. Issues6, 189–203. doi: 10.1504/IJGENVI.2006.010154

  • CrossRef
  • Google Scholar
  • View reference in article

• 7

  BaldacchiniG.MarchettiS.MontelaticiV. (1981). Diode laser measurements of NH3 absorption lines over the range 931–954 cm– 1. J. Mol. Spectrosc.86, 115–121. doi: 10.1016/0022-2852(81)90109-0

  • CrossRef
  • Google Scholar
  • View reference in article

• 8

  BatistaE. D.DetmannE.TitgemeyerE. C.Valadares FilhoS. C.ValadaresR. F. D.PratesL. L.et al. (2016). Effects of varying ruminally undegradable protein supplementation on forage digestion, nitrogen metabolism, and urea kinetics in Nellore cattle fed low-quality tropical forage. J. Anim. Sci.94, 201–216. doi: 10.2527/jas.2015-9493

  • CrossRef
  • Google Scholar
  • View reference in article

• 9

  BattyeR.BattyeW.OvercashC.FudgeS. (1994). Development and selection of ammonia emission factors (Washington D.C.: U.S. Environmental Protection Agency, Contract number 68-D3-0034).

  • Google Scholar
  • View reference in article

• 10

  BenedictK. B.DayD.SchwandnerF. M.KreidenweisS. M.SchichtelB.MalmW. C.et al. (2013). Observations of atmospheric reactive nitrogen species in Rocky Mountain National Park and across northern Colorado. Atmospheric Environ.64, 66–76. doi: 10.1016/j.atmosenv.2012.08.066

  • CrossRef
  • Google Scholar

• 11

  BonifacioH. F.MaghirangR. G.RazoteE. B.TrabueS. L.PruegerJ. H. (2013). Comparison of AERMOD and WindTrax dispersion models in determining PM10 emission rates from a beef cattle feedlot. J. Air Waste Manage. Assoc.63, 545–556. doi: 10.1080/10962247.2013.768311

  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  BrandaniC. B.LeeM.AuvermannB. W.ParkerD. B.CaseyK. D.CrosmanE. T.et al. (2023). Mitigating ammonia deposition derived from open-lot livestock facilities into Colorado’s Rocky Mountain National Park: State of the science. Atmosphere14, 1469. doi: 10.3390/atmos14101469

  • CrossRef
  • Google Scholar
  • View reference in article

• 13

  BristowA. W.WhiteheadD. C.CockburnJ. E. (1992). Nitrogenous constituents in the urine of cattle, sheep and goats. J. Sci. Food Agric.59, 387–394. doi: 10.1002/jsfa.2740590316

  • CrossRef
  • Google Scholar
  • View reference in article

• 14

  BussinkD. W.OenemaO. (1998). Ammonia volatilization from dairy farming systems in temperate areas: a review. Nutrient Cycling Agroecosystems51, 19–33. doi: 10.1023/A:1009747109538

  • CrossRef
  • Google Scholar
  • View reference in article

• 15

  CalsamigliaS.FerretA.ReynoldsC. K.KristensenN. B.Van VuurenA. M. (2010). Strategies for optimizing nitrogen use by ruminants. Animal4, 1184–1196. doi: 10.1017/S1751731110000911

  • CrossRef
  • Google Scholar
  • View reference in article

• 16

  ChenB.KozielJ. A.BialowiecA.O’BrienS. C. (2024). The potential role of biochar in mitigating gaseous emissions from livestock waste–A mini-review. J. Environ. Manage.370, 122692. doi: 10.1016/j.jenvman.2024.122692

  • CrossRef
  • Google Scholar
  • View reference in article

• 17

  ChenD.SunJ.BaiM.DassanayakeK. B.DenmeadO. T.HillJ. (2015). A new cost-effective method to mitigate ammonia loss from intensive cattle feedlots: application of lignite. Sci. Rep.5, 16689. doi: 10.1038/srep16689

  • CrossRef
  • Google Scholar
  • View reference in article

• 18

  CofieO.NikiemaJ.ImpraimR.AdamteyN.PaulJ.KonéD. (2016). Co-composting of solid waste and fecal sludge for nutrient and organic matter recovery Vol. 3 (IWMI. CGIAR Research program on Water, Land and Ecosystems), 47. doi: 10.5337/2016.204

  • CrossRef
  • Google Scholar
  • View reference in article

• 19

  ColeN. A. (2003). “Precision feeding: Opportunities and limitations,” in Proc. Plains Nutr. Council Spring Conf. Publ. No. AREC, 03-13. (Texas).

  • Google Scholar
  • View reference in article

• 20

  ColeN. A.ClarkR. N.ToddR. W.RichardsonC. R.GueyeA.GreeneL. W.et al. (2005). Influence of dietary crude protein concentration and source on potential ammonia emissions from beef cattle manure. J. Anim. Sci.83, 722–731. doi: 10.2527/2005.833722x

  • CrossRef
  • Google Scholar
  • View reference in article

• 21

  ColeN. A.DefoorP. J.GalyeanM. L.DuffG. C.GleghornJ. F. (2006). Effects of phase-feeding of crude protein on performance, carcass characteristics, serum urea nitrogen concentrations, and manure nitrogen of finishing beef steers. J. Anim. Sci.84, 3421–3432. doi: 10.2527/jas.2006-150

  • CrossRef
  • Google Scholar
  • View reference in article

• 22

  ColeN. A.ToddR. W. (2009). “Nitrogen and phosphorus balance of beef cattle feedyards,” in Proceedings of the Texas animal manure management issues conference, TX, USA. 17–24 (St. Joseph, Michigan: Round Rock).

  • Google Scholar
  • View reference in article

• 23

  ColmeneroJ. O.BroderickG. A. (2006). Effect of dietary crude protein concentration on milk production and nitrogen utilization in lactating dairy cows. J. Dairy Sci.89, 1704–1712. doi: 10.3168/jds.S0022-0302(06)72238-X

  • CrossRef
  • Google Scholar
  • View reference in article

• 24

  DawarK.ZamanM.RowarthJ. S.BlennerhassettJ.TurnbullM. H. (2011). Urease inhibitor reduces N losses and improves plant-bioavailability of urea applied in fine particle and granular forms under field conditions. Agriculture, Ecosystems & Environment144 (1), 41–50. doi: 10.1016/j.agee.2011.08.007

  • CrossRef
  • Google Scholar
  • View reference in article

• 25

  DenmeadO. T.ChenD.GriffithD. W. T.LohZ. M.BaiM.NaylorT. (2008). Emissions of the indirect greenhouse gases NH3 and NOx from Australian beef cattle feedlots. Australian. J. Exp. Agric.48, 213–218. doi: 10.1071/EA07276

  • CrossRef
  • Google Scholar
  • View reference in article

• 26

  EgglestonH. S.BuendiaL.MiwaK.NgaraT.TanabeK. (2006). 2006 IPCC guidelines for national greenhouse gas inventories (Kanagawa, Japan: U.S. Department of Energy Office of Scientific and Technical Information). Available at: https://www.osti.gov/etdeweb/biblio/20880391.

  • Google Scholar
  • View reference in article

• 27

  EmersonK.RussoR. C.LundR. E.ThurstonR. V. (1975). Aqueous ammonia equilibrium calculations: effect of pH and temperature. J. Fisheries Board Canada32, 2379–2383. doi: 10.1139/f75-274

  • CrossRef
  • Google Scholar
  • View reference in article

• 28

  EricksonG.KlopfensteinT. (2010). Nutritional and management methods to decrease nitrogen losses from beef feedlots. J. Anim. Sci.88, E172–E180. doi: 10.2527/jas.2009-2358

  • CrossRef
  • Google Scholar
  • View reference in article

• 29

  EricksonG. E.MiltonC. T.KlopfensteinT. J. (2000). “Dietary protein effects on nitrogen excretion and volatilization in open-dirt feedlots,” in Animal, agricultural and food processsing wastes. Proceedings of the Eighth International Symposium, Des Moines, Iowa, USA, Vol. 9. 297–304. doi: 10.5555/20013011378

  • CrossRef
  • Google Scholar
  • View reference in article

• 30

  FaulknerW. B.ShawB. W. (2008). Review of ammonia emission factors for United States animal agriculture. Atmospheric Environ.42, 6567–6574. doi: 10.1016/j.atmosenv.2008.04.021

  • CrossRef
  • Google Scholar
  • View reference in article

• 31

  FleschT. K.WilsonJ. D. (2005). Estimating tracer emissions with a backward Lagrangian stochastic technique. Micrometeorol. Agric. Syst.47, 513–531. doi: 10.2134/agronmonogr47.c22

  • CrossRef
  • Google Scholar
  • View reference in article

• 32

  FleschT. K.WilsonJ. D.HarperL. A.CrennaB. P.SharpeR. R. (2004). Deducing ground-to-air emissions from observed trace gas concentrations: A field trial. J. Appl. Meteorol. Climatol.43, 487–502. doi: 10.1175/1520-0450(2004)043<0487:DGEFOT>2.0.CO;2

  • CrossRef
  • Google Scholar
  • View reference in article

• 33

  FleschT. K.WilsonJ. D.HarperL. A.ToddR. W.ColeN. A. (2007). Determining ammonia emissions from a cattle feedlot with an inverse dispersion technique. Agric. For. Meteorol.144, 139–155. doi: 10.1016/j.agrformet.2007.02.006

  • CrossRef
  • Google Scholar
  • View reference in article

• 34

  FleschT. K.WilsonJ. D.YeeE. (1995). Backward-time Lagrangian stochastic dispersion models and their application to estimate gaseous emissions. J. Appl. Meteorol. Climatol.34, 1320–1332. doi: 10.1175/1520-0450(1995)034<1320:BTLSDM>2.0.CO;2

  • CrossRef
  • Google Scholar

• 35

  ForrestalP. J.HartyM.CarolanR.LaniganG. J.WatsonC. J.LaughlinR. Jet al. (2016). Ammonia emissions from urea, stabilized urea and calcium ammonium nitrate: insights into loss abatement intemperate grassland. Soil Use and Management32, doi: 92-100. doi: 10.1111/sum.12232

  • CrossRef
  • Google Scholar
  • View reference in article

• 36

  FreneyJ. R.SimpsonJ. R. (2013). Gaseous loss of nitrogen from plant-soil systems Vol. 9 (Springer Science & Business Media). doi: 10.1007/978-94-017-1662-8

  • CrossRef
  • Google Scholar
  • View reference in article

• 37

  GallesK. J. (2011). Practical strategies for reducing ammonia volatilization from feedlots along Colorado’s Front Range. (Master thesis), Colorado State University, Fort Collins, Clorado.

  • Google Scholar
  • View reference in article

• 38

  GallesK. J.HamJ.WestoverE.StrattonJ.WagnerJ.EngleT.et al. (2011). Influence of reduced nitrogen diets on ammonia emissions from cattle feedlot pens. Atmosphere2, 655–670. doi: 10.3390/atmos2040655

  • CrossRef
  • Google Scholar

• 39

  GalyeanM. L.HalesK. E. (2023). Feeding management strategies to mitigate methane and improve production efficiency in feedlot cattle. Animals13, 758. doi: 10.3390/ani13040758

  • CrossRef
  • Google Scholar
  • View reference in article

• 40

  GelardiD. L.LiC.ParikhS. J. (2019). An emerging environmental concern: Biochar-induced dust emissions and their potentially toxic properties. Sci. Total Environ.678, 813–820. doi: 10.1016/j.scitotenv.2019.05.007

  • CrossRef
  • Google Scholar
  • View reference in article

• 41

  GolstonL. M.PanD.SunK.TaoL.ZondloM. A.EilermanS. J.et al. (2020). Variability of ammonia and methane emissions from animal feeding operations in northeastern Colorado. Environ. Sci. Technol.54, 11015–11024. doi: 10.1021/acs.est.0c00301

  • CrossRef
  • Google Scholar
  • View reference in article

• 42

  GoodliffM.FletcherS.KliewerA.ForsytheJ.JonesA. (2020). Detection of non-Gaussian behavior using machine learning techniques: a case study on the Lorenz 63 model. J. Geophysical Research: Atmospheres125, e2019JD031551. doi: 10.1029/2019JD031551

  • CrossRef
  • Google Scholar

• 43

  GuB.ZhangL.Van DingenenR.VienoM.Van GrinsvenH. J.ZhangX.et al. (2021). Abating ammonia is more cost-effective than nitrogen oxides for mitigating PM2.5 air pollution. Science374, 758–762. doi: 10.1126/science.abf8623

  • CrossRef
  • Google Scholar

• 44

  HadasA.FeigenbaumS.MolinaJ. A. E.ClappC. E. (1992). Factors affecting nitrogen immobilization in soil as estimated by simulation models. Soil Sci. Soc. America J.56, 1481–1486. doi: 10.2136/sssaj1992.03615995005600050024x

  • CrossRef
  • Google Scholar

• 45

  HarperL. A. (2005). Ammonia: measurement issues. Micrometeorol. Agric. Syst.47, 345–379. doi: 10.2134/agronmonogr47.c15

  • CrossRef
  • Google Scholar

• 46

  HarperL. A.DenmeadO. T.FleschT. K. (2011). Micrometeorological techniques for measurement of enteric greenhouse gas emissions. Anim. Feed Sci. Technol.166, 227–239. doi: 10.1016/j.anifeedsci.2011.04.013

  • CrossRef
  • Google Scholar

• 47

  HarperL. A.FleschT. K.WeaverK. H.WilsonJ. D. (2010b). The effect of biofuel production on swine farm methane and ammonia emissions. J. Environ. Qual.39, 1984–1992. doi: 10.2134/jeq2010.0172

  • CrossRef
  • Google Scholar

• 48

  HarperL. A.FleschT. K.WilsonJ. D. (2010a). Ammonia emissions from broiler production in the San Joaquin Valley. Poultry Sci.89, 1802–1814. doi: 10.3382/ps.2010-00718

  • CrossRef
  • Google Scholar

• 49

  HarperL. A.SharpeR. R.ParkinT. B.De VisscherA.Van CleemputO.ByersF. M. (2004). Nitrogen cycling through swine production systems: Ammonia, dinitrogen, and nitrous oxide emissions. J. Environ. Qual.33, 1189–1201. doi: 10.2134/jeq2004.1189

  • CrossRef
  • Google Scholar

• 50

  HermanussenJ.BizzarriA.BaldacchiniG. (1986). Diode laser measurements of ammonia absorption lines over the range 620–740 cm– 1. J. Mol. Spectrosc.119, 291–298. doi: 10.1016/0022-2852(86)90025-1

  • CrossRef
  • Google Scholar

• 51

  HortonH. R.MoranL. A.ScrimgeourK. G.PerryM. D.RawnJ. D. (2006). “Principles of biochemistry,” in Principles of biochemistry (New Jersey: Pearson College Div), 852–852.

  • Google Scholar

• 52

  HribarC. (2010). Understanding concentrated animal feeding operations and their impact on communities (National Association of Local Boards of Health, Bowling Green, Ohio: Centers for Disease Control and Prevention). Available at: https://stacks.cdc.gov/view/cdc/59792.

  • Google Scholar

• 53

  HristovA. N.HaniganM.ColeA.ToddR.McAllisterT. A.NdegwaP. M.et al. (2011). Ammonia emissions from dairy farms and beef feedlots. Can. J. Anim. Sci.91, 1–35. doi: 10.4141/CJAS10034

  • CrossRef
  • Google Scholar

• 54

  HristovA. N.JouanyJ. P. (2005). “Factors affecting the efficiency of nitrogen utilization in the rumen,” in Nitrogen and phosphorus nutrition of cattle: reducing the environmental impact of cattle operations, (CABI Digital Library) 117–166. doi: 10.1079/9780851990132.0117

  • CrossRef
  • Google Scholar

• 55

  HutchesonJ. P.JohnsonD. E.GerkenC. L.MorganJ. B.TatumJ. D. (1997). Anabolic implant effects on visceral organ mass, chemical body composition, and estimated energetic efficiency in cloned (genetically identical) beef steers. J. Anim. Sci.75, 2620–2626. doi: 10.2527/1997.75102620x

  • CrossRef
  • Google Scholar

• 56

  HutchinsonG. L.MosierA. R.AndreC. E. (1982). Ammonia and amine emissions from a large cattle feedlot. J. Environ. Qual.11, 288–293. doi: 10.2134/jeq1982.00472425001100020028x

  • CrossRef
  • Google Scholar

• 57

  JamesT.MeyerD.EsparzaE.DepetersE. J.Perez-MontiH. (1999). Effects of dietary nitrogen manipulation on ammonia volatilization from manure from Holstein heifers. J. Dairy Sci.82, 2430–2439. doi: 10.3168/jds.S0022-0302(99)75494-9

  • CrossRef
  • Google Scholar

• 58

  KaikitiK.StylianouM.AgapiouA. (2021). Use of biochar for the sorption of volatile organic compounds (VOCs) emitted from cattle manure. Environ. Sci. pollut. Res.28, 59141–59149. doi: 10.1007/s11356-020-09545-y

  • CrossRef
  • Google Scholar

• 59

  KozielJ. A.BaekB. H.SpinhirneJ. P.ParkerD.ColeN. A. (2004). “Emissions of ammonia and hydrogen sulfide from beef cattle pens in Texas,” in In the proceedings of the AgEng, ‘Engineering the Future’ conference, Leuven, Belgium.

  • Google Scholar

• 60

  KrolD. J.ForrestalP. J.WallD.LaniganG. J.Sanz-GomezJ.RichardsK. G. (2020). Nitrogen fertilisers with urease inhibitors reduce nitrous oxide and ammonia losses, while retaining yield in temperate grassland. Sci. Total Environ. 725, 138329. doi: 10.1016/j.scitotenv.2020.138329

  • CrossRef
  • Google Scholar

• 61

  LeanI. J.ThompsonJ. M.DunsheaF. R. (2014). A meta-analysis of zilpaterol and ractopamine effects on feedlot performance, carcass traits and shear strength of meat in cattle. PloS One9, e115904. doi: 10.1371/journal.pone.0115904

  • CrossRef
  • Google Scholar

• 62

  LeeM.BrandaniC. B.BushK. J.FergusonG. B.WillisW.CampbellT. N.et al. (2023). “The effect of water application on ammonia emissions from open-lot livestock-feeding surfaces,” in 2023 ASABE Annual International Meeting (St. Joseph, Michigan: American Society of Agricultural and Biological Engineers). doi: 10.13031/aim.223011050

  • CrossRef
  • Google Scholar

• 63

  LeeC.HristovA. N. (2010). Origin of ammonia nitrogen volatilized from dairy manure. J. Dairy Sci.93, 691–691.

  • Google Scholar

• 64

  LeeC.HristovA. N.SilvaS. (2009). Effect of ammonia volatilization on manure nitrogen isotope composition. J. Dairy Sci.92, 146.

  • Google Scholar

• 65

  LegesseG.KroebelR.AlemuA. W.OminskiK. H.McGeoughE. J.BeaucheminK. A.et al. (2018). Effect of changes in management practices and animal performance on ammonia emissions from Canadian beef production in 1981 as compared with 2011. Can. J. Anim. Sci.98, 833–844. doi: 10.1139/cjas-2017-0184

  • CrossRef
  • Google Scholar

• 66

  LiS.DuK. (2020). Comparisons of forward-in-time and backward-in-time Lagrangian stochastic dispersion models for micro-scale atmospheric dispersion. J. Air Waste Manage. Assoc.70, 425–435. doi: 10.1080/10962247.2020.1728424

  • CrossRef
  • Google Scholar

• 67

  LiH.XinH.BurnsR. T.HoffS. J.HarmonJ. D.JacobsonL. D.et al. (2008). “Ammonia and PM emissions from a tom Turkey barn in Iowa,” in 2008 ASABE Annual International Meeting (St. Joseph, Michigan: American Society of Agricultural and Biological Engineers). doi: 10.13031/2013.24972

  • CrossRef
  • Google Scholar

• 68

  LupisS. G.GallesK. J.HamJ. M.StrattonJ. J.DavisJ. G.EmbertsonN. (2012). Best management practices for reducing ammonia emissions: feedlot pen management. Environment40, 5137–5145.

  • Google Scholar

• 69

  MaderT. L.DavisM. S. (2004). Effect of management strategies on reducing heat stress of feedlot cattle: feed and water intake. J. Anim. Sci.82, 3077–3087. doi: 10.2527/2004.82103077x

  • CrossRef
  • Google Scholar

• 70

  MaurerD. L.KozielJ. A.HarmonJ. D.HoffS. J.Rieck-HinzA. M.AndersenD. S. (2016). Summary of performance data for technologies to control gaseous, odor, and particulate emissions from livestock operations: Air management practices assessment tool (AMPAT). Data Brief7, 1413–1429. doi: 10.1016/j.dib.2016.03.070

  • CrossRef
  • Google Scholar

• 71

  McGinnS. M.FleschT. K. (2018). Ammonia and greenhouse gas emissions at beef cattle feedlots in Alberta Canada. Agric. For. Meteorol.258, 43–49. doi: 10.1016/j.agrformet.2018.01.024

  • CrossRef
  • Google Scholar

• 72

  McGinnS. M.FleschT. K.CrennaB. P.BeaucheminK. A.CoatesT. (2007). Quantifying ammonia emissions from a cattle feedlot using a dispersion model. J. Environ. Qual.36, 1585–1590. doi: 10.2134/jeq2007.0167

  • CrossRef
  • Google Scholar

• 73

  McGinnS. M.JanzenH. H.CoatesT. W.BeaucheminK. A.FleschT. K. (2016). Ammonia emission from a beef cattle feedlot and its local dry deposition and re-emission. J. Environ. Qual.45, 1178–1185. doi: 10.2134/jeq2016.01.0009

  • CrossRef
  • Google Scholar

• 74

  Mejia TurciosS. E. (2024). Evaluation of different approaches to reduce greenhouse gases and air pollutants from feedlot cattle production. Doctoral dissertation, UC Davis, CA, US.

  • Google Scholar

• 75

  MenezesA. C. B.Valadares FilhoS. C.e SilvaL. C.PachecoM. V. C.PereiraJ. M. V.RottaP. P.et al. (2016). Does a reduction in dietary crude protein content affect performance, nutrient requirements, nitrogen losses, and methane emissions in finishing Nellore bulls? Agriculture Ecosyst. Environ.223, 239–249. doi: 10.1016/j.agee.2016.03.015

  • CrossRef
  • Google Scholar

• 76

  MengZ.LinW.ZhangR.HanZ.JiaX. (2017). Summertime ambient ammonia and its effects on ammonium aerosol in urban Beijing, China. Sci. Total Environ.579, 1521–1530. doi: 10.1016/j.scitotenv.2016.11.159

  • CrossRef
  • Google Scholar

• 77

  MohiuddinS. S.KhattarD. (2019). Biochemistry, ammonia (Europe: PMC).

  • Google Scholar

• 78

  MontesF.RotzC. A.ChaouiH. (2009). “Process modeling of ammonia volatilization from ammonium solution and manure surfaces: a review with recommended models,” in 2009 ASABE Annual International Meeting, Vol. 52. 1707–1720 (St. Joseph, Michigan: American Society of Agricultural and Biological Engineers). doi: 10.13031/2013.29133

  • CrossRef
  • Google Scholar

• 79

  MorrisK. (2016). 2014 data summary of wet nitrogen deposition at Rocky Mountain National Park. Health Environ. Res. Oline.

  • Google Scholar

• 80

  MuckR. E. (1982). Urease activity in bovine feces. J. Dairy Sci.65, 2157–2163. doi: 10.3168/jds.S0022-0302(82)82475-2

  • CrossRef
  • Google Scholar

• 81

  MuckR. E.SteenhuisT. S. (1982). Nitrogen losses from manure storages. Agric. Wastes4, 41–54. doi: 10.1016/0141-4607(82)90053-1

  • CrossRef
  • Google Scholar

• 82

  NASEM (2016). Nutrient Requirements of Beef Cattle. Eighth Revised Edition (Washington, DC, USA: National Academies of Sciences, Engineering, and Medicine).

  • Google Scholar

• 83

  NiJ. (1999). Mechanistic models of ammonia release from liquid manure: a review. J. Agric. Eng. Res.72, 1–17. doi: 10.1006/jaer.1998.0342

  • CrossRef
  • Google Scholar

• 84

  NicholsW. T.GalyeanM. L.ThomsonD. U.HutchesonJ. P. (2002). Effects of steroid implants on the tenderness of beef. Prof. Anim. Scientist18, 202–210. doi: 10.15232/S1080-7446(15)31523-0

  • CrossRef
  • Google Scholar

• 85

  NRC (1985). Ruminant Nitrogen Usage (Washington DC. USA: National Academy Press).

  • Google Scholar

• 86

  NRC (2001). Nutrient requirements of dairy cattle. 7th rev. ed (Washington, DC. USA: National Academy Press).

  • Google Scholar

• 87

  NRC (2002). The scientific basis for estimating air emissions from animal feeding operations: Interim report (Washington, DC: National Academies Press).

  • Google Scholar

• 88

  NRC (2003). Air emissions from animal feeding operations: Current knowledge, future needs (Washington, DC: National Academies Press).

  • Google Scholar

• 89

  OhnoutkaC. A.BondurantR. G.BoydB. M.HilscherF. H.NuttelmanB. L.CrawfordG. I.et al. (2021). Evaluation of coated steroidal combination implants on feedlot performance and carcass characteristics of beef heifers fed for constant or varying days on feed. Appl. Anim. Sci.37, 41–51. doi: 10.15232/aas.2020-02013

  • CrossRef
  • Google Scholar

• 90

  OkopiS.LiY.XuF. (2024). “Biomass Digestion,” in Encyclopedia of Sustainable Technologies (Elsevier), 236–251. doi: 10.1016/B978-0-323-90386-8.00051-6

  • CrossRef
  • Google Scholar

• 91

  PandrangiS.ParkerD. B.GreeneL. W.AlmasL. K.RhoadesM. B.ColeN. A. (2003). “Effect of dietary crude protein on ammonia emissions from open-lot beef cattle feedyards,” in 2003 ASAE Annual International Meeting (St. Joseph, Michigan: American Society of Agricultural and Biological Engineers). doi: 10.13031/2013.13889

  • CrossRef
  • Google Scholar

• 92

  ParkerD. B.CaseyK. D.WillisW.MeyerB. (2021). “Nitrous oxide and methane emissions from beef cattle feedyard pens following large rainfall events,” in 2021 ASABE Annual Meeting, Vol. 64. 1211–1225 (St. Joseph, Michigan: American Society of Agricultural and Biological Engineers). doi: 10.13031/trans.14480

  • CrossRef
  • Google Scholar

• 93

  ParkerD. B.PandrangiS.GreeneL. W.AlmasL. K.ColeN. A.RhoadesM. B.et al. (2004). “Application rate and timing effects on urease inhibitor performance for minimizing ammonia emissions from beef cattle feedyards,” in 2004 ASAE Annual International Meeting (St. Joseph, Michigan: American Society of Agricultural and Biological Engineers). doi: 10.13031/2013.16787

  • CrossRef
  • Google Scholar

• 94

  ParkerD. B.RhoadesM. B.ColeN. A.SambanaV. P. (2011). “Effect of urease inhibitor application rate and rainfall on ammonia emissions from beef manure,” in 2011 ASABE Annual Meeting, Vol. 55. 211–218 (St. Joseph, Michigan: American Society of Agricultural and Biological Engineers). doi: 10.13031/2013.41248

  • CrossRef
  • Google Scholar

• 95

  ParkerD. B.RhoadesM. B.KozielJ. A.BaekB. H.WaldripH. M.ToddR. W. (2016). Urease inhibitor for reducing ammonia emissions from an open-lot beef cattle feedyard in the Texas High Plains. Appl. Eng. Agric.32, 823–832. doi: 10.13031/aea.32.11897

  • CrossRef
  • Google Scholar

• 96

  ParkerD. B.WaldripH. M.CaseyK. D.ToddR. W.WillisW. M.WebbK. (2017). Temporal nitrous oxide emissions from beef cattle feedlot manure after a simulated rainfall event. J. Environ. Qual.46, 733–740. doi: 10.2134/jeq2017.02.0042

  • CrossRef
  • Google Scholar

• 97

  ParkerD. B.WaldripH. M.CaseyK. D.WoodburyB. L.SpiehsM. J.WebbK.et al. (2018). How do temperature and rainfall affect nitrous oxide emissions from open-lot beef cattle feedyard pens? Trans. ASABE61, 1049–1061. doi: 10.13031/trans.12788

  • CrossRef
  • Google Scholar

• 98

  PerryT. W.BeesonW. M.MohlerM. T. (1976). Effect of monensin on beef cattle performance. J. Anim. Sci.42, 761–765. doi: 10.2527/jas1976.423761x

  • CrossRef
  • Google Scholar

• 99

  PrestonR. L.HerschlerR. C. (1992). Controlled release estradiol/progesterone anabolic implant in cattle (Lubbock (TX: Texas Tech Univ Agric Sci Tech Rep), 5.

  • Google Scholar

• 100

  ProctorJ. A. (2023). Implications of site and extent of protein digestion in growing and finishing cattle. [Doctoral dissertation] (College Station, Texas: Texas A&M University).

  • Google Scholar

• 101

  QuinnS. A.EricksonG. E.KlopfensteinT. J.StowellR. R.SherwoodD. M. (2007). Effect of phase feeding protein on cattle performance and nitrogen mass balance in open feedlots. Board Regents Univ. Nebraska.

  • Google Scholar

• 102

  ReddingM. R.LewisR.ShortenP. R. (2019). Simultaneous measurements of ammonia volatilisation and deposition at a beef feedlot. Anim. Production Sci.59, 160–168. doi: 10.1071/AN17310

  • CrossRef
  • Google Scholar

• 103

  ReddyD. V.KrishnaN. (2009). Precision animal nutrition: A tool for economic and eco-friendly animal production in ruminants. Livestock Res. Rural Dev.21, 36.

  • Google Scholar

• 104

  RenardJ. J.CalidonnaS. E.HenleyM. V. (2004). Fate of ammonia in the atmosphere—a review for applicability to hazardous releases. J. Hazardous Materials108, 29–60. doi: 10.1016/j.jhazmat.2004.01.015

  • CrossRef
  • Google Scholar

• 105

  ReynalS. M.BroderickG. A. (2005). Effect of dietary level of rumen-degraded protein on production and nitrogen metabolism in lactating dairy cows. J. Dairy Sci.88, 4045–4064. doi: 10.3168/jds.S0022-0302(05)73090-3

  • CrossRef
  • Google Scholar

• 106

  RhoadesM. B.AuvermannB. W.ColeN. A.ToddR. W.ParkerD. B.CarawayE. A.et al. (2008). “Ammonia concentration and modeled emission rates from a beef cattle feedyard,” in 2008 Providence, Rhode Island, June 29–July 2, 2008 ( St. Joseph, Michigan: American Society of Agricultural and Biological Engineers). doi: 10.13031/2013.24774

  • CrossRef
  • Google Scholar

• 107

  RhoadesM. B.ParkerD. B.ColeN. A.ToddR. W.CarawayE. A.AuvermannB. W.et al. (2010). Continuous ammonia emission measurements from a commercial beef feedyard in Texas. Trans. ASABE53, 1823–1831. doi: 10.13031/2013.35808

  • CrossRef
  • Google Scholar

• 108

  RossE. G. (2021). Mitigation of gaseous emissions from beef and dairy cattle through feed additives and manure supplements. University of California, Davis.

  • Google Scholar

• 109

  RussellJ. B.StrobelH. J. (1988). Effects of additives on in vitro ruminal fermentation: a comparison of monensin and bacitracin, another gram-positive antibiotic. J. Anim. Sci.66, 552–558. doi: 10.2527/jas1988.662552x

  • CrossRef
  • Google Scholar

• 110

  SaarijärviK.MattilaP. K.VirkajärviP. (2006). Ammonia volatilization from artificial dung and urine patches measured by the equilibrium concentration technique (JTI method). Atmospheric Environ.40, 5137–5145. doi: 10.1016/j.atmosenv.2006.03.052

  • CrossRef
  • Google Scholar

• 111

  SawyerC. N.McCARTYP. L.ParkinG. F. (1978). “Chemistry for environmental engineering,” in National Meeting of the American Chemical Society. (New York).

  • Google Scholar

• 112

  SchultzA. A.PeppardP.GangnonR. E.MaleckiK. M. (2019). Residential proximity to concentrated animal feeding operations and allergic and respiratory disease. Environ. Int.130, 104911. doi: 10.1016/j.envint.2019.104911

  • CrossRef
  • Google Scholar

• 113

  SelkG. (1999). Implants for suckling steer and heifer calves and potential replacement heifers. Compendium Continuing Educ. Practicing Veterinarian21.

  • Google Scholar

• 114

  ShenJ.ChenD.BaiM.SunJ.CoatesT.LamS. K.et al. (2016). Ammonia deposition in the neighbourhood of an intensive cattle feedlot in Victoria, Australia. Sci. Rep.6, 32793. doi: 10.1038/srep32793

  • CrossRef
  • Google Scholar

• 115

  ShiY.ParkerD. B.ColeN. A.AuvermannB. W.MehlhornJ. E. (2001). Surface amendments to minimize ammonia emissions from beef cattle feedlots. Trans. ASAE44, 677. doi: 10.13031/2013.6105

  • CrossRef
  • Google Scholar

• 116

  ShonkwilerK. B.HamJ. M. (2018). Ammonia emissions from a beef feedlot: Comparison of inverse modeling techniques using long-path and point measurements of fenceline NH3. Agric. For. Meteorol.258, 29–42. doi: 10.1016/j.agrformet.2017.10.031

  • CrossRef
  • Google Scholar

• 117

  SommerS. G.OlesenJ. E.ChristensenB. T. (1991). Effects of temperature, wind speed and air humidity on ammonia volatilization from surface applied cattle slurry. J. Agric. Sci.117, 91–100. doi: 10.1017/S0021859600079016

  • CrossRef
  • Google Scholar

• 118

  SpiehsM. J.WoodburyB. (2022). Effect of using aluminum sulfate (alum) as a surface amendment in beef cattle feedlots on ammonia and sulfide emissions. Sustainability14, 1984. doi: 10.3390/su14041984

  • CrossRef
  • Google Scholar

• 119

  SpiehsM. J.WoodburyB. L.ParkerD. B. (2019). Ammonia, hydrogen sulfide, and greenhouse gas emissions from lab-scaled manure bedpacks with and without aluminum sulfate additions. Environments6, 108. doi: 10.3390/environments6100108

  • CrossRef
  • Google Scholar

• 120

  StackhouseK. R.RotzC. A.OltjenJ. W.MitloehnerF. M. (2012). Growth-promoting technologies decrease the carbon footprint, ammonia emissions, and costs of California beef production systems. J. Anim. Sci.90, 4656–4665. doi: 10.2527/jas.2011-4654

  • CrossRef
  • Google Scholar

• 121

  StaeblerR. M.McGinnS. M.CrennaB. P.FleschT. K.HaydenK. L.LiS. M. (2009). Three-dimensional characterization of the ammonia plume from a beef cattle feedlot. Atmospheric Environ.43, 6091–6099. doi: 10.1016/j.atmosenv.2009.08.045

  • CrossRef
  • Google Scholar

• 122

  StewartB. A. (1970). Volatilization and nitrification of nitrogen from urine under simulated cattle feedlot conditions. Environ. Sci. Technol.4, 579–582. doi: 10.1021/es60042a004

  • CrossRef
  • Google Scholar

• 123

  SunK.TaoL.MillerD. J.ZondloM. A.ShonkwilerK. B.NashC.et al. (2015). Open-path eddy covariance measurements of ammonia fluxes from a beef cattle feedlot. Agric. For. Meteorol.213, 193–202. doi: 10.1016/j.agrformet.2015.06.007

  • CrossRef
  • Google Scholar

• 124

  SzymulaA.WlazłoŁ.SasákováN.WnukW.Nowakowicz-DębekB. (2021). The use of natural sorbents to reduce ammonia emissions from cattle faeces. Agronomy11, 2543. doi: 10.3390/agronomy11122543

  • CrossRef
  • Google Scholar

• 125

  TedeschiL. O.FoxD. G. (2020). The Ruminant Nutrition System: Vol. 1 - An Applied Model for Predicting Nutrient Requirement and Feed Utilization in Ruminants. 3rd Edition (Ann Arbor, MI: XanEdu publication).

  • Google Scholar

• 126

  TedeschiL. O.FoxD. G.TylutkiT. P. (2003). Potential environmental benefits of ionophores in ruminant diets. J. Environ. Qual.32, 1591–1602. doi: 10.2134/jeq2003.1591

  • CrossRef
  • Google Scholar

• 127

  ThompsonT. M.RodriguezM. A.BarnaM. G.GebhartK. A.HandJ. L.DayD. E.et al. (2015). Rocky Mountain National Park reduced nitrogen source apportionment. J. Geophysical Research: Atmospheres120, 4370–4384. doi: 10.1002/2014JD022675

  • CrossRef
  • Google Scholar

• 128

  ToddR. W.ColeN. A.ClarkR. N. (2006). Reducing crude protein in beef cattle diet reduces ammonia emissions from artificial feedyard surfaces. J. Environ. Qual.35, 404–411. doi: 10.2134/jeq2005.0045

  • CrossRef
  • Google Scholar

• 129

  ToddR. W.ColeN. A.ClarkR. N.FleschT. K.HarperL. A.BaekB. H. (2008). Ammonia emissions from a beef cattle feedyard on the southern High Plains. Atmospheric Environ.42, 6797–6805. doi: 10.1016/j.atmosenv.2008.05.013

  • CrossRef
  • Google Scholar

• 130

  ToddR. W.ColeN. A.HarperL. A.FleschT. K. (2007). “Flux-gradient estimates of ammonia emissions from beef cattle feedyard pens,” in International Symposium on Air Quality and Waste Management for Agriculture, 16–19 September 2007, Broomfield, Colorado. 81 (St. Joseph, Michigan: American Society of Agricultural and Biological Engineers). doi: 10.13031/2013.23877

  • CrossRef
  • Google Scholar

• 131

  ToddR. W.ColeN. A.HarperL. A.FleschT. K.BaekB. H. (2005). “Ammonia and gaseous nitrogen emissions from a commercial beef cattle feedyard estimated using the flux-gradient method and N: P ratio analysis,” in Proc. Symposium State of the Science: Animal Manure and Waste Management. (San Antonio, Texas).

  • Google Scholar

• 132

  ToddR. W.ColeN. A.ParkerD. B.RhoadesM.CaseyK. (2009). “Effect of feeding distiller’s grain on dietary crude protein and ammonia emissions from beef cattle feedyards,” in Proceedings of the Texas Animal Manure Management Issues (TAMMI) Conference. (Round Rock, Texas) 37–44.

  • Google Scholar

• 133

  ToddR. W.ColeN. A.RhoadesM. B.ParkerD. B.CaseyK. D. (2011). Daily, monthly, seasonal, and annual ammonia emissions from Southern High Plains cattle feedyards. J. Environ. Qual.40, 1090–1095. doi: 10.2134/jeq2010.0307

  • CrossRef
  • Google Scholar

• 134

  ToddR. W.ColeN. A.WaldripH. M.AikenR. M. (2013). Arrhenius equation for modeling feedyard ammonia emissions using temperature and diet crude protein. J. Environ. Qual.42, 666–671. doi: 10.2134/jeq2012.0371

  • CrossRef
  • Google Scholar

• 135

  USDA-NRCSConservation management practices. Available online at: https://www.nrcs.usda.gov/resources/guides-and-instructions/conservation-practice-standards (Accessed March 21, 2025).

  • Google Scholar

• 136

  USEPA (2004). Ammonia emissions from animal husbandry operations (Washington D.C.: United States Environmental Protection Agency, National emission inventory).

  • Google Scholar

• 137

  USEPA (2010). Methane and nitrous oxide emissions from natural sources (Washington D.C.: United States Environmental Protection Agency).

  • Google Scholar

• 138

  USEPA (2024). National ambient air quality standards (NAAQS) for PM (Washington D.C.: United States Environmental Protection Agency). Available at: https://www.epa.gov/pm-pollution/national-ambient-air-quality-standards-naaqs-pm.

  • Google Scholar

• 139

  VagaM. (2017). Investigating ruminal nitrogen metabolism. (Doctoral dissertation): Swedish Universitatis of Agricultural Sciences, Umeå, Sweden.

  • Google Scholar

• 140

  Vander PolM.HristovA. N.ZamanS.DelanoN. (2008). Peas can replace soybean meal and corn grain in dairy cow diets. J. Dairy Sci.91, 698–703. doi: 10.3168/jds.2007-0543

  • CrossRef
  • Google Scholar

• 141

  Van HaarlemR. P.DesjardinsR. L.GaoZ.FleschT. K.LiX. (2008). Methane and ammonia emissions from a beef feedlot in western Canada for a twelve-day period in the fall. Can. J. Anim. Sci.88, 641–649. doi: 10.4141/CJAS08034

  • CrossRef
  • Google Scholar

• 142

  Van SoestP. J. (1994). Nutritional ecology of the ruminant Vol. 476 (Ithaca, NY: Cornell University Press).

  • Google Scholar

• 143

  VarelV. H.NienaberJ. A.FreetlyH. C. (1999). Conservation of nitrogen in cattle feedlot waste with urease inhibitors. J. Anim. Sci.77, 1162–1168. doi: 10.2527/1999.7751162x

  • CrossRef
  • Google Scholar

• 144

  VavilinV. A.FernandezB.PalatsiJ.FlotatsX. (2008). Hydrolysis kinetics in anaerobic degradation of particulate organic material: an overview. Waste Manage.28, 939–951. doi: 10.1016/j.wasman.2007.03.028

  • CrossRef
  • Google Scholar

• 145

  WaldripH. M.ColeN. A.ToddR. W. (2015). Nitrogen sustainability and beef cattle feedyards: II. Ammonia emissions. Prof. Anim. Scientist31, 395–411. doi: 10.15232/pas.2015-01395

  • CrossRef
  • Google Scholar

• 146

  WaldripH. M.RotzC. A.HafnerS. D.ToddR. W.ColeN. A. (2014). Process-based modeling of ammonia emission from beef cattle feedyards with the integrated farm systems model. J. Environ. Qual.43, 1159–1168. doi: 10.2134/jeq2013.09.0354

  • CrossRef
  • Google Scholar

• 147

  WaldripH. M.ToddR. W.ColeN. A. (2013a). Prediction of nitrogen excretion by beef cattle: A meta-analysis. J. Anim. Sci.91, 4290–4302. doi: 10.2527/jas.2012-5818

  • CrossRef
  • Google Scholar

• 148

  WaldripH. M.ToddR. W.LiC.ColeN. A.SalasW. H. (2013b). Estimation of ammonia emissions from beef cattle feedyards using the process-based model Manure-DNDC. Trans. ASABE56, 1103–1114. doi: 10.13031/trans.56.9906

  • CrossRef
  • Google Scholar

• 149

  WangQ.FleschT. K.BaiM.ZhangM.ChenD. (2024). Seasonal ammonia emissions from an intensive beef cattle feedlot in Victoria Australia. J. Environ. Manage.351, 119898. doi: 10.1016/j.jenvman.2023.119898

  • CrossRef
  • Google Scholar

• 150

  WendlerW. M.DavisM. S.KoersW. C.RinckerP. J.PyattN. A.LucherkL. Wet al. (2025). Effect of beta-agonist type and timing of Experior feeding on calculated cumulative ammonia gas emissions, live growth performance, and carcass outcomes, and objective tenderness outcomes of feedlot steers. Translational Animal Science, txaf009. doi: 10.1093/tas/txaf009

  • CrossRef
  • Google Scholar

• 151

  WheelerE. F.Adviento-BorbeM. A. A.BrandtR. C.TopperP. A.TopperD. A.ElliottH. A.et al. (2011). Evaluation of odor emissions from amended dairy manure: preliminary screening. Agric. Eng. International: CIGR J.13.

  • Google Scholar

• 152

  WilsonJ. D.SawfordB. L. (1996). Review of Lagrangian stochastic models for trajectories in the turbulent atmosphere. Boundary-layer meteorol.78, 191–210. doi: 10.1007/BF00122492

  • CrossRef
  • Google Scholar

• 153

  WyerK. E.KelleghanD. B.Blanes-VidalV.SchaubergerG.CurranT. P. (2022). Ammonia emissions from agriculture and their contribution to fine particulate matter: A review of implications for human health. J. Environ. Manage.323, 116285. doi: 10.1016/j.jenvman.2022.116285

  • CrossRef
  • Google Scholar

• 154

  XieY.WangW.ChenY.QianZ.ChenJ.TongJ.et al. (2024). NH3 emissions and lifetime estimated by satellite observations with differential evolution algorithm. Atmosphere15, 251. doi: 10.3390/atmos15030251

  • CrossRef
  • Google Scholar

• 155

  YonkofskiC. M.AppriouD.DownsJ. L. (2019). Dust Control Planning (No. PNNL-29152) (Richland, WA, USA: U.S. Department of Energy, Pacific Northwest National Lab.(PNNL). doi: 10.2172/1593515

  • CrossRef
  • Google Scholar

• 156

  ZhangB.HeP. J.LüF.ShaoL. M.WangP. (2007). Extracellular enzyme activities during regulated hydrolysis of high-solid organic wastes. Water Res.41, 4468–4478. doi: 10.1016/j.watres.2007.06.061

  • CrossRef
  • Google Scholar

• 157

  ZuidhofM. J. (2020). Precision livestock feeding: matching nutrient supply with nutrient requirements of individual animals. J. Appl. Poultry Res.29, 11–14. doi: 10.1016/j.japr.2019.12.009

  • CrossRef
  • Google Scholar